Scholarlink

<p>The Saihanba Forest Farm (SHB), China’s largest plantation base, has experienced significant habitat quality services (HQS) changes following six decades of afforestation. This study developed an integrated “CLUE-S–InVEST–BBN” framework to simulate and optimize HQS spatial patterns under alternative land use/cover change (LUCC) scenarios for 2035. Using Landsat imagery (2002–2022) and twelve biophysical and socio-economic drivers, we projected LUCC under three scenarios: ecological protection (EP), natural development (ND), and economic development (ED). HQS exhibited a “decline-then-recovery” trend from 2002 to 2022, with forests and grasslands contributing 79.6% and 48.2% of total HQS, respectively. The EP scenario projected 89.96% forest cover by 2035 with the highest mean HQS (0.8925), while the ED scenario showed a 1.54% decrease due to urban expansion. Bayesian belief network analysis identified LUCC, NDVI, road proximity, and precipitation as primary HQS determinants and delineated three management zones: key optimization, ecological conservation, and general management. This framework provides a replicable approach for enhancing HQS and supporting sustainable land-use planning in ecologically sensitive mountainous forest areas.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatial optimization of habitat quality service patterns under multi-scenario land-use/cover change in the Saihanba Forest Farm, China

  • 期刊论文

  • 发表日期：

    2026年1月23日

查看全文
  • Chong Liu,
  • Liren Xu,
  • Xuanrui Huang,
  • Zhidong Zhang

摘要

The Saihanba Forest Farm (SHB), China’s largest plantation base, has experienced significant habitat quality services (HQS) changes following six decades of afforestation. This study developed an integrated “CLUE-S–InVEST–BBN” framework to simulate and optimize HQS spatial patterns under alternative land use/cover change (LUCC) scenarios for 2035. Using Landsat imagery (2002–2022) and twelve biophysical and socio-economic drivers, we projected LUCC under three scenarios: ecological protection (EP), natural development (ND), and economic development (ED). HQS exhibited a “decline-then-recovery” trend from 2002 to 2022, with forests and grasslands contributing 79.6% and 48.2% of total HQS, respectively. The EP scenario projected 89.96% forest cover by 2035 with the highest mean HQS (0.8925), while the ED scenario showed a 1.54% decrease due to urban expansion. Bayesian belief network analysis identified LUCC, NDVI, road proximity, and precipitation as primary HQS determinants and delineated three management zones: key optimization, ecological conservation, and general management. This framework provides a replicable approach for enhancing HQS and supporting sustainable land-use planning in ecologically sensitive mountainous forest areas.