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dc.creatorPedraza, Carlos 
dc.creatorClerici, Nicola 
dc.creatorForero, Cristian Fabián 
dc.creatorMelo, América 
dc.creatorNavarrete, Diego 
dc.creatorLizcano, Diego 
dc.creatorZuluaga, Andrés Felipe 
dc.creatorDelgado, Juliana 
dc.creatorGalindo, Gustavo 
dc.date.accessioned2019-02-12T20:58:57Z
dc.date.available2019-02-12T20:58:57Z
dc.date.created2018
dc.date.issued2018 
dc.identifier.issn2072-4292
dc.identifier.urihttp://repository.urosario.edu.co/handle/10336/19053
dc.descriptionDue 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. © 2018 by the authors.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.relation.ispartofRemote Sensing, ISSN:2072-4292, Vol. 10 (2018)
dc.relation.urihttps://www.mdpi.com/2072-4292/10/9/1464
dc.subjectSynthetic Aperture Radar
dc.subjectAlos Palsar
dc.subjectCarbon Cycles
dc.subjectCattle Ranching
dc.subjectClassification Errors
dc.subjectColombia
dc.subjectComplex Topographies
dc.subjectHeterogeneous Region
dc.subjectPayment For Environmental Services
dc.subjectDeforestation
dc.subject.ddcHuertos, frutas, silvicultura 
dc.subject.lembDeforestación
dc.subject.lembCiclo del carbono (Biogeoquímica)
dc.titleZero deforestation agreement assessment at farm level in Colombia using ALOS PALSAR
dc.typearticle
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.type.spaArtículo
dc.rights.accesoAbierto (Texto Completo)
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.source.bibliographicCitationKeenan, R.J., Reams, G.A., Achard, F., de Freitas, J.V., Grainger, A., Lindquist, E., Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015 (2015) For. Ecol. Manag, 352, pp. 9-20
dc.creator.googlePedraza, Carlos
dc.creator.googleClerici, Nicola
dc.creator.googleForero, Cristian Fabián
dc.creator.googleMelo, América
dc.creator.googleNavarrete, Diego
dc.creator.googleLizcano, Diego
dc.creator.googleZuluaga, Andrés Felipe
dc.creator.googleDelgado, Juliana
dc.creator.googleGalindo, Gustavo
dc.identifier.doi10.3390/rs10091464


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