A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

Question: Can recent satellite imagery of coarse spatial resolution support forest cover assessment and mapping at the regional level? Location: Continental southeast Asia. Methods: Forest cover mapping was based on digital classification of SPOT4‐VEGETATION satellite images of 1 km spatial resolution from the dry seasons 1998/1999 and 1999/2000. Following a geographical stratification, the spectral clusters were visually assigned to land cover classes. The forest classes were validated by an independent set of maps, derived from interpretation of satellite imagery of high spatial resolution (Landsat TM, 30 m). Forest area estimates from the regional forest cover map were compared to the forest figures of the FAO database. Results: The regional forest cover map displays 12 forest and land cover classes. The mapping of the region's deciduous and fragmented forest cover remained challenging. A high correlation was found between forest area estimates obtained from this map and from the Landsat TM derived maps. The regional and sub‐regional forest area estimates were close to those reported by FAO. Conclusion: SPOT4‐VEGETATION satellite imagery can be used for mapping consistently and uniformly the extent and distribution of the broad forest cover types at the regional scale. The new map can be considered as an update and improvement on existing regional forest cover maps.     

Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications

This paper provides a methodology for comparing global land cover maps that allows for differences in legend definitions between products to be taken into account. The legends of the two maps are first reconciled by creating a legend lookup table that shows how the legends map onto one another. Where there is overlap, the specific definitions for each legend class are used to calculate the degree of overlap between legend classes. In this way, one‐to‐many mappings are accounted for unlike in most methods where the legend definitions are often forced into place. Another advantage over previous map comparison methods is that application‐specific requirements are captured using expert input, whereby the user rates the importance of disagreement between different legend classes based on the needs of the application. This user‐defined matrix in conjunction with the degree of overlap between legend classes is applied on a pixel‐by‐pixel basis to create maps of spatial disagreement and uncertainty. The user can then highlight the areas of highest thematic uncertainty and disagreement between the different land cover maps allowing for areas that require further detailed examination to be readily identified. It would also be possible for several users to input their knowledge into the process, leading to a potentially more robust comparison of land cover products. The methodology of map comparison is illustrated using different land cover products including Global Land Cover 2000 (GLC‐2000) and the MODIS land cover data set. Two diverse applications are provided including the estimation of global forest cover and monitoring of agricultural land. In the case of global forest cover, an example was provided for Columbia, which showed that the MODIS land cover map overestimates forest cover in comparison with the GLC‐2000. The agricultural example, on the other hand, served to illustrate that for Sudan, MODIS tends to underestimate crop areas while GLC‐2000 overestimates them.     

Estimating soil carbon fluxes following land-cover change: a test of some critical assumptions for a region in Costa Rica

Changes in soil carbon storage that accompany land‐cover change may have significant effects on the global carbon cycle. The objective of this work was to examine how assumptions about preconversion soil C storage and the effects of land‐cover change influence estimates of regional soil C storage. We applied three models of land‐cover change effects to two maps of preconversion soil C in a 140 000 ha area of northeastern Costa Rica. One preconversion soil C map was generated using values assigned to tropical wet forest from the literature, the second used values obtained from extensive field sampling. The first model of land‐cover change effects used values that are typically applied in global assessments, the second and third models used field data but differed in how the data were aggregated (one was based on land‐cover transitions and one was based on terrain attributes). Changes in regional soil C storage were estimated for each combination of model and preconversion soil C for three time periods defined by geo‐referenced land‐cover maps. The estimated regional soil C under forest vegetation (to 0.3 m) was higher in the map based on field data (10.03 Tg C) than in the map based on literature data (8.90 Tg C), although the range of values derived from propagating estimation errors was large (7.67–12.40 Tg C). Regional soil C storage declined through time due to forest clearing for pasture and crops. Estimated CO₂ fluxes depended more on the model of land‐cover change effects than on preconversion soil C. Cumulative soil C losses (1950–1996) under the literature model of land‐cover effects exceeded estimates based on field data by factors of 3.8–8.0. In order to better constrain regional and global‐scale assessments of carbon fluxes from soils in the tropics, future research should focus on methods for extrapolating regional‐scale constraints on soil C dynamics to larger spatial and temporal scales.     

Mapping global cropland and field size

A new 1 km global IIASA‐IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo‐Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA‐IFPRI cropland product has been validated using very high‐resolution satellite imagery via Geo‐Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo‐Wiki. The first ever global field size map was produced at the same resolution as the IIASA‐IFPRI cropland map based on interpolation of field size data collected via a Geo‐Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.     

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.     

A large‐area,spatially continuous assessment of land cover map error and its impact on downstream analyses

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high‐resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel‐wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative “downstream” (map‐based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps’ spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ～45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National‐scale maps derived from higher‐resolution imagery were most accurate, followed by multi‐map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland‐adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%–500% greater than in input cropland maps, but ～40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users.     

A new land-cover map of Africa for the year 2000

Aim In the framework of the Global Land Cover 2000 (GLC 2000), a land‐cover map of Africa has been produced at a spatial resolution of 1 km using data from four sensors on‐board four different Earth observing satellites. Location The map documents the location and distribution of major vegetation types and non‐vegetated land surface formations for the entire African continent plus Madagascar and the other surrounding islands. Methods The bulk of these data were acquired on a daily basis throughout the year 2000 by the VEGETATION sensor on‐board the SPOT‐4 satellite. The map of vegetation cover has been produced based upon the spectral response and the temporal profile of the vegetation cover. Digital image processing and geographical information systems techniques were employed, together with local knowledge, high resolution imagery and expert consultation, to compile a cartographic map product. Radar data and thermal sensors were also used for specific land‐cover classes. Results A total of 27 land cover categories are documented, which has more thematic classes than previously published land cover maps of Africa contain. Systematic comparison with existing land cover data and 30‐m resolution imagery from Landsat are presented, and the map is also compared with other pan‐continental land cover maps. The map and digital data base are freely available for non‐commercial uses from http://www.gvm.jrc.it/tem/africa/products.htm Main conclusions The map improves our state of knowledge of the land‐cover of Africa and presents the most spatially detailed view yet published at this scale. This first version of the map should provide an important input for regional stratification and planning purposes for natural resources, biodiversity and climate studies.     

Estimation of forest area and its dynamics in Russia based on synthesis of remote sensing products

We review up-to-date, open access remote sensing (RS) products related to forest. We created a hybrid forest/non-forest map using geographically weighted regression (GWR) based on a number of recent RS products and crowdsourcing. The hybrid map has spatial resolution of 230 m and shows the extent of forest in Russia in 2010. We estimate area of Russian forest as 711 million ha (in accordance with Russian national forest definition). Compared to official data of the State Forest Register (SFR), RS estimates the area of forest to be considerably larger in European part (+12.2 million ha or +8%) and smaller in Asian (–39.8 million ha or–7%) part of Russia. We report the changing forest area in 2001–2010 and discuss main drivers: wildfire and encroachment of abandoned arable land. The methodology used here can by applied for monitoring of forest cover and enhancing the forest accounting system in Russia.     

Accuracy assessments of the GLOBCOVER dataset using global statistical inventories and FLUXNET site data

The spatio-temporal distribution of land cover provides fundamental data for global climate and environmental change research. In recent decades, five global land cover maps have been produced based on remote sensing data sources and methodologies. Related research have shown that the availability and quality of the first four global land cover datasets are poor at the regional or the continental scale for a variety of reasons. There is still no consensus on the accuracy of the latest global land cover map. Based on comparison of the land cover dataset with the statistical cropland data from FAO and the FLUXNET site data, this paper discusses the accuracy of the fifth global land cover map, namely, the GLOBCOVER dataset, at different spatial scales. At the global scale, the cropland area obtained from the GLOBCOVER dataset is greater than that of the FAO statistical data by 47.06–84.49%, and the land cover types of the GLOBCOVER dataset have a 65.02% consistency with that of the FLUXNET site data. At the continental scale, the difference between cropland areas obtained from the GLOBCOVER dataset and the statistical cropland area vary from ?43.42% to 502.36%; continents that have a more accurate cropland area compared to the FAO statistical data tend to be less consistent with the FLUXNET site data. In general, North America has a higher accuracy and Oceania has a lower accuracy. At the country scale, the accuracy estimates vary sharply over a wide range: between ?100.00% and 190670.37%. It is recommended that future studies should pay careful attention to the data validation step before using the GLOBCOVER dataset for any particular problem. Future studies are also required for the development of a universal land cover classification system and advanced algorithms for remote sensing classification of global land cover maps.     

Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000

Aim

This study provides regional estimates of forest cover in dry African ecoregions and the changes in forest cover that occurred there between 1990 and 2000, using a systematic sample of medium‐resolution satellite imagery which was processed consistently across the continent.

Location

The study area corresponds to the dry forests and woodlands of Africa between the humid forests and the semi‐arid regions. This area covers the Sudanian and Zambezian ecoregions.

Methods

A systematic sample of 1600 Landsat satellite imagery subsets, each 20 km × 20 km in size, were analysed for two reference years: 1990 and 2000. At each sample site and for both years, dense tree cover, open tree cover, other wooded land and other vegetation cover were identified from the analysis of satellite imagery, which comprised multidate segmentation and automatic classification steps followed by visual control by national forestry experts.

Results

Land cover and land‐cover changes were estimated at continental and ecoregion scales and compared with existing pan‐continental, regional and local studies. The overall accuracy of our land‐cover maps was estimated at 87%. Between 1990 and 2000, 3.3 million hectares (Mha) of dense tree cover, 5.8 Mha of open tree cover and 8.9 Mha of other wooded land were lost, with a further 3.9 Mha degraded from dense to open tree cover. These results are substantially lower than the 34 Mha of forest loss reported in the FAO's 2010 Global Forest Resources Assessment for the same period and area.

Main conclusions

Our method generates the first consistent and robust estimates of forest cover and change in dry Africa with known statistical precision at continental and ecoregion scales. These results reduce the uncertainty regarding vegetation cover and its dynamics in these previously poorly studied ecosystems and provide crucial information for both science and environmental policies.     

Spatial distribution of phytomass carbon in Indian forests

A spatial analysis of phytomass carbon (C) in Indian forests for the period (1988–94) at the district‐level is reported. District‐level forest phytomass C (1988, 1994) was computed by combining remote sensing‐based forest area inventories on 1:250 000 scale, field inventories of growing stock volume by the Forest Survey of India (FSI), and crown density‐based biomass expansion factors. The estimates of forest area inventory, forest phytomass C density, and forest phytomass C pools (1988–94) were linked to the district boundary coverage of India in ARC/INFO Geographic Information System (GIS). Of the total 386 districts examined, only 17 districts had more than 75% forest cover (as percent of their geographic area) in 1988. Estimated district‐level forest phytomass C densities ranged from 4.3 to 206.8 MgC ha^?1*. At the national level, forest phytomass C pool was estimated to be 3871.2 and 3874.3 TgC# in 1988 and 1994, respectively. Although the aggregated national estimates were similar, the district‐level change analysis indicated a decrease of 77.8 TgC and an increase of 81 TgC (1988–94). The highest decrease of 10.4 TgC was observed in Vishakapatnam district (Andhra Pradesh) while largest increase of 9.8 TgC in Bastar district (Madhya Pradesh) resulting from deforestation and afforestation activities, respectively. At the national level, the total forest cover decreased by ～0.6 Mha, however, the district‐level spatial analysis indicated an increase of 1.07 Mha, and decrease of 1.65 Mha forest cover during 1988–94 period. Although, this is the first district level phytomass C analysis of Indian forests, the aggregated results at state‐level were close to the earlier estimates. The spatial analysis identified the districts that have undergone significant changes in their forest phytomass C during the study period. This improved understanding of forest phytomass C pools is important to sustainable development and conservation of forests, mitigation strategies for C sequestration, and accurate estimation of contribution of land use changes to C emission in India.     

Forest transition in Vietnam and its environmental impacts

This study assesses the presence of a forest transition – that is, a shift from net deforestation to net reforestation – in Vietnam during the 1990s, and describes its key attributes relevant for global environmental change issues. Using Fuzzy Kappa and other indicators, we compared forest cover estimates and spatial patterns from global and national land cover maps from the early and late 1990s, and compiled other available statistics for years before and after that period. This showed that a forest transition indeed occurred in Vietnam: the forest cover dropped to 25–31% of the country area in 1991–1993, and then increased to 32–37% in 1999–2001. The reforestation occurred at a higher rate than deforestation in the previous decades, and was due in similar proportions, to natural forest regeneration and to planted forests. The carbon stock in forests followed a similar transition, decreasing to 903 (770–1307) Tg C in 1991–1993, and then increasing to 1374 (1058–1744) Tg C in 2005. However, forest density declined during the same period, with an increasing proportion of young and degraded forests. The effects on habitats measured with landscape pattern indices were contrasted: in several regions, the reforestation decreased forest fragmentation, while in others, clearing of old‐growth forests continued and/or forest fragmentation increased. This shows that a transition in forest area is not sufficient to rehabilitate the different ecosystem functions and services of forests. Other forest transitions exist in Tropical Asia and in Latin America. Knowledge about the causes, pattern and environmental impacts of the forest transition in Vietnam is therefore relevant to understand possible emerging regional trends that would have implications for global environmental change.     

Phylogenetic diversity meets conservation policy: small areas are key to preserving eucalypt lineages

Evolutionary and genetic knowledge is increasingly being valued in conservation theory, but is rarely considered in conservation planning and policy. Here, we integrate phylogenetic diversity (PD) with spatial reserve prioritization to evaluate how well the existing reserve system in Victoria, Australia captures the evolutionary lineages of eucalypts, which dominate forest canopies across the state. Forty-three per cent of remaining native woody vegetation in Victoria is located in protected areas (mostly national parks) representing 48% of the extant PD found in the state. A modest expansion in protected areas of 5% (less than 1% of the state area) would increase protected PD by 33% over current levels. In a recent policy change, portions of the national parks were opened for development. These tourism development zones hold over half the PD found in national parks with some species and clades falling entirely outside of protected zones within the national parks. This approach of using PD in spatial prioritization could be extended to any clade or area that has spatial and phylogenetic data. Our results demonstrate the relevance of PD to regional conservation policy by highlighting that small but strategically located areas disproportionally impact the preservation of evolutionary lineages.     

A Land System representation for global assessments and land‐use modeling

Current global scale land‐change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land‐use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human‐environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi‐)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land‐use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land‐change models that include the human drivers of land change are best parameterized at sub‐global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.     

Multidate,multisensor remote sensing reveals high density of carbon‐rich mountain peatlands in the páramo of Ecuador

Tropical peatlands store a significant portion of the global soil carbon (C) pool. However, tropical mountain peatlands contain extensive peat soils that have yet to be mapped or included in global C estimates. This lack of data hinders our ability to inform policy and apply sustainable management practices to these peatlands that are experiencing unprecedented high rates of land use and land cover change. Rapid large‐scale mapping activities are urgently needed to quantify tropical wetland extent and rate of degradation. We tested a combination of multidate, multisensor radar and optical imagery (Landsat TM/PALSAR/RADARSAT‐1/TPI image stack) for detecting peatlands in a 2715 km² area in the high elevation mountains of the Ecuadorian páramo. The map was combined with an extensive soil coring data set to produce the first estimate of regional peatland soil C storage in the páramo. Our map displayed a high coverage of peatlands (614 km²) containing an estimated 128.2 ± 9.1 Tg of peatland belowground soil C within the mapping area. Scaling‐up to the country level, páramo peatlands likely represent less than 1% of the total land area of Ecuador but could contain as much as ~23% of the above‐ and belowground vegetation C stocks in Ecuadorian forests. These mapping approaches provide an essential methodological improvement applicable to mountain peatlands across the globe, facilitating mapping efforts in support of effective policy and sustainable management, including national and global C accounting and C management efforts.     

Land cover change and carbon emissions over 100 years in an African biodiversity hotspot

Agricultural expansion has resulted in both land use and land cover change (LULCC) across the tropics. However, the spatial and temporal patterns of such change and their resulting impacts are poorly understood, particularly for the presatellite era. Here, we quantify the LULCC history across the 33.9 million ha watershed of Tanzania's Eastern Arc Mountains, using geo‐referenced and digitized historical land cover maps (dated 1908, 1923, 1949 and 2000). Our time series from this biodiversity hotspot shows that forest and savanna area both declined, by 74% (2.8 million ha) and 10% (2.9 million ha), respectively, between 1908 and 2000. This vegetation was replaced by a fivefold increase in cropland, from 1.2 million ha to 6.7 million ha. This LULCC implies a committed release of 0.9 Pg C (95% CI: 0.4–1.5) across the watershed for the same period, equivalent to 0.3 Mg C ha^?1 yr^?1. This is at least threefold higher than previous estimates from global models for the same study area. We then used the LULCC data from before and after protected area creation, as well as from areas where no protection was established, to analyse the effectiveness of legal protection on land cover change despite the underlying spatial variation in protected areas. We found that, between 1949 and 2000, forest expanded within legally protected areas, resulting in carbon uptake of 4.8 (3.8–5.7) Mg C ha^?1, compared to a committed loss of 11.9 (7.2–16.6) Mg C ha^?1 within areas lacking such protection. Furthermore, for nine protected areas where LULCC data are available prior to and following establishment, we show that protection reduces deforestation rates by 150% relative to unprotected portions of the watershed. Our results highlight that considerable LULCC occurred prior to the satellite era, thus other data sources are required to better understand long‐term land cover trends in the tropics.     

A land cover map of South America

A digital land cover map of South America has been produced using remotely sensed satellite data acquired between 1995 and the year 2000. The mapping scale is defined by the 1 km spatial resolution of the map grid‐cell. In order to realize the product, different sources of satellite data were used, each source providing either a particular parameter of land cover characteristic required by the legend, or mapping a particular land cover class. The map legend is designed both to fit requirements for regional climate modelling and for studies on land cover change. The legend is also compatible with a wider, global, land cover mapping exercise, which seeks to characterize the world's land surface for the year 2000. As a first step, the humid forest domain has been validated using a sample of high‐resolution satellite images. The map demonstrates both the major incursions of agriculture into the remaining forest domains and the extensive areas of agriculture, which now dominate South America's grasslands.     

The urgent need to develop a new grassland map in China: based on the consistency and accuracy of ten land cover products

Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years. Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets (GLC2000, GlobCover, CCI-LC, MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×10⁴ to 332.46×10⁴ km², with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of east-central Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products, with an overall accuracy (OA) reaching 77.51% and a kappa coefficient of 0.51; CLCD is slightly less accurate than MCD12Q1 (OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its 30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China’s grasslands and call for researchers and the government to actively produce a new generation of grassland maps.

     

Spatial evaluation of Indonesia's 2015 fire‐affected area and estimated carbon emissions using Sentinel‐1

Fires raged once again across Indonesia in the latter half of 2015, creating a state of emergency due to poisonous smoke and haze across Southeast Asia as well as incurring great financial costs to the government. A strong El Niño‐Southern Oscillation (ENSO) led to drought in many parts of Indonesia, resulting in elevated fire occurrence comparable with the previous catastrophic event in 1997/1998. Synthetic Aperture Radar (SAR) data promise to provide improved detection of land use and land cover changes in the tropics as compared to methodologies dependent upon cloud‐ and haze‐free images. This study presents the first spatially explicit estimates of burned area across Sumatra, Kalimantan, and West Papua based on high‐resolution Sentinel‐1A SAR imagery. Here, we show that 4,604,569 hectares (ha) were burned during the 2015 fire season (overall accuracy 84%), and compare this with other existing operational burned area products (MCD64, GFED4.0, GFED4.1s). Intersection of burned area with fine‐scale land cover and peat layer maps indicates that 0.89 gigatons carbon dioxide equivalents (Gt CO₂e) were released through the fire event. This result is compared to other estimates based on nonspatially explicit thermal anomaly measurements or atmospheric monitoring. Using freely available SAR C‐band data from the Sentinel mission, we argue that the presented methodology is able to quickly and precisely detect burned areas, supporting improvement in fire control management as well as enhancing accuracy of emissions estimation.