Effects of Armed Conflict on Deforestation and Coca Crops — A Case Study: Caño Cristales, La Macarena National Natural Park Colombia
Publisher: University of Western Australia
Author(s): Anagibeth Choconta Lopez
Date: 2021
Topics: Assessment, Conflict Causes, Land, Monitoring and Evaluation, Peace Agreements, Programming, Renewable Resources
Countries: Colombia
Colombian natural forest occupies more than 50% of the total terrestrial area and has almost 10% of global biodiversity (Prem, Saavedra, & Vargas, 2020). In addition, the current percentage of national protected areas in Colombia corresponds to 28% approximately (RUNAP, 2020). Nonetheless, the deforestation rate in Colombia is significant (0.6% per year, approximately) (Armenteras, Cabrera, Rodríguez, & Retana, 2013), and the main drivers of the natural forest loss in Colombia have been identified as five decades of armed conflict and coca crops, mainly (Mendoza, 2020; Prem et al., 2020).
Colombia has had an armed conflict since the 1960s, and over 8.5 million direct victims. Colombian government and FARC (the oldest and most influence armed group) started negotiations in 2012, endorsing a peace agreement in 2016 in Habana, Cuba; supported by the international community and conferring the Novel Peace Prize in 2016 to the Colombian President (Pena et al., 2018; Prem et al., 2020).
Voluntary coca eradication was included as part of this arrangement, and monitor those areas is not a simple task due to several limitations (access, security, remote, large, and disperse areas). Therefore, remote sensing plays an imperative part in monitoring deforestation and coca cultivation in Colombia (Pena et al., 2018).
Methods utilised to identify forest-loss and coca crops have been hotspot detection using the Point Density Spatial Analyst tool (Armenteras, Schneider, & Dávalos, 2019), subspace classification methods (Bagan, Millington, Takeuchi, & Yamagata, 2020), reduce region function in Google Earth Engine (GEE) in Bolivia (Bagan et al., 2020), spatial panel regression (Mendoza, 2020; Prem et al., 2020), and linear spectral mixture model in Venezuela (Pena et al., 2018), using mainly the normalized difference vegetation index (NDVI). Nevertheless, supervised classification has not been used to monitor the coca crops since there is no significant NDVI difference between 1) forest and mature coca crops and 2) grass and young coca crops (Angel, 2012; Pesaresi, 2008).