Several global maps providing tree cover information are available at different scales to support forest and environment protection policies. Yet, there is a lack of global and reliable forest/non-forest information at fine spatial resolution that can support the EU Regulation on deforestation-free supply chains. This paper presents a harmonized and globally consistent map of forest cover presence/absence for the year 2020 at 10m spatial resolution that may serve stakeholders and entities implementing this regulation. We assimilated all relevant freely available global data layers of tree cover and excluded areas that do not meet the definition. The paper outlines the legal context, the mapping approach and a preliminary assessment. Reviewers confirmed that there is a general alignment with forest stands in structured landscapes. Dry forests and ecotones remain the areas of high uncertainty, also from an assessment point of view. We envision an improved map before the Regulation becomes applicable by the end of 2024.

Recent advances in developing global forest monitoring approaches are important to address the role of tropical forests and the impact of deforestation, on global environmental issues such as climate change and biodiversity loss. In order to support the REDD+ monitoring and reporting, this paper assesses and compares recent products derived from Earth Observation at pan tropical scale regarding the forest status for year 2019 and forest cover changes between 2010 and 2016 over a test site in East Africa. A dedicated approach allows comparing six different datasets and deriving statistically robust accuracy estimates. This study aims at supporting the design of a future potential operational forest monitoring component within the Copernicus Land Service.

In this paper we examine the impact of the growing urbanization of Dar es Salaam, Tanzania, on the forest areas around the city. Since 1990 the city has seen an annual population growth rate of over 4%. We document the expansion of the city over the last 30 using the Global Human Settlement Layers, derived from the global Landsat archive. At the same time we show the depletion, of the surrounding forests that has occurred to meet the growing demand for agricultural land, fuel wood and construction material over the same period. The impact on these forests as a result of the demand from an expanding urban conglomeration is higher than would be expected from a dispersed population increase. The results show that to satisfy the demands of the city and of its expanding suburbs, forests from further afield are being depleted and that protected areas nearer the city are being compromised.

This paper presents a multidisciplinary approach used to assess forest degradation in the Brazilian Amazon based on remote sensing and spatial pattern analysis techniques. The selected study area is located in the Mato Grosso State, which is one of the states of the Brazilian legal Amazon with the highest deforestation rates and with a high concentration of selective logging activities and forest fires. We used an object-based image analysis for mapping degraded forest areas and compared their spatial distribution with that of fragmentation and edge indicators and the distance to roads. Our results show that the majority of these disturbed forest areas occur within a distance of less than 5 km from the main roads, are located between 100m and 5 km from the forest edge, and show higher entropy (used as a measure of fragmentation). However, circa 30% of the degradation occurred in areas considered as “core areas”.

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.

The need for quantitative and accurate information to characterize the state and evolution of forest types at the regional and continental scales is widely recognized. Various global land cover maps derived from satellite imagery depict several forest classes, but these maps are either too coarse or present large discrepancies. Recently a global forest change product (GFC) has been produced at 30 m resolution which is more adequate for forest monitoring purposes. However this product does not contain any thematic class and it is not possible to use a single global tree cover threshold for identifying forest areas. In order to map the main forest types at the continental scale, we developed a methodology that combines the best available spatial information to produce a hybrid map at 30m resolution for the year 2012. Such hybrid map was produced for the African continent. This map provides a much finer spatial resolution compared to existing global and continental land cover products in particular for the delineation of linear features such as the gallery forests.

Time series of SPOT4Take5 and RapidEye acquired in the period from February to June 2013 are used to monitor the dynamic of logging-related disturbances in the forest cover of a selective logging concession situated in the North of the Republic of Congo. First results of the time series analysis highlights the very rapid changes in the forest cover, as most logging gaps are totally disappearing in the space of a few months.

This paper presents a method for mapping and assessing burned areas at a regional scale, using a systematic sample of medium spatial resolution satellite images (Landsat). The State of Mato Grosso, located in the Brazilian Amazon region, comprising an area of approximately 903,366 km2, was selected for this study. 77 sample sites (20km × 20km in size) located at each full degree confluence of latitude and longitude were analyzed. The results showed that 52,663 km2or approximately 5.8% of the State land cover was burned in 2010. Our method produced results comparable with PanAmazonia Project data and useful for evaluating burned area products based on coarse spatial resolution imagery like MODIS or SPOT-VEGETATION.

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 Global Rain Forest Mapping (GRFM) radar mosaics, generated from L-band Japanese Earth Resources Satellite 1 imagery downsampled to 100-m pixel size, provide a two-season spatially continuous coverage of the humid tropical ecosystems of the world. This paper presents a novel classification approach suitable for regional-scale vegetation mapping using the GRFM datasets. The mapping system consists of: 1) an application-dependent wavelet-based edge-preserving smoothing algorithm and 2) a two-stage per-pixel hybrid learning nearest multiple-prototype (NMP) classifier, whose unsupervised first stage is a per-pixel near-optimal vector quantizer, called enhanced Linde-Buzo-Gray (ELBG), recently proposed in pattern recognition literature. Identified as ENMP (NMP with ELBG), this novel classification approach is compared against two alternative systems in the classification of forest cover disturbances located across an area in the Amazon Basin. Surface classes of interest are primary forest, degraded forest, nonforest, and water bodies. Reference maps, derived from 30-m resolution Landsat Thematic Mapper imagery, are provided by the National Aeronautics and Space Administration and the Food and Agriculture Organization of the United Nations. Abundant quantitative and qualitative evidence shows that: 1) in a forest/nonforest data-mapping task, ENMP provides a testing accuracy of 87%, in line with training accuracies, i.e., the proposed method seems capable of generalizing well over the GRFM South America dataset and 2) among three competing approaches, ENMP provides the best compromise between ease of use, mapping accuracy, and computational time. Starting from these results, ENMP is employed to generate a swamp forest map of the whole Amazon Basin from the two-season GRFM radar mosaic of South America, in the context of the Global Land Cover project (GLC 2000).

Daily global mosaics of data acquired by the VEGETATION instrument onboard the SPOT 4 between Nov. 1st, 1999 and Dec. 31st, 2000 have been used to produce a reference high quality global land cover map (GLC 2000) and the global survey of burned surfaces (GBA 2000). These products have been generated through an international partnership coordinated by the Joint Research Centre and are now freely available to users. This paper reviews briefly the results achieved and provides an assessment of the specific properties of the VEGETATION data for land cover and burn scar mapping at continental to global scales.

The modified adaptive pappas clustering (MPAC) algorithm, previously published in the image processing literature, is proposed as a valuable tool in the analysis of remotely sensed images where texture information is negligible. Owing to its contextual, adaptive, and multiresolutional labeling approach, MPAC preserves genuine but small regions, is easy to use (i.e., it requires minor user interaction to run), and is robust to changes in input parameters. As an application example, an MPAC-based three-stage classifier is applied to degraded forest detection in Landsat Thematic Mapper (TM) scenes of the Brazilian Amazon, where intermediate states of forest alterations caused by anthropogenic activities can be characterized by image structures 1-3 pixels wide. In three TM images of the Para test site, where classification results are validated by means of qualitative and quantitative comparisons with aerial photos, degraded forest areas cover 13% to 45% of the image ground coverage. In the Mato Grosso test site, the degraded forest class overlaps with 1) 10% of the closed-canopy forest detected by the deforestation mapping program of the Food and Agriculture Organization (FAO, 1992), and 2) 19% of the closed-canopy forest detected by the Tropical Rain Forest Information Center (TRFIC, 1996). These figures are in line with the conclusions of a study where present estimates of annual deforestation for the Brazilian Amazon are speculated to capture less than half of the forest area that is actually impoverished each year.

Abstract Motivation: The eXtensible Markup Language (XML) is an emerging standard for structuring documents, notably for the World Wide Web. In this paper, the authors present XML and examine its use as a data language for bioinformatics. In particular, XML is compared to other languages, and some of the potential uses of XML in bioinformatics applications are presented. The authors propose to adopt XML for data interchange between databases and other sources of data. Finally the discussion is illustrated by a test case of a pedigree data model in XML. Contact: [email protected] * To whom correspondence should be addressed. 3 Present address: Cereon Genomics, 45 Sidney St, Cambridge, MA 02139, USA.

MOTIVATION: Links between biological objects are frequently used by researchers in biology. However, many of the links found in public databases are insufficiently documented and difficult to retrieve. Virgil introduces the idea of a rich link, i.e. the link itself and the related pieces of information. Virgil was developed to collect, manage and distribute such links. RESULTS: At the moment, Virgil is a prototype database that contains rich links between GDB genes and Genbank sequences. The Virgil data model is rich enough to describe comprehensively a link between two biological objects. Two different means to access the information were developed: a schema-driven Web interface and a CORBA server. AVAILABILITY: http://www.infobiogen. fr/services/virgil/home.html CONTACT: [email protected]

MOTIVATION: A large proportion of the information found in public databases is not sufficiently cross-referenced. We developed genXref, an automated system for link inference, because embarking on a manual cross-referencing of genome data would require too much expensive human expertise. It uses information retrieval technology to generate links between objects of heterogeneous databases. RESULTS: GenXref was used to generate links between GDB genes and Genbank human sequences. It resulted in > 10,000 links with a precision of 83% and a recall of approximately 32%.