Explore the vast range of titles available.

Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

The net flux of carbon from land use and land-cover change (LULCC) accounted for 12.5% of anthropogenic carbon emissions from 1990 to 2010. This net flux is the most uncertain term in the global carbon budget, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and not all analyses consider the same types of management activities. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. In addition to deforestation, all analyses considered changes in the area of agricultural lands (croplands and pastures). Some considered, also, forest management (wood harvest, shifting cultivation). None included emissions from the degradation of tropical peatlands. Means and standard deviations across the thirteen model estimates of annual emissions for the 1980s and 1990s, respectively, are 1.14 ± 0.23 and 1.12 ± 0.25 Pg C yr−1 (1 Pg = 1015 g carbon). Four studies also considered the period 2000–2009, and the mean and standard deviations across these four for the three decades are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The standard deviations across model means shown here are smaller than previous estimates of uncertainty as they do not account for the errors that result from data uncertainty and from an incomplete understanding of all the processes affecting the net flux of carbon from LULCC. Although these errors have not been systematically evaluated, based on partial analyses available in the literature and expert opinion, they are estimated to be on the order of ± 0.5 Pg C yr−1.

Tropical forests provide global climate regulation ecosystem services and their clearing is a significant source of anthropogenic greenhouse gas (GHG) emissions and resultant radiative forcing of climate change. However, consensus on pan-tropical forest carbon dynamics is lacking. We present a new estimate that employs recommended good practices to quantify gross tropical forest aboveground carbon (AGC) loss from 2000 to 2012 through the integration of Landsat-derived tree canopy cover, height, intactness and forest cover loss and GLAS-lidar derived forest biomass. An unbiased estimate of forest loss area is produced using a stratified random sample with strata derived from a wall-to-wall 30 m forest cover loss map. Our sample-based results separate the gross loss of forest AGC into losses from natural forests (0.59 PgC yr−1) and losses from managed forests (0.43 PgC yr−1) including plantations, agroforestry systems and subsistence agriculture. Latin America accounts for 43% of gross AGC loss and 54% of natural forest AGC loss, with Brazil experiencing the highest AGC loss for both categories at national scales. We estimate gross tropical forest AGC loss and natural forest loss to account for 11% and 6% of global year 2012 CO2 emissions, respectively. Given recent trends, natural forests will likely constitute an increasingly smaller proportion of tropical forest GHG emissions and of global emissions as fossil fuel consumption increases, with implications for the valuation of co-benefits in tropical forest conservation.

Shifting cultivation has traditionally been practiced in the Democratic Republic of Congo by carving agricultural fields out of primary and secondary forest, resulting in the rural complex: a characteristic land cover mosaic of roads, villages, active and fallow fields and secondary forest. Forest clearing has varying impacts depending on where it occurs relative to this area: whether inside it, along its primary forest interface, or in more isolated primary forest areas. The spatial contextualization of forest cover loss is therefore necessary to understand its impacts and plan its management. We characterized forest clearing using spatial models in a Geographical Information System, applying morphological image processing to the Forets d'Afrique Central Evaluee par Teledetection product. This process allowed us to create forest fragmentation maps for 2000, 2005 and 2010, classifying previously homogenous primary forest into separate patch, edge, perforated, fragmented and core forest subtypes. Subsequently we used spatial rules to map the established rural complex separately from isolated forest perforations, tracking the growth of these areas in time. Results confirm that the expansion of the rural complex and forest perforations has high variance throughout the country, with consequent differences in local impacts on forest ecology and habitat fragmentation. Between 2000 and 2010 the rural complex grew by 10.2% (46 182 ha), increasing from 11.9% to 13.1% of the total land area (1.2% change) while perforated forest grew by 74.4% (23 856 ha), from 0.8% to 1.5%. Core forest decreased by 3.8% (54 852 ha), from 38% to 36.6% of the 2010 land area. Of particular concern is the nearly doubling of perforated forest, a land dynamic that represents greater spatial intrusion of forest clearing within core forest areas and a move away from the established rural complex.

AIM: Tropical forest degradation is a significant source of carbon emissions due to selective logging, fragmentation and other disturbance factors. However, methods for mapping and monitoring pan‐tropical forest degradation are still in their infancy. Here we present a new and automated approach to differentiate forests likely to be affected by degradation dynamics from more structurally intact forests, referred to as hinterland forests. LOCATION: Pan‐tropical. METHODS: Inputs required for hinterland forest mapping include the extent of the initial forest cover and subsequent forest cover loss data, in this case global‐scale Landsat‐derived tree cover and stand‐replacement disturbance maps. User‐defined parameters employed to generate the extent and change of hinterland forest include: (1) minimum size of hinterland forest patch, (2) minimum corridor width, (3) distance from disturbance, and (4) extant history. RESULTS: Hinterland forest extent was mapped using forest cover loss data from 2000 to 2012 and hinterland forest loss was quantified from 2007 to 2013. Lidar‐modelled forest height data were shown to be different within and outside hinterland forests, demonstrating the biophysical basis of the hinterland concept in discriminating likely degradation. Overall, hinterland forests experienced an 18% decline from 2007 to 2013. Regional variation in hinterland forest extent and loss was high. Data on 2013 pan‐tropical hinterland forest extent can be downloaded from http://glad.geog.umd.edu/hinterland/index.html and viewed online at http://earthenginepartners.appspot.com/science‐2013‐global‐forest. MAIN CONCLUSIONS: The largest extent of hinterland forests and of hinterland forest loss was found in Latin America, followed by Africa and Southeast Asia, respectively. The highest proportional loss of hinterland forest occurred in Southeast Asia, followed by Africa and Latin America, respectively. Nearly 95% of all 2013 hinterland forests were found in 17 of the 69 tropical forest countries studied. The extent and loss of hinterland forest can be an input to national monitoring and management programmes focused on forest carbon stocks, biodiversity conservation and other ecosystem services.

A novel approach for satellite-based comprehensive national tree cover change assessment was developed and applied in Bangladesh, a country where trees outside of forests play an important role in the national economy and carbon sequestration. Tree cover change area was quantified using the integration of wall-to-wall Landsat-based mapping with a higher spatial resolution sample-based assessment. The total national tree canopy cover area was estimated as 3165 500 ± 186 600 ha in the year 2000, with trees outside forests making up 54% of total canopy cover. Total tree canopy cover increased by 135 700 (± 116 600) ha (4.3%) during the 2000-2014 time interval. Bangladesh exhibits a national tree cover dynamic where net change is rather small, but gross dynamics significant and variable by forest type. Despite the overall gain in tree cover, results revealed the ongoing clearing of natural forests, especially within the Chittagong hill tracts. While forests decreased their tree cover area by 83 600 ha, the trees outside forests (including tree plantations, village woodlots, and agroforestry) increased their canopy area by 219 300 ha. Our results demonstrated method capability to quantify tree canopy cover dynamics within a fine-scale agricultural landscape. Our approach for comprehensive monitoring of tree canopy cover may be recommended for operational implementation in Bangladesh and other countries with significant tree cover outside of forests.

Transparent, consistent, and accurate national forest monitoring is required for successful implementation of reducing emissions from deforestation and forest degradation (REDD+) programs. Collecting baseline information on forest extent and rates of forest loss is a first step for national forest monitoring in support of REDD+. Peru, with the second largest extent of Amazon basin rainforest, has made significant progress in advancing its forest monitoring capabilities. We present a national-scale humid tropical forest cover loss map derived by the Ministry of Environment REDD+ team in Peru. The map quantifies forest loss from 2000 to 2011 within the Peruvian portion of the Amazon basin using a rapid, semi-automated approach. The available archive of Landsat imagery (11 654 scenes) was processed and employed for change detection to obtain annual gross forest cover loss maps. A stratified sampling design and a combination of Landsat (30 m) and RapidEye (5 m) imagery as reference data were used to estimate the primary forest cover area, total gross forest cover loss area, proportion of primary forest clearing, and to validate the Landsat-based map. Sample-based estimates showed that 92.63% (SE = 2.16%) of the humid tropical forest biome area within the country was covered by primary forest in the year 2000. Total gross forest cover loss from 2000 to 2011 equaled 2.44% (SE = 0.16%) of the humid tropical forest biome area. Forest loss comprised 1.32% (SE = 0.37%) of primary forest area and 9.08% (SE = 4.04%) of secondary forest area. Validation confirmed a high accuracy of the Landsat-based forest cover loss map, with a producer's accuracy of 75.4% and user's accuracy of 92.2%. The majority of forest loss was due to clearing (92%) with the rest attributed to natural processes (flooding, fires, and windstorms). The implemented Landsat data processing and classification system may be used for operational annual forest cover loss updates at the national level for REDD+ applications.

The rural complex is the inhabited agricultural land cover mosaic found along the network of rivers and roads in the forest of the Democratic Republic of Congo. It is a product of traditional small-holder shifting cultivation. To date, thanks to its distinction from primary forest, this area has been mapped as relatively homogenous, leaving the proportions of land cover heterogeneity within it unknown. However, the success of strategies for sustainable development, including land use planning and payment for ecosystem services, such as Reduced Emissions from Deforestation and Degradation, depends on the accurate characterization of the impacts of land use on natural resources, including within the rural complex. We photo-interpreted a simple random sample of 1000 points in the established rural complex, using 3106 high resolution satellite images obtained from the National Geospatial-Intelligence Agency, together with 406 images from Google Earth, spanning the period 2008-2016. Results indicate that nationally the established rural complex includes 5% clearings, 10% active fields, 26% fallows, 34% secondary forest, 2% wetland forest, 11% primary forest, 6% grasslands, 3% roads and settlements and 2% commercial plantations. Only a small proportion of sample points were plantations, while other commercial dynamics, such as logging and mining, were not detected in the sample. The area of current shifting cultivation accounts for 76% of the established rural complex. Added to primary forest (11%), this means that 87% of the rural complex is available for shifting cultivation. At the current clearing rate, it would take ~18 years for a complete rotation of the rural complex to occur. Additional pressure on land results in either the cultivation of non-preferred land types within the rural complex (such as wetland forest), or expansion of agriculture into nearby primary forests, with attendant impacts on emissions, habitat loss and other ecosystems services.

Agricultural intensification and forest conservation are often seen as incompatible. Agricultural interventions can help boost food security for poor rural communities but in certain cases can exacerbate deforestation, known as the rebound effect. We tested whether coupling agricultural interventions with participatory forest zoning could improve food security and promote forest conservation in the Democratic Republic of the Congo. Simple agricultural interventions led to a >60% increase in cassava yields and a spill-over effect of improved cassava variety uptake in non-intervention zones. Household surveys conducted at the end of the 8 year project implementation period revealed that households that received agricultural interventions had more favorable attitudes toward forest zoning and conservation. The surveys also showed that farmers in the intervention domain practiced less land-intensive field and fallow management strategies compared to those practiced in the non-intervention domain. However, an 18 year time series analysis of Landsat satellite data revealed that agricultural expansion persisted in areas both with and without intervention assistance, and there is risk of a rebound effect. Approximately 70% of the tree cover loss that occurred outside of the agricultural areas was located within a 3 km buffer zone surrounding the outermost edges of the agricultural areas, which suggested that the majority of tree cover loss was caused by agricultural expansion. Within that 3 km buffer, average annual tree cover loss during the post-intervention period was higher in the intervention domain compared to the non-intervention domain (0.17% yr −1 compared to 0.11% yr −1 respectively, p < 0.001), suggesting risk of a rebound effect. The disconnection between household perceptions of zoning adherence and actual behavior indicates the importance of strengthening governance structures for community-based monitoring and enforcement.