Deep learning-driven land cover monitoring and landscape ecological health assessment: A dynamic study in coastal regions of the China–Pakistan Economic Corridor from 2000 to 2023 - Chen Xu; Juanle Wang; Yamin Sun; Meng Liu; Jingxuan Liu; Meer Muhammad Sajjad (Ecological Indicators)
The coastal regions of the China–Pakistan Economic Corridor (CPEC) are crucial links for the “21st Century Maritime Silk Road”. Nonetheless, this region is facing significant ecological challenges due to natural disasters and intensive human activity. To effectively monitor and assess the ecological health of these critical coastal zones, this study employed integrated labels and a deep learning model to obtain land cover data spanning from 2000 to 2023. It then constructed a vigour-organisation-resilience (VOR) model with 12 assessment indicators to evaluate the landscape ecological health of this region. The evaluation results showed distinct spatial patterns. Gwadar and Ormara’s “Bare land” areas remained “Sick,” while Karachi and Lower Indus’ “Impervious surfaces” were “Unhealthy” with minimal fluctuations. The Lower Indus region saw “Sub-healthy” expansion with increased “Crops” areas. Lasbela was “Healthy,” dominated by shrub-based “Other vegetation,” and the Indus Delta’s mangroves maintained a “Very healthy” state. Overall, the CPEC coastal regions were rated “Unhealthy,” with signs of moderate improvement. We recommend that the CPEC coastal areas focus on restoring “Sick” areas, promoting sustainable agriculture in “Sub-healthy” regions, and conserving “Healthy” and “Very healthy” areas. This study demonstrates the efficacy of deep learning and VOR model in assessing long-term ecological health, providing a valuable framework that can be applied in other coastal regions facing similar challenges.