Remote sensing monitoring of the SDG indicator mountain green cover index in China from 2000 to 2022

Abstract

Mountains provide vital ecosystem services that support the livelihoods of billions of people worldwide, playing a crucial role in biodiversity conservation and climate regulation. The United Nations 2030 Agenda for Sustainable Development has established a specific target (SDG 15.4) dedicated to mountain protection. The Mountain Green Cover Index (MGCI) serves as a key indicator for assessing the health of mountain ecosystems. As the 2030 Agenda passes its midpoint, the mid-term assessment of the MGCI is essential for adjusting implementation strategies and ensuring the realization of the 2030 Agenda for the protection of mountain ecosystems. However, existing country-level MGCI values fail to account for the three-dimensional characteristics unique to mountains. Additionally, quantifying the detailed mechanisms of change and dynamics in highly heterogeneous mountain areas within countries remains challenging. In this study, we developed a high-resolution grid-based MGCI model for China and estimated MGCI values from 2000 to 2022 using 30 m annual land cover data and the true surface area of mountains. We analyzed the spatiotemporal patterns of the MGCI and quantified the impacts of anthropogenic and natural factors on MGCI dynamics during the 2022 mid-term assessment. The results show that from 2000 to 2022, China’s overall MGCI increased from 78.15 % to 82.23 %, with an average annual growth rate of 0.18 %. Notably, 8.48 % of mountains experienced an MGCI increase within the (0, 0.5) range, while only 0.03 % of areas saw a decrease greater than 0.5, primarily concentrated on the Qinghai–Tibetan Plateau. Spatial pattern analysis revealed clear variations in MGCI along elevation and hydrothermal gradients. Driving factor analysis indicated that water-related variables explain MGCI spatial distribution more effectively than thermal conditions. Furthermore, the interaction between grazing intensity and water factors demonstrated a strong synergistic effect on MGCI distribution. This research enhances the understanding of MGCI dynamics and its driving factors in China’s mountain ecosystems, offering valuable reference for the timely achievement of mountain sustainable development goals.