Early warning tropical forest loss alerts in Peru using Landsat

Abstract

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

The Road to Operationalization of Effective Tropical Forest Monitoring Systems

“…However, given that the tree cover concept includes secondary vegetation, tree plantations, and some crops as forests, further adaptation through refinement and processing is needed for its local use [23][24][25]. Vargas et al [26] reported that Terra-I and GLAD alerts underestimate the number and extent of deforestation events occurring on the ground due to persistent cloud cover and a mismatch between MODIS spatial resolution and the small size of forest clearings (for Terra-I). In general, the PRODES, DETER, and GFW systems provide forest cover change information with ~80% accuracy when compared to ground data.…”

“…CLCC algorithms can generate maps showing the location and extent of deforestation at a continuous rate (daily, monthly) using diverse time-series analysis frameworks [30]. CLCCderived annual deforestation maps have been tested in various ecosystems, including tropical forests, achieving spatial accuracies of >80% when compared to reference ground data [26,[30][31][32][33][34][35][36][37][38]. Temporal accuracies, however, can be much lower due to cloud cover persistence, especially in tropical areas, with differences of up to 18 months between the estimated time of occurrence of the deforestation event and the moment it happened [39].…”

Automatic Annotation of Change Detection Images

“…This is particularly useful for the monitoring of large or very remote areas where on-site data acquisition is impractical. Indeed, the impact of environmental events such as deforestation [ 1 , 2 ], wildfires [ 3 ], and other natural disasters [ 4 , 5 ] can be assessed with data from EO satellites. With change detection techniques, the various changes that are happening on the Earth’s surface can be automatically detected by analyzing images of a given area taken at different times [ 6 ].…”

“…With change detection techniques, the various changes that are happening on the Earth’s surface can be automatically detected by analyzing images of a given area taken at different times [ 6 ]. Such techniques have been used to monitor loss and disturbances in forests [ 2 , 7 ], to track change in urban areas [ 8 ], and also to map out areas affected by natural disasters [ 4 , 5 ]. When a change detection method neither provides nor needs additional semantic information beyond the change/no-change pixels, it is called a binary change detection method.…”

Pérdida de bosque en zonas para la conservación y recuperación de la Zonificación Ecológica y Económica (ZEE) de la provincia Alto Amazonas, Loreto, Perú

“…La pérdida de cobertura del bosque es el cambio de la cobertura vegetal originado por la acción antrópica o natural, se da principalmente por la deforestación para expansión agrícola y actividades extractivas ilegales o informales, así como, de forma natural, debido a vendavales o deslizamientos de tierra (Vargas et al, 2019).…”