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Mapping Tree Canopy...
Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest
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- Karlson, Martin, 1980- (författare)
- Linköpings universitet,Tema Miljöförändring,Filosofiska fakulteten,Centrum för klimatpolitisk forskning
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- Ostwald, Madelene, 1966 (författare)
- Linköpings universitet,Gothenburg University,Göteborgs universitet,Göteborgs miljövetenskapliga centrum, GMV,Centre for Environment and Sustainability,Tema Miljöförändring,Filosofiska fakulteten,Centrum för klimatpolitisk forskning,Centre for Environment and Sustainability (GMV), University of Gothenburg, Gothenburg, Sweden; Chalmers University of Technology, Gothenburg, Sweden
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- Reese, Heather (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management,Section of Forest Remote Sensing, Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden
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- Sanou, Josias (författare)
- Institut de l'Environnement et de Recherches Agricoles (INERA),Institut de l'Environnement et de Recherches Agricoles (INERA), Département Productions Forestières, Burkina Faso
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- Tankoano, Boalidioa (författare)
- Polytechnic University of Bobo-Dioulasso, Development Rural Institute/Department of Forestery, Bobo-Dioulasso, Burkina Faso
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- Mattsson, Eskil, 1981 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Division of Physical Resource Theory, Department of Energy and Environment, Chalmers University of Technology, Gothenburg, Sweden
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(creator_code:org_t)
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- 2015-08-06
- 2015
- Engelska.
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 7:8, s. 10017-10041
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Abstract
Ämnesord
Stäng
- Accurate and timely maps of tree cover attributes are important tools for environmental research and natural resource management. We evaluate the utility of Landsat 8 for mapping tree canopy cover (TCC) and aboveground biomass (AGB) in a woodland landscape in Burkina Faso. Field data and WorldView-2 imagery were used to assemble the reference dataset. Spectral, texture, and phenology predictor variables were extracted from Landsat 8 imagery and used as input to Random Forest (RF) models. RF models based on multi-temporal and single date imagery were compared to determine the influence of phenology predictor variables. The effect of reducing the number of predictor variables on the RF predictions was also investigated. The model error was assessed using 10-fold cross validation. The most accurate models were created using multi-temporal imagery and variable selection, for both TCC (five predictor variables) and AGB (four predictor variables). The coefficient of determination of predicted versus observed values was 0.77 for TCC (RMSE = 8.9%) and 0.57 for AGB (RMSE = 17.6 tons∙ha−1). This mapping approach is based on freely available Landsat 8 data and relatively simple analytical methods, and is therefore applicable in woodland areas where sufficient reference data are available.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Remote Sensing (hsv//eng)
- LANTBRUKSVETENSKAPER -- Annan lantbruksvetenskap -- Miljö- och naturvårdsvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Other Agricultural Sciences -- Environmental Sciences related to Agriculture and Land-use (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Multidisciplinär geovetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Geosciences, Multidisciplinary (hsv//eng)
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Skogsvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Forest Science (hsv//eng)
Nyckelord
- aboveground biomass
- Random Forest
- tree canopy cover
- Landsat 8
- phenology
- variable selection
- woodland
- Sudano-Sahel
- multi-temporal imagery
- Landsat 8; woodland; Sudano-Sahel; tree canopy cover; aboveground biomass; multi-temporal imagery; Random Forest; variable selection; phenology
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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