ABSTRACT

In tropical dry forests, wildfires are likely to become a major disturbance as a result of anthropogenic pressures and dryer conditions due to climate warming. Based on remote sensing techniques, this paper assesses the probability of fires occurring in the dry region of the Guanacaste Conservation Area (GCA), northwestern Costa Rica, testing the roles as fire determinants of topography, early successional forest stages, between-area susceptibility, and accessibility to human (roads and trails). Probability of fire occurrence and fire danger were determined based on a machine learning algorithm. Fire occurrence model was inferred from burned areas and fire line density; while fire danger was inferred from the probability of fire occurrence, the proportion of burned areas, and the number of fires per area. Results indicate that the presence of early successional vegetation on flat lowlands highly accessible by roads and trails are key components of fire occurrence. Three of the six investigated sectors show high probability of fire occurrence and fire danger, indicating the spatial heterogeneity of fire risk in the landscape. The results could be useful for the management of the conservation area.

摘要

在热带干燥森林中，由于气候变暖导致的人为压力和干燥条件，野火很可能成为主要干扰。本文基于遥感技术，评估了哥斯达黎加西北部瓜纳卡斯特保护区 (GCA) 干旱地区发生火灾的概率，测试了作为火灾决定因素的地形、早期演替森林阶段、区域间易感性、和人类的可达性（道路和小径）。基于机器学习算法确定火灾发生的概率和火灾危险。根据燃烧面积和火线密度推断火灾发生模型；而火灾危险则从火灾发生的概率、燃烧面积的比例、单位面积的火灾数量来推断。结果表明，道路和小径容易到达的平坦低地早期演替植被的存在是火灾发生的关键组成部分。六个被调查的扇区中有三个显示出火灾发生和火灾危险的高概率，表明景观中火灾风险的空间异质性。研究结果可用于保护区的管理。