Transparent, consistent, and accurate national forest monitoring is required for successful implementation of reducing emissions from deforestation and forest degradation (REDD+) programs. Collecting baseline information on forest extent and rates of forest loss is a first step for national forest monitoring in support of REDD+. Peru, with the second largest extent of Amazon basin rainforest, has made significant progress in advancing its forest monitoring capabilities. We present a national-scale humid tropical forest cover loss map derived by the Ministry of Environment REDD+ team in Peru. The map quantifies forest loss from 2000 to 2011 within the Peruvian portion of the Amazon basin using a rapid, semi-automated approach. The available archive of Landsat imagery (11 654 scenes) was processed and employed for change detection to obtain annual gross forest cover loss maps. A stratified sampling design and a combination of Landsat (30 m) and RapidEye (5 m) imagery as reference data were used to estimate the primary forest cover area, total gross forest cover loss area, proportion of primary forest clearing, and to validate the Landsat-based map. Sample-based estimates showed that 92.63% (SE = 2.16%) of the humid tropical forest biome area within the country was covered by primary forest in the year 2000. Total gross forest cover loss from 2000 to 2011 equaled 2.44% (SE = 0.16%) of the humid tropical forest biome area. Forest loss comprised 1.32% (SE = 0.37%) of primary forest area and 9.08% (SE = 4.04%) of secondary forest area. Validation confirmed a high accuracy of the Landsat-based forest cover loss map, with a producer's accuracy of 75.4% and user's accuracy of 92.2%. The majority of forest loss was due to clearing (92%) with the rest attributed to natural processes (flooding, fires, and windstorms). The implemented Landsat data processing and classification system may be used for operational annual forest cover loss updates at the national level for REDD+ applications.

Since March 16, 2017, the National Forest Conservation Program for Climate Change Mitigation (PNCBMCC) of Peru's Ministry of the Environment (MINAM) has been implementing a methodology to detect early warning alerts of humid tropical forest cover loss in Peru using data from the Landsat 7 and 8 satellites. The method uses Direct Spectral Unmixing (DSU) to detect forest loss as small as 25% of a pixel. Between March 16 and December 25 of 2017, 500 Landsat images have been used to detect 137,143 hectares of humid tropical forest cover loss, including deforestation for agricultural expansion and illegal or informal extractive activities, such as the opening of roads for selective logging. Natural forest loss was also detected, produced by windstorms and landslides in mountainous areas, among others. The results were verified with high-resolution satellite images and the accuracy was evaluated using a stratified random sample, showing a high level of both user's and producer's accuracy. The early warning alerts are distributed and available through the Geobosques platform

Deforestation and the unsustainable management of agricultural and livestock production systems in tropical mountain areas have caused fragmented and degraded landscapes. Payment for ecosystem services (PES) could be an effective policy instrument with which to reduce deforestation and restore disturbed ecosystems. The national-scale PES program in Costa Rica is recognized as being successful; however, its financial resources have been mostly dedicated to forest protection, and much less to reforestation projects. This paper aims to construct a micro-scale PES scheme by using primary data generated through spatial modeling and socio-economic and stated preference surveys (choice experiment) in southern Costa Rica. The results suggest that, on average, landholders would agree to implement restoration projects on their own private pasturelands if an appropriate holistic place-based approach was applied encompassing biophysical, social, economic, and institutional aspects. Willingness-to-accept values allow payments to be linked to cattle farmers’ estimates of specific ecosystem services (ES) and land opportunity costs. The economic valuation of three ESs (erosion control, water availability, and biodiversity) allows construction of a layered payment scheme, which could encourage the development of a potential partnership between national and local institutions and NGOs as alternative buyers of ESs, reduce transaction costs, and improve household well-being.

[Introducción]: La deforestación y la gestión insostenible de los sistemas de producción agrícola y ganadero en áreas montañosas han provocado la degradación de la tierra y una progresiva reducción en la provisión de los servicios ecosistémicos. [Objetivo]: En este artículo se desarrolla un análisis espacial de susceptibilidad de erosión en la subcuenca del río Claro, en el cordón montañoso Fila Cruces, en la región del Pacífico Sur de Costa Rica. [Metodología]: Para ello se aplicaron los métodos de regresión logística y redes neuronales artificiales integrados en un entorno de sistemas de información geográfica (GIS) y empleando herramientas de teledetección. En ambos modelos se consideraron los siguientes factores explicativos: uso del suelo, geomorfología, pendiente, distancia euclidiana a la red de drenaje e índice de vegetación diferencial normalizado (NDVI, en sus siglas en inglés). Los mapas de susceptibilidad de erosión fueron validados independientemente por medio de la función características operativas del receptor (ROC, por sus siglas en inglés). [Resultados]: El modelo de redes neuronales artificiales obtuvo un poder predictivo superior al de regresión logística con base en el valor calculado del área debajo de la curva (AUC, por sus siglas en inglés). Los factores con mayor poder explicativo variaron en función del modelo utilizado [Conclusiones]: Los mapas de susceptibilidad de erosión mostraron una elevada alteración ecológica en términos de la probabilidad de ocurrencia de procesos de erosión, especialmente en la parte alta de la subcuenca, en terrenos ocupados por fincas de ganadería extensiva y elevada pendiente.

We develop a DSGE model of the Colombian economy to assess the effect of tax policy on informal employment and income distribution.The model recreates a small open economy, with persistent income inequality, a substantial degree of informality, and different possibilities of government intervention. This paper evaluates the consequences of government transfer payments to households with lower incomes. We find that although transfer payments have a positive effect on income distribution, financing them requires an adjustment in government finances (cut spending or increase revenue through the use of various taxes), which have negative effects on the economic as a whole.JEL Classification: E62, D58.