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

Artikel i vetenskaplig tidskrift
Författare Marin Karlson
Madelene Ostwald
Heather Reese
Josias Sanou
Boalidioa Tankoano
Eskil Mattsson
Publicerad i Remote Sensing
Volym 7
Nummer/häfte 8
Sidor 10017-10041
ISSN 2072-4292
Publiceringsår 2015
Publicerad vid Göteborgs miljövetenskapliga centrum, GMV
Sidor 10017-10041
Språk en
Länkar dx.doi.org/10.3390/rs70810017
www.mdpi.com/2072-4292/7/8/10017
https://gup.ub.gu.se/file/169651
Ämnesord Landsat 8; woodland; Sudano-Sahel; tree canopy cover; aboveground biomass; multi-temporal imagery; Random Forest; variable selection; phenology
Ämneskategorier Miljö- och naturvårdsvetenskap, Fjärranalysteknik, Multidisciplinär geovetenskap

Sammanfattning

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.

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