Search

We assess the fulfillment of zero deforestation 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 non-forest extension for 2615 participant farms in five regions of Colombia. 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. 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.

Colombia is a key actor related with the adoption of the relevant UNFCC provisions referred in the context of obtaining result-based payments for REDD+ actions. Since 2012, the Colombian Forest Monitoring System (SMByC, Spanish acronym) has generated wall-to-wall historical information about the deforestation process over the last 27 years at national and regional levels. The SMByC generates a methodology that integrates tools for the preprocessing and semi-automated processing of satellite imagery to detect and quantify the loss of forest cover by deforestation. Also, the SMByC has done an extensive literature review, collecting qualitative and quantitative information to identify how the drivers of deforestation (illicit crops, illegal mining and postconflict scenario) could result in an increased forest lost during a postconflict scenario. Given that Colombia is a country conformed by regions that have differentially socioeconomic and environmental conditions, the study shows how the methods could generate official information and specifies the context of deforestation process of those regions.

SOLAP data cubes are a main tool to help on the processing of satellite imagery. This article presents the work developed for adapting an existing SOLAP Data Cube to the current Colombian Protocol for processing satellite images to analyze deforestation. We studied different technological alternatives to support such protocol and extend the capabilities of the Australian data cube to include the whole analysis process. In this way, it is possible for different institutions to produce and consume information to/from the data cube allowing the standardization of such information. In consequence, an institution can generate new results based on previous information generated for another institution, keeping the associated metadata along the whole process. This paper introduces the defined architecture, a first implementation and the first results obtained by IDEAM, the Colombian official institution responsible for the monitoring of deforestation in the country.

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&ndash, 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.

Biodiversity assessment represents a baseline for developing conservation strategies, but the assessment of future impact of some policies also requires the development of scenarios. These assessments are particularly important and difficult in areas with high biodiversity such as the Tropical Andes. Therefore three countries were analyzed: Colombia, Ecuador and Peru using the framework of GLOBIO3 to assess the remaining biodiversity for 2000 and for two 2030 scenarios: market forces and policy reforms. The purpose was to identify the most vulnerable areas to biodiversity loss, the most important drivers and the implications of such losses for conservation. Detailed information for each country was used to build the drivers of biodiversity loss (land use/land cover, infrastructure, fragmentation and climate change). The authors discuss the use of this methodology for Andean countries, how the results can be useful for policy and decision makers, and provide suggestions to improve GLOBIO3 at national scales.