Analysis of spatiotemporal heterogeneity of habitat quality and their driving factors based on optimal parameters-based geographic detector for Fuzhou City, China
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摘要:
揭示生境质量的时空演变特征和影响因素可为区域可持续发展提供参考依据。基于2000年、2010年和2020年的土地利用数据，运用InVEST模型、土地利用转移矩阵和参数最优地理探测器等方法，综合分析福州市生境质量时空演变及其驱动因素。结果表明：福州市2000年、2010年和2020年生境质量指数分别为0.812、0.806和0.793，生境质量改善的面积小于生境质量退化的面积，福州主城区和东南沿海区域的生境状况亟须改善。3 km网格为本研究的最佳空间尺度，最佳数据离散化分类数为6，自然间断点法更能解释驱动因素的驱动程度。高程、坡度和夜间灯光是福州市生境质量空间分异的主要影响因素，坡度和夜间灯光的交互作用对生境质量变化的解释力最强。
Abstract:Revealing the spatiotemporal evolution characteristics of habitat quality and their influencing factors can provide reference basis for regional sustainable development. Based on the land use data in 2000, 2010 and 2020, InVEST model, landuse transfer matrix and optimal parameters-based geographical detector (OPGD) were used to comprehensively analyze the spatiotemporal evolution and driving factors of habitat quality in Fuzhou City. The results revealed that the habitat quality index of Fuzhou City in 2000, 2010 and 2020 was 0.812, 0.806 and 0.793, respectively. The area of habitat improvement was less than the area of habitat deterioration. Habitat conditions in the main urban area of Fuzhou and the southeast coastal region were in urgent need of improvement. 3 km was the best optional spatial unit for this study, the optimal data discretization classification number is 6, and the natural break method better explained the explanatory power of the driving factors. Elevation, slope and night-time lights were the main influencing factors of spatial variation in habitat quality in Fuzhou City, and the interaction of slope and nighttime lights had the strongest explanation for habitat quality changes.
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图 2 2000—2020年福州市的生境质量分布
Figure 2. Habitat quality distribution of Fuzhou City from 2000 to 2020
图 3 2000—2020年福州市生境质量变化的空间分布
Figure 3. Spatial distribution of changes in the habitat quality of Fuzhou City from 2000 to 2020
图 6 生境质量指数在各驱动因素上的分区统计数据
Figure 6. Statistical data of habitat quality in each driving factor
表 1 福州市土地二级分类体系
Table 1. Two-level land classification system of Fuzhou City
一级类型 二级类型 一级
类型二级类型 耕地 水田、旱地 水域 河渠、湖泊、水库、
滩涂、滩地林地 有林地、灌木林、疏林地、
其他林地建设
用地城镇用地、农村居民点、其他建设用地 草地 高覆盖度草地、中覆盖度草地、低覆盖度草地 未利用土地 裸地、裸岩石质地 
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表 2 威胁因子的最大影响距离及其权重
Table 2. Threat factors and its maximum influence distance and weight
威胁因子 最大影响距离/km 权重 衰减类型 水田 8 0.7 指数 旱地 8 0.5 线性 城镇用地 10 1 指数 农村居民点 5 0.7 指数 其他建设用地 10 1 指数
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表 3 各土地利用类型的生境适宜度及其对威胁因子的敏感性
Table 3. Habitat suitability of each land use type and its sensitivity to threat factors
一级地类 二级地类 生境适
宜度水田 旱地 城镇
用地农村居
民点其他建设
用地耕地 水田 0.4 0.3 0.3 0.5 0.4 0.1 旱地 0.6 0.3 0.3 0.5 0.4 0.1 林地 有林地 1 0.8 0.8 0.95 0.85 0.6 灌木林 1 0.4 0.4 0.6 0.45 0.2 疏林地 1 0.9 0.9 1 0.9 0.65 其他林地 1 0.9 0.9 1 0.9 0.65 草地 髙覆盖度草地 0.8 0.4 0.4 0.6 0.5 0.2 中覆盖度草地 0.7 0.45 0.45 0.65 0.55 0.25 低覆盖度草地 0.6 0.55 0.55 0.7 0.6 0.3 水域 河渠 1 0.7 0.7 0.9 0.8 0.5 湖泊 1 0.7 0.7 0.9 0.8 0.5 水库 1 0.7 0.7 0.9 0.8 0.6 滩涂 0.6 0.8 0.8 0.9 0.8 0.55 滩地 0.6 0.75 0.75 0.9 0.8 0.55 建设用地 城镇用地 0 0 0 0 0 0 农村居民点 0 0 0 0 0 0 其他建设用地 0 0 0 0 0 0 未利用土地 裸地 0 0 0 0 0 0 裸岩石质地 0 0 0 0 0 0
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表 4 驱动因素栅格数据介绍
Table 4. Introduction to driver factor raster data
维度 因素 分辨率/m 数据来源 自然环境 高程(X1) 30 www.gscloud.cn 坡度(X2) 30 年均温度(X3) 1 000 data.cma.cn 年均降水量(X4) 1 000 社会经济因素 夜间灯光(X5) 1 000 www.resdc.cn 人口数量(X6) 100 www.worldpop.org GDP(X7) 1 000 www.resdc.cn 区位条件因素 与行政中心距离(X8) 30 www.resdc.cn 与一级道路距离(X9) 30 与二级道路距离(X10) 30 与三级道路距离(X11) 30 与四级道路距离(X12) 30
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表 5 2000—2020年福州市生境质量等级和面积统计
Table 5. Statistics of habitat quality grade and area of Fuzhou City from 2000 to 2020
生境质量等级 2000年 2010年 2020年 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 低[0，0.2) 572.33 4.94 740.74 6.39 952.22 8.22 较低[0.2，0.4） 1612.24 13.92 1493.44 12.89 1417.19 12.23 中等[0.4，0.6） 679.49 5.87 622.37 5.37 610.90 5.27 较高[0.6，0.8） 1486.70 12.83 1457.08 12.58 1445.37 12.48 高[0.8，1] 7233.67 62.44 7270.80 62.77 7158.75 61.80 生境质量指数 0.812 0.806 0.793
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表 6 2000—2020年福州市生境质量等级变化
Table 6. Changes in the habitat quality grade of Fuzhou City from 2000 to 2020
生境质量
变化趋势2000—2010年 2010—2020年 2000—2020年 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 改善 404.40 3.49 359.13 3.10 469.16 4.05 稳定 10663.69 92.05 10683.45 92.22 10359.61 89.43 退化 516.34 4.46 541.85 4.68 755.66 6.52
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表 7 驱动因素q和90%分位数的空间单元尺度效应比较
Table 7. Comparison of size effects of spatial units for q values and 90% quantile of driving factors
驱动因素 0.5 km 1 km 1.5 km 2 km 2.5 km 3 km 3.5 km 4 km X1 0.457 0.516 0.559 0.579 0.584 0.599 0.591 0.572 X2 0.522 0.569 0.593 0.604 0.605 0.630 0.601 0.603 X3 0.225 0.254 0.272 0.277 0.269 0.279 0.259 0.277 X4 0.103 0.131 0.153 0.162 0.182 0.192 0.190 0.212 X5 0.321 0.376 0.423 0.447 0.459 0.486 0.495 0.503 X6 0.086 0.106 0.120 0.135 0.142 0.165 0.172 0.184 X7 0.104 0.122 0.131 0.141 0.147 0.160 0.157 0.182 X8 0.074 0.087 0.092 0.090 0.076 0.082 0.082 0.082 X9 0.106 0.124 0.137 0.142 0.150 0.150 0.162 0.173 X10 0.080 0.089 0.096 0.099 0.091 0.099 0.096 0.094 X11 0.031 0.035 0.041 0.037 0.044 0.060 0.060 0.075 X12 0.037 0.053 0.070 0.083 0.120 0.109 0.113 0.144 q的90%
分位数0.443 0.502 0.545 0.566 0.572 0.588 0.581 0.565
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表 8 单因子探测结果
Table 8. Results of single factor detection
因素 q 排序 因素 q 排序 X1 0.599 2 X7 0.160 7 X2 0.630 1 X8 0.082 11 X3 0.279 4 X9 0.150 8 X4 0.192 5 X10 0.099 10 X5 0.486 3 X11 0.060 12 X6 0.165 6 X12 0.109 9
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