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

phenology

variable selection

woodland

Sudano-Sahel

multi-temporal imagery

Author

Martin Karlson

Linköping University

Madelene Ostwald

University of Gothenburg

Heather Reese

Swedish University of Agricultural Sciences (SLU)

Josias Sanou

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

Boalidioa Tankoano

Bobo-Dioulasso

Eskil Mattsson

Chalmers, Energy and Environment, Physical Resource Theory

Remote Sensing

2072-4292 (ISSN)

Vol. 7 8 10017-10041

Driving Forces

Sustainable development

Subject Categories

Remote Sensing

Environmental Sciences related to Agriculture and Land-use

Geosciences, Multidisciplinary

Areas of Advance

Energy

DOI

10.3390/rs70810017

More information

Latest update

4/11/2018