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Southeast Asia is a region known for active land-use changes (LUC) over the past 60 years; yet, how trends in net CO2 uptake and release resulting from LUC activities (net LUC flux) have changed through past decades remains uncertain. The level of uncertainty in net LUC flux from process-based models is so high that it cannot be concluded that newer estimates are necessarily more reliable than older ones. Here, we examined net LUC flux estimates of Southeast Asia for the 1980s−2010s from older and newer sets of Dynamic Global Vegetation Model simulations (TRENDY v2 and v7, respectively), and forcing data used for running those simulations, along with two book-keeping estimates (H&N and BLUE). These estimates yielded two contrasting historical LUC transitions, such that TRENDY v2 and H&N showed a transition from increased emissions from the 1980s to 1990s to declining emissions in the 2000s, while TRENDY v7 and BLUE showed the opposite transition. We found that these contrasting transitions originated in the update of LUC forcing data, which reduced the loss of forest area during the 1990s. Further evaluation of remote sensing studies, atmospheric inversions, and the history of forestry and environmental policies in Southeast Asia supported the occurrence of peak emissions in the 1990s and declining thereafter. However, whether LUC emissions continue to decline in Southeast Asia remains uncertain as key processes in recent years, such as conversion of peat forest to oil-palm plantation, are yet to be represented in the forcing data, suggesting a need for further revision.

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year-1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year-1 Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement's bold targets.

In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011–2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom- up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

Abstract Background Climate change and high rates of global carbon emissions have focussed attention on the need for high-quality monitoring systems to assess how much carbon is present in terrestrial systems and how these change over time. The choice of system to adopt should be guided by good science. There is a growing body of scientific and technical information on ground-based and remote sensing methods of carbon measurement. The adequacy and comparability of these different systems have not been fully evaluated. Methods A systematic review will compare methods of assessing carbon stocks and carbon stock changes in key land use categories, including, forest land, cropland, grassland, and wetlands, in terrestrial carbon pools that can be accounted for under the Kyoto protocol (above- ground biomass, below-ground biomass, dead wood, litter and soil carbon). Assessing carbon in harvested wood products will not be considered in this review. Discussion Developing effective mitigation strategies to reduce carbon emissions and equitable adaptation strategies to cope with increasing global temperatures will rely on robust scientific information that is free from biases imposed by national and commercial interests. A systematic review of the methods used for assessing carbon stocks and carbon stock changes will contribute to the transparent analysis of complex and often contradictory science.

Abstract: The growing access to Earth Observations and processing capabilities have stimulated the production of global and regional products that are commonly used to assess tree‐covered habitats and their changes. The popularity of these products has led to their use for defining baselines and to assess progress in conserving natural habitats, in particular, in the context of the conservation targets to 2020 set by the UN Convention on Biological Diversity. In this paper, we reviewed three tree cover products commonly used over Sub‐Saharan Africa: (1) MODIS Vegetation Continuous Field percent tree cover map, (2) Global Forest Change map and (3) TREES product. Over a systematic sample of 2045 map subsets, each having a size of 10 × 10 km², we calculated the extent and change of tree cover from each product for the period between 2000 and 2010. Our statistical and spatial comparison shows noticeable discrepancies between the three products, which lead to uncertainties when assessing tree cover across varying ecosystems. These differences are highest in habitats where tree cover is fragmented or reaches medium density levels and overlap with areas of high economic development potential, where habitat changes are likely to occur in the near future. We discuss these findings in the context of using these remotely sensed tree cover products to support current global biodiversity conservation policies. [ABSTRACT FROM AUTHOR]

In this paper we analyse the extent of fire-induced forest degradation in Mato Grosso State, Brazil. Weutilise a sample based approach used in a previous pan-tropical deforestation survey to derive information on land cover and burned areas in the two major biomes of Mato Grosso: Amazon and Cerrado. Land cover and burned area are mapped for three years (2000-2005-2010) over 77 sample sites (10 000 ha each) distributed systematically throughout the state which covers 90.337 Mha. Our results indicate continuing forest degradation by fires in the state and potentially increasing fire susceptibility of the Amazon forests, regardless of the decrease in deforestation. 2010 witnessed the most extensive fireinduced forest degradation (~300 000 ha) in the forests of the Amazon biome among the study years, regardless of the fact that the fire season was less severe than in 2005. Deforestation in the Amazon biome in Mato Grosso dropped from 590 000 ha year-1 in the 2000-2005 period to 190 000 ha year-1 in the second half of the decade. The findings of this study advocate the inclusion of forest fire effects into carbon accounting initiatives. [ABSTRACT FROM AUTHOR]

The area of land covered by forest and trees is an important indicator of environmental condition. This study presents and analyses results from the Global Forest Resources Assessment 2015 (FRA 2015) of the Food and Agriculture Organization of the United Nations. FRA 2015 was based on responses to surveys by individual countries using a common reporting framework, agreed definitions and reporting standards. Results indicated that total forest area declined by 3%, from 4128 M ha in 1990 to 3999 M ha in 2015. The annual rate of net forest loss halved from 7.3 M ha y −1 in the 1990s to 3.3 M ha y −1 between 2010 and 2015. Natural forest area declined from 3961 M ha to 3721 M ha between 1990 and 2015, while planted forest (including rubber plantations) increased from 168 M ha to 278 M ha. From 2010 to 2015, tropical forest area declined at a rate of 5.5 M ha y −1 – only 58% of the rate in the 1990s – while temperate forest area expanded at a rate of 2.2 M ha y −1 . Boreal and sub-tropical forest areas showed little net change. Forest area expanded in Europe, North America, the Caribbean, East Asia, and Western-Central Asia, but declined in Central America, South America, South and Southeast Asia and all three regions in Africa. Analysis indicates that, between 1990 and 2015, 13 tropical countries may have either passed through their forest transitions from net forest loss to net forest expansion, or continued along the path of forest expansion that follows these transitions. Comparing FRA 2015 statistics with the findings of global and pan-tropical remote-sensing forest area surveys was challenging, due to differences in assessment periods, the definitions of forest and remote sensing methods. More investment in national and global forest monitoring is needed to provide better support for international initiatives to increase sustainable forest management and reduce forest loss, particularly in tropical countries. [ABSTRACT FROM AUTHOR]

A method has been developed to identify and map areas of forest degradation caused by either selective logging or fires in tropical humid ecosystems. Our study area is located in the Mato Grosso state of Brazil, in a region known as ‘Deforestation Arc’. Eight consecutive Landsat Enhanced Thematic Mapper Plus (ETM+) images were available over this study area during the dry season of 2002 (from June to October). The proposed method is based on multi-temporal image segmentation and classification of a data set of soil and shade fraction images derived from Landsat ETM+ imagery. Areas of selectively logged forest are identified and mapped from the soil fraction images, whereas burned forest areas are identified and mapped from the shade fraction images combined with a map of deforestation happening during 2002. The main benefit of this approach is the capability to discriminate selectively logged forest from burned forest, which is a very important issue for estimating carbon emissions from forest degradation. [ABSTRACT FROM AUTHOR]

We estimate changes in forest cover (deforestation and forest regrowth) in the tropics for the two last decades (1990-2000 and 2000-2010) based on a sample of 4000 units of 10 ×10 km size. Forest cover is interpreted from satellite imagery at 30 × 30 m resolution. Forest cover changes are then combined with pan-tropical biomass maps to estimate carbon losses. We show that there was a gross loss of tropical forests of 8.0 million ha yr−1 in the 1990s and 7.6 million ha yr−1 in the 2000s (0.49% annual rate), with no statistically significant difference. Humid forests account for 64% of the total forest cover in 2010 and 54% of the net forest loss during second study decade. Losses of forest cover and Other Wooded Land (OWL) cover result in estimates of carbon losses which are similar for 1990s and 2000s at 887 MtC yr−1 (range: 646-1238) and 880 MtC yr−1 (range: 602-1237) respectively, with humid regions contributing two-thirds. The estimates of forest area changes have small statistical standard errors due to large sample size. We also reduce uncertainties of previous estimates of carbon losses and removals. Our estimates of forest area change are significantly lower as compared to national survey data. We reconcile recent low estimates of carbon emissions from tropical deforestation for early 2000s and show that carbon loss rates did not change between the two last decades. Carbon losses from deforestation represent circa 10% of Carbon emissions from fossil fuel combustion and cement production during the last decade (2000-2010). Our estimates of annual removals of carbon from forest regrowth at 115 MtC yr−1 (range: 61-168) and 97 MtC yr−1 (53-141) for the 1990s and 2000s respectively are five to fifteen times lower than earlier published estimates. [ABSTRACT FROM AUTHOR]

The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level. The main aim of this paper is to demonstrate that a coarse-resolution land-cover map of the Russian Federation, the ‘TerraNorte’ map at 230 m × 230 m resolution for the year 2010, can be used in combination with a sample of reference forest maps at 30 m resolution to correctly assess forest cover in the Russian federation. First, an accuracy assessment of the TerraNorte map is carried out through the use of reference forest maps derived from finer-resolution satellite imagery (Landsat Thematic Mapper (TM) sensor). A sample of 32 sites was selected for the detailed identification of forest cover from Landsat TM imagery. A methodological approach is developed to process and analyse the Landsat imagery based on unsupervised classification and cluster-based visual labelling. The resulting forest maps over the 32 sites are then used to evaluate the accuracy of the forest classes of the TerraNorte land-cover map. A regression analysis shows that the TerraNorte map produces satisfactory results for areas south of 65° N, whereas several forest classes in more northern areas have lower accuracy. This might be explained by the strong reflectance of background (i.e. non-tree) cover. A forest area estimate is then derived by calibration of the TerraNorte Russian map using a sample of Landsat-derived reference maps (using a regression estimator approach). This estimate compares very well with the FAO FRA exercise for 2010 (1% difference for total forested area). We conclude that the TerraNorte map combined with finer-resolution reference maps can be used as a reliable spatial information layer for forest resources assessment over the Russian Federation at national scale. [ABSTRACT FROM PUBLISHER]

The European Commission funded Tropical Ecosystem Environment Observation by Satellite (TREES) project aims to assess the forest cover and its changes within the pan- tropical belt. Statistics for the monitoring target periods 1990, 2000 and 2010 are derived from analyzing a systematic sample set of satellite imagery mainly from the Landsat TM and ETM+ sensors. To reach full coverage of all sample sites for the year 2010 epoch and to ensure data continuity for possible future epochs the usage of alternative satellite data is inevitable. In this study the replacement of Landsat data with ALOS AVNIR-2 data was tested. Adaptations of the existing processing chain are presented as well as the results of an accuracy assessment. The results show that AVNIR-2 data are generally suitable to substitute Landsat data in this context. [ABSTRACT FROM PUBLISHER]

Forest monitoring from earth observation is crucial over tropical regions to assess forest extent and provide up-to-date estimates of deforestation rates. Based on a systematic sample of 20x20 km size sites, a processing chain has been developed at the European Commission's Joint Research Centre (JRC) for producing deforestation estimates between years 1990, 2000 and 2005. Whereas this monitoring exercise was based on Landsat imagery, limitations in Landsat availability over Central Africa for year 2010 required alternative imagery such as the Disaster Monitoring Constellation (DMC). The classification module of the existing JRC processing chain is based on tasseled caps analysis (TCap-based). We adapted this module to DMC imagery by selecting the most suitable object-features through their assessments using a sub-sample of existing land-cover maps of years 1990 and 2000. The processing chain is adapted for the production of land-cover change maps between years 2000 and 2010. The accuracy of the land-cover maps produced for year 2010 with the two methods (original TCap-based and adapted Multi-Sensor) is assessed through a reference dataset. Overall accuracies are similar for both approaches (93% and 95% respectively), but the Multi-Sensor approach shows a significant improvement when considering only changed objects (83% overall accuracy versus 56% for TCap-based). Our results show that, even by using DMC imagery with lower radiometric quality (compared to Landsat) an automated classification can provide land-cover maps with similar accuracy thanks to an appropriate object-features selection. Similar adaptations need to be developed for other satellite imagery such as SPOT and RapidEye. [ABSTRACT FROM PUBLISHER]

The TREES-3 project of the European Commission's Joint Research Centre is producing estimates of tropical forest cover changes for two time periods: 1990–2000 and 2000–2010. This paper presents the method developed for the automatic change detection and classification of year 2010 imagery integrating the existing segmentation and classification results of the period 1990–2000. The year 2010 imagery is processed in three automatic steps: segmentation, change detection and object spectral classification. The validated maps of forest cover for the years 1990 and 2000 are integrated as thematic input layer into the image segmentation and classification process for the year 2010 data. An object-based change detection technique is applied using Tasseled Cap components and spectral Euclidean distances. Objects detected as changed are classified in two steps: parametric classification based on membership functions and change vector analysis for the remaining unclassified objects. All objects identified as ‘unchanged’ are used as training areas for parametric classification and spectral signatures are extracted from 2010 imagery. The change vectors are defined according to the validated land cover classification of the year 2000. The segmentation approach was tested on 568 sample units spread over Brazil. The segmentation results for year 2010 demonstrated consistency with the segmentation of imagery for the period 1990–2000. The resulting overall accuracy of the automatic classification was calculated for the 281 sample units of the Brazilian Amazon biome and for 201 sample units of three more complex Brazilian biomes, Caatinga, Cerrado and Pantanal, at 92% and 91% respectively. [ABSTRACT FROM PUBLISHER]

This paper outlines the methods and results for monitoring forest change and resulting carbon emissions for the 1990-2000 and 200-2005 periods carried out over tropical Central and South America. To produce our forest change estimates we used a systematic sample of medium resolution satellite data processed to forest change maps covering 1230 sites of 20 km by 20 km, each located at the degree confluence. Biomass data were spatially associated to each individual sample site so that annual carbon emissions could be estimated. For our study area we estimate that forest cover in the study area had fallen from 763 Mha (s.e. 10 Mha) in 1990 to 715 Mha (s.e. 10 Mha) in 2005. During the same period other wooded land (i.e., non-forest woody vegetation) had fallen from 191 Mha (s.e. 5.5 Mha) to 184 Mha (s.e. 5.5 Mha). This equates to an annual gross loss of 3.74 Mha·y-1 of forests (0.50% annually) between 1990 and 2000, rising to 4.40 Mha·y-1 in the early 2000s (0.61% annually), with Brazil accounting for 69% of the total losses. The annual carbon emissions from the combined loss of forests and other wooded land were calculated to be 482 MtC·y-1 (s.e. 29 MtC·y-1) for the 1990s, and 583 MtC·y-1 (s.e. 48 MtC·y-1) for the 2000 to 2005 period. Our maximum estimate of sinks from forest regrowth in tropical South America is 92 MtC·y-1. These estimates of gross emissions correspond well with the national estimates reported by Brazil, however, they are less than half of those reported in a recent study based on the FAO country statistics, highlighting the need for continued research in this area. [ABSTRACT FROM AUTHOR]

This paper presents a new accounting mechanism in the context of the UNFCCC issue on reducing emissions from deforestation in developing countries, including technical options for determining baselines of forest conversions. This proposal builds on the recent scientific achievements related to the estimation of tropical deforestation rates and to the assessment of 'intac' forest areas. The distinction between 'intact' and 'non intact' forests used here arises from experience with satellite-based deforestation measurements and allows accounting for carbon losses from forest degradation. The proposed accounting system would use forest area conversion rates as input data. An optimal technical solution to set baselines would be to use historical average figures during the time period from 1990 to 2005. The system introduces two different schemes to account for preserved carbon: one for countries with high forest conversion rates where the desired outcome would be a reduction in their rates, and another for countries with low rates. A 'global' baseline rate would be used to discriminate between these two country categories (high and low rates). For the hypothetical accounting period 2013-2017 and considering 72% of the total tropical forest domain for which data are available, the scenario of a 10% reduction of the high rates and of the preservation of low rates would result in approximately 1.6 billion tCO2 of avoided emissions. The resulting benefits of this reduction would be shared between those high-rate countries which reduced deforestation and those low-rate countries which did not increase their deforestation over an agreed threshold (e.g., half of "global" baseline rate). [ABSTRACT FROM AUTHOR]

An accurate estimate of carbon fluxes associated with tropical deforestation from the last two decades is needed to balance the global carbon budget. Several studies have already estimated carbon emissions from tropical deforestation, but the estimates vary greatly and are difficult to compare due to differences in data sources, assumptions, and methodologies. In this paper, we review the different estimates and datasets, and the various challenges associated with comparing them and with accurately estimating carbon emissions from deforestation. We performed a simulation study over legal Amazonia to illustrate some of these major issues. Our analysis demonstrates the importance of considering land-cover dynamics following deforestation, including the fluxes from reclearing of secondary vegetation, the decay of product and slash pools, and the fluxes from regrowing forest. It also suggests that accurate carbon-flux estimates will need to consider historical land-cover changes for at least the previous 20 years. However, this result is highly sensitive to estimates of the partitioning of cleared carbon into instantaneous burning vs. Long-timescale slash pools. We also show that carbon flux estimates based on ‘committed flux’ calculations, as used by a few studies, are not comparable with the ‘annual balance’ calculation method used by other studies. [ABSTRACT FROM AUTHOR]

Abstract: Changes in forest cover have become recognized as an important global environmental issue. This article presents a synthesis of what is known about areas of rapid forest-cover change in boreal Eurasia from the end of the 1990s, based on data compiled from expert opinion and remote sensing data. The broad geographic patterns of rapid forest-cover change have been mapped at regional scale with the characterization of the main causes and drivers of these changes. Around 40millionha of rapid change areas with clear-cutting activities and 70millionha with increased fire frequency are depicted. The main processes of rapid forest-cover changes in boreal Eurasia are logging and increase of fire frequency. Ancillary processes are forest conversion for built over area or dam construction, forest regrowths and conversion of bogs. This study revealed that human influence on the Russian forest landscape has been growing over recent decades, mainly as a consequence of logging activities and human-induced fires, with in particular: (i) clear cuts; (ii) high intensity selective logging; (iii) increased fire frequency. Rapid land-cover change is not randomly or uniformly distributed but is clustered in some locations, e.g., high intensity logging mostly takes place in the European part of Russia (e.g., in the Karelian Isthmus) and along the Southern border of the Taiga. Except for forest regrowths on abandoned agricultural land in the southern Taiga, all other processes lead to the decrease of forest cover or to its degradation. Logging activities are driven by regular timber harvesting and irregular cutting for public revenue or individual profit in response to growing demand in national and international markets, particularly in China and Japan. Forest degradation in Siberia, mostly related to increase of fire frequency and development of logging activities, is extending rapidly. Annual forest-cover change rates in areas identified as rapid change areas may range from 0.26% for diffuse logging activities to around 0.65% for areas affected by intense clear-cutting activities, up to 2.3% for areas affected by fires or a combination of fires and logging. While this approach does not directly lead to quantified estimates of forest-cover changes, it highlights those areas where intensive monitoring would be devised for an improved estimation of the changes at continental scale. [Copyright &y& Elsevier]

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, ENMJ 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). [ABSTRACT FROM AUTHOR]

Reports the application of contextual clustering of image labeling to degraded forest assessment in Landsat Thematic Mapper images of Brazilian Amazon. Analysis of remote sensed images; Cause of intermediate states of forest alterations; Detection on the deforestation mapping program of the Food and Agriculture Organization.