Abstract
The Colombian Andes foothills have seen an expansion of forest disturbance since the 1950s. While understanding the drivers of disturbance is important for quantifying the implications of land use change on regional biodiversity, methods for attributing disturbance to specific drivers of change at a high temporal and spatial resolution are still lacking in the Andes region, in part due to persistent cloud cover. Using 20 years of Landsat images (1996–2015) covering Picachos National Park in the Colombian Andes, we detected sub-annual forest cover disturbances using the Breaks For Additive Season and Trend (BFAST) Monitor algorithm; characterized different types of disturbance using spectral, spatial, and topographic indicators; and attributed causes of forest disturbance such as conversion to pasture, conversion to agriculture, and non-stand replacing disturbance (i.e., thinning) using a Random Forest (RF) classifier. Conversion to pasture has been the main driver of forest disturbance in Picachos, responsible for 11,395 ± 72 ha (17%) of forest cover loss, followed by non-stand replacing disturbance and conversion to agriculture. Disturbance detection had 96% overall agreement with validation data, although we had a high omission error of 21% primarily associated with forest to agriculture conversion. Other change drivers had a much more reliable attribution with forest to pasture conversion or non-stand-replacing disturbance, showing only 1–5% commission and 2–14% omission errors. Our results provide spatially-explicit information on sub-annual disturbances and associated drivers of change that are necessary for evaluating and improving domestic conservation efforts and establishing systematic ecological observations, which is currently absent from Colombia. While effective at revealing forest change dynamics in a geographically remote and socio-politically complex region like Picachos, our approach is highly automated and it can be easily extended to the rest of Andes-Amazon transition belt where low availability of remote sensing data and high cloud cover impede efforts at consistent monitoring of forest cover change dynamics and drivers.
Highlights
Colombia is a “megadiverse” country given its high level of endemism and species richness [1].over the last 50 years, the spread of informal settlements into forested regions, internal socialForests 2018, 9, 269; doi:10.3390/f9050269 www.mdpi.com/journal/forestsForests 2018, 9, 269 conflict, and the growing demand for agricultural land have increased the pressure on Colombia’s forest ecosystems even in remote or protected regions [2]
We focused on the mostand common driverschanges of disturbance, including conversion to pasture, conversion foothills of Picachos National Park as a case of study, we (1) detected forest disturbances using to agriculture, and non-stand replacing disturbance
We focused on the structure, most common of disturbance, includingof forest disturbance were characterized using metrics derived from time-series trajectories such as spectral conversion to pasture, conversion to agriculture, and non-stand replacing disturbance
Summary
Forests 2018, 9, 269 conflict, and the growing demand for agricultural land have increased the pressure on Colombia’s forest ecosystems even in remote or protected regions [2]. Between 1990 and 2015, Colombia lost more than six million hectares of forest, and more significantly, witnessed a 44% increase in deforestation in 2016 compared to 2015 [3]. The Colombian Andes foothills were rather inaccessible three decades ago but have seen recent forest disturbance [4,5,6] due to favorable incentives for cattle ranching in the 1980s [7], the enlargement of coca cultivation in the 1990s through 2002 [2], and the more recent expansion of cattle ranching and other agricultural land uses [8,9]. Though National Protected Areas make up 12% of Colombia’s land mass [10], protected areas are threatened by migration and displacement, economic development, and illicit cultivation of coca (Erythroxylum coca Lam.) [11], with attendant effects on socio-ecological relationships [4,7,12,13,14]
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