Jean-François Pekel

A New, Global, Multi-annual (2000-2007) Burnt Area Product at 1 Km Resolution

This paper reports on the development and validation of a new, global, burnt area product. Burnt areas are reported at a resolution of 1 km for seven fire years (2000 to 2007). A modified version of a Global Burnt Area (GBA) 2000 algorithm is used to compute global burnt area. The total area burnt each year (2000-2007) is estimated to be between 3.5 million km 2 and 4.5 million km(2). The total amount of vegetation burnt by cover type according to the Global Land Cover (GLC) 2000 product is reported. Validation was undertaken using 72 Landsat TM scenes was undertaken. Correlation statistics between estimated burnt areas are reported for major vegetation types. The accuracy of this new global data set depends on vegetation type.

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.

Broad-scale spatial pattern of forest landscape types in the Guiana Shield

Abstract Detecting broad scale spatial patterns across the South American rainforest biome is still a major challenge. Although several countries do possess their own, more or less detailed land-cover map, these are based on classifications that appear largely discordant from a country to another. Up to now, continental scale remote sensing studies failed to fill this gap. They mostly result in crude representations of the rainforest biome as a single, uniform vegetation class, in contrast with open vegetations. A few studies identified broad scale spatial patterns, but only when they managed to map a particular forest characteristic such as biomass. The main objective of this study is to identify, characterize and map distinct forest landscape types within the evergreen lowland rainforest at the sub-continental scale of the Guiana Shield (north-east tropical South-America 10° North-2° South; 66° West-50° West). This study is based on the analysis of a 1-year daily data set (from January 1st to December 31st, 2000) from the VEGETATION sensor onboard the SPOT-4 satellite (1-km spatial resolution). We interpreted remotely sensed landscape classes (RSLC) from field and high resolution remote sensing data of 21 sites in French Guiana. We cross-analyzed remote sensing data, field observations and environmental data using multivariate analysis. We obtained 33 remotely sensed landscape classes (RSLC) among which five forest-RSLC representing 78% of the forested area. The latter were classified as different broad forest landscape types according to a gradient of canopy openness. Their mapping revealed a new and meaningful broad-scale spatial pattern of forest landscape types. At the scale of the Guiana Shield, we observed a spatial patterns similarity between climatic and forest landscape types. The two most open forest-RSLCs were observed mainly within the north-west to south-east dry belt. The three other forest-RSLCs were observed in wetter and less anthropized areas, particularly in the newly recognized “Guianan dense forest arch”. Better management and conservation policies, as well as improvement of biological and ecological knowledge, require accurate and stable representations of the geographical components of ecosystems. Our results represent a decisive step in this way for the Guiana Shield area and contribute to fill one of the major shortfall in the knowledge of tropical forests.

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)

Development and Application of Multi-Temporal Colorimetric Transformation to Monitor Vegetation in the Desert Locust Habitat

The Desert Locust (Schistocerca gregaria) is the most feared of all the locusts worldwide. Satellite imagery can provide a continuous overview of ecological conditions (i.e., vegetation, soil moisture) suitable for the Desert Locust at the continental scale and in near real time. To monitor green vegetation, most remote sensing techniques are based on vegetation indices (e.g., NDVI). However, several limitations have been observed for this index based approaches in sparsely vegetated areas. To guarantee a more robust and reliable image-independent discrimination between vegetation and non-vegetated surface types, an innovative multi-temporal and multi-spectral image analysis method was developed based on a combination of MIR, NIR and Red reflectance measurements. The proposed approach is based on a transformation of the RGB color space into HSV that decouples chromaticity and luminance. A complete automatic processing chain combining the daily observations of MODIS and SPOT VEGETATION, was designed to provide user-friendly vegetation dynamic maps at 250 m resolution over the entire locust area every 10 days. This new product informs users about the location of green vegetation and its temporal evolution. The methodology is currently implemented at the Vlaamse instelling voor technologisch onderzoek (VITO) to provide vegetation dynamic maps every dekade to the Desert Locust Information Service at FAO.

The eStation, an Earth Observation processing service in support to ecological monitoring

Abstract The eStation is a collecting and processing system designed to automatically deal with the reception, processing, analysis and dissemination of key environmental parameters derived from remotely sensed data. Developed mainly at the Joint Research Centre of the European Commission, the eStation has been distributed to 47 sub-Saharan countries in the frame of the AMESD (Africa n Monitoring of Environment for Sustainable Development) project to provide local institutions with the capacity to easily access a large range of remote sensing products on vegetation, precipitation, fires and oceans. These products, derived from the processing of images coming from various instruments including SPOT-Vegetation, MSG-SEVIRI and MODIS are developed to allow end-users to make local and regional assessments of the state of marine and terrestrial ecosystems. The products, dispatched to the users through the EUMETSAT data broadcasting system (EUMETCast) or provided by other Earth Observation (EO) data agencies (e.g. NASA), are further processed by the eStation to allow end-users to generate their own environmental, whether terrestrial or marine, assessments and reports. Initially designed as a stand-alone system using an open source development framework, the eStation has recently been further developed as a web processing service to allow a broader range of end-users to access the data and services over the Internet. It is the purpose of this paper to introduce the readers to the eStation and its products, to share the lessons learnt in deploying these services as well as to discuss its more recent use in chained environmental web based modeling services.

Effects of increase in temperature and open water on transmigration and access to health care by the Nenets reindeer herders in northern Russia

Background The indigenous Nenets reindeer herders in northern Russia annually migrate several hundred kilometers between summer and winter pastures. In the warming climate, ice-rich permafrost and glaciers are being significantly reduced and will eventually disappear from parts of the Arctic. The emergent changes in hydrological cycles have already led to substantial increases in open water that stays unfrozen for longer periods of time. This environmental change has been reported to compromise the nomadic Nenets’ traditional way of life because the presence of new water in the tundra reduces the Nenets’ ability to travel by foot, sled, or motor vehicle from the summer transitory tundra campsites in order to access healthcare centers in villages. New water can also impede their access to family and community at other herder camps and in the villages. Although regional and global models predicting hydrologic changes due to climate changes exist, the spatial resolution of these models is too coarse for studying how increases in open water affect health and livelihoods. To anticipate the full health impact of hydrologic changes, the current gap between globally forecasted scenarios and locally forecasted hydrologic scenarios needs to be bridged. Objectives We studied the effects of the autumn temperature anomalies and increases in open water on health care access and transmigration of reindeer herders on the Kanin Peninsula. Design Correlational and time series analyses were completed. Methods The study population consisted of 370 full-time, nomadic reindeer herders. We utilized clinical visit records, studied surface temperature anomalies during autumn migrations, and used remotely sensed imagery to detect water bodies. Spearman correlation was used to measure the relationship between temperature anomalies and the annual arrival of the herders at the Nes clinic for preventive and primary care. Piecewise regression was used to model change in mean autumnal temperature anomalies over time. We also created a water body product to detect inter-annual changes in water area. Results Correlation between arrivals to the Nes clinic and temperature anomalies during the fall transmigration (1979–2011) was r = 0.64, p = 0.0004; 95% CI (0.31; 0.82). Regression analysis estimated that mean temperature anomalies during the fall migration in September–December were stochastically stationary pre-1991 and have been rising significantly (p < 0.001) since then. The rate of change was estimated at +0.1351°C/year, SE = 0.0328, 95% CI (+0.0694, +0.2007). The amount of detected water fluctuated significantly interannually (620–800 km2). Conclusions Later arrival of freezing temperatures in the autumn followed by the earlier spring thaws and more open water delay transmigration and reduce herders’ access to health care. The recently observed delays in arrival to the clinic are likely related to the warming trend and to concomitant hydrologic changes.

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.