Evaluation and prediction of ecological sensitivity of the Xuebaoding region based on PLUS-InVEST
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摘要: 生态敏感性是指在不损失或不降低环境质量情况下，环境因子对外界压力或干扰适应能力，其实质反映了区域生态环境问题发生的难易程度与可能性。位于青藏高原东缘的雪宝顶地区，岩溶景观丰富，生态系统类型独特，对气候变化与人类活动高度敏感，是研究生态敏感性演变规律的理想区域。文章以雪宝顶地区为研究对象，基于PLUS-InVEST模型模拟土地利用变化和分析生境质量的动态变化，系统评估2000—2020年环境质量，并对2030年的生境质量进行预测，运用地理探测器量化环境因子，通过质量变化研究生态敏感性。结果表明：（1）2000—2020年土地利用结构变化呈现出“两减四增”，即草地、耕地减少，建设用地、林地、水域和未利用地增加。（2）2000—2020年间，生境质量均在85.2%以上，研究区生境质量总体较高，生态结构相对稳定。但建设用地以每年8.4%的速度扩张，引起局部生境下降。（3）2030年生境质量将有所下降，未来流域生态环境面临较大的压力。（4）该流域生态环境质量由自然环境因素（高程、坡度、温度、降水因子）起主导作用，未来更应关注流域气候变化引起的生态环境效应。研究结果揭示了该区域生态系统在土地利用变化背景下的响应机制，对于优化保护区管理策略、推动地区人与自然和谐共生具有重要科学价值与现实意义。Abstract:
Ecological sensitivity refers to the capacity of environmental factors to adapt to external pressures or disturbances without degrading or compromising environmental quality. In essence, it reflects the likelihood and ease of occurrence of regional ecological and environmental problems. The Xuebaoding region, located on the eastern of the Qinghai-Xizang Plateau, features abundant karst landscapes and unique ecosystem. Highly sensitive to climate change and human activities, it serves as an ideal area for studying the evolution of ecological sensitivity. It is of great scientific value and practical significance to reveal the response mechanism of the regional ecosystem under the background of land use change for optimizing the management strategy of protected areas and promoting the harmonious coexistence between human and nature. This study focuses on the Xuebaoding region, employing the PLUS-InVEST model to simulate land use changes and analyze the dynamic variations in habitat quality. It systematically evaluates environmental quality from 2000 to 2020, predicts habitat quality for 2030, and applies the Geo Detector to quantify environmental factors. Based on these quality changes, the study investigates ecological sensitivity within the study area. The results show that: (1) From 2000 to 2020, the change of land use structure showed the characteristics of "two decreases and four increases", in which the area of grassland and cultivated land continued to decrease, with a net decrease of 294.98 km2 and 32.79 km2, respectively. However, the area of woodland, construction land, water area and unused land all showed an increasing trend. The net transfer of woodland was 227.78 km2, and the construction land increased by 166.98%. (2) From 2000 to 2020, the cultivated land area further decreased with the acceleration of urbanization, and the construction land continued to expand at an average annual rate of 8.35%. The results of habitat quality assessment show that the area of extremely low ecological environment increased by 156.90% in 2020 compared with 2000, and the spatial distribution of expansion area and construction land was highly coincident, indicating that urban expansion was the main reason for the decline of ecological environment quality and the growth of extremely low-grade areas. (3) Despite the changes in land use structure, the overall habitat quality in the study area remained above 85.2% from 2000 to 2020, and the average habitat quality remained relatively stable after it was increased from 85.26% in 2000 to 86.11% in 2010, indicating that the overall ecosystem structure in the basin remained at a high level and the ecological carrying capacity was strong. However, the forecast results in 2030 based on PLUS model show the overall accuracy, and the average value of future habitat quality will drop to 85.9%, which is lower than that in 2020, and the area of extremely low area is expected to increase by 192.47% compared with that in 2000, indicating that the potential pressure on regional ecosystems continues to increase. (4) The results of Geo Detectors show that natural factors,such as elevation,slope,temperature,and precipitation, are still the dominant factors to determine the spatial pattern of ecological environment quality in the study area. Interactive detection shows that the factors are enhanced by two factors, which indicates that human intervention is significantly affecting the evolution direction and range of ecological environment quality. -
Key words:
- ecological sensitivity /
- PLUS /
- InVEST /
- land use /
- Xuebaoding region
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图 2 数据处理及模型构建技术路线图
Figure 2. Technology roadmap for data processing and model construction
图 6 土地利用转移矩阵（a.2000—2005年；b.2005—2010年； c.2010—2015年；d.2015—2020年）
Figure 6. Land use transition matrix ( a.From 2000 to 2005; b.From 2005 to 2010; c.From 2010 to 2015; d.From 2015 to 2020)
图 8 土地利用及生境质量分布（2000—2030年）
Figure 8. Spatial dstribution of land use and habitat quality (from 2000 to 2030)
图 10 因子交互探测结果（a.单因子探测结果；b.双因子2000年；c.双因子2005年；d. 双因子2010年；e.双因子2015年；f.双因子2020年）
Figure 10. Results of factor interaction detection (a.Univariate detection results; b.Two-factor interactions in 2000; c.Two-factor interactions in 2005; d.Two-factor interactions in 2010; e.Two-factor interactions in 2015; f.Two-factor interactions in 2020)
表 1 评价指标体系
Table 1. Assessment indicator system
评价方法 基础影像集 提取的指标因子 指标分辨率/m 数据源 InVEST模型
生境质量
模块土地利用数据 30 Jie Yang & Xin Huang. (2023). The 30 m annual land cover datasets and its dynamics in China from 1985 to 2022 [Data set]. In Earth System Science Data (1.0.2, Vol. 13, Number 1, pp. 3907−3925). Zenodo. https://doi.org/10.5281/zenodo.8176941 PLUS模型及
地理探测
器模型自然生
态数据高程(X1) 30 地理空间数据云 距水域距离(X2) 30 Open Street Map 坡度(X3) 30 地理空间数据云 年均气温(X4) 30 资源环境科学数据中心 年均降水(X5) 30 资源环境科学数据中心 距断层距离(X6) 30 资源环境科学数据中心 社会经
济数据土壤类型(X7) 1000 资源环境科学数据中心 距一级道路距离(X8) 30 Open Street Map 距二级道路距离(X9) 30 Open Street Map 距三级道路距离(X10) 30 Open Street Map 距乡政府距离(X11) 30 天地图 距县政府距离(X12) 30 天地图 人口密度(X13) 1000 资源环境科学数据中心 GDP水平 (X14) 1000 资源环境科学数据中心 
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表 2 威胁因子权重、最大影响距离、衰退类型
Table 2. Threat factor weights, maximum influence distance, and decay type
威胁因子 最大影响距离 权重 衰退类型 建设用地 8 km 1 指数 耕地 3 km 0.85 线性 未利用地 3 km 0.30 指数
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表 3 各土地利用/覆被对威胁因子的敏感度
Table 3. Sensitivity of land use/land cover types to threat factors
土地利用类型 HABITAT L_GD L_JSYD L_WLY 耕地 0.50 0.30 0.50 0.50 林地 1.00 0.80 0.75 0.80 草地 1.00 0.45 0.60 0.60 水域 0.75 0.75 0.60 0.40 建设用地 0.00 0.00 0.00 0.10 未利用 0.60 0.40 0.60 0.00
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