Assessing the socioecological shocks of infrastructure-driven roadside deforestation using a participatory GIS approach framed by Panarchy theory

Infrastructure-driven roadside deforestation presents significant socio-ecological challenges, including microclimatic disruption and livelihood vulnerability. While spatial metrics are widely used to map these changes, there is a lack of an integrated framework that combine community knowledge with geospatial evidence within a social-ecological theory. This study assesses the socio-ecological shocks of roadside deforestation along a 10-km corridor of the Z-6006 highway in Charghat, Rajshahi, using a participatory GIS approach framed by the Panarchy adaptive cycle. Primary data were collected through 7 focus group discussions (FGDs) and 10 key informant interviews (KIIs) involving 71 participants, using Participatory Rural Appraisal tools. A geospatial analysis is developed using Sentinel-2 NDVI, Landsat 8 LST, and MODIS AOD data for the pre-monsoon periods of 2016, 2020, and 2024. The geospatial analysis was blinded to the qualitative information, and the NDVI and LST extraction boundaries were established independently before the satellite metrics were compared with the community-identified hotspots. The results show roadside vegetation loss, with mean NDVI declining from 0.39 to 0.36. Mean LST was 39.5 °C in 2016, 35.5 °C in 2020, and 39.8 °C in 2024, and NDVI and LST showed a statistically significant negative association (r = − 0.382, p < 0.001, N = 1,500). The qualitative themes highlight heat stress prioritized by youth groups, alongside dust pollution and livelihood insecurity emphasized by farmers and vendors, which have prompted fragmented coping strategies. Interpreted through the Panarchy adaptive cycle using explicit criteria (reported tree loss above 50% and negative sentiment above 80%, corroborated by satellite indicators), these disruptions indicate a shift from a stable Conservation (K) phase to a chaotic Release (Ω) phase, with the community now in a Reorganization (α) phase. The demonstrated methodology is a novel contribution of this study, as it integrates community knowledge with satellite indicators within an adaptive-cycle frame. While limited by a single-corridor scope, the findings offer context-specific policy recommendations, in line with Sustainable Development Goals 13 and 15.