Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery

Published Date
International Journal of Applied Earth Observation and Geoinformation
July 2015, Vol.39:18–27, doi:10.1016/j.jag.2015.02.001

Author 

K. Dons ^a,,

C. Smith-Hall ^a

H. Meilby ^a

R. Fensholt ^b

aDepartment of Food and Resource Economics, Faculty of Science, University of Copenhagen, Rolighedsvej 25, 1958 Frederiksberg C, Denmark

bDepartment of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Øster Voldgade 10, 1350, Københaven K, Denmark

Received 19 December 2013. Revised 2 February 2015. Accepted 6 February 2015. Available online 27 February 2015. 

Highlights

• Burn marks from charcoal production were successfully detected with QuickBird.
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  Adaptive thresholding of the NIR band was superior for burn mark detection.
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  Delineation of burn mark areas was most accurate with supervised classification.
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  Regression models estimate charcoal production based on burn mark properties.

Abstract 

Quantification of forest degradation in monitoring and reporting as well as in historic baselines is among the most challenging tasks in national REDD+ strategies. However, a recently introduced option is to base monitoring systems on subnational conditions such as prevalent degradation activities. In Tanzania, charcoal production is considered a major cause of forest degradation, but is challenging to quantify due to sub-canopy biomass loss, remote production sites and illegal trade. We studied two charcoal production sites in dry Miombo woodland representing open woodland conditions near human settlements and remote forest with nearly closed canopies. Supervised classification and adaptive thresholding were applied on a pansharpened QuickBird (QB) image to detect kiln burn marks (KBMs). Supervised classification showed reasonable detection accuracy in the remote forest site only, while adaptive thresholding was found acceptable at both locations. We used supervised classification and manual digitizing for KBM delineation and found acceptable delineation accuracy at both sites with RMSEs of 25–32% compared to ground measurements. Regression of charcoal production on KBM area delineated from QB resulted in R²s of 0.86–0.88 with cross-validation RMSE ranging from 2.22 to 2.29 Mg charcoal per kiln. This study demonstrates, how locally calibrated remote sensing techniques may be used to identify and delineate charcoal production sites for estimation of charcoal production and associated extraction of woody biomass.

Keywords

REDD+

Feature extraction

Very-high-resolution imagery

Burn mark detection

Supervised classification

Miombo woodlands

Tanzania

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