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Kevin Bishop - One of the best experts on this subject based on the ideXlab platform.
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Forest Cover change over four decades in the blue nile basin ethiopia comparison of three watersheds
Regional Environmental Change, 2014Co-Authors: Kevin Bishop, Solomon Gebreyohannis Gebrehiwot, Woldeamlak Bewket, Annemieke I GardenasAbstract:The objective of this study was to quantify Forest Cover changes in three watersheds (Gilgel Abbay (1,646 km2), Birr (980 km2), and Upper-Didesa (1,980 km2) of the Blue Nile Basin between 1957 and 2001. Four land Cover maps were produced for each watershed for 1957/1958, 1975, 1986, and 2000/2001. Nine different types of land Cover were identified, five of which were Forest Cover classes. Between 1957 and 2001, the total Forest Cover increased in Gilgel Abbay (from 10 to 22 % Cover) and decreased in Birr (from 29 to 22 % Cover) as well as in Upper-Didesa (from 89 to 45 % Cover). The increase in Gilgel Abbay was primarily due to the expansion of eucalyptus plantations. Natural Forest Cover decreased in all three watersheds. Wooded grassland decreased by two-thirds, dry/moist mixed Forests decreased by half, and riverine Forests had disappeared by 1975 in Gilgel Abbay and Birr. Major deForestation had already taken place in the northern watersheds, Gilgel Abbay and Birr, before the 1960s and 1970s, while in the southern watershed, Upper-Didesa, much of the deForestation occurred after 1975. The southern watershed still remained by far the most Forested watershed in 2001 despite the strong ongoing deForestation. The changes in Forest Cover could affect natural resource management, greenhouse gas emissions, water resources, and agricultural production including coffee production. The patterns of change are different in the three watersheds. We therefore recommend further studies of the local conditions and drivers of change as the basis for designing effective policy to halt further loss of natural Forest, which offers a wealth of ecosystem services.
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on the Forest Cover water yield debate from demand to supply side thinking
Global Change Biology, 2012Co-Authors: Martyn N Futter, David Ellison, Kevin BishopAbstract:Several major articles from the past decade and beyond conclude the impact of reForestation or afForestation on water yield is negative: additional Forest Cover will reduce and removing Forests will raise downstream water availability. A second group of authors argue the opposite: planting additional Forests should raise downstream water availability and intensify the hydrologic cycle. Obtaining supporting evidence for this second group of authors has been more difficult due to the larger scales at which the positive effects of Forests on the water cycle may be seen. We argue that Forest Cover is inextricably linked to precipitation. Forest-driven evapotranspiration removed from a particular catchment contributes to the availability of atmospheric moisture vapor and its cross-continental transport, raising the likelihood of precipitation events and increasing water yield, in particular in continental interiors more distant from oceans. Seasonal relationships heighten the importance of this phenomenon. We review the arguments from different scales and perspectives. This clarifies the generally beneficial relationship between Forest Cover and the intensity of the hydrologic cycle. While evidence supports both sides of the argument – trees can reduce runoff at the small catchment scale – at larger scales, trees are more clearly linked to increased precipitation and water availability. Progressive deForestation, land conversion from Forest to agriculture and urbanization have potentially negative consequences for global precipitation, prompting us to think of Forest ecosystems as global public goods. Policy-making attempts to measure product water footprints, estimate the value of ecosystem services, promote afForestation, develop drought mitigation strategies and otherwise manage land use must consider the linkage of Forests to the supply of precipitation.
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Forest Cover and stream flow in a headwater of the blue nile complementing observational data analysis with community perception
AMBIO: A Journal of the Human Environment, 2010Co-Authors: Solomon Gebreyohannis Gebrehiwot, Ayele Taye, Kevin BishopAbstract:This study analyses the relation of Forest Cover and stream flow on the 266 km2 Koga watershed in a headwater of Blue Nile Basin using both observed hydrological data and community perception. The watershed declined from 16% Forest Cover in 1957 to 1% by 1986. The hydrological record did not reveal changes in the flow regime between 1960 and 2002 despite the reduction in Forest area. This agrees with the perception of the downstream community living near the gauging station. The upstream community, however, reported both decreases in low flows and increases in high flows shortly after the Forest Cover was reduced. The upstream deForestation effect appeared to have been buffered by a wetland lower in the watershed. This study concludes that community perception can be a complement to observational data for better understanding how Forest Cover influences the flow regime.
Peter Potapov - One of the best experts on this subject based on the ideXlab platform.
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drivers of Forest Cover change in eastern europe and european russia 1985 2012
Land Use Policy, 2016Co-Authors: Jennifer Alixgarcia, Peter Potapov, Catalina Munteanu, Na Zhao, Alexander V Prishchepov, Volker C Radeloff, Alexander Krylov, Eugenia V BraginaAbstract:The relative importance of geography, history, and policy in driving Forest Cover change at broad scales remains poorly understood. We examine variation in Forest Cover dynamics over the period 1985–2012 across 19 countries in Eastern Europe and European Russia in order to shed light on the role of these in driving Forest Cover change after the collapse of socialism. Using a combination of cross-section and panel regression methods, we find that privatization of Forest lands increased Forest Cover loss due to logging, as did increases in agricultural land between 1850 and 1900. Land quality has no power to explain variation in Forest loss between countries, nor does trade and price liberalization policy. None of our covariates explain Forest regrowth on non-Forested land over the period. We conclude that history and land privatization drove important cross-country variation in Forest dynamics in the region, but that the majority of Forest Cover change over the period results from shocks, both political and economic, shared by all countries in the sample. This highlights the importance of broad-scale shocks as drivers of Forest change, relative to geographic and policy variability across individual countries.
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eastern europe s Forest Cover dynamics from 1985 to 2012 quantified from the full landsat archive
Remote Sensing of Environment, 2015Co-Authors: Peter Potapov, Svetlana Turubanova, Volker C Radeloff, Alexander Krylov, Alexandra Tyukavina, Jessica L Mccarty, Matthew C HansenAbstract:Abstract In the former “Eastern Bloc” countries, there have been dramatic changes in Forest disturbance and Forest reCovery rates since the collapse of the Soviet Union, due to the transition to open-market economies, and the recent economic crisis. Unfortunately though, Eastern European countries collected their Forest statistics inconsistently, and their boundaries have changed, making it difficult to analyze Forest dynamics over time. Our goal here was to consistently quantify Forest Cover change across Eastern Europe since the 1980s based on the Landsat image archive. We developed an algorithm to simultaneously process data from different Landsat platforms and sensors (TM and ETM +) to map annual Forest Cover loss and decadal Forest Cover gain. We processed 59,539 Landsat images for 527 footprints across Eastern Europe and European Russia. Our results were highly accurate, with gross Forest loss producer's and user's accuracy of > 88% and > 89%, respectively, and gross Forest gain producer's and user's accuracy of > 75% and > 91%, based on a sample of probability-based validation points. We found substantial changes in the Forest Cover of Eastern Europe. Net Forest Cover increased from 1985 to 2012 by 4.7% across the region, but decreased in Estonia and Latvia. Average annual gross Forest Cover loss was 0.41% of total Forest Cover area, with a statistically significant increase from 1985 to 2012. Timber harvesting was the main cause of Forest loss, accompanied by some insect defoliation and Forest conversion, while only 7.4% of the total Forest Cover loss was due to large-scale wildfires and windstorms. Overall, the countries of Eastern Europe experienced constant levels or declines in Forest loss after the collapse of socialism in the late 1980s, but a pronounced increase in loss in the early 2000s. By the late 2000s, however, the global economic crisis coincided with reduced timber harvesting in most countries, except Poland, Czech Republic, Slovakia, and the Baltic states. Most Forest disturbance did not result in a permanent Forest loss during our study period. Indeed, Forest generally reCovered fast and only 12% of the areas of Forest loss prior to 1995 had not yet reCovered by 2012. Our results allow national and sub-national level analysis and are available on-line ( http://glad.geog.umd.edu/europe/ ) to serve as a baseline for further analyses of Forest dynamics and its drivers.
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quantifying Forest Cover loss in democratic republic of the congo 2000 2010 with landsat etm data
Remote Sensing of Environment, 2012Co-Authors: Peter Potapov, M C Hansen, Svetlana Turubanova, Bernard Adusei, Mark Broich, Alice Altstatt, Landing Mane, C O JusticeAbstract:Abstract Forest Cover and Forest Cover loss for the last decade, 2000–2010, have been quantified for the Democratic Republic of the Congo (DRC) using Landsat time-series data set. This was made possible via an exhaustive mining of the Landsat Enhanced Thematic Mapper Plus (ETM +) archive. A total of 8881 images were processed to create multi-temporal image metrics resulting in 99.6% of the DRC land area Covered by cloud-free Landsat observations. To facilitate image compositing, a top-of-atmosphere (TOA) reflectance calibration and image normalization using Moderate Resolution Imaging Spectroradiometer (MODIS) top of canopy (TOC) reflectance data sets were performed. Mapping and change detection was implemented using a classification tree algorithm. The national year 2000 Forest Cover was estimated to be 159,529.2 thousand hectares, with gross Forest Cover loss for the last decade totaling 2.3% of Forest area. Forest Cover loss area increased by 13.8% between the 2000–2005 and 2005–2010 intervals, with the greatest increase occurring within primary humid tropical Forests. Forest loss intensity was distributed unevenly and associated with areas of high population density and mining activity. While Forest Cover loss is comparatively low in protected areas and priority conservation landscapes compared to Forests outside of such areas, gross Forest Cover loss for all nature protection areas increased by 64% over the 2000 to 2005 and 2005 to 2010 intervals.
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time series analysis of multi resolution optical imagery for quantifying Forest Cover loss in sumatra and kalimantan indonesia
International Journal of Applied Earth Observation and Geoinformation, 2011Co-Authors: Mark Broich, Peter Potapov, Bernard Adusei, Matthew C Hansen, Erik Lindquist, Stephen V. StehmanAbstract:Monitoring loss of humid tropical Forests via remotely sensed imagery is critical for a number of environmental monitoring objectives, including carbon accounting, biodiversity, and climate modeling science applications. Landsat imagery, provided free of charge by the U.S. Geological Survey Center for Earth Resources Observation and Science (USGS/EROS), enables consistent and timely Forest Cover loss updates from regional to biome scales. The Indonesian islands of Sumatra and Kalimantan are a center of significant Forest Cover change within the humid tropics with implications for carbon dynamics, biodiversity maintenance and local livelihoods. Sumatra and Kalimantan feature poor observational Coverage compared to other centers of humid tropical Forest change, such as Mato Grosso, Brazil, due to the lack of ongoing acquisitions from nearby ground stations and the persistence of cloud Cover obscuring the land surface. At the same time, Forest change in Indonesia is transient and does not always result in deForestation, as cleared Forests are rapidly replaced by timber plantations and oil palm estates. Epochal composites, where single best observations are selected over a given time interval and used to quantify change, are one option for monitoring Forest change in cloudy regions. However, the frequency of Forest Cover change in Indonesia confounds the ability of image composite pairs to quantify all change. Transient change occurring between composite periods is often missed and the length of time required for creating a cloud-free composite often obscures change occurring within the composite period itself. In this paper, we analyzed all Landsat 7 imagery with <50% cloud Cover and data and products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify Forest Cover loss for Sumatra and Kalimantan from 2000 to 2005. We demonstrated that time-series approaches examining all good land observations are more accurate in mapping Forest Cover change in Indonesia than change maps based on image composites. Unlike other time-series analyses employing observations with a consistent periodicity, our study area was characterized by highly unequal observation counts and frequencies due to persistent cloud Cover, scan line corrector off (SLC-off) gaps, and the absence of a complete archive. Our method accounts for this variation by generating a generic variable space. We evaluated our results against an independent probability sample-based estimate of gross Forest Cover loss and expert mapped gross Forest Cover loss at 64 sample sites. The mapped gross Forest Cover loss for Sumatra and Kalimantan was 2.86% of the land area, or 2.86 Mha from 2000 to 2005, with the highest concentration having occurred in Riau and Kalimantan Tengah provinces.
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regional scale boreal Forest Cover and change mapping using landsat data composites for european russia
Remote Sensing of Environment, 2011Co-Authors: Peter Potapov, Svetlana Turubanova, M C HansenAbstract:Abstract Boreal Forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing Forest Cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely Forest Cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal Forest Cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud Cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize Forest Cover and gross Forest loss over the study period. Forest Cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official Forest Cover statistics of the Russian government. Gross Forest Cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross Forest Cover loss within European Russia 2000–2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 Forest Cover. At the regional scale, the highest proportional Forest Cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the Forest Cover depletion around large industrial cities as the hotspot of Forest Cover change in European Russia.
M C Hansen - One of the best experts on this subject based on the ideXlab platform.
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quantifying Forest Cover loss in democratic republic of the congo 2000 2010 with landsat etm data
Remote Sensing of Environment, 2012Co-Authors: Peter Potapov, M C Hansen, Svetlana Turubanova, Bernard Adusei, Mark Broich, Alice Altstatt, Landing Mane, C O JusticeAbstract:Abstract Forest Cover and Forest Cover loss for the last decade, 2000–2010, have been quantified for the Democratic Republic of the Congo (DRC) using Landsat time-series data set. This was made possible via an exhaustive mining of the Landsat Enhanced Thematic Mapper Plus (ETM +) archive. A total of 8881 images were processed to create multi-temporal image metrics resulting in 99.6% of the DRC land area Covered by cloud-free Landsat observations. To facilitate image compositing, a top-of-atmosphere (TOA) reflectance calibration and image normalization using Moderate Resolution Imaging Spectroradiometer (MODIS) top of canopy (TOC) reflectance data sets were performed. Mapping and change detection was implemented using a classification tree algorithm. The national year 2000 Forest Cover was estimated to be 159,529.2 thousand hectares, with gross Forest Cover loss for the last decade totaling 2.3% of Forest area. Forest Cover loss area increased by 13.8% between the 2000–2005 and 2005–2010 intervals, with the greatest increase occurring within primary humid tropical Forests. Forest loss intensity was distributed unevenly and associated with areas of high population density and mining activity. While Forest Cover loss is comparatively low in protected areas and priority conservation landscapes compared to Forests outside of such areas, gross Forest Cover loss for all nature protection areas increased by 64% over the 2000 to 2005 and 2005 to 2010 intervals.
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regional scale boreal Forest Cover and change mapping using landsat data composites for european russia
Remote Sensing of Environment, 2011Co-Authors: Peter Potapov, Svetlana Turubanova, M C HansenAbstract:Abstract Boreal Forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing Forest Cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely Forest Cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal Forest Cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud Cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize Forest Cover and gross Forest loss over the study period. Forest Cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official Forest Cover statistics of the Russian government. Gross Forest Cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross Forest Cover loss within European Russia 2000–2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 Forest Cover. At the regional scale, the highest proportional Forest Cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the Forest Cover depletion around large industrial cities as the hotspot of Forest Cover change in European Russia.
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remotely sensed Forest Cover loss shows high spatial and temporal variation across sumatera and kalimantan indonesia 2000 2008
Environmental Research Letters, 2011Co-Authors: Mark Broich, M C Hansen, Peter Potapov, Fred Stolle, Belinda Arunarwati Margono, Bernard AduseiAbstract:The Indonesian islands of Sumatera and Kalimantan (the Indonesian part of the island of Borneo) are a center of significant and rapid Forest Cover loss in the humid tropics with implications for carbon dynamics, biodiversity conservation, and local livelihoods. The aim of our research was to analyze and interpret annual trends of Forest Cover loss for different sub-regions of the study area. We mapped Forest Cover loss for 2000?2008 using multi-resolution remote sensing data from the Landsat enhanced thematic mapper plus (ETM +) and moderate resolution imaging spectroradiometer (MODIS) sensors and analyzed annual trends per island, province, and official land allocation zone. The total Forest Cover loss for Sumatera and Kalimantan 2000?2008 was 5.39?Mha, which represents 5.3% of the land area and 9.2% of the year 2000 Forest Cover of these two islands. At least 6.5% of all mapped Forest Cover loss occurred in land allocation zones prohibiting clearing. An additional 13.6% of Forest Cover loss occurred where clearing is legally restricted. The overall trend of Forest Cover loss increased until 2006 and decreased thereafter. The trends for Sumatera and Kalimantan were distinctly different, driven primarily by the trends of Riau and Central Kalimantan provinces, respectively. This analysis shows that annual mapping of Forest Cover change yields a clearer picture than a one-time overall national estimate. Monitoring Forest dynamics is important for national policy makers, especially given the commitment of Indonesia to reducing greenhouse gas emissions as part of the reducing emissions from deForestation and Forest degradation in developing countries initiative (REDD +). The improved spatio-temporal detail of Forest change monitoring products will make it possible to target policies and projects in meeting this commitment. Accurate, annual Forest Cover loss maps will be integral to many REDD + objectives, including policy formulation, definition of baselines, detection of displacement, and the evaluation of the permanence of emission reduction.
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quantification of global gross Forest Cover loss
Proceedings of the National Academy of Sciences of the United States of America, 2010Co-Authors: M C Hansen, Stephen V. Stehman, Peter PotapovAbstract:A globally consistent methodology using satellite imagery was implemented to quantify gross Forest Cover loss (GFCL) from 2000 to 2005 and to compare GFCL among biomes, continents, and countries. GFCL is defined as the area of Forest Cover removed because of any disturbance, including both natural and human-induced causes. GFCL was estimated to be 1,011,000 km2 from 2000 to 2005, representing 3.1% (0.6% per year) of the year 2000 estimated total Forest area of 32,688,000 km2. The boreal biome experienced the largest area of GFCL, followed by the humid tropical, dry tropical, and temperate biomes. GFCL expressed as the proportion of year 2000 Forest Cover was highest in the boreal biome and lowest in the humid tropics. Among continents, North America had the largest total area and largest proportion of year 2000 GFCL. At national scales, Brazil experienced the largest area of GFCL over the study period, 165,000 km2, followed by Canada at 160,000 km2. Of the countries with >1,000,000 km2 of Forest Cover, the United States exhibited the greatest proportional GFCL and the Democratic Republic of Congo the least. Our results illustrate a pervasive global GFCL dynamic. However, GFCL represents only one component of net change, and the processes driving GFCL and rates of reCovery from GFCL differ regionally. For example, the majority of estimated GFCL for the boreal biome is due to a naturally induced fire dynamic. To fully characterize global Forest change dynamics, remote sensing efforts must extend beyond estimating GFCL to identify proximate causes of Forest Cover loss and to estimate reCovery rates from GFCL.
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comparing annual modis and prodes Forest Cover change data for advancing monitoring of brazilian Forest Cover
Remote Sensing of Environment, 2008Co-Authors: M C Hansen, Peter Potapov, Yosio Edemir Shimabukuro, Kyle PittmanAbstract:Annual Forest Cover loss indicator maps for the humid tropics from 2000 to 2005 derived from time-series 500 m data from the MODerate Resolution Imaging Spectroradiometer (MODIS) were compared with annual deForestation data from the PRODES (Amazon DeForestation Monitoring Project) data set produced by the Brazilian National Institute for Space Research (INPE). The annual PRODES data were used to calibrate the MODIS annual change indicator data in estimating Forest loss for Brazil. Results indicate that MODIS data may be useful in providing a first estimate of national Forest Cover change on an annual basis for Brazil. When directly compared with PRODES change at the MODIS grid scale for all years of the analysis, MODIS change indicator maps accounted for 75% of the PRODES change. This ratio was used to scale the MODIS change indicators to the PRODES area estimates. A sliding threshold of percent PRODES Forest and 2000 to 2005 deForestation classes per MODIS grid cell was used to match the scaled MODIS to the official PRODES change estimates, and then to differentiate MODIS change within various sub-areas of the PRODES analysis. Results indicate significant change outside of the PRODES-defined intact Forest class. Total scaled MODIS change area within the PRODES historical deForestation and Forest area of study is 120% of the official PRODES estimate. Results emphasize the importance of synoptic monitoring of all Forest change dynamics, including the Cover dynamics of intact humid Forest, regrowth, plantations, and cerrado tree Cover assemblages. Results also indicate that operational MODIS-only Forest Cover loss algorithms may be useful in providing near-real time areal estimates of annual change within the Amazon Basin.
Thijs Van Leeuwen - One of the best experts on this subject based on the ideXlab platform.
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optimal use of land surface temperature data to detect changes in tropical Forest Cover
Journal of Geophysical Research, 2011Co-Authors: Thijs Van Leeuwen, Andrew Frank, Yufang Jin, Padhraic Smyth, Michael L Goulden, Guido R Van Der Werf, J T RandersonAbstract:[1] Rapid and accurate assessment of global Forest Cover change is needed to focus conservation efforts and to better understand how deForestation is contributing to the buildup of atmospheric CO2. Here we examined different ways to use land surface temperature (LST) to detect changes in tropical Forest Cover. In our analysis we used monthly 0.05° × 0.05° Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations of LST and Program for the Estimation of DeForestation in the Brazilian Amazon (PRODES) estimates of Forest Cover change. We also compared MODIS LST observations with an independent estimate of Forest Cover loss derived from MODIS and Landsat observations. Our study domain of approximately 10° × 10° included the Brazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropical Forest Cover in our study area, we found that using data sampled during the end of the dry season (∼1–2 months after minimum monthly precipitation) had the greatest predictive skill. During this part of the year, precipitation was low, surface humidity was at a minimum, and the difference between day and night LST was the largest. We used this information to develop a simple temporal sampling algorithm appropriate for use in pantropical deForestation classifiers. Combined with the normalized difference vegetation index, a logistic regression model using day-night LST did moderately well at predicting Forest Cover change. Annual changes in day-night LST decreased during 2006–2009 relative to 2001–2005 in many regions within the Amazon, providing independent confirmation of lower deForestation levels during the latter part of this decade as reported by PRODES.
Jari Miettinen - One of the best experts on this subject based on the ideXlab platform.
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Change in tropical Forest Cover of Southeast Asia from 1990 to 2010
Biogeosciences, 2014Co-Authors: Hans-jürgen Stibig, Rastislav Raši, Frederic Achard, SALVATORE CARBONI, Jari MiettinenAbstract:Abstract. The study assesses the extent and trends of Forest Cover in Southeast Asia for the periods 1990–2000 and 2000–2010 and provides an overview on the main causes of Forest Cover change. A systematic sample of 418 sites (10 km × 10 km size) located at the one-degree geographical confluence points and Covered with satellite imagery of 30 m resolution is used for the assessment. Techniques of image segmentation and automated classification are combined with visual satellite image interpretation and quality control, involving Forestry experts from Southeast Asian countries. The accuracy of our results is assessed through an independent consistency assessment, performed from a subsample of 1572 mapping units and resulting in an overall agreement of >85% for the general differentiation of Forest Cover versus non-Forest Cover. The total Forest Cover of Southeast Asia is estimated at 268 Mha in 1990, dropping to 236 Mha in 2010, with annual change rates of 1.75 Mha (∼0.67%) and 1.45 Mha (∼0.59%) for the periods 1990–2000 and 2000–2010, respectively. The vast majority of Forest Cover loss (∼2 / 3 for 2000–2010) occurred in insular Southeast Asia. Complementing our quantitative results by indicative information on patterns and on processes of Forest change, obtained from the screening of satellite imagery and through expert consultation, respectively, confirms the conversion of Forest to cash crops plantations (including oil palm) as the main cause of Forest loss in Southeast Asia. Logging and the replacement of natural Forests by Forest plantations are two further important change processes in the region.
