Christelle Vancutsem

GlobCover: ESA service for global land cover from MERIS

The Globcover initiative comprises the development and demonstration of a service that in first instance produces a global land cover map for year 2005/2006. Globcover uses MERIS fine resolution (300 m) mode data acquired between mid 2005 and mid 2006 and, for maximum user benefit, the thematic legend is compatible with the UN land cover classification system (LCCS). This new product updates and complements the other existing comparable global products, such as the global land cover map at 1 km resolution for the year 2000 (GLC2000) produced by JRC. It is expected to improve such previous global product, in particular because of the finer spatial resolution. The Globcover project is an initiative of ESA in cooperation with an international network of partner including EEA, FAO, GOFC-GOLD, IGBP, JRC and UNEP.

A vectorial capacity product to monitor changing malaria transmission potential in epidemic regions of Africa.

Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmit Plasmodium spp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model.

Mean Compositing, an alternative strategy for producing temporal syntheses. Concepts and performance assessment for SPOT VEGETATION time series

Various compositing criteria have been proposed to produce cloud‐free images from optical time series. However, they often favour specific atmospheric and geometric conditions, which may cause serious inconsistencies in the syntheses. Algorithms including BRDF normalization minimize variations induced by the anisotropy of the target. However, their operational implementation faces some issues. This study proposes to avoid these issues by using a new strategy based on a statistical approach, i.e. Mean Compositing, and by comparing it with three existing techniques. A quantitative evaluation methodology with statistical tests on reflectance and texture values as well as visual comparisons were applied to numerous SPOT VEGETATION time series. The performance criterion was to best mimic the information content of a single cloud‐free near‐nadir view image. Moreover a quantitative approach was used to assess the temporal consistency of the syntheses. The results showed that the proposed strategy combined with an efficient quality control produces images with greater spatial consistency than currently available VEGETATION products but produces slightly more uneven time series than the most advanced compositing algorithm.

Mapping and characterizing the vegetation types of the Democratic Republic of Congo using SPOT VEGETATION time series

The need for quantitative and accurate information to characterize the state and evolution of vegetation types at a national scale is widely recognized. This type of information is crucial for the Democratic Republic of Congo, which contains the majority of the tropical forest cover of Central Africa and a large diversity of habitats. In spite of recent progress in earth observation capabilities, vegetation mapping and seasonality analysis in equatorial areas still represent an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. On one hand, the use of coarse-resolution optical data is constrained by performance in the presence of cloud screening and by noise arising from the compositing process, which limits the spatial consistency of the composite and the temporal resolution. On the other hand, the use of high-resolution data suffers from heterogeneity of acquisition dates, images and interpretation from one scene to another. The objective of the present study was to propose and demonstrate a semi-automatic processing method for vegetation mapping and seasonality characterization based on temporal and spectral information from SPOT VEGETATION time series. A land cover map with 18 vegetation classes was produced using the proposed method that was fed by ecological knowledge gathered from botanists and reference documents. The floristic composition and physiognomy of each vegetation type are described using the Land Cover Classification System developed by the FAO. Moreover, the seasonality of each class is characterized on a monthly basis and the variation in different vegetation indicators is discussed from a phenological point of view. This mapping exercise delivers the first area estimates of seven different forest types, five different savannas characterized by specific seasonality behavior and two aquatic vegetation types. Finally, the result is compared to two recent land cover maps derived from coarse-resolution (GLC2000) and high-resolution imagery (Africover)

A decision support tool for the optimization of compositing parameters

Temporal syntheses of surface reflectance are one of the most common data products from high temporal resolution instruments. Such an image combination procedure is sensitive to several control parameters, including the compositing period. Unfortunately, these choices usually rely on a unique and global solution delivered by the data provider to ensure temporal and spatial consistency across applications. However, many applications require customized composites to achieve their goals and meet regional constraints. Although this need is now widely recognized by the scientific community, practitioners still rely on trial and error and ad hoc adjustments in designing their approach. The objective of this paper is to design, implement, and test a decision support tool that can find the most appropriate compositing parameters for coarse‐ to medium‐resolution sensors. The key innovation of this approach is that it incorporates external data on the cloud cover and seasonality of the region studied. The algorithm can be applied to any region of the globe and to any optical satellite instrument recording surface reflectance over time. The tool allows data users to optimize the compositing parameters to their application subject and to regional conditions. It can also be used to determine the feasibility of a proposed compositing process. The potential of this methodology to improve the relevancy and the quality of time series products is demonstrated by testing it on two specific applications.

From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities

Monitoring the start of the crop season in Sahel provides decision makers with valuable information for an early assessment of potential production and food security threats. Presently, the most common method for the estimation of sowing dates in West African countries consists of applying given thresholds on rainfall estimations. However, the coarse spatial resolution and the possible inaccuracy of these estimations are limiting factors. In this context, the remote sensing approach, which consists of deriving green-up onset dates from satellite remote sensing data, appears as an interesting alternative. It builds upon a novel statistic model that translates vegetation onset detections derived from MODIS time series into sowing probabilities at the village level. Results for Niger show that this approach outperforms the standard method adopted in the region based on rainfall thresholds.

Harmonization of pan-tropical biomass maps using an R2-weighted data fusion approach — A case study for the Amazon biome

Using an R2-weighted data-fusion model two existing above-ground biomass (AGB) maps (Saatchi and Baccini) are combined to derive improved AGB estimates for the Amazon biome. Advantage of this methodology is the increased transparency to hitherto existing approaches and the fact that no AGB reference datasets are necessary for the implementation. Instead, local correlations with independent vegetation cover-related spectral data are analyzed to derive an R2-weighted combination of the input maps. This approach also accounts for vegetation cover changes between the acquisition dates of the input maps. The analysis of three major forest cover types shows a higher consistency with the Baccini map for tropical rainforest (244 t/ha) and tropical mountain forest (269 t/ha), while tropical moist deciduous forest (163 t/ha) is more consistently depicted in the Saatchi dataset. The local harmonization is expected to increase accuracy - but due to missing high-quality AGB reference maps a validation is not yet feasible.