Vasquez, MCV; Chen, CF; Lin, YJ; Kuo, YC; Chen, YY; Medina, D; Diaz, K (2020). Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season's in Honduras with the aid of geographic information systems and remote sensing data. FOREST ECOLOGY AND MANAGEMENT, 467, 118162.

Coniferous and mixed forests cover approximately 42% of Honduras forested areas, however; pine bark beetle (PBB) (Dendroctonus spp.) outbreaks are an environmental hazard that has caused incalculable ecological and economic impacts in Honduras. In this research, in order to plan more focalized measures for controlling the PBB outbreaks, it is essential to identify those areas that have a high susceptibility to a PBB outbreak during the dry and rainy seasons. For this purpose, we require to associate the historical PBB outbreak points (2017-2019), with a series of environmental and anthropogenic variables that according to the literature review have affected the initiation and the spread of PBB outbreaks. To assess the current climatic variables we used MODIS land surface temperature product (LST-MOD11A2), wind speed, precipitation, and temperature acquired from the WorldClim data. To assess vegetation vigor, we use the Normalized drought moisture index estimated from the MODIS surface reflectance product (MODO9A3). To give us an understanding of the density of the forest, we compare two products, the MODIS Leaf Area Index (LAI-MOD15A2H), and the MODIS Vegetation Continuous Fields (VFC-MOD44B). We included elevation, aspect, and slope as variables and acquired this data from the Global multiresolution terrain elevation data (GMTED-2010). Finally, we used geographical information systems data to derive proximity to different types of roads as anthropogenic data and wildfire density. We do a preliminary analysis of the variables and eliminated those which show the least importance. Furthermore, we integrated the most relevant variables identified with the PBB points by using the Random Forest (RF) algorithm to fit the model and then predicted the current PBB outbreak susceptibility for the dry and rainy season. Results indicated that climatic variables weigh heavily in determining high susceptibility areas. Our prediction results show high and very high susceptibility in the North-eastern and Central parts of the country especially. The results acquired, can lead to improved preventive and control measures to reduce the negative ecological effects that are caused by PBB outbreaks.