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Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 36191

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Special Issue Editors

Dr. Eva Ivits

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Guest Editor

European Environment Agency, Copenhagen, Denmark
Interests: land use; land use intensity; land take; land recycling; habitats segmentation; carbon sequestration; land and ecosystem degradation
Special Issues, Collections and Topics in MDPI journals

Dr. Stephanie Horion

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Guest Editor

Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Interests: terrestrial ecosystems and climate studies; global environmental change; abrupt changes in ecosystem functioning; drought monitoring and impact assessment
Special Issues, Collections and Topics in MDPI journals

Dr. Roel Van Hoolst

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Guest Editor

Remote Sensing unit, Flemish Institute for Technological Research (VITO), Belgium
Interests: vegetation productivity and dynamics; multiple EO data; crop and pasture monitoring

Special Issue Information

Dear Colleagues,

Land is our shared, essential resource. It maintains healthy ecosystems and supports the flora and fauna; it is the foundation of our society and it is also a source for economic growth. Yet, it is under intense pressure, far beyond the limit of sustainable development for our planet. Food demand is increasing globally, whereas climate change and extreme events are critically impacting the functioning of all ecosystems on Earth. To satisfy the ever-growing demands for land resources, intensive use of lands manifests in the over-exploitation of ecosystems, which may cause the irreversible loss of ecosystem functions, in many cases leading to land degradation. A comprehensive understanding of all interactions between the biosphere, the climate, the biogeochemical cycle, and socio-economic impacts requires one to properly linking the related drivers of land productivity dynamics.

Land productivity dynamics should be monitored and mapped over time and space and mapped on a large scale using high-repeat frequency and spatially continuous observation approaches. These approaches have to be supported by quantitative, robust, reliable, and comparable methods to supply a standardized framework for land degradation studies. Earth Observation sensors, techniques, methods, and approaches are now advanced enough to support policy makers with sound, quantitative, and comparable assessments. Remote sensing-derived land productivity parameters have a strong added value in completing the mapping of the condition and degradation of ecosystems. They capture the spatial patterns of vegetation dynamics repetitively over vast areas; they are directly related to key aspects of vegetation dynamism such as seasonality, productivity, and inter-annual variation; and they provide an integrated measure of ecosystem responses to climatic factors, to extreme events, as well as to anthropogenic disturbances.

This special issue aims at presenting the latest advances in EO-based research for monitoring land productivity dynamics and related land degradation patterns. We welcome papers discussing challenges and opportunities in mapping land productivity dynamics and related land degradation over case studies. Emphasis is put on those interdisciplinary approaches, which link Earth Observation-derived land productivity, and other vegetation remote sensing-related paramaters to climatic and socio-economic drivers of land degradation. Articles covering but not limited to recent research on the following topics are invited to this Special Issue:

Remote Sensing data and methods for estimating land productivity dynamics (e.g., biophysical variables, Gross Primary Production, and above-ground biomass);
Integration of RS-derived land productivity data with in situ data and other expert knowledge;
Approaches to monitoring land degradation using Remote Sensing-derived land productivity;
Research demonstrating new use of EO-based land productivity dynamics for assessing of, and accounting for sustainable land use and/or change in ecosystem functioning;
Apporaches to facilitate reporting on land degradation addressing international targets or conventions (such as the SDGs, the UNCCD, the UNFCCC, etc.);
Methods using high and very high resolution satellite images such as the Sentinel systems;
Advanced processing methods to process time series of land porductivity dynamics such as Artificial Intelligence, cloud computing environments, and big data cubes.

We are looking forward to high-quality submissions on the topic of monitoring land productivity dynamics and to an exciting and fruitful collaboration with the research community in these critical times of increasing global environmental crises.

Dr. Eva Ivits
Dr. Stephanie Horion
Dr. Roel Van Hoolst
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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 productivity
Land degradation
Climatic impacts on lands
Anthropogenic impacts on lands
Land use/land cover
Ecosystem condition assessment
Time series analysis of vegetation indices

Published Papers (9 papers)

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Research

34 pages, 40413 KiB

Open AccessArticle

Prospects for Long-Term Agriculture in Southern Africa: Emergent Dynamics of Savannah Ecosystems from Remote Sensing Observations

by Tiffany M. Wei and Ana P. Barros

Remote Sens. 2021, 13(15), 2954; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152954 - 27 Jul 2021

Cited by 3 | Viewed by 2344

Abstract

Hydro-climatic resilience is an essential element of food security. The miombo ecosystem in Southern Africa supports varied land uses for a growing population. Albedo, Leaf Area Index (LAI), Fractional Vegetation Cover (FVC), Solar-Induced chlorophyll Fluorescence (SIF), and precipitation remote-sensing data for current climate were jointly analyzed to explore vegetation dynamics and water availability feedbacks. Changes in the surface energy balance tied to vegetation status were examined in the light of an hourly albedo product with improved atmospheric correction derived for this study. Phase-space analysis shows that the albedo’s seasonality tracks the landscape-scale functional stability of miombo and woody savanna with respect to precipitation variations. Miombo exhibits the best adaptive traits to water stress which highlights synergies among root-system water uptake capacity, vegetation architecture, and landscape hydro-geomorphology. This explains why efforts to conserve the spatial structure of the miombo forest in sustainable farming of seasonal wetlands have led to significant crop yield increases. Grass savanna’s high vulnerability to water stress is illustrative of potential run-away impacts of miombo deforestation. This study suggests that phase-space analysis of albedo, SIF, and FVC can be used as operational diagnostics of ecosystem health. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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16 pages, 28703 KiB

Open AccessArticle

National Mapping of New Zealand Pasture Productivity Using Temporal Sentinel-2 Data

by Alexander C. Amies, John R. Dymond, James D. Shepherd, David Pairman, Coby Hoogendoorn, Marmar Sabetizade and Stella E. Belliss

Remote Sens. 2021, 13(8), 1481; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081481 - 12 Apr 2021

Cited by 12 | Viewed by 3459

Abstract

A national map of pasture productivity, in terms of mass of dry matter yield per unit area and time, enables evaluation of regional and local land-use suitability. Difficulty in measuring this quantity at scale directed this research, which utilises four years of Sentinel-2 satellite imagery and collected pasture yield measurements to develop a model of pasture productivity. The model uses a Normalised Difference Vegetation Index (NDVI), with spatio-temporal segmentation and averaging, to estimate mean annual pasture productivity across all of New Zealand’s grasslands with a standard error of prediction of 2.2 t/ha/y. Regional aggregates of pasture yield demonstrate expected spatial variations. The pasture productivity map may be used to classify grasslands objectively into stratified levels of production on a national scale. Due to its ability to highlight areas of land use intensification suitability, the national map of pasture productivity is of value to landowners, land users, and environmental scientists. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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30 pages, 5770 KiB

Open AccessArticle

Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

by Kyle D. Woodward, Narcisa G. Pricope, Forrest R. Stevens, Andrea E. Gaughan, Nicholas E. Kolarik, Michael D. Drake, Jonathan Salerno, Lin Cassidy, Joel Hartter, Karen M. Bailey and Henry Maseka Luwaya

Remote Sens. 2021, 13(4), 631; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040631 - 10 Feb 2021

Cited by 2 | Viewed by 3420

Abstract

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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19 pages, 7376 KiB

Open AccessArticle

Use of Sentinel-1 Multi-Configuration and Multi-Temporal Series for Monitoring Parameters of Winter Wheat

by Azza Gorrab, Maël Ameline, Clément Albergel and Frédéric Baup

Remote Sens. 2021, 13(4), 553; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040553 - 04 Feb 2021

Cited by 5 | Viewed by 2546

Abstract

The present study aims to investigate the potential of multi-configuration Sentinel-1 (S-1) synthetic aperture radar (SAR) images for characterizing four wheat parameters: total fresh mass (TFM), total dry mass (TDM), plant heights (He), and water content (WC). Because they are almost independent on the weather conditions, we have chosen to use only SAR. Samples of wheat parameters were collected over seven fields (three irrigated and four rainfed fields) in Southwestern France. We first analyzed the temporal behaviors of wheat parameters (He, TDM, TFM and WC) between February and June 2016. Then, the temporal profiles of the S-1 backscattering coefficients (VV, VH), the difference (VH − VV), the sum of the polarizations (VH + VV) and their cumulative values are analyzed for two orbits (30 and 132) during the wheat-growing season (from January to July 2016). After that, S-1 signals were statistically compared with all crop parameters considering the impact of pass orbit, irrigation and two vegetative periods in order to identify the best S-1 configuration for estimating crop parameters. Interesting S-1 backscattering behaviors were observed with the various wheat parameters after separating irrigation impacts and vegetative periods. For the orbit 30 (mean incidence angle of 33.6°); results show that the best S-1 configurations (with coefficient of determination (R²) > 0.7) were obtained using the VV and VH + VV as a function of the He, TDM and WC, over irrigated fields and during the second vegetative period. For the orbit 132 (mean incidence angle of 43.4°), the highest dynamic sensitivities (R² > 0.8) were observed for the VV and VH + VV configurations with He, TDM and TFM over irrigated fields during the first vegetative period. Overall, the sensitivity of S-1 data to wheat variables depended on the radar configuration (orbits and polarizations), the vegetative periods and was often better over irrigated fields in comparison with rainfed ones. Significant improvements of the determination coefficients were obtained when the cumulative (VH + VV) index was considered for He (R² > 0.9), TDM (R² > 0.9) and TFM (R² > 0.75) for irrigated fields, all along the crop cycle. The estimate of WC was more limited (R² > 0.6) and remained limited to the second period of the vegetation cycle (from flowering onwards). Whatever parameters were considered, the relative errors never exceeded 23%. This study has shown the importance of considering the agricultural practices (irrigation) and vegetative periods to effectively monitor some wheat parameters with S-1 data. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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21 pages, 3380 KiB

Open AccessArticle

Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

by Amal Chakhar, David Hernández-López, Rocío Ballesteros and Miguel A. Moreno

Remote Sens. 2021, 13(2), 243; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020243 - 12 Jan 2021

Cited by 39 | Viewed by 4254

Abstract

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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14 pages, 3802 KiB

Open AccessArticle

Green Vegetation Cover Has Steadily Increased since Establishment of Community Forests in Western Chitwan, Nepal

by Jie Dai, Dar A. Roberts, Douglas A. Stow, Li An and Qunshan Zhao

Remote Sens. 2020, 12(24), 4071; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244071 - 12 Dec 2020

Cited by 8 | Viewed by 2346

Abstract

Community forests have been established worldwide to sustainably manage forest ecosystem services while maintaining the livelihoods of local residents. The Chitwan National Park in Nepal is a world-renowned biodiversity hotspot, where community forests were consolidated in the park’s buffer zone after 1993. These western Chitwan community forests stand as the frontiers of human–environment interactions, nurturing endangered large mammal species while providing significant natural resources for local residents. Nevertheless, no systematic forest cover assessment has been conducted for these forests since their establishment. In this study, we examined the green vegetation dynamics of these community forests for the years 1988–2018 using Landsat surface reflectance products. Combining an automatic water extraction index, spectral mixture analysis and the normalized difference fraction index (NDFI), we developed water masks and quantified the water-adjusted green vegetation fractions and NDFI values in the forests. Results showed that all forests have been continuously greening up since their establishment, and the average green vegetation cover of all forests increased from approximately 30% in 1988 to above 70% in 2018. With possible contributions from the invasion of exotic understory plant species, we credit community forestry programs for some of the green-up signals. Monitoring of forest vegetation dynamics is critical for evaluating the effectiveness of community forestry as well as developing sustainable forest management policies. Our research will provide positive feedbacks to local community forest committees and users. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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42 pages, 23332 KiB

Open AccessArticle

Long-Term Grass Biomass Estimation of Pastures from Satellite Data

by Chiara Clementini, Andrea Pomente, Daniele Latini, Hideki Kanamaru, Maria Raffaella Vuolo, Ana Heureux, Mariko Fujisawa, Giovanni Schiavon and Fabio Del Frate

Remote Sens. 2020, 12(13), 2160; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12132160 - 06 Jul 2020

Cited by 13 | Viewed by 5001

Abstract

The general consensus on future climate projections poses new and increased concerns about climate change and its impacts. Droughts are primarily worrying, since they contribute to altering the composition, distribution, and abundance of species. Grasslands, for example, are the primary source for grazing mammals and modifications in climate determine variation in the available yields for cattle. To support the agriculture sector, international organizations such as the Food and Agriculture Organization (FAO) of the United Nations are promoting the development of dedicated monitoring initiatives, with particular attention for undeveloped and disadvantaged countries. The temporal scale is very important in this context, where long time series of data are required to compute consistent analyses. In this research, we discuss the results regarding long-term grass biomass estimation in an extended African region. The results are obtained by means of a procedure that is mostly automatic and replicable in other contexts. Zambia has been identified as a significant test area due to its vulnerability to the adverse impacts of climate change as a result of its geographic location, socioeconomic stresses, and low adaptive capacity. In fact, analysis and estimations were performed over a long time window (21 years) to identify correlations with climate variables, such as precipitation, to clarify sensitivity to climate change and possible effects already in place. From the analysis, decline in both grass quality and quantity was not currently evident in the study area. However, pastures in the considered area were found to be vulnerable to changing climate and, in particular, to the water shortages accompanying drought periods. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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19 pages, 5680 KiB

Open AccessArticle

Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data

by Amal Chakhar, Damián Ortega-Terol, David Hernández-López, Rocío Ballesteros, José F. Ortega and Miguel A. Moreno

Remote Sens. 2020, 12(11), 1735; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111735 - 28 May 2020

Cited by 49 | Viewed by 4637

Abstract

The launch of Sentinel-2A and B satellites has boosted the development of many applications that could benefit from the fine resolution of the supplied information, both in time and in space. Crop classification is a necessary task for efficient land management. We evaluated the benefits of combining Landsat-8 and Sentinel-2A information for irrigated crop classification. We also assessed the robustness and efficiency of 22 nonparametric classification algorithms for classifying irrigated crops in a semiarid region in the southeast of Spain. A parcel-based approach was proposed calculating the mean normalized difference vegetation index (NDVI) of each plot and the standard deviation to generate a calibration-testing set of data. More than 2000 visited plots for 12 different crops along the study site were utilized as ground truth. Ensemble classifiers were the most robust algorithms but not the most efficient because of their low prediction rate. Nearest neighbor methods and support vector machines have the best balance between robustness and efficiency as methods for classification. Although the F1 score is close to 90%, some misclassifications were found for spring crops (e.g., barley, wheat and peas). However, crops with quite similar cycles could be differentiated, such as purple garlic and white garlic, showing the powerfulness of the developed tool. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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18 pages, 8737 KiB

Open AccessArticle

Analysis of the Recent Agricultural Situation of Dakhla Oasis, Egypt, Using Meteorological and Satellite Data

by Reiji Kimura, Erina Iwasaki and Nobuhiro Matsuoka

Remote Sens. 2020, 12(8), 1264; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081264 - 16 Apr 2020

Cited by 6 | Viewed by 6765

Abstract

Dakhla Oasis is the most highly populated oasis in Egypt. Although the groundwater resource is very large, there is essentially no rainfall and the aquifer from which the water is drawn is not recharged. Therefore, for the future development and sustainability of Dakhla Oasis, it is important to understand how land and water are used in the oasis and meteorological conditions there. In this study, meteorological and satellite data were used to examine the recent agricultural situation and water use. The results showed that the meteorological conditions are suitable for plant production, and the maximum vegetation index value was comparable to the Nile delta. The cultivated area increased between 2001 and 2019 by 13.8 km² year⁻¹, with most of the increase occurring after the 2011 revolution (21.2 km² year⁻¹). People living in Dakhla Oasis derive their income primarily from agricultural activity, which requires abundant water. Thus, the increasing demand for water is likely to put pressure on the groundwater resource and limit its sustainability. Full article

(This article belongs to the Special Issue Mapping Land Productivity Dynamics with Time-Series of Remote Sensing Images)

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