IAFE   05512
INSTITUTO DE ASTRONOMIA Y FISICA DEL ESPACIO
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Estimating deforestation events in the Semiarid Chaco Forest in Argentina using GIS, Remote sensing and machine learning models.
Autor/es:
VANESA DOUNA; ESTEFANIA PIEGARI; FRANCISCO GRINGS; VERONICA BARRAZA; ESTEBAN ROITBERG
Lugar:
montevideo
Reunión:
Conferencia; KHIPU Latin American Meeting In Artificial Intelligence; 2019
Institución organizadora:
khipu, google, apple, assap, mercado libre
Resumen:
Semi-arid forest ecosystems play an important role in seasonal carbon cycle dynamics; however, these ecosystems are prone to heavy degradation. In subtropical Argentina, the Chaco region has the highest absolute deforestation rates in the country (200.000 ha/ year), and at the same time, it is the least represented ecoregion in the national protected areas system. There is a critical need for methods that enable the analysis of satellite image time series to detect forest disturbances, especially in developing countries (e.g. Argentina). The Forest Management Unit (UMSEF) in Argentina provides annual deforestation maps based on visual inspection of Landsat images (Landsat 7 ETM+ and Landsat 8 OLI), which take long processing times and the intensive and coordinated participation of many human resources. In this research, we assess the potential to use Random Forest (RF) algorithm with the Landsat dataset and geographic information system (GIS) information to detect cover change over the Dry Chaco Forest (DCF) in Argentina. To identify the factors that define the agricultural expansion we calculated feature importances. Results indicate that distance to previous deforestation areas, distance to rivers and remote sensing vegetation indices are sufficient to predict deforestation events.