Multi-source remote sensing of insect defoliation events in Abisko from point to regional scales

Insect infestation is projected to become more frequent in the Arctic regions under global climate change, posing high socioeconomic and ecological pressure on the local environment and ecosystem. Rapid insect induced defoliation detection is critical for understanding the impact of discernible disturbances on terrestrial ecosystem structures and functions. This paper uses Continuous Change Detection and Classification (CCDC) algorithm and the Harmonized Landsat and Sentinel-2 (HLS) dataset to build a systematic insect defoliation monitoring system in the Abisko region, subarctic Sweden. The Green Chromatic Coordinate (GCC) index was used to link different scale sensors, from close-up and time-lapse imagery to Unmanned Aerial Vehicle (UAV) and HLS observations, and biological processes of biogenic volatile organic compound (BVOC) emissions. The detected defoliation intensity was validated using the ground truth larvae abundance and Greenness Ratio from in situ digital photos. The new insect defoliation detection approach provides a new tool for assessing larvae outbreaks and the associated impact at different scales. The findings underscore the potential of integrating remote sensing data with ground observations from various sensors for effective insect defoliation monitoring and ecological impact assessment, paving the way for future large scale rapid mapping, early warning systems, and BVOC emission estimation.