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Research article - Peer-reviewed, 2015

Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest

Karlson, Martin; Ostwald, Madelene; Reese, Heather; Sanou, J.; Tankoano, Boalidioa; Mattsson, Eskil


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 center dot 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 areavailable.


Landsat 8; woodland; Sudano-Sahel; tree canopy cover; aboveground biomass; multi-temporal imagery; Random Forest; variable selection; phenology

Published in

Remote Sensing
2015, volume: 7, number: 8, pages: 10017-10041
Publisher: MDPI AG

Authors' information

Karlson, Martin
Linköping University
Ostwald, Madelene
Linköping University
Reese, Heather
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Sanou, J.
Tankoano, Boalidioa
Polytechnic University of Bobo-Dioulasso
Mattsson, Eskil
Chalmers University of Technology

UKÄ Subject classification

Remote Sensing
Geosciences, Multidisciplinary
Forest Science

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