Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery |
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| Institution: | 1. Department of Food and Resource Economics, Faculty of Science, University of Copenhagen, Rolighedsvej 25, 1958 Frederiksberg C, Denmark;2. Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Øster Voldgade 10, 1350, Københaven K, Denmark;1. School of Biological Sciences, University of Western Australia, Perth, Australia;2. Department of Biodiversity, Conservation and Attractions, Western Australia, Australia;1. Forest Sciences Department, Federal University of Lavras, PO Box 3037, Lavras, MG 37200-900, Brazil;2. Biology Department, Federal University of Lavras, PO Box 3037, Lavras, MG 37200-900, Brazil;3. Instituto Federal Goiano, Cx. P. 66, Rio Verde, GO, Zip Code 75901–970, Brazil;1. The Mountain Partnership Secretariat, Forestry Policy and Resources Division, Forestry Department, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy;2. Forest & Nature Lab, Department of Environment, Ghent University, Gontrode-Melle, Belgium;3. Department of Earth and Environmental Sciences, University of Leuven, Leuven, Belgium;4. Forest Policy and Resources Division, Forestry Department, Food and Agriculture Organization of the United Nations, Rome, Italy;5. School of Earth and Environmental Sciences, The University of Queensland, Australia;6. Department of Environmental Biology, Sapienza University of Rome, Italy |
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| 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. |
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| Keywords: | REDD+ Feature extraction Very-high-resolution imagery Burn mark detection Supervised classification Miombo woodlands Tanzania |
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