Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest
Artikel i vetenskaplig tidskrift, 2015

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.

aboveground biomass

Random Forest

tree canopy cover

Landsat 8


variable selection



multi-temporal imagery


Martin Karlson

Linköpings universitet

Madelene Ostwald

Göteborgs universitet

Heather Reese

Sveriges lantbruksuniversitet (SLU)

Josias Sanou

Institut de l'Environnement et de Recherches Agricoles (INERA)

Boalidioa Tankoano


Eskil Mattsson

Chalmers, Energi och miljö, Fysisk resursteori

Remote Sensing

20724292 (eISSN)

Vol. 7 8 10017-10041


Hållbar utveckling



Miljö- och naturvårdsvetenskap

Multidisciplinär geovetenskap





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