Zero Deforestation Agreement Assessment at Farm Level in Colombia Using ALOS PALSAR

Remote Sensing
Pedraza, Carlos; Clerici, Nicola; Forero, Cristian Fabian; Melo, America; Navarrete, Diego; Lizcano, Diego; Zuluaga, Andres Felipe; Delgado, Juliana; Galindo, Gustavo
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Volume / Issue10/9
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Total Pages18 pages
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Tagscarbon cycle; deforestation; Colombia; sustainable cattle ranching; Synthetic Aperture Radar; ALOS PALSAR
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Conference Date N/A
Publication DateSeptember 13, 2018
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AbstractDue to the fast deforestation rates in the tropics, multiple international efforts have been launched to reduce deforestation and develop consistent methodologies to assess forest extension and change. Since 2010 Colombia implemented the Mainstream Sustainable Cattle Ranching project with the participation of small farmers in a payment for environmental services (PES) scheme where zero deforestation agreements are signed. To assess the fulfillment of such agreements at farm level, ALOS-1 and ALOS-2 PALSAR fine beam dual imagery for years 2010 and 2016 was processed with ad-hoc routines to estimate stable forest, deforestation, and stable nonforest extension for 2615 participant farms in five heterogeneous regions of Colombia. Landsat VNIR imagery was integrated in the processing chain to reduce classification uncertainties due to radar limitations. Farms associated with Meta Foothills regions showed zero deforestation during the period analyzed (2010–2016), while other regions showed low deforestation rates with the exception of the Cesar River Valley (75 ha). Results, suggests that topography and dry weather conditions have an effect on radar-based mapping accuracy, i.e., deforestation and forest classes showed lower user accuracy values on mountainous and dry regions revealing overestimations in these environments. Nevertheless, overall ALOS Phased Array L-band SAR (PALSAR) data provided overall accurate, relevant, and consistent information for forest change analysis for local zero deforestation agreements assessment. Improvements to preprocessing routines and integration of high dense radar time series should be further investigated to reduce classification errors from complex topography conditions.
Created: 12/20/2018 4:39 PM (ET)
Modified: 12/20/2018 4:39 PM (ET)
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