Remote Sensing and Geoinformatics in Agriculture and Environment

Topic Information

Dear Colleagues,

Nowadays the evolution of new technologies for spatial data collection -UAV-drones, digital cameras, satellite data, sensors and more-, their transmission through IoT technologies (Internet of Things), their emergence via internet, and their analysis through GIS provide enhanced capabilities and significant impetus for solving and confronting contemporary issues faced by Agriculture, as well as by Environmental sustainability. New innovative sensors carried on earth observation instruments, tractors and field measuring devices are constantly collecting high resolution, multitemporal and multispectral data, which are supplement and integrate the data collected with more traditional approaches. GIS and other geospatial technologies shape this data into information that is accessible and interpretable by farmers and land managers to make efficient and informed decisions. At the same time, geospatial analyses of the human impact on the environment is crucial for a better understanding of the underlying relationships and processes. Advanced Earth Observation technologies and Geoinformatics are paving the way towards a better understanding of ecological and environmental interactions, identifying early indicators of environmental degradation and improving our capacity for risk assessment, timely forecast and response. Every single year brings much progress in Remote Sensing, GIS, and spatial analysis methodologies and technologies in agriculture and environment.

This topic will summarize the contemporary progress and achievements of Remote Sensing and Geoinformatics, highlight the recent advancements, and present applications in a wide spectrum of topics related to Remote Sensing and Geoinformatics in agriculture and environment. It is coordinated with the 4ᵗʰ Congress of Geographical Information Systems and Spatial Analysis in Agriculture and Environment, and it includes selected papers from this conference but also welcomes other papers that align with its topics:

Specifically, the Topic is focusing on, but not limited to earth observation, GIS, and spatial analysis applications such as Satellite Data, Geoinformatics & Geospatial Technologies, Web-GIS, GNSS and GPS, IoT, Land Information Systems, Spatial exploratory data analysis, Spatial Statistical Models, Spatial Interpolation, Geostatistics, Neural Networks and AI, Use of Cloud Service’s for the Management of Spatial Data of Large Volume, in the following topics:

- Land Suitability Classification

- Soil Resources Protection, Land Assessment, and Land Use Planning

- Water Resources Analysis, Planning, and Management

- Ecosystem Protection, Restoration and Management

- Forests Evaluation and Management

- Natural Hazards, Geohazards

- Natural Disasters (Floods, Droughts, Fires, Landslides etc.)

- Spatial Digital Management of Farms & Agricultural Holdings

- Precision Agriculture, Smart Farming, and Data Collection via Spatial Digital Technologies

- Agricultural Production and of Agricultural Ecosystems Monitoring

- Crop Protection, Pest and Diseases Management

- Weeds

– Invasive Species

- Soil nutrients and fertility management

- Sustainable Fishery through the contemporary geospatial technologies’ application

- Livestock and Pastures Management

Prof. Dr. Dionissios Kalivas
Prof. Dr. Christos Chalkias
Dr. Thomas Alexandridis
Dr. Konstantinos X. Soulis
Dr. Emmanouil Psomiadis
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Drones drones	4.8	6.1	2017	17.9 Days	CHF 2600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Agriculture agriculture	3.6	3.6	2011	17.7 Days	CHF 2600
Land land	3.9	3.7	2012	14.8 Days	CHF 2600

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Published Papers (44 papers)

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14 pages, 5142 KiB  

Open AccessArticle

Effect of the Red-Edge Band from Drone Altum Multispectral Camera in Mapping the Canopy Cover of Winter Wheat, Chickweed, and Hairy Buttercup

by Clement E. Akumu and Sam Dennis

Drones 2023, 7(4), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7040277 - 19 Apr 2023

Cited by 1 | Viewed by 1421

Abstract

The detection and mapping of winter wheat and the canopy cover of associated weeds, such as chickweed and hairy buttercup, are essential for crop and weed management. With emerging drone technologies, the use of a multispectral camera with the red-edge band, such as [...] Read more.

The detection and mapping of winter wheat and the canopy cover of associated weeds, such as chickweed and hairy buttercup, are essential for crop and weed management. With emerging drone technologies, the use of a multispectral camera with the red-edge band, such as Altum, is commonly used for crop and weed mapping. However, little is understood about the contribution of the red-edge band in mapping. The aim of this study was to examine the addition of the red-edge band from a drone with an Altum multispectral camera in improving the detection and mapping of the canopy cover of winter wheat, chickweed, and hairy buttercup. The canopy cover of winter wheat, chickweed, and hairy buttercup were classified and mapped with the red-edge band inclusively and exclusively using a random forest classification algorithm. Results showed that the addition of the red-edge band increased the overall mapping accuracy of about 7%. Furthermore, the red-edge wavelength was found to better detect winter wheat relative to chickweed and hairy buttercup. This study demonstrated the usefulness of the red-edge band in improving the detection and mapping of winter wheat and associated weeds (chickweed and hairy buttercup) in agricultural fields. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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22 pages, 6429 KiB  

Open AccessFeature PaperArticle

Monitoring of Land Degradation in Greece and Tunisia Using Trends.Earth with a Focus on Cereal Croplands

by Ines Cherif, Eleni Kolintziki and Thomas K. Alexandridis

Remote Sens. 2023, 15(7), 1766; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15071766 - 25 Mar 2023

Cited by 2 | Viewed by 2015

Abstract

Land degradation (LD) processes are widespread in drylands worldwide and are accelerated by climate change. As a result, food security and livelihoods are at risk. Thus, there is a need to monitor LD trends, especially in agricultural areas. Mediterranean countries, including Tunisia and [...] Read more.

Land degradation (LD) processes are widespread in drylands worldwide and are accelerated by climate change. As a result, food security and livelihoods are at risk. Thus, there is a need to monitor LD trends, especially in agricultural areas. Mediterranean countries, including Tunisia and Greece, are concerned due to the presence of drivers and pressures causing land degradation. Through the Trends.Earth plugin, the SDG 15.3.1 indicator can be implemented to map LD status. In this study, we mapped LD in Greece and Tunisia for the recommended baseline period of 2001–2015 and the selected reporting period of 2016–2020. The land productivity was assessed within Trends.Earth using the MODIS MOD13Q1 product, while the default datasets were used for the other sub-indicators. The main findings are: (i) the percentage of degraded land decreased from the baseline to the reporting period from 4.83% to 2.62% of total area in Greece and 9.97% to 6.26% in Tunisia—degradation rates that differ from those reported to the UNCCD (United Nations Convention to Combat Desertification) by the respective national authorities; (ii) the dominant land condition in Greece was improved, while in Tunisia, it was stable; (iii) land productivity presented a similar trend through the SDG 15.3.1 indicator over both countries, including the net land productivity dynamics over croplands; (iv) based on analysis using plant functional types performed with MODIS MCD12Q1, the highest portion of degraded land in Greece was located in grasslands and in Tunisia in cereal croplands (after desert areas); and (v) with a focus on LD over cereal croplands, the portion of degraded areas appeared to decrease in both Greece and Tunisia. The percentage was higher in Tunisia, representing 16.52% of the total degraded land during the reporting period compared to 10.83% in Greece. All the above stress the need to foster the adoption of sustainable land management practices, especially in Tunisia, and speed up the implementation of measures to achieve LD neutrality. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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18 pages, 2740 KiB  

Open AccessArticle

Delineating Natural Terroir Units in Wine Regions Using Geoinformatics

by Nikolaos Karapetsas, Thomas K. Alexandridis, George Bilas, Serafeim Theocharis and Stefanos Koundouras

Agriculture 2023, 13(3), 629; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13030629 - 06 Mar 2023

Viewed by 1362

Abstract

The terroir effect refers to the interactions between the grapes and their natural surroundings and has been recognized as an important factor in wine quality. The identification and mapping of viticultural terroir have long been relying on expert opinion coupled with land classification [...] Read more.

The terroir effect refers to the interactions between the grapes and their natural surroundings and has been recognized as an important factor in wine quality. The identification and mapping of viticultural terroir have long been relying on expert opinion coupled with land classification and soil/climate mapping. In this study, the data-driven approach has been implemented for mapping natural terroir units based on spatial modeling of public-access geospatial information regarding the three most important environmental factors that make up the terroir effect on different scales, climate, soil, and topography. K-means cluster analysis was applied to the comprehensive databases of relevant spatial information, and the optimum number of clusters was identified by the Dunn and CCC indices. The results have revealed ten clusters that cover the agricultural area of Drama (Greece), where it was applied, and displayed variable conditions on the climate, soil, and topographic factors. The implications of the resulting natural terroir units on the vini-viticultural management of the most common vine varieties are discussed. As more accurate and detailed input spatial data become available, the potential of such an approach is highlighted and paving the way toward a true understanding of the drivers of terroir. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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24 pages, 5962 KiB  

Open AccessArticle

Challenges in the Geo-Processing of Big Soil Spatial Data

by Leonidas Liakos and Panos Panagos

Land 2022, 11(12), 2287; https://0-doi-org.brum.beds.ac.uk/10.3390/land11122287 - 13 Dec 2022

Cited by 5 | Viewed by 2740

Abstract

This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic [...] Read more.

This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, the main highlights include the difficulties associated with using data infrastructures, managing big geospatial data, decentralizing operations through remote access, mass processing, and automating the data-processing workflow using advanced programming languages. Challenges to this study included the reproducibility of the results, their presentation in a communicative way, and the harmonization of complex heterogeneous data in space and time based on high standards of accuracy. Accuracy was especially important as the results needed to be identical at all spatial scales (from point counts to aggregated countrywide data). The geospatial modeling of soil requires analysis at multiple spatial scales, from the pixel level, through multiple territorial units (national or regional), and river catchments, to the global scale. Advanced mapping methods (e.g., zonal statistics, map algebra, choropleth maps, and proportional symbols) were used to convey comprehensive and substantial information that would be of use to policymakers. More specifically, a variety of cartographic practices were employed, including vector and raster visualization and hexagon grid maps at the global or European scale and in several cartographic projections. The information was rendered in both grid format and as aggregated statistics per polygon (zonal statistics), combined with diagrams and an advanced graphical interface. The uncertainty was estimated and the results were validated in order to present the outputs in the most robust way. The study was also interdisciplinary in nature, requiring large-scale datasets to be integrated from different scientific domains, such as soil science, geography, hydrology, chemistry, climate change, and agriculture. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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20 pages, 11239 KiB  

Open AccessArticle

Land Suitability Analysis as a Tool for Evaluating Soil-Improving Cropping Systems

by George Bilas, Nikolaos Karapetsas, Anne Gobin, Konstantinos Mesdanitis, Gergely Toth, Tamás Hermann, Yaosheng Wang, Liangguo Luo, Thomas M. Koutsos, Dimitrios Moshou and Thomas K. Alexandridis

Land 2022, 11(12), 2200; https://0-doi-org.brum.beds.ac.uk/10.3390/land11122200 - 04 Dec 2022

Cited by 5 | Viewed by 3399

Abstract

Agricultural land use planning is based on the capacity of the soil to support different types of crops and is a prerequisite for better use of cultivated land. Land Suitability Analysis (LSA) is used to measure the level of suitability of growing a [...] Read more.

Agricultural land use planning is based on the capacity of the soil to support different types of crops and is a prerequisite for better use of cultivated land. Land Suitability Analysis (LSA) is used to measure the level of suitability of growing a specific crop in the area and can also be used to evaluate future scenarios as a means for sustainable agriculture. LSA was employed to calculate current land suitability, as well as four scenarios of Soil-Improving Cropping Systems (SICS): (a) Conservation Tillage (CT), (b) Cover Crop (CC), (c) Crop Residue Management (CRM), and (d) Manure Application (MA). The scenarios of SICS were derived by increasing soil organic matter and cation exchange capacity values depending on the SICS hypothetically applied for a period of 100 years in the future. LSA was evaluated for maize in three sites: (a) Flanders (BE), (b) Somogy (HU), and (c) Hengshui (CH). LSA was performed using the Agricultural Land Use Evaluation System (ALUES) considering soil and climatic and topographic parameters. Weighing factors of input parameters were assigned using the Analytical Hierarchy Process (AHP). The results show that in Flanders, the highly suitable (S2) class covered 3.3% of the total area, and the best scenario for improving current LS was CRM, in which S2 expanded to 9.1%. In Somogy, the S2 class covered 18.3% of the total area, and the best scenarios for improving current land suitability were CT and CC, in both of which the S2 class expanded to 70.5% of the total area. In Hengshui, the S2 class covered 64.7% of the total area, and all SICS scenarios performed extremely well, converting almost all moderately suitable (S3) areas to S2. The main limiting factor that was recognized from a limiting factor analysis in all cases was the climatic conditions. This work proves that LSA can evaluate scenarios of management practices and recognize limiting factors. The proposed methodology is a novel approach that can provide land suitability maps to efficiently evaluate SICS scenarios by projecting soil characteristics and LSA in the future, thus facilitating management decisions of regional policy makers. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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14 pages, 1818 KiB  

Open AccessFeature PaperArticle

A Forward Future-Based Approach to Optimizing Agriculture and Climate Change Adaptation in Lower Eastern Kenya

by Lilian Wangui Ndungu, John Bosco Kyalo Kiema, David Nyangau Siriba, Denis Macharia Muthike and Samuel Wamathai Ndungu

Land 2022, 11(12), 2172; https://0-doi-org.brum.beds.ac.uk/10.3390/land11122172 - 30 Nov 2022

Viewed by 1854

Abstract

Kenya’s vulnerability to climate variability and change has been compounded by dependence on rain-fed agriculture with constrained capacity to adapt, a rapidly growing population, low-mechanized and low-input smallholder agricultural systems, and compromised soil fertility. The Ukraine war, COVID-19 and the desert locust invasion [...] Read more.

Kenya’s vulnerability to climate variability and change has been compounded by dependence on rain-fed agriculture with constrained capacity to adapt, a rapidly growing population, low-mechanized and low-input smallholder agricultural systems, and compromised soil fertility. The Ukraine war, COVID-19 and the desert locust invasion have only amplified the prevailing sensitivity to shocks in the agriculture sector, creating an emphasis on the need to strengthen local agricultural production to reduce reliance on imports. This paper seeks to assess the opportunities for improving agriculture adaptation and resilience based on future expected changes in climate, length of the growing period and agro-ecologies. The study uses 2020 as the baseline year and explores changes in agro-ecological zones (AEZs) in “near future” 2040 through two representative concentration pathways, 4.5 and 8.5, representing a medium carbon emissions and a dire emissions future, respectively. Google Earth Engine and R Statistics are used in data-processing. Down-scaled climate projections from CIMP5 are used for future analyses combined with static soil suitability and drainage data. Fuzzy logic is used to normalize inputs and compute the agro-ecological zones (AEZ). Interesting results emerge from the study that validate the hypothesis that the seasons and production potential are shifting. Lowland drylands will experience an increasingly long growing period, creating the potential for diversifying production systems from rangelands to agro-pastoral systems, with the capacity to grow more drought-resistant crops and the potential to take advantage of increased runoff for water harvesting. Midland highland areas, which form part of the food basket areas, have already started experiencing a reduction in the length of the growing period and agricultural potential. In these areas, resilience mechanisms will need to consider the expected future reduction in rain-fed agricultural potential, gendered preferences, convergence of technology and indigenous coping mechanisms, and drought-resilience-focused diversification. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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21 pages, 27023 KiB  

Open AccessArticle

Spatio-Temporal Changes of Vegetation Cover and Its Influencing Factors in Northeast China from 2000 to 2021

by Maolin Li, Qingwu Yan, Guie Li, Minghao Yi and Jie Li

Remote Sens. 2022, 14(22), 5720; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225720 - 12 Nov 2022

Cited by 13 | Viewed by 2127

Abstract

The foundation of study on regional environmental carrying capacity is the detection of vegetation changes. A case of Northeast China, we, with the support of normalized difference vegetation index (NDVI) of MOD13A3 (MOD13A3-NDVI), use a three-dimensional vegetation cover model (3DFVC) to acquire vegetation [...] Read more.

The foundation of study on regional environmental carrying capacity is the detection of vegetation changes. A case of Northeast China, we, with the support of normalized difference vegetation index (NDVI) of MOD13A3 (MOD13A3-NDVI), use a three-dimensional vegetation cover model (3DFVC) to acquire vegetation cover from 2000 to 2021. Vegetation trends are then monitored by the spatio-temporal analysis models including the empirical orthogonal function (EOF), the Sen’s slope (Sen), the Mann-Kendall test (MK) and the Hurst index (Hurst). Additionally, we, through the multi-scale geographically weighted regression model (MGWR), explore the spatial heterogeneity of vegetation response to its influencing factors. On the basis of this, it is by introducing the structural equation model (SEM) that we figure out the mechanisms of vegetation response to climate and human activity. The main results are as follows: (1) Compared with the dimidiate pixel model (FVC), 3DFVC, to some extent, weaken the influence of terrain on vegetation cover extraction with a good applicability. (2) From 2000 to 2021, the average annual vegetation cover has a fluctuating upward trend (0.03·22a${}^{-1}$, p < 0.05), and spatially vegetation cover is lower in the west and higher in the east with a strong climatic zoning feature. In general, vegetation cover is relatively stable, only 7.08% of the vegetation area with a trend of significant change. (3) In terms of EOF (EOF${}_1$+EOF${}_2$), EOF${}_1$ has a strong spatial heterogeneity but EOF${}_2$ has a strong temporal heterogeneity. As for the Hurst index, its mean value, with an anti-persistence feature, is 0.451, illustrating that vegetation is at some risk of degradation in future. (4) MGWR is slightly better than GWR. Vegetation growth is more influenced by the climate (precipitation and temperature) or human activity and less by the terrain or soil. Besides, precipitation plays a leading role on vegetation growth, while temperature plays a moderating role on vegetation growth. What is more, precipitation, on different temperature conditions, shows a different effect on vegetation growth. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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15 pages, 257 KiB  

Open AccessArticle

An Overview of Frontier Technologies for Land Tenure: How to Avoid the Hype and Focus on What Matters

by Simon Hull, Harold Liversage, Maria Paola Rizzo and Vladimir Evtimov

Land 2022, 11(11), 1939; https://0-doi-org.brum.beds.ac.uk/10.3390/land11111939 - 31 Oct 2022

Cited by 3 | Viewed by 2712

Abstract

Secure land and natural resource rights are key ingredients for rural transformation, social inclusion, and the realization of the Sustainable Development Goals. In many cases, these rights are not formally recorded, and statutory land administration systems are inaccessible to rural communities. The rapid [...] Read more.

Secure land and natural resource rights are key ingredients for rural transformation, social inclusion, and the realization of the Sustainable Development Goals. In many cases, these rights are not formally recorded, and statutory land administration systems are inaccessible to rural communities. The rapid development of geospatial technologies and systems, combined with participatory methods for social empowerment, have contributed significantly to addressing these challenges and in developing fit-for-purpose land administration/land recordation systems that promote land tenure security, but with the plethora of options currently available, it is challenging to know which technologies are appropriate for what circumstances and purposes. This paper reports on the findings from a joint FAO/IFAD project that addresses this problem. Thirteen one-hour interviews were conducted with knowledgeable experts to showcase which technologies are being used for what purposes and by whom, the associated benefits and challenges, and what the future may hold. We conclude that technologies are best used in partnership with communities and as integrated solutions, that successful implementations must incorporate maintenance plans, and that the real challenge is not the technology–it is the social, legal, and political context. These findings are useful for governments, NGOs, academia, donors, and others involved in land-related projects aimed at benefitting small-scale farmers. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

23 pages, 2679 KiB  

Open AccessFeature PaperArticle

Evaluation of Different Modelling Techniques with Fusion of Satellite, Soil and Agro-Meteorological Data for the Assessment of Durum Wheat Yield under a Large Scale Application

by Emmanuel Lekakis, Athanasios Zaikos, Alexios Polychronidis, Christos Efthimiou, Ioannis Pourikas and Theano Mamouka

Agriculture 2022, 12(10), 1635; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12101635 - 08 Oct 2022

Cited by 3 | Viewed by 3157

Abstract

Food and feed production must be increased or maintained in order to meet the demands of the earth’s population. Under this scenario, the question that arises is how to address the demand for agricultural products given that the pressures on land use have [...] Read more.

Food and feed production must be increased or maintained in order to meet the demands of the earth’s population. Under this scenario, the question that arises is how to address the demand for agricultural products given that the pressures on land use have already increased. In addition, it is obvious that climate change will have a serious negative impact and threaten the productivity and sustainability of food production systems. Therefore, understanding and predicting the outcome of crop production, while considering adaptation and sustainability, is essential. The need for information on decision making at all levels, from crop management to adaptation strategies, is constantly increasing and methods for providing such information are urgently needed in a relatively short period of time. Thus arises the need to use effective data, such as satellite and meteorological data, but also operational tools, to assess crop yields over local, regional, national, and global scales. In this work, three modeling approaches built on a fusion of satellite-derived vegetation indices, agro-meteorological indicators, and crop phenology are tested and evaluated in terms of data intensiveness for the prediction of wheat yields in large scale applications. The obtained results indicated that medium input data intensity methods are effective tools for yield assessments. The methods, namely, a semi-empirical regression model, a machine learning regression model, and a process-based model, provided high to moderate accuracies by fully relying on freely available datasets as sources of input data. The findings are comparable with those reported in the literature for detailed field experiments, thereby introducing a promising framework that can support operational platforms for dynamic yield forecasting, operating at the administrative or regional unit scale. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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15 pages, 10315 KiB  

Open AccessArticle

Remote Sensing Monitoring and Analytical Evaluation of Grasslands in the Muli Region of Qinghai, China from 2000 to 2021

by Lu Jiang, Tengfei Cui, Hui Liu and Yong Xue

Land 2022, 11(10), 1733; https://0-doi-org.brum.beds.ac.uk/10.3390/land11101733 - 07 Oct 2022

Cited by 3 | Viewed by 1351

Abstract

The mining area in the Muli region, Qinghai Province, China, is an important source of water and an ecological security barrier in the Qilian Mountains region and has a very important ecological status. A series of ecological problems such as vegetation degradation and [...] Read more.

The mining area in the Muli region, Qinghai Province, China, is an important source of water and an ecological security barrier in the Qilian Mountains region and has a very important ecological status. A series of ecological problems such as vegetation degradation and loss of biodiversity caused by mining have attracted widespread attention. In this paper, we used Landsat secondary data from 2000 to 2021 from the Muli region to obtain the spatial and temporal distribution characteristics of the vegetation in the Muli region by inversion of the fractional vegetation cover, above-ground biomass and the land surface phenology to comprehensively analyze the ecological changes in the vegetation in the Muli region. The results showed the following: (1) the above-ground biomass and cover of grassland in the Muli region showed a decreasing trend between 2000 and 2021, with a particularly pronounced decrease in grassland cover between 2009 and 2016; (2) the start of the vegetation growth cycle, i.e., the beginning of the vegetation growing season (SOG) became more advanced, the end of the vegetation growing season (EOG) was delayed, and the length of the growing cycle (LOG) became longer for most of the vegetation in the Muli region; (3) the results of this comprehensive analysis showed that the grassland in the Muli region showed dynamic changes with complex characteristics from 2000 to 2021, and anthropogenic disturbances had some influence on ecological indicators such as fractional vegetation cover and biomass. The extension of the vegetation growing season might be related to climate change. Based on the results of this paper, it is recommended to utilize biomass and fractional vegetation cover as indicators to assess the grass growth status of mining sites. This study analyzed the spatial and temporal characteristics of grasslands in the Muli area with several indicators, which will help relevant departments continue to improve and optimize ecological restoration measures. In addition, this study provides a reference for achieving comprehensive restoration of the ecological environment and ecological functions in mining areas. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 3641 KiB  

Open AccessArticle

Optimization of Open-Access Optical and Radar Satellite Data in Google Earth Engine for Oil Palm Mapping in the Muda River Basin, Malaysia

by Ju Zeng, Mou Leong Tan, Yi Lin Tew, Fei Zhang, Tao Wang, Narimah Samat, Fredolin Tangang and Zulkifli Yusop

Agriculture 2022, 12(9), 1435; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12091435 - 10 Sep 2022

Cited by 4 | Viewed by 2420

Abstract

Continuous oil palm distribution maps are essential for effective agricultural planning and management. Due to the significant cloud cover issue in tropical regions, the identification of oil palm from other crops using only optical satellites is difficult. Based on the Google Earth Engine [...] Read more.

Continuous oil palm distribution maps are essential for effective agricultural planning and management. Due to the significant cloud cover issue in tropical regions, the identification of oil palm from other crops using only optical satellites is difficult. Based on the Google Earth Engine (GEE), this study aims to evaluate the best combination of open-source optical and microwave satellite data in oil palm mapping by utilizing the C-band Sentinel-1, L-band PALSAR-2, Landsat 8, Sentinel-2, and topographic images, with the Muda River Basin (MRB) as the test site. The results show that the land use land cover maps generated from the combined images have accuracies from 95 to 97%; the best combination goes to Sentinel-1 and Sentinel-2 for the overall classification. Meanwhile, the best combination for oil palm classification is C5 (PALSAR-2 + Landsat 8), with the highest producer accuracy (96%) and consumer accuracy (100%) values. The combination of C-band radar images can improve the classification accuracy of oil palm, but compared with the combination of L-band images, the oil palm area was underestimated. The oil palm area had increased from 2015 to 2020, ranging from 10% to 60% across all combinations. This shows that the selection of optimal images is important for oil palm mapping. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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22 pages, 7756 KiB  

Open AccessArticle

Timely Plastic-Mulched Cropland Extraction Method from Complex Mixed Surfaces in Arid Regions

by Chenhao Fu, Lei Cheng, Shujing Qin, Aqil Tariq, Pan Liu, Kaijie Zou and Liwei Chang

Remote Sens. 2022, 14(16), 4051; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164051 - 19 Aug 2022

Cited by 14 | Viewed by 1966

Abstract

Plastic mulch is extensively applied in agricultural production in arid regions. It significantly influences the interactions between land and atmosphere by altering underlying surface characteristics. An accurate and timely extraction method for Plastic-Mulched Cropland (PMC) is required to understand land surface energy transfer [...] Read more.

Plastic mulch is extensively applied in agricultural production in arid regions. It significantly influences the interactions between land and atmosphere by altering underlying surface characteristics. An accurate and timely extraction method for Plastic-Mulched Cropland (PMC) is required to understand land surface energy transfer processes, eco-hydrological cycle, the climate effect of PMC, and in the management of water resources. In this study, we proposed a Timely Plastic-mulched cropland Extraction Method (TPEM) from complex mixed surfaces with multi-source remote sensing data in the Shiyanghe River Basin (SRB), a typical representation of a complex and inhomogeneous arid region in the northwest of China. We defined TPEM in three phases; in the first phase, the spectral characteristic curves were drawn from ground object points labeled by visual interpretation with multi-source remote sensing data. In the second phase, a spectral characteristic analysis of the modified index was proposed to amplify the difference between PMC and non-PMC ground objects. Finally, the Classification and Regression Tree (CART) classifier was used to generate thresholds of indices as PMC extraction rules. The results showed that it can extract the boundary of PMC in large-scale farmland, distinguish PMC from ground objects in complex mixed surfaces, and separate the PMC from desert land that shares same spectral characteristics with PMC. The TPEM is verified to be efficient and robust, with an overall accuracy of 0.9234, quantity disagreement of 0.0541, and allocation disagreement of 0.0224, and outperformed two extensively used PMC extraction methods, especially for timely PMC extraction when satellite data only during the period that ground surface incomplete covered by plastic mulch is available. This study will provide us with an accurate and timely method to extract PMC, especially in the widely distributed complex mixed surfaces. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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22 pages, 4043 KiB  

Open AccessArticle

A Novel Hybrid GOA-XGB Model for Estimating Wheat Aboveground Biomass Using UAV-Based Multispectral Vegetation Indices

by Yixiu Han, Rui Tang, Zhenqi Liao, Bingnian Zhai and Junliang Fan

Remote Sens. 2022, 14(14), 3506; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143506 - 21 Jul 2022

Cited by 6 | Viewed by 1909

Abstract

The rapid and nondestructive determination of wheat aboveground biomass (AGB) is important for accurate and efficient agricultural management. In this study, we established a novel hybrid model, known as extreme gradient boosting (XGBoost) optimization using the grasshopper optimization algorithm (GOA-XGB), which could accurately [...] Read more.

The rapid and nondestructive determination of wheat aboveground biomass (AGB) is important for accurate and efficient agricultural management. In this study, we established a novel hybrid model, known as extreme gradient boosting (XGBoost) optimization using the grasshopper optimization algorithm (GOA-XGB), which could accurately determine an ideal combination of vegetation indices (VIs) for simulating wheat AGB. Five multispectral bands of the unmanned aerial vehicle platform and 56 types of VIs obtained based on the five bands were used to drive the new model. The GOA-XGB model was compared with many state-of-the-art models, for example, multiple linear regression (MLR), multilayer perceptron (MLP), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), random forest (RF), support vector machine (SVM), XGBoost, SVM optimization by particle swarm optimization (PSO), SVM optimization by the whale optimization algorithm (WOA), SVM optimization by the GOA (GOA-SVM), XGBoost optimization by PSO, XGBoost optimization by the WOA. The results demonstrated that MLR and GOA-MLR models had poor prediction accuracy for AGB, and the accuracy did not significantly improve when input factors were more than three. Among single-factor-driven machine learning (ML) models, the GPR model had the highest accuracy, followed by the XGBoost model. When the input combinations of multispectral bands and VIs were used, the GOA-XGB model (having 37 input factors) had the highest accuracy, with RMSE = 0.232 kg m⁻², R² = 0.847, MAE = 0.178 kg m⁻², and NRMSE = 0.127. When the XGBoost feature selection was used to reduce the input factors to 16, the model accuracy improved further to RMSE = 0.226 kg m⁻², R² = 0.855, MAE = 0.172 kg m⁻², and NRMSE = 0.123. Based on the developed model, the average AGB of the plot was 1.49 ± 0.34 kg. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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17 pages, 3351 KiB  

Open AccessArticle

Evaluation of Ecological Carrying Capacity and Identification of Its Influencing Factors Based on Remote Sensing and Geographic Information System: A Case Study of the Yellow River Basin in Shaanxi

by Zhiyuan Zhu, Zhikun Mei, Shilin Li, Guangxin Ren and Yongzhong Feng

Land 2022, 11(7), 1080; https://0-doi-org.brum.beds.ac.uk/10.3390/land11071080 - 14 Jul 2022

Cited by 7 | Viewed by 2137

Abstract

Ecological carrying capacity (ECC), which requires simple scientific evaluation methods, is an important evaluation index for assessing the sustainability of ecosystems. We integrate an innovative research method. Geographic information systems (GIS) and remote sensing (RS) were used to evaluate the ECC of the [...] Read more.

Ecological carrying capacity (ECC), which requires simple scientific evaluation methods, is an important evaluation index for assessing the sustainability of ecosystems. We integrate an innovative research method. Geographic information systems (GIS) and remote sensing (RS) were used to evaluate the ECC of the Yellow River Basin in Shaanxi (YRBS) and to identify the underlying factors that influence it. A calculation method that combines RS and GIS data to estimate ECC based on net primary productivity (NPP) was established. The Carnegie–Ames–Stanford approach model was applied to estimate NPP. The NPP of each land type was used as an indicator to determine the yield factors. The ECC of the watershed was calculated with the carrying capacities of each land-use type. The geographical detector model was used to study the influencing factors of ECC, which provides a scientific basis for the formulation of ecological management policies in YRBS. The results show that from 2000 to 2010, it first decreased by 45.46%, and then increased by 37.06% in 2020, an overall decrease of 13.49 × 10⁵ wha in 20 years. Precipitation is the dominant factor that affects ECC, while the impact of human activities on ECC was significantly enhanced during the study period. The developed method based on RS data serves as a reference for ecological evaluation in other similar regions. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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27 pages, 7622 KiB  

Open AccessArticle

Estimation of Maize Foliar Temperature and Stomatal Conductance as Indicators of Water Stress Based on Optical and Thermal Imagery Acquired Using an Unmanned Aerial Vehicle (UAV) Platform

by Kiara Brewer, Alistair Clulow, Mbulisi Sibanda, Shaeden Gokool, John Odindi, Onisimo Mutanga, Vivek Naiken, Vimbayi G. P. Chimonyo and Tafadzwanashe Mabhaudhi

Drones 2022, 6(7), 169; https://0-doi-org.brum.beds.ac.uk/10.3390/drones6070169 - 08 Jul 2022

Cited by 16 | Viewed by 4413

Abstract

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, [...] Read more.

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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27 pages, 3046 KiB  

Open AccessArticle

GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization

by Amir Azemati, Amer Melebari, James D. Campbell, Jeffrey P. Walker and Mahta Moghaddam

Remote Sens. 2022, 14(13), 3129; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133129 - 29 Jun 2022

Cited by 5 | Viewed by 2050

Abstract

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model [...] Read more.

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized bistatic radar cross section DDM of the land surface. This method was tested on both simulated DDMs and CYGNSS DDMs over the Soil Moisture Active Passive (SMAP) Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDMs were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The vegetation water content (VWC) was $0.19 \mathrm{kg}$${\mathrm{m}}^{-2}$ and $4.89 \mathrm{kg}$${\mathrm{m}}^{-2}$ for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than $0.03 {\mathrm{m}}^3$${\mathrm{m}}^{-3}$ while it was approximately $0.06 {\mathrm{m}}^3$${\mathrm{m}}^{-3}$ and $0.09 {\mathrm{m}}^3$${\mathrm{m}}^{-3}$ for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an an ubRMSE of less than $0.12 {\mathrm{m}}^3$${\mathrm{m}}^{-3}$. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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21 pages, 5227 KiB  

Open AccessArticle

Detection of Irrigated Permanent Grasslands with Sentinel-2 Based on Temporal Patterns of the Leaf Area Index (LAI)

by Mukhtar Abubakar, André Chanzy, Guillaume Pouget, Fabrice Flamain and Dominique Courault

Remote Sens. 2022, 14(13), 3056; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133056 - 25 Jun 2022

Cited by 6 | Viewed by 2064

Abstract

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are [...] Read more.

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France), which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May–October period, which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016–2020) was used. The detection mowing number was conducted at the pixel level, and then the results were aggregated at the plot level. An evaluation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94 and 0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 5513 KiB  

Open AccessArticle

Estimating Productivity Measures in Guayule Using UAS Imagery and Sentinel-2 Satellite Data

by Truman P. Combs, Kamel Didan, David Dierig, Christopher J. Jarchow and Armando Barreto-Muñoz

Remote Sens. 2022, 14(12), 2867; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122867 - 15 Jun 2022

Cited by 2 | Viewed by 1968

Abstract

Guayule (Parthenium argentatum Gray) is a perennial desert shrub currently under investigation as a viable commercial alternative to the Pará rubber tree (Hevea brasiliensis), the traditional source of natural rubber. Previous studies on guayule have shown a close association [...] Read more.

Guayule (Parthenium argentatum Gray) is a perennial desert shrub currently under investigation as a viable commercial alternative to the Pará rubber tree (Hevea brasiliensis), the traditional source of natural rubber. Previous studies on guayule have shown a close association between morphological traits or biomass and rubber content. We collected multispectral and RGB-derived Structure-from-motion (SfM) data using an unmanned aircraft system (UAS; drone) to determine if incorporating both high-resolution normalized difference vegetation index (NDVI; an indicator of plant health) and canopy height (CH) information could support model predictions of crop productivity. Ground-truth resource allocation in guayule was measured at four elevations (i.e., tiers) along the crop’s vertical profile using both traditional biomass measurement techniques and a novel volumetric measurement technique. Multiple linear regression models estimating fresh weight (FW), dry weight (DW), fresh volume (FV), fresh-weight-density (FWD), and dry-weight-density (DWD) were developed and their performance compared. Of the crop productivity measures considered, a model predicting FWD (i.e., the fresh weight of plant material adjusted by its freshly harvested volume) and incorporating NDVI, CH, NDVI:CH interaction, and tier parameters reported the lowest mean absolute percentage error (MAPE) between field measurements and predictions, ranging from 9 to 13%. A reduced FWD model incorporating only NDVI and tier parameters was developed to explore the scalability of model predictions to medium spatial resolutions with Sentinel-2 satellite data. Across all UAS surveys and corresponding satellite imagery compared, MAPE between FWD model predictions for UAS and satellite data were below 3% irrespective of soil pixel influence. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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14 pages, 2974 KiB  

Open AccessArticle

Study of the Agglomeration Characteristics of Cultivated Land in Underdeveloped Mountainous Areas Based on Spatial Auto-Correlation: A Case of Pengshui County, Chongqing, China

by Guanglian Luo, Bin Wang, Ruiwei Li, Dongqi Luo and Chaofu Wei

Land 2022, 11(6), 854; https://0-doi-org.brum.beds.ac.uk/10.3390/land11060854 - 06 Jun 2022

Cited by 3 | Viewed by 1730

Abstract

The economic and social orientation of cultivated land in underdeveloped mountainous areas is obvious. A study of the spatial agglomeration characteristics of cultivated land quality can provide guidance for regional economic and social development. Taking Pengshui County, Chongqing, China as the study area, [...] Read more.

The economic and social orientation of cultivated land in underdeveloped mountainous areas is obvious. A study of the spatial agglomeration characteristics of cultivated land quality can provide guidance for regional economic and social development. Taking Pengshui County, Chongqing, China as the study area, the spatial agglomeration characteristics of cultivated land quality indexes at county, township and village levels were analyzed by using the auto-correlation analysis method. The results showed that: (1) At different spatial scales, the cultivated land quality index showed spatial agglomeration characteristics. (2) Moran’s I values of the cultivated land quality index at county, township and village level decreased successively, but three indexes still showed significant positive spatial correlation. (3) The spatial scale affects the spatial agglomeration of the cultivated land quality index, and its influence is physical, with a utilization and economic quality grade index from large to small. In underdeveloped mountainous areas, the spatial agglomeration characteristics of township scale and physical quality grade index are the most stable and significant, which can be used as the direct basis for zoning of cultivated land protection and site selection of rural residents’ agglomeration points. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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17 pages, 5893 KiB  

Open AccessArticle

Two-Stepwise Hierarchical Adaptive Threshold Method for Automatic Rapeseed Mapping over Jiangsu Using Harmonized Landsat/Sentinel-2

by Shaomei Chen, Zhaofu Li, Tingli Ji, Haiyan Zhao, Xiaosan Jiang, Xiang Gao, Jianjun Pan and Wenmin Zhang

Remote Sens. 2022, 14(11), 2715; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112715 - 06 Jun 2022

Cited by 4 | Viewed by 2879

Abstract

Rapeseed distribution mapping is a crucial issue for food and oil security, entertainment, and tourism development. Previous studies have used various remote sensing approaches to map rapeseed. However, the time-consuming and labor-intensive sample data used in these supervised classification methods greatly limit the [...] Read more.

Rapeseed distribution mapping is a crucial issue for food and oil security, entertainment, and tourism development. Previous studies have used various remote sensing approaches to map rapeseed. However, the time-consuming and labor-intensive sample data used in these supervised classification methods greatly limit the development of large-scale mapping in rapeseed studies. Regarding threshold methods, some empirical thresholding methods still need sample data to select the optimal threshold value, and their accuracies decrease when a fixed threshold is applied in complex and diverse environments. This study first developed the Normalized Difference Rapeseed Index (NDRI), defined as the difference in green and short-wave infrared bands divided by their sum, to find a suitable feature to distinguish rapeseed from other types of crops. Next, a two-stepwise hierarchical adaptive thresholding (THAT) algorithm requiring no training data was used to automatically extract rapeseed in Xinghua. Finally, two adaptive thresholding methods of the standalone Otsu and Otsu with Canny Edge Detection (OCED) were used to extract rapeseed across Jiangsu province. The results show that (1) NDRI can separate rapeseed from other vegetation well; (2) the OCED-THAT method can accurately map rapeseed in Jiangsu with an overall accuracy (OA) of 0.9559 and a Kappa coefficient of 0.8569, and it performed better than the Otsu-THAT method; (3) the OCED-THAT method had a lower but acceptable accuracy than the Random Forest method (OA = 0.9806 and Kappa = 0.9391). This study indicates that the THAT model is a promising automatic method for mapping rapeseed. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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27 pages, 8304 KiB  

Open AccessArticle

Monitoring Maize Growth and Calculating Plant Heights with Synthetic Aperture Radar (SAR) and Optical Satellite Images

by İbrahim Arslan, Mehmet Topakcı and Nusret Demir

Agriculture 2022, 12(6), 800; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12060800 - 01 Jun 2022

Cited by 7 | Viewed by 7077

Abstract

The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain [...] Read more.

The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain an adequate level of crop production in a world where the population is constantly increasing. Therefore, agricultural activities must be closely monitored, especially in maize fields since maize is of great importance to both humans and animals. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite images were used to monitor maize growth in this study. Backscatter and interferometric coherence values derived from Sentinel-1 images, as well as Normalized Difference Vegetation Index (NDVI) and values related to biophysical variables (such as Leaf Area Index (LAI), Fraction of Vegetation Cover (fCover or FVC), and Canopy Water Content (CW)) derived from Sentinel-2 images were investigated. Sentinel-1 images were also used to calculate plant heights. The Interferometric SAR (InSAR) technique was applied to calculate interferometric coherence values and plant heights. For the plant height calculation, two image pairs with the largest possible perpendicular baseline were selected. Backscatter, NDVI, LAI, fCover, and CW values were low before planting, while the interferometric coherence values were generally high. Backscatter, NDVI, LAI, fCover, and CW values increased as the maize grew, while the interferometric coherence values decreased. Among all Sentinel-derived values, fCover had the best correlation with maize height until maize height exceeded 260 cm (R² = 0.97). After harvest, a decrease in backscatter, NDVI, LAI, fCover, and CW values and an increase in interferometric coherence values were observed. NDVI, LAI, fCover, and CW values remained insensitive to tillage practices, whereas backscatter and interferometric coherence values were found to be sensitive to planting operations. In addition, backscatter values were also sensitive to irrigation operations, even when the average maize height was about 235 cm. Cloud cover and/or fog near the study area were found to affect NDVI, LAI, fCover, and CW values, while precipitation events had a significant impact on backscatter and interferometric coherence values. Furthermore, using Sentinel-1 images, the average plant height was calculated with an error of about 50 cm. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 3316 KiB  

Open AccessArticle

Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach

by Ran Pelta, Ofer Beeri, Rom Tarshish and Tal Shilo

Remote Sens. 2022, 14(11), 2600; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112600 - 28 May 2022

Cited by 8 | Viewed by 6023

Abstract

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture. It has been used globally since the 1970s as a proxy to monitor crop growth and correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. [...] Read more.

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture. It has been used globally since the 1970s as a proxy to monitor crop growth and correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions that might alter the crop’s real NDVI value. Synthetic Aperture Radar (SAR), on the other hand, can penetrate clouds and is hardly affected by atmospheric conditions, but it is sensitive to the physical structure of the crop and therefore does not give a direct indication of the NDVI. Several SAR indices and methods have been suggested to estimate NDVIs via SAR; however, they tend to work for local spatial and temporal conditions and do not work well globally. This is because they are not flexible enough to capture the changing NDVI–SAR relationship throughout the crop-growing season. This study suggests a new method for converting Sentinel-1 to NDVIs for Agricultural Fields (SNAF) by utilizing a hyperlocal machine learning approach. This method generates multiple on-the-fly disposal field- and time-specific models for every available Sentinel-1 image across 2021. Each model learns the field-specific NDVI (from Sentinel-2 and Landsat-8) –SAR (Sentinel-1) relationship based on recent NDVI and SAR time series and consequently estimates the optimal NDVI value from the current SAR image. The SNAF was tested on 548 commercial fields from 18 countries with 28 crop types and, based on 6880 paired NDVI–SAR images, achieved an RMSE, bias, and R² of 0.06, 0.00, and 0.92, respectively. The outcome of this study aspires to a persistent seamless stream of NDVI values, regardless of the atmospheric conditions, illumination, or local conditions, which can assist in agricultural decision making. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 12202 KiB  

Open AccessArticle

Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops

by Caixia Yin, Xin Lv, Lifu Zhang, Lulu Ma, Huihan Wang, Linshan Zhang and Ze Zhang

Remote Sens. 2022, 14(11), 2576; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112576 - 27 May 2022

Cited by 9 | Viewed by 2587

Abstract

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use [...] Read more.

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha⁻¹. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R², RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R², RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 6032 KiB  

Open AccessArticle

Surface Reflectance–Derived Spectral Indices for Drought Detection: Application to the Guadalupe Valley Basin, Baja California, Mexico

by Francisco José Del-Toro-Guerrero, Luis Walter Daesslé, Rodrigo Méndez-Alonzo and Thomas Kretzschmar

Land 2022, 11(6), 783; https://0-doi-org.brum.beds.ac.uk/10.3390/land11060783 - 26 May 2022

Cited by 3 | Viewed by 2359

Abstract

Evaluating how meteorological drought affects areas covered by natural ecosystems is challenging due to the lack of ground-based climate data, historical records, and weather station observation with limited coverage. This research tests how the surface reflectance–derived indices (SRDI) may solve this problem by [...] Read more.

Evaluating how meteorological drought affects areas covered by natural ecosystems is challenging due to the lack of ground-based climate data, historical records, and weather station observation with limited coverage. This research tests how the surface reflectance–derived indices (SRDI) may solve this problem by assessing the condition and vegetation dynamics. We use long–term, monthly surface reflectance data (26 hydrological years, 1992/93–2017/18) from Landsat 5 TM, 7 ETM+, and 8 OLI/TIRS satellites and calculated the following five SRDI: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Health Index (VHI), Normalized Difference Water Index (NDWI), and Modified Soil Adjusted Vegetation Index (MSAVI). The SRDI allows us to detect, classify, and quantify the area affected by drought in the Guadalupe Valley Basin (GVB) via correlations with the Reconnaissance Drought Index (RDI) and the Standardized Precipitation Index (SPI) (weather station-based data). For particular SRDI–RDI and SRDI–SPI combinations, we find positive seasonal correlations during April–May (IS2) and for annual (AN) values (MSAVI IS2–RDI AN, R = 0.90; NDWI IS2–SPI AN, R = 0.89; VHI AN–RDI AN, R = 0.86). The drought–affected GVB area accounted for >87% during 2001/02, 2006/07, 2013/14, and 2017/18. MSAVI and NDWI are the best meteorological drought indicators in this region, and their application minimizes the dependence on the availability of climatic data series. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 3980 KiB  

Open AccessArticle

Maize Yield Estimation in Intercropped Smallholder Fields Using Satellite Data in Southern Malawi

by Chengxiu Li, Ellasy Gulule Chimimba, Oscar Kambombe, Luke A. Brown, Tendai Polite Chibarabada, Yang Lu, Daniela Anghileri, Cosmo Ngongondo, Justin Sheffield and Jadunandan Dash

Remote Sens. 2022, 14(10), 2458; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102458 - 20 May 2022

Cited by 10 | Viewed by 3303

Abstract

Satellite data provide high potential for estimating crop yield, which is crucial to understanding determinants of yield gaps and therefore improving food production, particularly in sub-Saharan Africa (SSA) regions. However, accurate assessment of crop yield and its spatial variation is challenging in SSA [...] Read more.

Satellite data provide high potential for estimating crop yield, which is crucial to understanding determinants of yield gaps and therefore improving food production, particularly in sub-Saharan Africa (SSA) regions. However, accurate assessment of crop yield and its spatial variation is challenging in SSA because of small field sizes, widespread intercropping practices, and inadequate field observations. This study aimed to firstly evaluate the potential of satellite data in estimating maize yield in intercropped smallholder fields and secondly assess how factors such as satellite data spatial and temporal resolution, within-field variability, field size, harvest index and intercropping practices affect model performance. Having collected in situ data (field size, yield, intercrops occurrence, harvest index, and leaf area index), statistical models were developed to predict yield from multisource satellite data (i.e., Sentinel-2 and PlanetScope). Model accuracy and residuals were assessed against the above factors. Among 150 investigated fields, our study found that nearly half were intercropped with legumes, with an average plot size of 0.17 ha. Despite mixed pixels resulting from intercrops, the model based on the Sentinel-2 red-edge vegetation index (VI) could estimate maize yield with moderate accuracy (R² = 0.51, nRMSE = 19.95%), while higher spatial resolution satellite data (e.g., PlanetScope 3 m) only showed a marginal improvement in performance (R² = 0.52, nRMSE = 19.95%). Seasonal peak VI values provided better accuracy than seasonal mean/median VI, suggesting peak VI values may capture the signal of the dominant upper maize foliage layer and may be less impacted by understory intercrop effects. Still, intercropping practice reduces model accuracy, as the model residuals are lower in fields with pure maize (1 t/ha) compared to intercropped fields (1.3 t/ha). This study provides a reference for operational maize yield estimation in intercropped smallholder fields, using free satellite data in Southern Malawi. It also highlights the difficulties of estimating yield in intercropped fields using satellite imagery, and stresses the importance of sufficient satellite observations for monitoring intercropping practices in SSA. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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23 pages, 23060 KiB  

Open AccessArticle

The Iterative Extraction of the Boundary of Coherence Region and Iterative Look-Up Table for Forest Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar Data

by Zenghui Huang, Ye Yun, Huiming Chai and Xiaolei Lv

Remote Sens. 2022, 14(10), 2438; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102438 - 19 May 2022

Cited by 5 | Viewed by 1584

Abstract

In this paper, we introduce a refined three-stage inversion algorithm (TSIA) for forest height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR). Specifically, the iterative extraction of the boundary of the coherence region (IEBCR) and iterative look-up table (ILUT) are proposed to improve [...] Read more.

In this paper, we introduce a refined three-stage inversion algorithm (TSIA) for forest height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR). Specifically, the iterative extraction of the boundary of the coherence region (IEBCR) and iterative look-up table (ILUT) are proposed to improve the efficiency of traditional TSIA. A class of refined TSIA utilizes the boundary of the coherence region (BCR) to alleviate the underestimation phenomenon in forest height estimation. Given many eigendecompositions in the extraction of BCR (EBCR), we analyze the relationship of eigenvectors between the adjacent points on the BCR and propose the IEBCR utilizing the power methods. In the final inversion stage of TSIA, the look-up table (LUT) uses the exhaustive search method to minimize the loss function in the 2-D grid with defined step sizes and thus costs high computational complexity. To alleviate the deficiency, we define the random volume over ground (RVoG) function based on the RVoG model and prove its monotonicity and convergence from the analytical and numerical points of view. After analyzing the relationship between the RVoG function and the loss function, we propose the ILUT for the inversion stage. The simulation and experiments based on the BioSAR 2008 campaign data illustrate that the IEBCR and ILUT greatly improve the computational efficiency almost without compromising on accuracy. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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16 pages, 1200 KiB  

Open AccessArticle

A First Attempt to Combine NIRS and Plenoptic Cameras for the Assessment of Grasslands Functional Diversity and Species Composition

by Simon Taugourdeau, Mathilde Dionisi, Mylène Lascoste, Matthieu Lesnoff, Jean Marie Capron, Fréderic Borne, Philippe Borianne and Lionel Julien

Agriculture 2022, 12(5), 704; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12050704 - 17 May 2022

Cited by 1 | Viewed by 1799

Abstract

Grassland represents more than half of the agricultural land. Numerous metrics (biomass, functional trait, species composition) can be used to describe grassland vegetation and its multiple functions. The measures of these metrics are generally destructive and laborious. Indirect measurements using optical tools are [...] Read more.

Grassland represents more than half of the agricultural land. Numerous metrics (biomass, functional trait, species composition) can be used to describe grassland vegetation and its multiple functions. The measures of these metrics are generally destructive and laborious. Indirect measurements using optical tools are a possible alternative. Some tools have high spatial resolutions (digital camera), and others have high spectral resolutions (Near Infrared Spectrometry NIRS). A plenoptic camera is a multifocal camera that produces clear images at different depths in an image. The objective of this study was to test the interest of combining plenoptic images and NIRS data to characterize different descriptors of two Mediterranean legumes mixtures. On these mixtures, we measured biomass, species biomass, and functional trait diversity. NIRS and plenoptic images were acquired just before the field measurements. The plenoptic images were analyzed using Trainable Weka Segmentation ImageJ to evaluate the percentage of each species in the image. We calculated the average and standard deviation of the different colors (red, green, blue reflectance) in the image. We assessed the percentage of explanation of outputs of the images and NIRS analyses using variance partition and partial least squares. The biomass Trifolium michelianum and Vicia sativa were predicted with more than 50% variability explained. For the other descriptors, the variability explained was lower but nevertheless significant. The percentage variance explained was nevertheless quite low, and further work is required to produce a useable tool, but this work already demonstrates the interest in combining image analysis and NIRS. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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20 pages, 2902 KiB  

Open AccessArticle

Obstacle Avoidance and Profile Ground Flight Test and Analysis for Plant Protection UAV

by Shubo Wang, Shaoqing Xu, Congwei Yu, Hecheng Wu, Qiang Liu, Dian Liu, Liujian Jin, Yi Zheng, Jianli Song and Xiongkui He

Drones 2022, 6(5), 125; https://0-doi-org.brum.beds.ac.uk/10.3390/drones6050125 - 13 May 2022

Cited by 8 | Viewed by 2644

Abstract

In recent years, with the further development of agricultural aviation technology, the plant protection UAV has been widely used, especially in some agricultural environments with limited operating conditions due to its advantages of high efficiency, environmental protection and safety guarantee. A plant protection [...] Read more.

In recent years, with the further development of agricultural aviation technology, the plant protection UAV has been widely used, especially in some agricultural environments with limited operating conditions due to its advantages of high efficiency, environmental protection and safety guarantee. A plant protection UAV generally flies at low altitude during operation. However, the low altitude operation environment, such as farmland and mountainous areas, is relatively complex, and is faced with many types of obstacles, proposing higher requirements for obstacle avoidance and the profiling system of a plant protection UAV. In order to test the obstacle avoidance and profiling performance of the commercialized plant protection UAV at this stage and explore the performance boundary of obstacle avoidance and profiling of the UAV, EAVISION E-A2021 and XAG P80, the flagship models of the plant protection UAV manufacturer on the market, were hereby selected as the experimental test objects in the paper. Firstly, the obstacle avoidance and profiling test scheme of plant protection UAVs is designed; then, the above two UAVs are adopted for corresponding tests, and the test data are discussed based on the analysis of software and hardware technology; finally, the practical application status of different obstacle avoidance and profiling technologies of plant protection UAVs is clarified, and the shortcomings of obstacle avoidance and profiling technology of plant protection UAVs on the market are summarized, providing a reliable reference for the future development of plant protection UAVs. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 3199 KiB  

Open AccessArticle

A Simplified Spatial Methodology for Assessing Land Productivity Status in Africa

by Barasa Bernard, Majaliwa J. G. Mwanjalolo, Banduga Moses, Katwere James, Magaya Paul, Sadadi Ojoatre, Wanjiru Lydia and Margaret N. Walusimbi

Land 2022, 11(5), 730; https://0-doi-org.brum.beds.ac.uk/10.3390/land11050730 - 12 May 2022

Cited by 1 | Viewed by 3284

Abstract

The degradation of soil, vegetation and socio-economic transformations are a huge threat to Africa’s land production. This study aimed to (i) assess the soil and land productivity of standing biomass and (ii) determine the effect of rainfall on the standing biomass in Eastern [...] Read more.

The degradation of soil, vegetation and socio-economic transformations are a huge threat to Africa’s land production. This study aimed to (i) assess the soil and land productivity of standing biomass and (ii) determine the effect of rainfall on the standing biomass in Eastern Africa. Soil productivity was determined using the Soil Productivity Index (SPI) and a simplified model was developed to estimate the Net Primary Productivity (NPP). The SPI indicators used included soil-organic matter, texture, soil moisture, base-saturation, pH, cation-exchange-capacity, soil-depth and drainage. The inputs of the simplified model are: MODIS Soil Adjusted Vegetation Index (SAVI), soil erosion, soil nutrient content and input, rainfall, land-use/cover and agro-ecological zones. The findings reveal that the countries with the most productive soils are Mauritius, Rwanda and South Sudan—while, for standing biomass, the countries with the highest spatial extent are Mauritius (97%), Rwanda (96%), Uganda (95%), South Sudan (89%), Ethiopia (47%) and Kenya (36%). Standing biomass is dominant in biomes such as natural forests, woodlands, croplands, grasslands, wetlands and tree-plantations. High land productivity was attributed to soil quality and management, land policy reforms, favourable climatic conditions and sustainable land husbandry activities. Rainfall was significantly correlated with standing biomass in most of the studied countries (p < 0.05) except Djibouti and Rwanda. Therefore, monitoring soil health, use and land reforms are key to sustaining vegetative biomass. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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16 pages, 3244 KiB  

Open AccessArticle

Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region

by Xue Wang, Jiahua Zhang, Lan Xun, Jingwen Wang, Zhenjiang Wu, Malak Henchiri, Shichao Zhang, Sha Zhang, Yun Bai, Shanshan Yang, Shuaishuai Li and Xiang Yu

Remote Sens. 2022, 14(10), 2341; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102341 - 12 May 2022

Cited by 23 | Viewed by 3865

Abstract

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness [...] Read more.

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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19 pages, 7742 KiB  

Open AccessArticle

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

by Shuo Chen, Weihang Liu, Puyu Feng, Tao Ye, Yuchi Ma and Zhou Zhang

Remote Sens. 2022, 14(10), 2340; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102340 - 12 May 2022

Cited by 12 | Viewed by 2686

Abstract

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an [...] Read more.

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R²) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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16 pages, 3736 KiB  

Open AccessArticle

Multi-Temporal Landsat-8 Images for Retrieval and Broad Scale Mapping of Soil Copper Concentration Using Empirical Models

by Yuan Fang, Linlin Xu, Alexander Wong and David A. Clausi

Remote Sens. 2022, 14(10), 2311; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102311 - 11 May 2022

Cited by 7 | Viewed by 1720

Abstract

Mapping soil heavy metal concentration using machine learning models based on readily available satellite remote sensing images is highly desirable. Accurate mapping relies on appropriate data, feature extraction, and model selection. To this end, a data processing pipeline for soil copper (Cu) concentration [...] Read more.

Mapping soil heavy metal concentration using machine learning models based on readily available satellite remote sensing images is highly desirable. Accurate mapping relies on appropriate data, feature extraction, and model selection. To this end, a data processing pipeline for soil copper (Cu) concentration estimation has been designed. First, instead of using single Landsat scenes, the utilization of multiple Landsat scenes of the same location over time is considered. Second, to generate a preferred feature set as input to a regression model, a number of feature extraction methods are motivated and compared. Third, to find a preferred regression model, a variety of approaches are implemented and compared for accuracy. In this research, 11 Landsat-8 images from 2013 to 2017 of Gulin County, Sichuan China, and 138 soil samples with lab-measured Cu concentrations collected from the area in 2015 are used. A variety a metrics under cross-validation are used for comparison. The results indicate that multi-temporal images increase accuracy compared to single Landsat images. The preferred feature extraction varies based on the regression model used; however, the best results are obtained using support vector regression and the original data. The final soil Cu map generated using the recommended data processing pipeline shows a consistent spatial pattern with a ground-truth land cover classification map. These results indicate that machine learning has the ability to perform large-scale soil heavy metal mapping from widely available satellite remote sensing images. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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23 pages, 3955 KiB  

Open AccessArticle

UAV-Based Landfill Land Cover Mapping: Optimizing Data Acquisition and Open-Source Processing Protocols

by Coraline Wyard, Benjamin Beaumont, Taïs Grippa and Eric Hallot

Drones 2022, 6(5), 123; https://0-doi-org.brum.beds.ac.uk/10.3390/drones6050123 - 09 May 2022

Cited by 9 | Viewed by 2914

Abstract

Earth observation technologies offer non-intrusive solutions for monitoring complex and risky sites, such as landfills. In particular, unmanned aerial vehicles (UAVs) offer the ability to acquire data at very high spatial resolution, with full control of the temporality required for the desired application. [...] Read more.

Earth observation technologies offer non-intrusive solutions for monitoring complex and risky sites, such as landfills. In particular, unmanned aerial vehicles (UAVs) offer the ability to acquire data at very high spatial resolution, with full control of the temporality required for the desired application. The versatility of UAVs, both in terms of flight characteristics and on-board sensors, makes it possible to generate relevant geodata for a wide range of landfill monitoring activities. This study aims to propose a robust tool and to provide data acquisition guidelines for the land cover mapping of complex sites using UAV multispectral imagery. For this purpose, the transferability of a state-of-the-art object-based image analysis open-source processing chain was assessed and its sensitivity to the segmentation approach, textural and contextual information, spectral and spatial resolution was tested over the landfill site of Hallembaye (Wallonia, Belgium). This study proposes a consistent open-source processing chain for the land cover mapping using UAV data with accuracies of at least 85%. It shows that low-cost red-green-blue standard sensors are sufficient to reach such accuracies and that spatial resolution of up to 10 cm can be adopted with limited impact on the performance of the processing chain. This study also results in the creation of a new operational service for the monitoring of the active landfill sites of Wallonia. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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14 pages, 1615 KiB  

Open AccessArticle

Land Cover Classification from Hyperspectral Images via Local Nearest Neighbor Collaborative Representation with Tikhonov Regularization

by Rongchao Yang, Qingbo Zhou, Beilei Fan and Yuting Wang

Land 2022, 11(5), 702; https://0-doi-org.brum.beds.ac.uk/10.3390/land11050702 - 07 May 2022

Cited by 4 | Viewed by 1361

Abstract

The accurate and timely monitoring of land cover types is of great significance for the scientific planning, rational utilization, effective protection and management of land resources. In recent years, land cover classification based on hyperspectral images and the collaborative representation (CR) model has [...] Read more.

The accurate and timely monitoring of land cover types is of great significance for the scientific planning, rational utilization, effective protection and management of land resources. In recent years, land cover classification based on hyperspectral images and the collaborative representation (CR) model has become a hot topic in the field of remote sensing. However, most of the existing CR models do not consider the problem of sample imbalance, which affects the classification performance of CR models. In addition, the Tikhonov regularization term can improve the classification performance of CR models, but greatly increases the computational complexity of CR models. To address the above problems, a local nearest neighbor (LNN) method is proposed in this paper to select the same number of nearest neighbor samples from each nearest class of the test sample to construct a dictionary. This is then introduced into the original collaborative representation classification (CRC) method and CRC with Tikhonov regularization (CRT) for land cover classification, denoted as LNNCRC and LNNCRT, respectively. To verify the effectiveness of the proposed LNNCRC and LNNCRT methods, the classification performance and running time of the proposed methods are compared with those of six popular CR models on a hyperspectral scene with nine land cover types. The experimental results show that the proposed LNNCRT method achieves the best land cover classification performance, and the proposed LNNCRC and LNNCRT methods not only further exclude the interference of irrelevant training samples and classes, but also effectively eliminate the influence of imbalanced training samples, so as to improve the classification performance of CR models and effectively reduce the computational complexity of CR models. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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13 pages, 2521 KiB  

Open AccessArticle

A Practical Assessment of Using sUASs (Drones) to Detect and Quantify Wright Fishhook Cactus (Sclerocactus wrightiae L.D. Benson) Populations in Desert Grazinglands

by Thomas H. Bates, Val J. Anderson, Robert L. Johnson, Loreen Allphin, Dustin Rooks and Steven L. Petersen

Land 2022, 11(5), 655; https://0-doi-org.brum.beds.ac.uk/10.3390/land11050655 - 28 Apr 2022

Viewed by 1461

Abstract

Obtaining accurate plant population estimates has been integral in listing, recovery, and delisting species under the U.S. Endangered Species Act of 1973 and for monitoring vegetation in response to livestock grazing. Obtaining accurate population estimates remains a daunting and labor-intensive task. Small unmanned [...] Read more.

Obtaining accurate plant population estimates has been integral in listing, recovery, and delisting species under the U.S. Endangered Species Act of 1973 and for monitoring vegetation in response to livestock grazing. Obtaining accurate population estimates remains a daunting and labor-intensive task. Small unmanned aircraft systems (sUASs or drones) may provide an effective alternative to ground surveys for rare and endangered plants. The objective of our study was to evaluate the efficacy of sUASs (DJI Phantom 4 Pro) for surveying the Wright fishhook cactus (Sclerocactus wrightiae), a small (1–8 cm diameter) endangered species endemic to grazinglands in the southwest desert of Utah, USA. We assessed sUAS-based remotely sensed imagery to detect and count individual cacti compared to ground surveys and estimated optimal altitudes (10 m, 15 m, or 20 m) for collecting imagery. Our results demonstrated that low altitude flights provided the best detection rates (p < 0.001) and counts (p < 0.001) compared to 15 m and 20 m. We suggest that sUASs can effectively locate cactus within grazingland areas, but should be coupled with ground surveys for higher accuracy and reliability. We also acknowledge that these technologies may have limitations in effectively detecting small, low-growing individual plants such as the small and obscure fishhook cactus species. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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16 pages, 4695 KiB  

Open AccessArticle

Intercomparison of Real and Simulated GEDI Observations across Sclerophyll Forests

by Sven Huettermann, Simon Jones, Mariela Soto-Berelov and Samuel Hislop

Remote Sens. 2022, 14(9), 2096; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092096 - 27 Apr 2022

Cited by 7 | Viewed by 3423

Abstract

Forest structure is an important variable in ecology, fire behaviour, and carbon management. New spaceborne lidar sensors, such as the Global Ecosystem Dynamics Investigation (GEDI), enable forest structure to be mapped at a global scale. Virtual GEDI-like observations can be derived from airborne [...] Read more.

Forest structure is an important variable in ecology, fire behaviour, and carbon management. New spaceborne lidar sensors, such as the Global Ecosystem Dynamics Investigation (GEDI), enable forest structure to be mapped at a global scale. Virtual GEDI-like observations can be derived from airborne laser scanning (ALS) data for given locations using the GEDI simulator, which was a tool initially developed for GEDI’s pre-launch calibration. This study compares the relative height (RH) and ground elevation metrics of real and simulated GEDI observations against ALS-derived benchmarks in southeast Australia. A total of 15,616 footprint locations were examined, covering a large range of forest types and topographic conditions. The impacts of canopy cover and height, terrain slope, and ALS point cloud density were assessed. The results indicate that the simulator produces more accurate canopy height (RH95) metrics (RMSE: 4.2 m, Bias: −1.3 m) than the actual GEDI sensor (RMSE: 9.6 m, Bias: −1.6 m). Similarly, the simulator outperforms GEDI in ground detection accuracy. In contrast to other studies, which favour the Gaussian algorithm for ground detection, we found that the Maximum algorithm performed better in most settings. Despite the determined differences between real and simulated GEDI observations, this study indicates the compatibility of both data sources, which may enable their combined use in multitemporal forest structure monitoring. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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18 pages, 5533 KiB  

Open AccessArticle

Use of Geographically Weighted Regression (GWR) to Reveal Spatially Varying Relationships between Cd Accumulation and Soil Properties at Field Scale

by Zhifan Chen, Sen Zhang, Wencai Geng, Yongfeng Ding and Xingyuan Jiang

Land 2022, 11(5), 635; https://0-doi-org.brum.beds.ac.uk/10.3390/land11050635 - 26 Apr 2022

Cited by 9 | Viewed by 2493

Abstract

The spatial variation of correlation between Cd accumulation and its impact factors plays an important role in precise management of Cd contaminated farmland. Samples of topsoils (n = 247) were collected from suburban farmland located at the junction of the Yellow River [...] Read more.

The spatial variation of correlation between Cd accumulation and its impact factors plays an important role in precise management of Cd contaminated farmland. Samples of topsoils (n = 247) were collected from suburban farmland located at the junction of the Yellow River Basin and the Huaihe River Basin in China using a 200 m × 200 m grid system. The total and available contents of Cd (T-Cd and A-Cd) in topsoils were analyzed by ICP-MS, and their spatial distribution was analyzed using kriging interpolation with the GIS technique. Geographically weighted regression (GWR) models were applied to explore the spatial variation and their influencing mechanisms of relationships between major environmental factors (pH, organic matter, available phosphorus (A-P)) and Cd accumulation. Spatial distribution showed that T-Cd, A-Cd and their influencing factors had obvious spatial variability, and high value areas primarily cluster near industrial agglomeration areas and irrigation canals. GWR analysis revealed that relationships between T-Cd, A-Cd and their environmental factors presented obvious spatial heterogeneity. Notably, there was a significant negative correlation between soil pH and T-Cd, A-Cd, but with the increase of pH in soil the correlation decreased. A novel finding of a positive correlation between OM and T-Cd, A-Cd was observed, but significant positive correlation only occurred in the high anthropogenic input area due to the complex effects of organic matter on Cd activity. The influence intensity of pH and OM on T-Cd and A-Cd increases under the strong influence of anthropogenic sources. Additionally, T-Cd and A-Cd were totally positively related to soil A-P, but mostly not significantly, which was attributed to the complexity of the available phosphorus source and the differences in Cd contents in chemical fertilizer. Furthermore, clay content might be an important factor affecting the correlation between Cd and soil properties, considering that the correlation between Cd and pH, SOM, A-P was significantly lower in areas with lower clay particles. This study suggested that GWR was an effective tool to reveal spatially varying relationships at field scale, which provided a new idea to further explore the related influencing factors on spatial distribution of contaminants and to realize precise management of a farmland environment. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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17 pages, 10061 KiB  

Open AccessArticle

Land-Greening Hotspot Changes in the Yangtze River Economic Belt during the Last Four Decades and Their Connections to Human Activities

by Liangsheng Zhang, Haijiang Luo and Xuezhen Zhang

Land 2022, 11(5), 605; https://0-doi-org.brum.beds.ac.uk/10.3390/land11050605 - 21 Apr 2022

Cited by 6 | Viewed by 1585

Abstract

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI [...] Read more.

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI changes using global inventory modelling and mapping studies (GIMMS) NDVI data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data. Secondly, the Mann–Kendall test, regression residual analysis and cluster analysis are used to diagnose the potential causes of the NDVI changes. The results show that the regional mean NDVI exhibited an uptrend from 1982 to 2019, which consists of two prominent uptrend periods, i.e., 1982–2003 and 2003–2019. There has been a shift of greening hotspots. The first prominent greening trend from 1982 to 2003 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2003 to 2019 mainly occurred at the junction of Chongqing, Guizhou and Yunnan. The greening trend and shift of greening hotspots were slightly caused by climate change, but mainly caused by human activities. The first greening trend was closely related to the agricultural progress, and the second greening trend was associated with the rapid economic development and implementation of ecology restoration policies. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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26 pages, 53673 KiB  

Open AccessArticle

Investigating the Potential of Sentinel-2 MSI in Early Crop Identification in Northeast China

by Mengfan Wei, Hongyan Wang, Yuan Zhang, Qiangzi Li, Xin Du, Guanwei Shi and Yiting Ren

Remote Sens. 2022, 14(8), 1928; https://doi.org/10.3390/rs14081928 - 15 Apr 2022

Cited by 8 | Viewed by 2825

Abstract

Early crop identification can provide timely and valuable information for agricultural planting management departments to make reasonable and correct decisions. At present, there is still a lack of systematic summary and analysis on how to obtain real-time samples in the early stage, what [...] Read more.

Early crop identification can provide timely and valuable information for agricultural planting management departments to make reasonable and correct decisions. At present, there is still a lack of systematic summary and analysis on how to obtain real-time samples in the early stage, what the optimal feature sets are, and what level of crop identification accuracy can be achieved at different stages. First, this study generated training samples with the help of historical crop maps in 2019 and remote sensing images in 2020. Then, a feature optimization method was used to obtain the optimal features in different stages. Finally, the differences of the four classifiers in identifying crops and the variation characteristics of crop identification accuracy at different stages were analyzed. These experiments were conducted at three sites in Heilongjiang Province to evaluate the reliability of the results. The results showed that the earliest identification time of corn can be obtained in early July (the seven leaves period) with an identification accuracy up to 86%. In the early stages, its accuracy was 40~79%, which was low, and could not reach the satisfied accuracy requirements. In the middle stages, a satisfactory recognition accuracy could be achieved, and its recognition accuracy was 79~100%. The late stage had a higher recognition accuracy, which was 90~100%. The accuracy of soybeans at each stage was similar to that of corn, and the earliest identification time of soybeans could also be obtained in early July (the blooming period) with an identification accuracy up to 87%. Its accuracy in the early growth stage was 35~71%; in the middle stage, it was 69~100%; and in the late stage, it was 92~100%. Unlike corn and soybeans, the earliest identification time of rice could be obtained at the end of April (the flooding period) with an identification accuracy up to 86%. In the early stage, its accuracy was 58~100%; in the middle stage, its accuracy was 93~100%; and in the late stage, its accuracy was 96~100%. In terms of crop identification accuracy in the whole growth stage, GBDT and RF performed better than other classifiers in our three study areas. This study systematically investigated the potential of early crop recognition in Northeast China, and the results are helpful for relevant applications and decision making of crop recognition in different crop growth stages. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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21 pages, 13506 KiB  

Open AccessArticle

An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring

by Zhiming Hong, Yijie Hu, Changlu Cui, Xining Yang, Chongxin Tao, Weiran Luo, Wen Zhang, Linyi Li and Lingkui Meng

Agriculture 2022, 12(4), 547; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12040547 - 12 Apr 2022

Cited by 4 | Viewed by 2711

Abstract

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The [...] Read more.

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite SIF hinders the resolution enhancement of SIF derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) SIF provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day SIF (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI SIF (TROPOSIF) with ${\mathrm{R}}^2=0.956$ and $\mathrm{RMSE}=0.054{\mathrm{mWm}}^{-2}{\mathrm{sr}}^{-1}{\mathrm{nm}}^{-1}$. Comparison with another SIF product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with ${\mathrm{R}}^2$ for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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16 pages, 7298 KiB  

Open AccessArticle

Retrieval of Chlorophyll-a Concentrations of Class II Water Bodies of Inland Lakes and Reservoirs Based on ZY1-02D Satellite Hyperspectral Data

by Li Lu, Zhaoning Gong, Yanan Liang and Shuang Liang

Remote Sens. 2022, 14(8), 1842; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081842 - 12 Apr 2022

Cited by 10 | Viewed by 2559

Abstract

Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps [...] Read more.

Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps to assess the Class II water bodies of inland lakes and reservoirs, making it an important tool for refined remote sensing detection of the environment. In this study, the Baiyangdian Nature Reserve in northern China, which contains a typical inland lake and wetland, was chosen as the study area. Using ZY1-02D hyperspectral synchronization transit images and in situ measured chlorophyll-a concentration as the data source, remote sensing of the chlorophyll-a concentration of inland lakes was conducted. By analyzing the correlation between the spectral reflectance of the ZY1-02D hyperspectral image and the chlorophyll-a concentration and using algorithms such as the single band, band ratio, and three bands to compare and filter characteristic wavelengths, a quantitative hyperspectral model of the chlorophyll-a concentration was established to determine the chlorophyll-a concentration of Baiyangdian Lake. The dynamic monitoring of the water body and the assessment of the nutritional status of the water body were determined. The results revealed that the estimation of the chlorophyll-a concentration of Baiyangdian Lake based on the hyperspectral Fluorescence Line Height (FLH) model was ideal, with an R² value of 0.78. The FLH model not only comprehensively considers the effects of suspended solids, yellow substances, and backscattering of the water body on the estimation of the chlorophyll-a concentration, but also considers the influence of the elastic scattering efficiency of the chlorophyll. Based on the ZY1-02D hyperspectral data, a spatial distribution map of the chlorophyll-a concentration of Baiyangdian Lake was created to provide new ideas and technical support for monitoring inland water environments. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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24 pages, 11395 KiB  

Open AccessArticle

Agroecological Efficiency Evaluation Based on Multi-Source Remote Sensing Data in a Typical County of the Tibetan Plateau

by Qizhi Wang, Maofang Gao and Huijie Zhang

Land 2022, 11(4), 561; https://0-doi-org.brum.beds.ac.uk/10.3390/land11040561 - 10 Apr 2022

Cited by 5 | Viewed by 1716

Abstract

Evaluating agricultural ecology can help us to understand regional environmental status and contribute to the sustainable development of agricultural ecosystems. Furthermore, the results of eco-environmental assessment can provide data support for policy-making and agricultural production. The application of multi-source remote-sensing technology has the [...] Read more.

Evaluating agricultural ecology can help us to understand regional environmental status and contribute to the sustainable development of agricultural ecosystems. Furthermore, the results of eco-environmental assessment can provide data support for policy-making and agricultural production. The application of multi-source remote-sensing technology has the advantages of being fast, accurate and wide ranging. It can reveal the status of regional ecological environments, and is of great significance to monitoring their quality. In this paper, an agroecological efficiency evaluation model was constructed by combining remote sensing data and ecological index (EI). Multi-source remote-sensing data were used to obtain the evaluation index. Indicators collected from satellites, such as biological richness, vegetation cover, water network density, land stress, and pollution load, were used to quantitatively evaluate the agroecological efficiency of Rangtang County in the Tibetan Plateau. The results showed that the EI of Rangtang County increased from 61.77 to 65.10 during 2000–2020, which means that the eco-environmental quality of this area was good, and it has shown an obviously improving trend over the past 20 years. Rangtang County has converted more than 30 km²of grassland into woodland over the past 20 years. Climate change and human activities have had combined effects on the ecological environment of this area. The change in ecological environment quality is greatly affected by human disturbance. Policymakers should continue setting up nature reserves and should implement the policy of returning farmland to forests. Unreasonable grazing and rational allocation of land resources are still critical points of concern for future ecological environment construction. EI, combined with remote sensing and statistical data, is proven to be able to reasonably represent changes in ecological environment in Rangtang County, thus providing more possibilities for ecological evaluation on the Tibetan Plateau, and even the whole world. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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16 pages, 4721 KiB  

Open AccessCommunication

Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning

by Rui Fu, Rui Chen, Changjing Wang, Xiao Chen, Hongfan Gu, Cong Wang, Baodong Xu, Guoxiang Liu and Gaofei Yin

Remote Sens. 2022, 14(7), 1662; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071662 - 30 Mar 2022

Cited by 4 | Viewed by 2436

Abstract

Drought is an event of shortages in the water supply, whether atmospheric, surface water or ground water. Prolonged droughts have negative impacts on ecosystems, agriculture, society, and the economy. Although existing drought index products are widely utilized in drought monitoring, the coarse spatial [...] Read more.

Drought is an event of shortages in the water supply, whether atmospheric, surface water or ground water. Prolonged droughts have negative impacts on ecosystems, agriculture, society, and the economy. Although existing drought index products are widely utilized in drought monitoring, the coarse spatial resolution greatly limits their applications on regional or local scales. Machine learning driven by remote sensing observations offers an opportunity to monitor regional scale droughts. However, the limited time range of remote sensing observations such as vegetation index (VI) resulted in a substantial gap in generating high resolution drought index products before 2000. This study generated spatiotemporally continuous Standardized Precipitation Evapotranspiration Index (SPEI) data spanning from 1901–2018 in southwestern China by machine learning. It indicated that four Classification and Regression Tree (CART) approaches, decision trees (DT), random forest (RF), gradient boosted regression trees (GBRT) and extra trees (ET), can provide valid local drought information by downscaling the Estación Experimental de Aula Dei (EEAD) data. The in-situ SPEI dataset produced by the Penman–Monteith method was used as a benchmark to evaluate the temporal and spatial performance of the downscaled SPEI. In addition, the necessity of VI in SPEI downscaling was also assessed. The results showed that: (1) the ET-based product has the best performance (R² = 0.889, MAE = 0.232, RMSE = 0.432); (2) the VI provides no significant improvement for SPEI re-construction; (3) topography exerts an obvious influence on the downscaling process, and (4) the downscaled SPEI shows more consistency with the in-situ SPEI compared with EEAD SPEI. The proposed method can be easily extended to other areas without in-situ data and enhance the ability of long-term drought monitoring. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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15 pages, 4144 KiB  

Open AccessArticle

Deriving First Floor Elevations within Residential Communities Located in Galveston Using UAS Based Data

by Nicholas D. Diaz, Wesley E. Highfield, Samuel D. Brody and Brent R. Fortenberry

Drones 2022, 6(4), 81; https://0-doi-org.brum.beds.ac.uk/10.3390/drones6040081 - 25 Mar 2022

Cited by 4 | Viewed by 2575

Abstract

Flood damages occur when just one inch of water enters a residential household and models of flood damage estimation are sensitive to first-floor elevation (FFE). The current sources for FFEs consist of costly survey-based elevation certificates (ECs) or assumptions based on year built, [...] Read more.

Flood damages occur when just one inch of water enters a residential household and models of flood damage estimation are sensitive to first-floor elevation (FFE). The current sources for FFEs consist of costly survey-based elevation certificates (ECs) or assumptions based on year built, foundation type, and flood zone. We sought to address these limitations by establishing the role of an Unmanned Aerial System (UAS) to efficiently derive accurate FFEs. Four residential communities within Galveston Island, Texas were selected to assess efficient flight parameters required for UAS photogrammetry within the built environment. A real-time kinematic positioning enabled (RTK) UAS was then used to gather georeferenced aerial imagery and create detailed 3D photogrammetric models with ±0.02 m horizontal and ±0.05 m vertical accuracies. From these residential models, FFEs and other structural measurements present in traditional ECs were obtained. Comparative statistical analyses were performed using the UAS-based measurements and traditional EC measurements. UAS based FFE measurements achieved 0.16 m mean absolute error (MAE) across all comparative observations and were not statistically different from traditional EC measures. We conclude the RTK enabled UAS approach is an efficient, cost-effective method in establishing accurate FFEs and other flood-sensitive measures in residential communities. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment)

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