TY - JOUR
T1 - Zero deforestation agreement assessment at farm level in Colombia using ALOS PALSAR
AU - Pedraza, Carlos
AU - Clerici, Nicola
AU - Forero, Cristian Fabián
AU - Melo, América
AU - Navarrete, Diego
AU - Lizcano, Diego
AU - Zuluaga, Andrés Felipe
AU - Delgado, Juliana
AU - Galindo, Gustavo
N1 - Publisher Copyright:
© 2018 by the authors.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Due 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.
AB - Due 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.
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U2 - 10.3390/rs10091464
DO - 10.3390/rs10091464
M3 - Research Article
AN - SCOPUS:85053610671
SN - 2072-4292
VL - 10
JO - Remote Sensing
JF - Remote Sensing
IS - 9
M1 - 1464
ER -