Characterizing forest disturbances across the Argentine Dry Chaco based on Landsat time series

Abstract

Forest loss in the tropics affects large areas, but whereas full forest conversions are routinely assessed, forest degradation patters remain often unclear. This is particularly so for the world’s tropical dry forests, where remote sensing of forest disturbances is challenging due to high canopy complexity, strong phenology and climate variability, and diverse degradation drivers. Here, we used the full depth of the Landsat archive and devised an approach to detect disturbances related to forest degradation across the entire Argentine Dry Chaco (about 489,000 km2) over a 30-year timespan. We used annual time series of different spectral indices, summarized for three seasonal windows, and applied LandTrendr to temporally segment each time series. The resulting pixel-level forest disturbance metrics then served as input for a Random Forests classification which we used to produce an area-wide disturbance map, and associated yearly area estimates of disturbed forest. Finally, we evaluated disturbance trends in relation to climate and soil conditions. Our best model produced a disturbance map with an overall accuracy of 79%, with a balanced error distribution. A total of 8% (24,877 ± 860 km2) of the remaining forest in the Argentine Dry Chaco have been affected by forest disturbances between 1990 and 2017. Diverse spatial patterns of forest disturbances indicate a variety of agents driving disturbances. We also found the disturbed area to vary strongly between years, with larger areas being disturbed during drought years. Our approach shows that it is possible to robustly map forest disturbances in tropical dry forests using Landsat time series, and demonstrates the value of ensemble approaches to capture spectrally-complex and heterogeneous land-change processes. For the Chaco, a global deforestation hotspot, our analyses provide the first Landsat-based assessment of forest disturbance in remaining forests, highlighting the need to better consider such disturbances in assessments of carbon budgets and biodiversity change.

Keywords

• Ensemble classification
• Forest degradation
• LandTrendr
• Trajectory analyses
• Tropical dry forests
• Random Forests