Habitat quality assessment and prediction in Suzhou based on CLUE-S and InVEST models
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摘要:
评估城市化进程中土地利用变化下的生境质量演变，对于构建综合生态规划体系、应对全球可持续发展的重大挑战具有重要意义。基于苏州市2010年和2018年遥感影像解译的土地利用数据和社会经济等数据，选取高程、坡度、到公路的距离、人口密度等驱动因子，采用CLUE-S模型模拟多情景下2030年土地利用变化，并通过生态系统服务评估与权衡（InVEST）模型评估过去并预测未来的生境质量时空演变，探讨土地利用变化对生境质量的影响。结果表明：最优拟合尺度为400 m×400 m，Kappa系数达到0.854 5，模型能够很好地模拟研究区未来的土地利用格局；苏州市2030年生态保护情景下的生境质量优于2018年，而2030年自然增长情景下的生境质量差于2010年和2018年；从空间分布看，苏州市中心城区、工业较集中和人口活动强度大的地区生境质量差，太湖周边等水系发达地区生境质量较好。因此，未来研究区在发展经济的同时，更要注重生态保护，实现高质量发展。
Abstract:Assessing the evolution of habitat quality under land use changes in the process of urbanization is of great significance for building a comprehensive ecological planning system and responding to major challenges in global sustainable development. Based on the land use data and socio-economic data interpreted by remote sensing images of Suzhou in 2010 and 2018, this paper selects driving factors such as elevation, slope, distance to highway, and population density, and uses the CLUE-S model to simulate land use changes in 2030 under multiple scenarios, and the InVEST model is used to assess and predict the spatial and temporal evolution of habitat quality in the past and future to explore the impact of land use change on habitat. The results showed that the optimal fitting scale was 400 m × 400 m, the Kappa coefficient reached 0.854 5, and the model was able to simulate the future land use cover pattern of the study area well. In terms of temporal distribution, the habitat quality under the ecological protection scenario in Suzhou in 2030 was the best, while the habitat quality under the natural growth scenario in 2030 was lower than that in 2010 and 2018. In terms of spatial distribution, the habitat quality in the central city of Suzhou, areas with higher concentration of industries and high intensity of population activities are poor, while the habitat quality in the areas with developed water systems such as around Taihu Lake is better. Therefore, the future research area should pay more attention to ecological protection and achieve high-quality development while developing the economy.
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Key words:
- CLUE-S model /
- InVEST model /
- scenario simulation /
- habitat quality /
- Suzhou City
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图 2 不同模拟尺度下各地类ROC变化
Figure 2. Changes in ROC of each land use type under different simulation scales
图 3 苏州市2018年实际土地利用与模拟土地利用对比
Figure 3. Comparison of actual land use and simulated land use in Suzhou City in 2018
图 4 苏州市2030年不同情景下土地利用模拟结果
Figure 4. Land use simulation results of different scenarios in Suzhou City in 2030
图 5 苏州市过去、现状和未来不同情景下生境质量空间分布
Figure 5. Spatial distribution of past, present and future habitat quality in Suzhou under different scenarios
表 1 各土地利用类型转换矩阵
Table 1. Transformation matrix of each land use type
土地利用类型 耕地 水域 建设用地 其他用地 耕地 1 1 1 1 水域 1 1 1 1 建设用地 1 1 1 1 其他用地 1 1 1 1 注：1表示可以相互转换。 
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表 2 胁迫因子的最大影响距离及其权重
Table 2. Maximum influence distance and weight of stress factors
胁迫因子 最大影响距离/km 权重 距离衰减函数 耕地 1 0.5 线性 建设用地 2.5 0.7 指数
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表 3 生境适宜度及其对不同胁迫因子的敏感度
Table 3. Habitat suitability and its sensitivity to different stress factors
土地利用类型 生境适宜度 敏感度 耕地 建设用地 耕地 0.5 0.3 0.5 水域 1.0 0.8 0.7 建设用地 0 0 0 其他用地 0.2 0.3 0.4
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表 4 土地覆被模拟结果与现状数据对比
Table 4. Comparison of land cover simulation results and current data
土地利用类型 模拟正确的栅格数 总栅格数 模拟正确格栅数占比/% 耕地 16 969 18 969 89.46 水域 17 276 19 190 90.03 建设用地 12 458 14 307 87.07 其他用地 1 502 1 644 91.36 合计 48 205 54 110 89.09
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表 5 2030年不同情景模拟下土地利用面积及占比
Table 5. Land use area and proportion under different scenarios in 2030
土地利用
类型2018年现状 2030年
自然增长情景2030年
生态保护情景面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 耕地 3 050.04 35.05 2 688.18 31.05 2 849.92 32.91 水域 3 070.40 35.46 3 040.57 35.12 3 068.96 35.44 建设用地 2 289.12 26.44 2 657.01 30.69 2 469.76 28.53 其他用地 263.04 3.05 271.84 3.14 268.96 3.12
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表 6 各等级生境质量面积与占比
Table 6. Area and proportion of each grade of habitat quality
等级 生境质量指数 2010年 2018年 2030年自然增长情景 2030年生态保护情景 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 差 0~0.2 1 045.60 12.08 2 548.64 29.44 2 503.20 28.92 2 007.36 23.18 较差 0.2~0.4 2 752.48 31.79 2 644.96 30.55 1 980.16 22.87 2 277.60 26.33 中等 0.4~0.6 2 159.20 24.93 914.56 10.56 462.40 5.34 1 425.60 16.46 较好 0.6~0.8 265.60 3.07 448.32 5.18 1 375.04 15.88 473.44 5.46 好 0.8~1.0 2 434.72 28.13 2 101.12 24.27 2 336.80 26.99 2 473.60 28.57
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