For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on ...remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.
Sentinel-2 images are expected to improve global crop monitoring even in challenging tropical small agricultural systems that are characterized by high intra- and inter-field spatial variability and ...where satellite observations are disturbed by the presence of clouds. To overcome these constraints, we analyzed and optimized the performance of a combined Random Forest (RF) classifier/object-based approach and applied it to multisource satellite data to produce land use maps of a smallholder agricultural zone in Madagascar at five different nomenclature levels. The RF classifier was first optimized by reducing the number of input variables. Experiments were then carried out to (i) test cropland masking prior to the classification of more detailed nomenclature levels, (ii) analyze the importance of each data source (a high spatial resolution (HSR) time series, a very high spatial resolution (VHSR) coverage and a digital elevation model (DEM)) and data type (spectral, textural or other), and (iii) quantify their contributions to classification accuracy levels. The results show that RF classifier optimization allowed for a reduction in the number of variables by 1.5- to 6-fold (depending on the classification level) and thus a reduction in the data processing time. Classification results were improved via the hierarchical approach at all classification levels, achieving an overall accuracy of 91.7% and 64.4% for the cropland and crop subclass levels, respectively. Spectral variables derived from an HSR time series were shown to be the most discriminating, with a better score for spectral indices over the reflectances. VHSR data were only found to be essential when implementing the segmentation of the area into objects and not for the spectral or textural features they can provide during classification.
•Change maps based on breakpoint magnitudes often highlight abrupt/amplitude changes.•Land use changes induced by Large-Scale Land Acquisitions are mostly seasonal.•BFASTm-L2, a fast and unsupervised ...new method to timely detect seasonal changes.•BFASTm-L2 over performed BFASTm, BFAST Lite, Edyn mapping regional LSLAs.•BFASTm-L2 may be used to highlight important land use changes at regional scale.
In the context of Global Change Research, detection, monitoring and characterization of land use/land cover (LULC) changes are of prime importance. The increasing availability of dense satellite image time series (SITS) has led to a shift in the change detection paradigm, with algorithms able to exploit the full temporal information laid down in SITS. So far, most of these algorithms have focused on the detection of abrupt and gradual changes, and thus developed breakpoint detection based on significant deviations from the mean. However, LULC changes may manifest themselves in other patterns, particularly changes in seasonality (amplitude, number and length of the growing seasons) that are harder to detect. In this paper, we propose a simple method to automatically select the breakpoint linked to the biggest seasonal change in long and dense SITS with multiple breakpoints. This approach - BFASTm-L2 - relies on linking a high-speed algorithm (BFAST monitor) with a time series similarity metric (Euclidian distance L2) sensitive to seasonal changes. The capacity of BFASTm-L2 to identify the date of change in different situations was tested on two data sets, and compared to the performances of three other algorithms (BFAST monitor, BFAST lite, and Edyn). The data sets are 1. a published benchmark data set composed of 25200 simulated SITS with different change types and change magnitudes, and 2. the 2000–2020 MODIS NDVI SITS over a 200x200 pixels area in Senegal including different study sites which have undergone recent LULC changes due to agricultural large-scale land acquisitions (LSLAs) (as reported in the ground field database used in this study). The results show that BFASTm-L2 is efficient in accurately detecting in time most of the changes, and, in contrast with BFAST Lite and BFASTmonitor, to spatially highlight LSLAs-induced changes without the need of any prior knowledge. The automatic proposed approach, faster than BFAST Lite and Edyn, and with very few tuneable parameters, may thus be easily implemented in unsupervised pipelines to map and analyse generic LULC changes at regional scale.
High urbanization rates in cities lead to rapid changes in land uses, particularly in southern cities where population growth is fast. Urban and peri-urban agricultural land is often seen as ...available space for the city to expand, but at the same time, agricultural land provides many benefits to cities pertaining to food, employment, and eco-services. In this context, there is an urgent need to provide spatial information to support planning in complex urban systems. The challenge is to integrate analysis of agriculture and urban land-cover classes, and of their spatial and functional patterns. This paper takes up this challenge in Antananarivo (Madagascar), where agricultural plots and homes are interlocked and very small. It innovates by using a methodology already tested in rural settings, but never applied to urban environments. The key step of the analysis is to produce landscape zoning based on multisource satellite data to identify agri-urban functional areas within the city, and to explore their relationships. Our results demonstrate that the proposed classification method is well suited for mapping agriculture and urban land cover (overall accuracy = 76.56% for the 20 classes of level 3) in such a complex setting. The systemic analysis of urban agriculture patterns and functions can help policymakers and urban planners to design and build resilient cities.
The use of consumer digital cameras or webcams to characterize and monitor different features has become prevalent in various domains, especially in environmental applications. Despite some promising ...results, such digital camera systems generally suffer from signal aberrations due to the on-board image processing systems and thus offer limited quantitative data acquisition capability. The objective of this study was to test a series of radiometric corrections having the potential to reduce radiometric distortions linked to camera optics and environmental conditions, and to quantify the effects of these corrections on our ability to monitor crop variables. In 2007, we conducted a five-month experiment on sugarcane trial plots using original RGB and modified RGB (Red-Edge and NIR) cameras fitted onto a light aircraft. The camera settings were kept unchanged throughout the acquisition period and the images were recorded in JPEG and RAW formats. These images were corrected to eliminate the vignetting effect, and normalized between acquisition dates. Our results suggest that 1) the use of unprocessed image data did not improve the results of image analyses; 2) vignetting had a significant effect, especially for the modified camera, and 3) normalized vegetation indices calculated with vignetting-corrected images were sufficient to correct for scene illumination conditions. These results are discussed in the light of the experimental protocol and recommendations are made for the use of these versatile systems for quantitative remote sensing of terrestrial surfaces.
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has ...the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel2 for Agriculture” system.
Locusts are grasshopper species that exhibit phase polyphenism resulting in the expression of gregarious behaviors that favor the development of large devastating bands and swarms. Desert locust ...preventative management aims to prevent crop damage by controlling populations before they can reach high densities and form mass migrating swarms. The areas of potential gregarization for Desert locust are large and need to be physically assessed by survey teams for efficient preventative management. An ongoing challenge is to be able to guide where prospection surveys should occur depending on local meteorological and vegetation conditions. In this study, we analyzed the relationship between historical prospection data of Desert locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing with the help of multiple models of logistic regression. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: (1) spatial means at different scales around the prospection point, (2) relative differences of NDVI variation through time before the prospection, and (3) large-scale summary of vegetation quantity. The multi-model framework showed that vegetation development a month and a half before the survey was amongst the best predictors of locust presence. Also, the local vegetation quantity was not enough to predict locust presence. Vegetation quantity on a scale of a few kilometers was a better predictor but varied non-linearly, reflecting specific biotope types that support Desert locust development. Using one of the best logistic regression models and NDVI data, we were able to derive a predictive model of probability of finding locusts in specific areas. This methodology should help in more efficiently focusing survey efforts on specific parts of the gregarization areas based on the predicted probability of locusts being present.
Wanderheuschrecken gehören zu den Heuschreckenarten, die einen saisonalen Polyphänismus zeigen, der sich in einem gregären Verhalten ausdrückt, das die Entwicklung von großen, verheerenden Gruppen und Schwärmen fördert. Das präventive Management von Wüstenwanderheuschrecken zielt darauf ab, Ernteschäden zu verhindern, indem die Populationen unter Kontrolle gehalten werden bevor sie hohe Dichten erreichen und wandernde Massenschwärme bilden können. Die Gebiete für potenzielle Aggregationen von Wüstenwanderheuschrecken sind riesig und müssen für ein effektives präventives Management durch Erfassungsteams kontrolliert werden. Die derzeitige Herausforderung besteht darin, sagen zu können, wo in Abhängigkeit von lokalen meteorologischen und Vegetationsbedingungen Erfassungen stattfinden sollten. In dieser Untersuchung analysierten wir mithilfe von verschiedenen Modellen der logistischen Regression die Beziehung zwischen historischen Erfassungsdaten von Beobachtungen der Wüstenwanderheuschrecken zwischen 2005 und 2009 und den räumlich-zeitlichen Statistiken eines Vegetationsindex, der durch Fernerkundung gewonnen wurde. Der Vegetationsindex war der zusammengesetzte, normalisierte Differenz-Vegetations-Index (NDVI), der alle 16 Tage mit einer räumlichen Auflösung von 250m (MOD13Q1 vom MODIS-Satelliten) gegeben war. Die Statistiken, die aus diesem Index ermittelt wurden, waren: (1) räumliche Mittelwerte auf verschiedenen Skalen rund um den Kontrollpunkt, (2) relative Unterschiede in der Variation des NDVI während der Zeit vor der Kontrolle und (3) die großräumige Zusammenfassung der Vegetationsmenge. Das Bezugssystem aus mehreren Modellen zeigte, dass die Vegetationsentwicklung eineinhalb Monate vor der Erfassung zu den besten Vorhersagefaktoren für die Anwesenheit der Wanderheuschrecken zählte. Es zeigte sich auch, dass die lokale Vegetationsqualität nicht ausreichte, um die Anwesenheit von Wanderheuschrecken vorherzusagen. Die Vegetationsmenge auf einer Skala von einigen Kilometern war ein besserer Indikator, variierte jedoch nicht-linear und spiegelte damit die spezifischen Biotoptypen wider, welche die Entwicklung von Wüstenwanderheuschrecken förderten. Unter der Verwendung des besten logistischen Regressionsmodell und der NDVI-Daten konnten wir ein Vorhersagemodell für die Wahrscheinlichkeit entwickeln, mit der Wanderheuschrecken in bestimmten Gebieten gefunden werden. Diese Methodik sollte helfen, den Erfassungsaufwand aufgrund der vorhergesagten Wahrscheinlichkeit für die Anwesenheit der Wanderheuschrecken viel effektiver auf spezifische Bereiche der Aggregationsgebiete zu fokussieren.
The convergence of new EO data flows, new methodological developments and cloud computing infrastructure calls for a paradigm shift in operational agriculture monitoring. The Copernicus Sentinel-2 ...mission providing a systematic 5-day revisit cycle and free data access opens a completely new avenue for near real-time crop specific monitoring at parcel level over large countries. This research investigated the feasibility to propose methods and to develop an open source system able to generate, at national scale, cloud-free composites, dynamic cropland masks, crop type maps and vegetation status indicators suitable for most cropping systems. The so-called Sen2-Agri system automatically ingests and processes Sentinel-2 and Landsat 8 time series in a seamless way to derive these four products, thanks to streamlined processes based on machine learning algorithms and quality controlled in situ data. It embeds a set of key principles proposed to address the new challenges arising from countrywide 10 m resolution agriculture monitoring. The full-scale demonstration of this system for three entire countries (Ukraine, Mali, South Africa) and five local sites distributed across the world was a major challenge met successfully despite the availability of only one Sentinel-2 satellite in orbit. In situ data were collected for calibration and validation in a timely manner allowing the production of the four Sen2-Agri products over all the demonstration sites. The independent validation of the monthly cropland masks provided for most sites overall accuracy values higher than 90%, and already higher than 80% as early as the mid-season. The crop type maps depicting the 5 main crops for the considered study sites were also successfully validated: overall accuracy values higher than 80% and F1 Scores of the different crop type classes were most often higher than 0.65. These respective results pave the way for countrywide crop specific monitoring system at parcel level bridging the gap between parcel visits and national scale assessment. These full-scale demonstration results clearly highlight the operational agriculture monitoring capacity of the Sen2-Agri system to exploit in near real-time the observation acquired by the Sentinel-2 mission over very large areas. Scaling this open source system on cloud computing infrastructure becomes instrumental to support market transparency while building national monitoring capacity as requested by the AMIS and GEOGLAM G-20 initiatives.
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•First ever national crop mapping at 10 m for Mali, Ukraine and South-Africa•Near real time agriculture monitoring at parcel level made operational nationwide•Demonstration across the world of multi-sensor EO exploitation for crop monitoring•Sentinel-2 time series mapping crop type at 10 m resolution along the growing season•Sen2-Agri: an innovative system to monitor crops in any country around the globe
•BFASTm-L2 change characterization provides insight into national land dynamics.•Differentiation between change types improves understanding of LULC change drivers.•The RGB change map is useful for ...inferring main drivers of LULC change (LULCC)•Time series shape dissimilarity is sensitive to agricultural-driven LULC changes.•Large-Scale Agricultural Investments driven LULCC are unsupervisely detectable.
As global land cover/ land use change (LULCC) threatens the human’s well-being, accurate detection and characterization of LULCC is of paramount importance. The increasing availability of dense satellite image time series (SITS), together with the ever-improving change detection algorithms, has allowed significant progress to be made. However, much remains to be done in its characterization.
This study aims to uncover potential relationships between changes in Normalized Difference Vegetation Index (NDVI) SITS patterns and their drivers. It distinguishes itself by representing phenological changes not only as transitions between specific patterns, but also by examining the nature of these changes—whether abrupt, gradual, or seasonal. For seasonal changes, it further refines the analysis to determine their impact on the amplitude, number of seasons (NOS), or length of seasons (LOS) components. Our focus is to provide insights into the land dynamics and drivers of change in Senegal using an RGB (red, green, blue) composite change map. This map is derived from three MODIS NDVI time series change metrics detected by BFASTm-L2 within the MODIS NDVI 2000–2021 SITS: magnitude of change, direction of change, and dissimilarity of time series shape. The 250-meter resolution MODIS data served as an optimal data source for this analysis due to its high temporal resolution (near daily) and extensive coverage over 20 years.
The sensitivity of each metric to different types of change was first tested on a simulated dataset before being applied to the MODIS SITS. The RGB change map enabled visualization of different “signatures” of change, which, combined with ground information, rainfall data, NDVI time series analysis, and Google Earth imagery, helped link these signatures to various drivers of change. Climatic and anthropogenic changes, such as those induced by Large Scale Agricultural Investments (LSAI) or mining, were visually inferred from the RGB map.This approach demonstrates the usefulness of integrating the type of change, especially seasonal change, into the characterization of land change. This method has the advantage of being fast, interpretable, robust to noise and easily transferable to different regions.
Highlight textThermal infrared imagery contributes to the phenotyping of crop response to water stress. Based on multispectral images, the Vegetation Index–Temperature (VIT) concept constitutes a ...relevant approach.
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