Pierre Defourny

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.

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.

The Anopheles dirus complex: spatial distribution and environmental drivers

BackgroundThe Anopheles dirus complex includes efficient malaria vectors of the Asian forested zone. Studies suggest ecological and biological differences between the species of the complex but variations within species suggest possible environmental influences. Behavioural variation might determine vector capacity and adaptation to changing environment. It is thus necessary to clarify the species distributions and the influences of environment on behavioural heterogeneity.MethodsA literature review highlights variation between species, influences of environmental drivers, and consequences on vector status and control. The localisation of collection sites from the literature and from a recent project (MALVECASIA) produces detailed species distributions maps. These facilitate species identification and analysis of environmental influences.ResultsThe maps give a good overview of species distributions. If species status partly explains behavioural heterogeneity, occurrence and vectorial status, some environmental drivers have at least the same importance. Those include rainfall, temperature, humidity, shade, soil type, water chemistry and moon phase. Most factors are probably constantly favourable in forest. Biological specificities, behaviour and high human-vector contact in the forest can explain the association of this complex with high malaria prevalence, multi-drug resistant Plasmodium falciparum and partial control failure of forest malaria in Southeast Asia.ConclusionEnvironmental and human factors seem better than species specificities at explaining behavioural heterogeneity. Although forest seems essential for mosquito survival, adaptations to orchards and wells have been recorded. Understanding the relationship between landscape components and mosquito population is a priority in foreseeing the influence of land-cover changes on malaria occurrence and in shaping control strategies for the future.

The Forests of the Congo Basin: State of the Forest 2010

Meat from wild terrestrial or semi-terrestrial animals, termed „bushmeat‟, is a significant source of animal protein in Central African countries, and a crucial component of food security and livelihoods in rural areas. Estimates of bushmeat consumption across the Congo Basin range between 1 million tonnes (Wilkie and Carpenter 1999) and 5 million tonnes (Fa et al. 2003) and harvest rates are estimated to range from 23 to 897 kg/km 2 /year (Nasi et al. 2008). Many sustainability assessments focusing on tropical forest wildlife in the region have warned about the increasing unsustainability of hunting and associated ecological impacts (e.g. examples within Bennett and Robinson, 2000).The term “value chain” is useful to understand the activities involved in bringing a product from the forest, through processing and production, to delivery to final consumers and ultimately disposal (Kaplinsky & morris, 2000). Value chain analysis is a conceptual framework for mapping and categorizing the economic, social and environmental processes. It helps to understand how and where enterprises and institutions are positioned in chains, and to identify opportunities and possible leverage points for upgrading. This analysis encompasses the organization, coordination, equity, power relationships, linkages and governance between organizations and actors. Photo 7.1: Kola nuts (Cola acuminata) for sale in a market in Kisangani, DRC

Land cover characterization and mapping of continental southeast Asia using multi-resolution satellite sensor data

Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.

National forest cover change in Congo Basin: deforestation, reforestation, degradation and regeneration for the years 1990, 2000 and 2005

This research refers to an object-based automatic method combined with a national expert validation to produce regional and national forest cover change statistics over Congo Basin. A total of 547 sampling sites systematically distributed over the whole humid forest domain are required to cover the six Central African countries containing tropical moist forest. High resolution imagery is used to accurately estimate not only deforestation and reforestation but also degradation and regeneration. The overall method consists of four steps: (i) image automatic preprocessing and preinterpretation, (ii) interpretation by national expert, (iii) statistic computation and (iv) accuracy assessment. The annual rate of net deforestation in Congo Basin is estimated to 0.09% between 1990 and 2000 and of net degradation to 0.05%. Between 2000 and 2005, this unique exercise estimates annual net deforestation to 0.17% and annual net degradation to 0.09%. An accuracy assessment reveals that 92.7% of tree cover (TC) classes agree with independent expert interpretation. In the discussion, we underline the direct causes and the drivers of deforestation. Population density, small-scale agriculture, fuelwood collection and forests accessibility are closely linked to deforestation, whereas timber extraction has no major impact on the reduction in the canopy cover. The analysis also shows the efficiency of protected areas to reduce deforestation. These results are expected to contribute to the discussion on the reduction in CO2 emissions from deforestation and forest degradation (REDD+) and serve as reference for the period.

Optimization of a coupled hydrology–crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter

It is well known that the presence and development stage of vegetation largely influences the soil moisture content. In its turn, soil moisture availability is of major importance for the development of vegetation. The objective of this paper is to assess to what extent the results of a fully coupled hydrology-crop growth model can be optimized through the assimilation of observed leaf area index ( LAI) or soil moisture values. For this purpose the crop growth module of the World Food Studies ( WOFOST) model has been coupled to a fully process based water and energy balance model ( TOPMODEL-Based Land-Atmosphere Transfer Scheme ( TOPLATS)). LAI and soil moisture observations from 18 fields in the loamy region in the central part of Belgium have been used to thoroughly validate the coupled model. An observing system simulation experiment ( OSSE) has been performed in order to assess whether soil moisture and LAI observations with realistic uncertainties are useful for data assimilation purposes. Under realistic conditions ( biweekly observations with a noise level of 5 volumetric percent for soil moisture and 0.5 for LAI) an improvement in the model results can be expected. The results show that the modeled LAI values are not sensitive to the assimilation of soil moisture values before the initiation of crop growth. Also, the modeled soil moisture profile does not necessarily improve through the assimilation of LAI values during the growing season. In order to improve both the vegetation and soil moisture state of the model, observations of both variables need to be assimilated.

Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.

Thematic accuracy assessment of geographic object-based image classification

Geographic object-based image analysis is an image-processing method where groups of spatially adjacent pixels are classified as if they were behaving as a whole unit. This approach raises concerns about the way subsequent validation studies must be conducted. Indeed, classical point-based sampling strategies based on the spatial distribution of sample points (using systematic, probabilistic or stratified probabilistic sampling) do not rely on the same concept of objects and may prove to be less appropriate than the methods explicitly built on the concept of objects used for the classification step. In this study, an original object-based sampling strategy is compared with other approaches used in the literature for the thematic accuracy assessment of object-based classifications. The new sampling scheme and sample analysis are founded on a sound theoretical framework based on few working hypotheses. The performance of the sampling strategies is quantified using simulated object-based classifications results of a Quickbird imagery. The bias and the variance of the overall accuracy estimates were used as indicators of the methods benefits. The main advantage of the object-based predictor of the overall accuracy is its performance: for a given confidence interval, it requires fewer sampling units than the other methods. In many cases, this can help to noticeably reduce the sampling effort. Beyond the efficiency, more conceptual differences between point-based and object-based samplings are discussed. First, geolocation errors do not influence the object-based thematic accuracy as they do for point-based accuracy. These errors need to be addressed independently to provide the geolocation precision. Second, the response design is more complex in object-based accuracy assessment. This is interesting for complex classes but might be an issue in case of large segmentation errors. Finally, there is a larger likelihood to reach the minimum sample size for each class with an object-based sampling than in a point-based sampling. Further work is necessary to reach the same suitability than point-based sampling for pixel-based classification, but this pioneer study shows that object-based sampling could be implemented within a statistically sound framework.

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.