Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier

Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat 8 images. Our goal was to provide practitioners with recommendations for the best sample size and composition. The study area was located in the Yaqui Valley in Mexico. Using polygons of more than 6000 labeled crop fields, we prepared data sets for training, in which the nine crops had an equal or proportional representation, called Equal or Ratio, respectively. Increasing the size of the training set improved the overall accuracy (OA). Gains became marginal once the total number of fields approximated 500 or 40 to 45 fields per crop type. Equal achieved slightly higher OAs than Ratio for a given number of fields. However, recall and F-scores of the individual crops tended to be higher for Ratio than for Equal. The high number of wheat fields in the Ratio scenarios, ranging from 275 to 2128, produced a more accurate classification of wheat than the maximal 80 fields of Equal. This resulted in a higher recall for wheat in the Ratio than in the Equal scenarios, which in turn limited the errors of commission of the non-wheat crops. Thus, a proportional representation of the crops in the training data is preferable and yields better accuracies, even for the minority crops

Sentinel-2 for Agriculture project : preparing Sentinel-2 operational exploitation for supporting national and global crop monitoring

Developing better agricultural monitoring capabilities based on Earth Observation (EO) data is critical for strengthening food production information and market transparency. The Sentinel-2 (S2) mission has the optimal capacity for agriculture monitoring in terms of resolution (10-20 meter), revisit frequency (5 days) and coverage (global). In 2014, the European Space Agency launched the “Sentinel-2 for Agriculture” project, which aims to provide to the international community an open-source system to process S2 data in an operational manner into relevant EO agricultural products for major worldwide representative agriculture systems. These products consist of (i) cloud-free surface reflectance composites, (ii) monthly cropland masks delivered along the agricultural season, (iii) cultivated crop type map for main crop groups and (iv) LAI and NDVI indicators describing on a 10-day basis the vegetative development of crops. The first phases of the project focused on algorithms selection, system design and implementation. They were achieved using S2-like time series (SPOT Take 5, Landsat 8) over 12 globally distributed sites as well as in-situ data shared by teams working in the various agrosystems. The last phase started in March 2016 and aims at demonstrating the developed system to deliver the products in operational conditions, i.e. over the 2016 growing season with S2 data in near real-time. The demonstration is done at national scale (~500.000 km²) over Ukraine, Mali and South Africa and at local scale (a full S2 swath) for seven sites located in France, Belgium, Czech Republic, Morocco, Madagascar, Sudan and China. It is carried out in close interaction with teams working on the field, with the additional objective to transfer the system to their operations. Doing so, the project wants to contribute to filling the gap between state-of-the-art remote sensing practices and operational systems, thus providing a strong scientific contribution to the GEOGLAM initiative

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications

Estimation de la biomasse aérienne par utilisation des grands arbres dans la réserve forestière de Yoko

Depuis l’avènement du mécanisme REDD+, l’estimation précise de la biomasse ligneuse aérienne est au centre des enjeux environnementaux actuels. La majorité des équations allométriques disponibles ont été établies au moyen de variables dendrométriques issues de méthodes destructives et incluant, hormis la hauteur et la densité de bois, un spectre limité de diamètre à hauteur de poitrine. Cette étude a pour objectif de tester une méthode non destructive de mesure de variables dendrométriques en vue d’estimer la biomasse aérienne en forêt tropicale dense humide, à partir des équations allométriques les plus adaptées pour une forêt dense humide sur terre ferme au nord-est de la République démocratique du Congo. Les résultats indiquent que les arbres émergents stockent à eux seuls 41 % de la biomasse aérienne de l’ensemble de la placette. Ce pourcentage augmente jusqu’à 67 % lorsqu’on regroupe les espèces dominantes et émergentes. Cette étude montre aussi qu’il est possible d’estimer le diamètre à partir de la couronne de l’arbre mesurée sur des images satellites à très haute résolution spatiale pour des espèces émergentes et dominantes. De très fortes corrélations entre le diamètre mesuré sur le terrain et le diamètre de la couronne mesurée par satellite ont été obtenues pour les espèces Prioria oxyphylla (Harms) Breteler et Pericopsis elata (Harms) Van Meeuwen, tandis que cette corrélation était très satisfaisante pour les espèces Entandrophragma spp., Piptadeniastrum africanum (Hook.f.) Brenan et Albizia gummifera (J.F. Gmel.) C.A. Sm

Sent2Agri System Based Crop Type Mapping in Yellow River Irrigation Area

Ａｇｒｉｃｕｌｔｕｒａｌ ｍｏｎｉｔｏｒｉｎｇ ｉｓ ｅｓｓｅｎｔｉａｌ ｆｏｒ ａｄｅｑｕａｔｅ ｍａｎａｇｅｍｅｎｔ ｏｆ ｆｏｏｄ ｐｒｏｄｕｃｔｉｏｎ ａｎｄ ｄｉｓｔｒｉｂｕｔｉｏｎ. Ｃｒｏｐ ｌａｎｄ ａｎｄ ｃｒｏｐ ｔｙｐｅ ｃｌａｓｓｉｆｉｃａｔｉｏｎꎬ ｕｓｉｎｇ ｒｅｍｏｔｅ ｓｅｎｓｉｎｇ ｔｉｍｅ ｓｅｒｉｅｓꎬ ｆｏｒｍ ａｎ ｉｍｐｏｒｔａｎｔ ｔｏｏｌ ｔｏ ｃａｐｔｕｒｅ ｔｈｅ ａｇｒｉｃｕｌｔｕｒａｌ ｐｒｏｄｕｃｔｉｏｎ ｉｎｆｏｒｍａｔｉｏｎ. Ｔｈｅ ｒｅｃｅｎｔｌｙ ｌａｕｎｃｈｅｄ Ｓｅｎｔｉｎｅｌ￣２ ｓａｔｅｌｌｉｔｅｓ ｐｒｏｖｉｄｅ ｕｎｐｒｅｃｅｄｅｎｔｅｄ ｍｏｎｉｔｏｒｉｎｇ ｃａｐａｃｉｔｉｅｓ ｉｎ ｔｅｒｍｓ ｏｆ ｓｐａｔｉａｌ ｒｅｓｏｌｕｔｉｏｎꎬ ｓｗａｔｈ ｗｉｄｔｈꎬ ａｎｄ ｒｅｖｉｓｉｔ ｆｒｅｑｕｅｎｃｙ. Ｔｈｅ Ｓｅｎｔｉｎｅｌ￣２ ｆｏｒ Ａｇｒｉｃｕｌｔｕｒｅ (Ｓｅｎ２￣Ａｇｒｉ) ｓｙｓｔｅｍ ｈａｓ ｂｅｅｎ ｄｅｖｅｌｏｐｅｄ ｔｏ ｆｕｌｌｙ ｅｘｐｌｏｉｔ ｔｈｏｓｅ ｃａｐａｃｉｔｉｅｓꎬ ｂｙ ｐｒｏｖｉｄｉｎｇ ｆｏｕｒ ｒｅｌｅｖａｎｔ ｅａｒｔｈ ｏｂｓｅｒｖａｔｉｏｎ ｐｒｏｄｕｃｔｓ ｆｏｒ ａｇｒｉｃｕｌｔｕｒａｌ ｍｏｎｉｔｏｒｉｎｇ. Ｕｎｄｅｒ ｔｈｅ Ｄｒａｇｏｎ ４ Ｐｒｏｇｒａｍꎬ ｔｈｅ ｃｒｏｐ ｍａｐｐｉｎｇ ｗｉｔｈ ｖａｒｉｏｕｓ ｓａｔｅｌｌｉｔｅ ｉｍａｇｅｓ ａｎｄ ａ ｓｐｅｃｉｆｉｃ ｆｏｃｕｓ ｏｎ ｔｈｅ Ｙｅｌｌｏｗ Ｒｉｖｅｒ ｉｒｒｉｇａｔｅｄ ａｇｒｉｃｕｌｔｕｒａｌ ａｒｅａ ｉｎ ｔｈｅ Ｎｉｎｇｘｉａ Ｈｕｉ Ａｕｔｏｎｏｍｏｕｓ Ｒｅｇｉｏｎ ｉｎ Ｃｈｉｎａ ｗａｓ ｃａｒｒｉｅｄ ｏｕｔ ｗｉｔｈ ｔｈｅ Ｓｅｎｔｉｎｅｌ￣２ ｆｏｒ Ａｇｒｉｃｕｌｔｕｒｅ ｓｙｓｔｅｍ (Ｓｅｎｔ２Ａｇｒｉ). ９ ｔｙｐｅｓ ｏｆ ｃｒｏｐｓ ｗｅｒｅ ｃｌａｓｓｉｆｉｅｄ ａｎｄ ｔｈｅ ｃｒｏｐ ｔｙｐｅ ｍａｐ ｉｎ ２０１７ ｗａｓ ｐｒｏｄｕｃｅｄ ｂａｓｅｄ ｏｎ ３５ ｓｃｅｎｅｓ Ｓｅｎｔｉｎｅｌ ２Ａ/ Ｂ ｉｍａｇｅｓ. Ｔｈｅ ｏｖｅｒａｌｌ ａｃｃｕｒａｃｙ ｃｏｍｐｕｔｅｄ ｆｒｏｍ ｔｈｅ ｅｒｒｏｒ ｃｏｎｆｕｓｉｏｎ ｍａｔｒｉｘ ｉｓ ８８％ꎬ ｗｈｉｃｈ ｉｎｃｌｕｄｅｓ ｔｈｅ ｃｒｏｐｐｅｄ ａｎｄ ｕｎｃｒｏｐｐｅｄ ｔｙｐｅｓ. Ａｆｔｅｒ ｔｈｅ ｒｅｍｏｖａｌ ｏｆ ｔｈｅ ｕｎｃｒｏｐｐｅｄ ａｒｅａꎬ ｔｈｅ ｏｖｅｒａｌｌ ａｃｃｕｒａｃｙ ｆｏｒ ａ ｃｒｏｐｐｅｄ ｄｅｃｒｅａｓｅ ｔｏ ７３％. Ｉｎ ｏｒｄｅｒ ｔｏ ｆｕｒｔｈｅｒ ｉｍｐｒｏｖｅ ｔｈｅ ｃｒｏｐ ｃｌａｓｓｉｆｉｃａｔｉｏｎ ａｃｃｕｒａｃｙꎬ ｔｈｅ ｔｒａｉｎｉｎｇ ｄａｔａｓｅｔ ｓｈｏｕｌｄ ｂｅ ｆｕｒｔｈｅｒ ｉｍｐｒｏｖｅｄ ａｎｄ ｔｕｎｅｄ

The Flemish health and environment biomonitoring prgram: relation between PCB levels in cord blood and dietary habits

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Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed

Cropland maps derived from satellite imagery have become a common source of information to estimate food production, support land use policies, and measure the environmental impacts of agriculture. Cropland classification models are typically calibrated with data collected from roadside surveys which enable the sampling of large areas at a relatively low cost. However, there is a risk of providing biased data as environmental and management gradients may not be fully captured from road networks, thereby violating the assumption of representativeness of calibration data. Despite being widely adopted, the potential biases of roadside sampling have so far not been thoroughly addressed. In this study, we looked for evidence of these biases by comparing three sampling strategies: Random sampling, Roadside sampling, and Transect sampling – a spatially constrained variant of Roadside sampling. In these three strategies, non-cropland data are randomly distributed as they can be photo-interpreted. Based on reference maps at 30 m in four study sites, we followed a Monte Carlo approach to generate multiple realizations of each sampling strategy for ten sample sizes. The effect of the sampling strategy was then assessed in terms of representativeness of the data set collected and accuracy of the resulting maps. Results showed that data sets obtained from Roadside sampling were significantly less representative than those obtained from Random sampling but the resulting maps were only marginally less accurate (2% difference). Transect sampling captured systematically less variability than Random or Roadside sampling which led to differences in accuracy as large as 15%. The effect of sample size on accuracy varied across sites but generally leveled off after reaching 3000 pixels. Augmenting the size of Transect samples improved the classification accuracy but not sufficiently to match the performance of the other sampling strategies. Finally, we found that Random and Roadside training sets with similar representativeness yield comparable accuracy. Therefore, we conclude that roadside sampling can be a viable source of training data for cropland mapping if the range of environmental and management gradients is surveyed. This underlines the importance of survey planning to identify those routes that capture most variability808293COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão tem2014/50715-9The authors received funding from the CSIRO Future Science Platform “GrainCast”; the SIGMA project (Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM; FP7-ENV-2013 no. 603719); the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES); the project “Characterizing And Predicting Biomass Production In Sugarcane And Eucalyptus Plantations In Brazil” (FAPESP-Microsoft Research 2014/50715-9); the CESOSO project (TOSCA program Grant of the French Space Agency, CNES

Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world

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 10m 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: overal

The standardized computerized 24-h dietary recall method EPIC-Soft adapted for pan-European dietary monitoring

Background/Objectives: The EPIC-Soft program (the software initially developed to conduct 24-h dietary recalls (24-HDRs) in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study) was recommended as the best way to standardize 24-HDRs for future pan-European dietary monitoring. Within European Food Consumption Validation (EFCOVAL), EPIC-Soft was adapted and further developed on various aspects that were required to optimize its use. In this paper, we present the structure and main interview steps of the EPIC-Soft program, after implementation of a series of new specifications deemed to satisfy specific requirements of pan-European monitoring surveys and other international studies. Subjects/Methods: Updates to optimize the EPIC-Soft program were ascertained according to the following stepwise approach: (1) identification of requested specifications to be potentially implemented through an ad hoc ‘EPIC-Soft specifications questionnaire’ sent to past, current and possible future users of the software; (2) evaluation of the specifications in collaboration with two ad hoc task force groups and through a workshop; (3) development of a technical solution for each retained specification; (4) implementation of the specifications by software developers; (5) testing and amendment of bugs. Results: A number of new specifications and facilities were implemented to EPIC-Soft program. In addition, the software underwent a full reprogramming and migration to a modern Windows environment, including changes in its internal architecture and user interface. Although the overall concept and structure of the initial software were not changed substantially, these improvements ease the current and future use of EPIC-Soft and increase further its adaptation to other countries and study contexts. Conclusions: EPIC-Soft is enriched with further functions and facilities expected to fulfil specific needs of pan-European dietary monitoring and risk assessment purposes. The validity, feasibility and relevance of this software for different national and international study designs, and the logistical aspects related to its implementation are reported elsewhere