Mapping Congo Basin vegetation types from 300 m and 1 km multi-sensor time series for carbon stocks and forest areas estimation

This study aims to contribute to the understanding of the Congo Basin forests by delivering a detailed map of vegetation types with an improved spatial discrimination and coherence for the whole Congo Basin region. A total of 20 land cover classes were described with the standardized Land Cover Classification System (LCCS) developed by the FAO. Based on a semi-automatic processing chain, the Congo Basin vegetation types map was produced by combining 19 months of observations from the Envisat MERIS full resolution products (300 m) and 8 yr of daily SPOT VEGETATION (VGT) reflectances (1 km). Four zones (north, south and two central) were delineated and processed separately according to their seasonal and cloud cover specificities. The discrimination between different vegetation types (e.g. forest and savannas) was significantly improved thanks to the MERIS sharp spatial resolution. A better discrimination was achieved in cloudy areas by taking advantage of the temporal consistency of the SPOT VGT observations. This resulted in a precise delineation of the spatial extent of the rural complex in the countries situated along the Atlantic coast. Based on this new map, more accurate estimates of the surface areas of forest types were produced for each country of the Congo Basin. Carbon stocks of the Basin were evaluated to a total of 49 360 million metric tons. The regional scale of the map was an opportunity to investigate what could be an appropriate tree cover threshold for a forest class definition in the Congo Basin countries. A 30% tree cover threshold was suggested. Furthermore, the phenology of the different vegetation types was illustrated systematically with EVI temporal profiles. This Congo Basin forest types map reached a satisfactory overall accuracy of 71.5% and even 78.9% when some classes are aggregated. The values of the Cohen's kappa coefficient, respectively 0.64 and 0.76 indicates a result significantly better than random

Social Enterprise in Europe: At the Crossroads of Market, Public Policies and Third Sector

Peer reviewe

A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficultie

Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community

Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90°N/90°S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100%. The CCI global map of open water bodies provided the best water class representation (F-score of 89%) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89%. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km2 ± 0.24 million km2. The dataset is freely available through the ESA CCI Land Cover viewer

Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt

In Mali's cotton belt, spatial variability in management practices, soil fertility and rainfall strongly impact crop productivity and the livelihoods of smallholder farmers. To identify crop growth conditions and hence improve food security, accurate assessment of local crop production is key. However, production estimates in heterogeneous smallholder farming systems often rely on labor-intensive surveys that are not easily scalable, nor exhaustive. Recent advances in high-resolution earth observation (EO) open up new possibilities to work in heterogeneous smallholder systems. This paper develops a method to estimate individual crop production at farm-to-community scales using high-resolution Sentinel-2 time series and ground data in the commune of Koningue, Mali. Our estimation of agricultural production relies on (i) a supervised, pixel-based crop type classification inside an existing cropland mask, (ii) a comparison of yield estimators based on spectral indices and derived leaf area index (LAI), and (iii) a Monte Carlo approach combining the resulting unbiased crop area estimate and the uncertainty on the associated yield estimate. Results show that crop types can be mapped from Sentinel-2 data with 80% overall accuracy (OA), with best performances observed for cotton (Fscore 94%), maize (88%) and millet (83%), while peanut (71%) and sorghum (46%) achieve less. Incorporation of parcel limits extracted from very high-resolution imagery is shown to increase OA to 85%. Obtained through inverse radiative transfer modeling, Sen2-Agri estimates of LAI achieve better prediction of final grain yield than various vegetation indices, reaching R2 of 0.68, 0.62, 0.8 and 0.48 for cotton, maize, millet and sorghum respectively. The uncertainty of Monte Carlo production estimates does not exceed 0.3% of the total production for each crop type

Spatial fields’ dispersion as a farmer strategy to reduce agro-climatic risk at the household level in pearl millet-based systems in the Sahel: A modeling perspective

The rainfall pattern in the Sahel is very erratic with a high spatial variability. We tested the often reported hypothesis that the dispersion of farmers’ fields around the village territory helps mitigate agro-climatic risk by increasing yield stability from year to year. We also wished to evaluate whether this strategy had an effect on the yield disparity among households in a village. Based on a network of approximately 60 rain gauges spread over 500 km2 in the Fakara region (Southwest Niger), daily rainfall was interpolated at 300 m × 300 m resolution over a 12-year period. This data was used to compute, by means of the APSIM crop simulation model, millet biomass and grain yields at the pixel scale. Simulated yields were combined with the land tenure map of the Banizoumbou village in a GIS to assess millet yield at field and household level. Agro-climatic risk analysis was performed using linear regression between a spatial dispersion index of household fields and the inter-annual (instability) and inter-household (disparity) millet yield variability of 107 households in the village territory. We find that the spatial variability of annual rainfall induces an even higher spatial variability of millet production at pixel, field and household levels. The dispersion of farm fields reduces moderately but significantly the disparity of millet yield between households each year and increases the inter-annual yield stability of a given household. The less the household fields are scattered, the more the presence of a fertility gradient around the village enhances the inter-annual stability but also the disparity between households. Our results provide evidence that field dispersion is an effective strategy to mitigate agro-climatic risk, as claimed by farmers in the Sahelian Niger. Although the results should be confirmed by further research on longer term rainfall spatial data, it is clearly advisable that any land reforms in the area take into account the benefits of field dispersion to mitigate climatic risk

Scenario trees and policy selection for multistage stochastic programming using machine learning

We propose a hybrid algorithmic strategy for complex stochastic optimization problems, which combines the use of scenario trees from multistage stochastic programming with machine learning techniques for learning a policy in the form of a statistical model, in the context of constrained vector-valued decisions. Such a policy allows one to run out-of-sample simulations over a large number of independent scenarios, and obtain a signal on the quality of the approximation scheme used to solve the multistage stochastic program. We propose to apply this fast simulation technique to choose the best tree from a set of scenario trees. A solution scheme is introduced, where several scenario trees with random branching structure are solved in parallel, and where the tree from which the best policy for the true problem could be learned is ultimately retained. Numerical tests show that excellent trade-offs can be achieved between run times and solution quality

Detection of astrovirus in a cow with neurological signs by nanopore technology, Italy

In this study, starting from nucleic acids purified from the brain tissue, Nanopore technology was used to identify the etiological agent of severe neurological signs observed in a cow which was immediately slaughtered. Histological examination revealed acute non-suppurative encephalomyelitis affecting the brainstem, cerebrum, cerebellum, and medulla oblongata, while by using PCR-based assays, the nucleic acids of major agents for neurological signs were not detected. By using Nanopore technology, 151 sequence reads were assigned to Bovine Astrovirus (BoAstV). Real-time RT-PCR and in situ hybridization (ISH) confirmed the presence of viral RNA in the brain. Moreover, using the combination of fluorescent ISH and immunofluorescence (IF) techniques, it was possible to detect BoAstV RNA and antigens in the same cells, suggesting the active replication of the virus in infected neurons. The nearly whole genome of the occurring strain (BoAstV PE3373/2019/Italy), obtained by Illumina NextSeq 500, showed the highest nucleotide sequence identity (94.11%) with BoAstV CH13/NeuroS1 26,730 strain, an encephalitis-associated bovine astrovirus. Here, we provide further evidence of the role of AstV as a neurotropic agent. Considering that in a high proportion of non-suppurative encephalitis cases, which are mostly indicative of a viral infection, the etiologic agent remains unknown, our result underscores the value and versatility of Nanopore technology for a rapid diagnosis when the PCR-based algorithm gives negative results