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Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 33474

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Special Issue Editors

Dr. Ahamed Tofael

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Guest Editor

Associate Professor, Faculty and Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
Interests: remote sensing; precision agriculture; big data; GIS; decision support systems; agricultural machinery sensing systems
Special Issues, Collections and Topics in MDPI journals

Dr. Ryozo Noguchi

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Guest Editor

Associate Professor, Faculty and Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, 305-8572, Tsukuba, Japan
Interests: decision support systems; LCA-based land management for crop production; biomass and bioenergy

Prof. Dr. Abdul Rashid Mohamed Sharif

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Guest Editor

Department of Biological and Agricultural Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
Interests: remote sensing; precision agriculture; GIS; decision support systems; land management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lilik Sutiarso

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Guest Editor

Department of Agricultural and Biosystem Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281, Indonesia
Interests: precision agriculture; intelligent control system; decision support systems; agricultural system and machinery; human knowledge management

Dr. Kaewtrakulpong Kriengkri

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Guest Editor

Assistant Professor, Faculty of Agriculture, Department of Farm Mechanics, Kasetsart University, Bangkok, Thailand
Interests: decision support systems; logistics and supply chain management

Special Issue Information

Dear Colleagues,

I am happy to tell you that Remote Sensing has taken on the interesting project of a Special Issue on land suitability assessment using remote sensing and GIS. The recent changes of land uses present users with challenges due to the intensification of growing foods and fibers over the years. I am sure that you all have problems in your countries in dealing with land uses and the impact of adding fertilizers to increase crop growth over the years.

At present, land management for sustainable intensification is a challenging issue which requires detailed land suitability analysis. How can we construct an appropriate land use management system to adapt to the microclimatic environment? In this regard, land suitability assessment offers detailed information on the soil fertility, climatic variability, and the environmental, economic, social, and cultural effects of land use for decision-making. We need to keep in mind the impacts of climate change on land uses. The intensified uses of fertilizers, pesticides, and herbicides might not be effective for plants due to resistance and the carrying capacity of soil fertility in the long run. Local expert knowledge is required on how land suitability assessment can expand, adapting site-specific crop selection for sustainability. As agronomy is local, we need to deal with climate issues for adoption in the micro scale. Conventionally, the indices from satellite remote sensing and meteorological observation (e.g., precipitation and air temperature) are the characterized conditions worldwide. These indices are the basis of measurement, which are spatially interpolated with crop and land biophysical properties. Satellite remote sensing has the opportunity to provide spatially accurate and localized information about land fertility and crop phenotyping ranging from higher to coarser resolutions. Land suitability assessment using remote sensing and GIS could increase the productivity of agricultural production, aid decision-making on early warning information, and inform life cycle assessment (LCA), forest protection, and policy formation for stakeholders to enhance management decisions to establish a big data scheme.

Therefore, the aim of this Special Issue of Remote Sensing is to collect articles (original research papers, review articles, and case studies) to provide insight into the application of satellite remote sensing and GIS datasets to generate solutions for more site-specific management of lands which involves monitoring, change detection, and modelling for selecting suitable sites (e.g., flooding and drought) at various spatial and temporal changes.

Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture is an open Special Issue welcoming a variety of novel scientific articles including innovative and cutting-edge research using remote sensing techniques and data from many platforms (ground truth data, satellite, aircraft, radar, drones, etc.) to the study related issues in agriculture and forestry. The Editor invites contributions on social, economic, and legal aspects of agriculture and forest management.

Dr. Tofael Ahamed
Dr. Ryozo Noguchi
Prof. Dr. Abdul Rashid Mohamed Sharif
Prof. Dr. Lilik Sutiarso
Dr. Kaewtrakulpong Kriengkri
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

land suitability
remote sensing
GIS
decision support system
land use and land cover
climate changes
precision agriculture

Published Papers (7 papers)

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Research

26 pages, 4305 KiB

Open AccessArticle

Assessment of Land Use Land Cover Changes for Predicting Vulnerable Agricultural Lands in River Basins of Bangladesh Using Remote Sensing and a Fuzzy Expert System

by Kazi Faiz Alam and Tofael Ahamed

Remote Sens. 2022, 14(21), 5582; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215582 - 05 Nov 2022

Cited by 3 | Viewed by 2647

Abstract

The aim of this study was to assess the LULC changes over 26 years from 1995 to 2021 to find the most changed land use conditions within the 25 km territory of the main river systems of Bangladesh. In addition, the prediction of vulnerable areas for agricultural land use in terms of inundation by river water was also analyzed. The study area includes river networks distributed through eight administrative divisions (Rangpur, Rajshahi, Mymensingh, Sylhet, Dhaka, Khulna, Barishal and Chittagong) of Bangladesh, covering an area of 64,556 km². The study was conducted by identifying permanent water bodies from NDWI indices and preparing LULC maps that include the five main land use classes (water body, bare land, vegetation, agricultural land, and urban area) in the Google Earth Engine platform using supervised classification. The LULC maps were then analyzed in the ArcGIS^® environment. A vulnerability map for agricultural land use was also prepared using a fuzzy expert-based system applying multicriteria analysis. From the land use land cover map of the study area, it was found that among the five land use classes, water bodies, bare land, vegetation, and urban areas increased in size by 3.65%, 2.18%, 3.31% and 2.55%, respectively, whereas agricultural land use significantly decreased by 11.68%. This decrease in agricultural land use was common for the analyzed area of all administrative divisions. According to the vulnerable area map of the eight divisions, more than 50% of the analyzed area of the Khulna and Dhaka divisions and more than 40% of the analyzed area of the Rajshahi, Mymensingh, Sylhet, Barishal and Chittagong divisions were highly vulnerable to agricultural land use due to the possibility of inundation by water. However, approximately 44% of the analyzed area of the Rangpur division was not vulnerable for agricultural land use. The prepared LULC and vulnerability maps can be helpful for the future land use planning of Bangladesh to meet the increasing demand for food production and livelihoods for increasing populations. Full article

(This article belongs to the Special Issue Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture)

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19 pages, 9231 KiB

Open AccessArticle

Land Suitability Analysis for Potential Vineyards Extension in Afghanistan at Regional Scale Using Remote Sensing Datasets

by Sara Tokhi Arab and Tofael Ahamed

Remote Sens. 2022, 14(18), 4450; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184450 - 06 Sep 2022

Cited by 8 | Viewed by 3744

Abstract

Grapes are one of the world’s most widely distributed crops and are cultivated in more than 100 countries in the global scheme. Due to climate change and improper vine growth variable selection, production has significantly decreased across countries. Therefore, the primary purpose of this study was to develop a land suitability analysis method using a fuzzy expert system at a regional scale. The fuzzy membership function was used in the ArcGIS^® environment to perform the spatial analysis, and the overlay function was used to generate the final suitability map for Afghanistan considering policy planning. The results indicated that 23% (15,760,144 ha) of the areas were potential and located in the highly suitable region for grape production; however, 11% (7,370,025 ha) of the regions were not suitable for vineyards throughout the country of Afghanistan. In the present study, it was observed that most of the vineyards were in highly suitable areas (90%, 80,466 ha), while 0.01% (5 ha) of the vineyards were in less suitable areas. The present analysis demonstrated that the significant extension of grape vines can be possible in highly suitable areas. The results of this research can support decision-makers, farm managers and land developers to find more prospective acreage for expanding vineyards in Afghanistan. Full article

(This article belongs to the Special Issue Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture)

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24 pages, 7338 KiB

Open AccessArticle

Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology

by Wenyi Lu, Tsuyoshi Okayama and Masakazu Komatsuzaki

Remote Sens. 2022, 14(1), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010078 - 24 Dec 2021

Cited by 9 | Viewed by 3473

Abstract

Unmanned aerial vehicle (UAV) photogrammetry was used to monitor crop height in a flooded paddy field. Three multi-rotor UAVs were utilized to conduct flight missions in order to capture RGB (RedGreenBlue) and multispectral images, and these images were analyzed using several different models to provide the best results. Two image sets taken by two UAVs, mounted with RGB cameras of the same resolution and Global Navigation Satellite System (GNSS) receivers of different accuracies, were applied to perform photogrammetry. Two methods were then proposed for creating crop height models (CHMs), one of which was denoted as the M1 method and was based on the Digital Surface Point Cloud (DSPC) and the Digital Terrain Point Cloud (DSPT). The other was denoted as the M2 method and was based on the DSPC and a bathymetric sensor. An image set taken by another UAV mounted with a multispectral camera was used for multispectral-based photogrammetry. A Normal Differential Vegetation Index (NDVI) and a Vegetation Fraction (VF) were then extracted. A new method based on multiple linear regression (MLR) combining the NDVI, the VF, and a Soil Plant Analysis Development (SPAD) value for estimating the measured height (MH) of rice was then proposed and denoted as the M3 method. The results show that the M1 method, the UAV with a GNSS receiver with a higher accuracy, obtained more reliable estimations, while the M2 method, the UAV with a GNSS receiver of moderate accuracy, was actually slightly better. The effect on the performance of CHMs created by the M1 and M2 methods is more negligible in different plots with different treatments; however, remarkably, the more uniform the distribution of vegetation over the water surface, the better the performance. The M3 method, which was created using only a SPAD value and a canopy NDVI value, showed the highest coefficient of determination (

R^{2}

) for overall MH estimation, 0.838, compared with other combinations. Full article

(This article belongs to the Special Issue Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture)

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20 pages, 7336 KiB

Open AccessArticle

An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

by Animesh Chandra Das, Ryozo Noguchi and Tofael Ahamed

Remote Sens. 2021, 13(14), 2730; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142730 - 12 Jul 2021

Cited by 12 | Viewed by 5528

Abstract

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates. Full article

(This article belongs to the Special Issue Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture)

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28 pages, 4916 KiB

Open AccessArticle

Plot-Based Classification of Macronutrient Levels in Oil Palm Trees with Landsat-8 Images and Machine Learning

by Zhi Hong Kok, Abdul Rashid Bin Mohamed Shariff, Siti Khairunniza-Bejo, Hyeon-Tae Kim, Tofael Ahamed, See Siang Cheah and Siti Aishah Abd Wahid

Remote Sens. 2021, 13(11), 2029; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112029 - 21 May 2021

Cited by 3 | Viewed by 2858

Abstract

Oil palm crops are essential for ensuring sustainable edible oil production, in which production is highly dependent on fertilizer applications. Using Landsat-8 imageries, the feasibility of macronutrient level classification with Machine Learning (ML) was studied. Variable rates of compost and inorganic fertilizer were applied to experimental plots and the following nutrients were studied: nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca). By applying image filters, separability metrics, vegetation indices (VI) and feature selection, spectral features for each plot were acquired and used with ML models to classify macronutrient levels of palm stands from chemical foliar analysis of their 17th frond. The models were calibrated and validated with 30 repetitions, with the best mean overall accuracy reported for N and K at 79.7 ± 4.3% and 76.6 ± 4.1% respectively, while accuracies for P, Mg and Ca could not be accurately classified due to the limitations of the dataset used. The study highlighted the effectiveness of separability metrics in quantifying class separability, the importance of indices for N and K level classification, and the effects of filter and feature selection on model performance, as well as concluding RF or SVM models for excessive N and K level detection. Future improvements should focus on further model validation and the use of higher-resolution imaging. Full article

(This article belongs to the Special Issue Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture)

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24 pages, 4343 KiB

Open AccessArticle

Integrating an Expert System, GIS, and Satellite Remote Sensing to Evaluate Land Suitability for Sustainable Tea Production in Bangladesh

by Animesh Chandra Das, Ryozo Noguchi and Tofael Ahamed

Remote Sens. 2020, 12(24), 4136; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244136 - 17 Dec 2020

Cited by 20 | Viewed by 8877

Abstract

Land evaluation is important for assessing environmental limitations that inhibit higher yield and productivity in tea. The aim of this research was to determine the suitable lands for sustainable tea production in the northeastern part of Bangladesh using phenological datasets from remote sensing, geospatial datasets of soil–plant biophysical properties, and expert opinions. Sentinel-2 satellite images were processed to obtain layers for land use and land cover (LULC) as well as the normalized difference vegetation index (NDVI). Data from the Shuttle Radar Topography Mission (SRTM) were used to generate the elevation layer. Other vector and raster layers of edaphic, climatic parameters, and vegetation indices were processed in ArcGIS 10.7.1^® software. Finally, suitability classes were determined using weighted overlay of spatial analysis based on reclassified raster layers of all parameters along with the results from multicriteria analysis. The results of the study showed that only 41,460 hectares of land (3.37% of the total land) were in the highly suitable category. The proportions of moderately suitable, marginally suitable, and not suitable land categories for tea cultivation in the Sylhet Division were 9.01%, 49.87%, and 37.75%, respectively. Thirty-one tea estates were located in highly suitable areas, 79 in moderately suitable areas, 24 in marginally suitable areas, and only one in a not suitable area. Yield estimation was performed with the NDVI (R² = 0.69, 0.66, and 0.67) and the LAI (R² = 0.68, 0.65, and 0.63) for 2017, 2018, and 2019, respectively. This research suggests that satellite remote sensing and GIS application with the analytical hierarchy process (AHP) could be used by agricultural land use planners and land policy makers to select suitable lands for increasing tea production. Full article

(This article belongs to the Special Issue Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture)

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17 pages, 7165 KiB

Open AccessArticle

Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

by Yan Nie, Ying Tan, Yuqin Deng and Jing Yu

Remote Sens. 2020, 12(16), 2587; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162587 - 11 Aug 2020

Cited by 13 | Viewed by 2626

Abstract

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions. Full article

(This article belongs to the Special Issue Remote Sensing Application in Big Data: GIS-Based Land Suitability Assessments for Precision Agriculture)

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