地理科学 ›› 2022, Vol. 42 ›› Issue (4): 589-601.doi: 10.13249/j.cnki.sgs.2022.04.004
陈万旭1,2,3(), 梁加乐4, 卞娇娇1,2, 曾杰1,2,*(
), 潘思佩4
收稿日期:
2021-01-19
修回日期:
2021-07-11
出版日期:
2022-04-10
发布日期:
2022-06-07
通讯作者:
曾杰
E-mail:cugcwx@sina.com;zengjie@cug.edu.cn
作者简介:
陈万旭(1989-),男,河南信阳人,副教授,博士,主要从事资源环境、区域经济研究。E-mail: cugcwx@sina.com
基金资助:
Chen Wanxu1,2,3(), Liang Jiale4, Bian Jiaojiao1,2, Zeng Jie1,2,*(
), Pan Sipei4
Received:
2021-01-19
Revised:
2021-07-11
Online:
2022-04-10
Published:
2022-06-07
Contact:
Zeng Jie
E-mail:cugcwx@sina.com;zengjie@cug.edu.cn
Supported by:
摘要:
基于2000年、2005年、2010年和2015年土地利用现状遥感监测数据,分别采用景观格局指数和当量因子法测度了黄河流域多尺度景观破碎化和土壤保持服务的时空特征,并且综合运用热点分析工具、普通最小二乘法和空间回归分析模型,探索了2000—2015年黄河流域多尺度土壤保持服务时空演变特征及景观破碎化对土壤保持服务的影响机制。结果如下:① 黄河流域土壤保持服务低值区主要集中分布在黄河流域上游青海-甘肃-宁夏-陕西-内蒙古沿线地区以及流域下游河南和山东,另外省会城市以及周边地区同样是土壤保持服务低值区,土壤保持服务具有显著的尺度依赖性;② 研究期间黄河流域土壤保持服务变化的热点区域主要分布在流域上游黄土高原地区,冷点区域主要分布在黄土高原的周边地区,不同尺度下土壤保持服务变化冷热点差异显著;③ 回归结果显示,经济社会因子与土壤保持服务具有显著的负相关;自然本底因子中海拔和林地面积比重与土壤保持服务具有显著的正相关;景观破碎化指数对土壤保持服务的影响差异性显著。未来黄河流域不同分区土壤保持和土地利用政策的制定不仅需要考虑自然本底和经济社会驱动因子,还应考虑多尺度景观格局破碎化以及空间溢出效应,跨区域协同治理对于黄河流域土地利用政策制定和生态系统保护具有重要现实意义。
中图分类号:
陈万旭, 梁加乐, 卞娇娇, 曾杰, 潘思佩. 黄河流域景观破碎化对土壤保持服务影响研究[J]. 地理科学, 2022, 42(4): 589-601.
Chen Wanxu, Liang Jiale, Bian Jiaojiao, Zeng Jie, Pan Sipei. Impact of Landscape Fragmentation on Soil Conservation Services in the Yellow River Basin[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(4): 589-601.
表1
县域尺度回归结果(样本数=461)
变量 | 2000年 | 2005年 | 2010年 | 2015年 | |||||||
OLS | SEMLD | OLS | SEMLD | OLS | SEMLD | OLS | SEMLD | ||||
注:研究采用queen’s contiguity构建空间权重矩阵。其中***P<0.001,**P<0.01,*P<0.05;括号内是标准差;LM为拉格朗日乘子法系数;PD ,斑块密度;ED,边缘密度;LSI,景观形状指数;ENN_MN,平均欧式最近邻域距离;CONTAG,蔓延度指数;COHESION,凝聚度指数;SPLIT,分离度指数;SHDI,香农多样性指数;—为无此项。 | |||||||||||
人口密度 | -0.632*** (0.138) | -0.004 (0.088) | -0.478*** (0.129) | 0.073 (0.082) | -0.255** (0.098) | 0.096 (0.068) | -0.157* (0.070) | 0.037 (0.047) | |||
建设用地面积比重 | -0.111 (0.068) | -0.295*** (0.039) | -0.154* (0.063) | -0.345*** (0.036) | -0.298*** (0.052) | -0.359*** (0.033) | -0.335*** (0.051) | -0.328*** (0.031) | |||
林地面积比重 | 0.931*** (0.025) | 0.745*** (0.019) | 0.872*** (0.024) | 0.703*** (0.019) | 0.857*** (0.022) | 0.710*** (0.018) | 0.924*** (0.025) | 0.771*** (0.019) | |||
PD | 0.193*** (0.049) | 0.090** (0.030) | 0.140** (0.048) | 0.084** (0.030) | 0.076 (0.043) | 0.070* (0.030) | 0.085 (0.048) | 0.086** (0.032) | |||
ED | 0.085* (0.034) | 0.085*** (0.024) | 0.181*** (0.034) | 0.102*** (0.024) | 0.180*** (0.030) | 0.114*** (0.023) | 0.233*** (0.033) | 0.139*** (0.025) | |||
LSI | -0.320*** (0.046) | -0.191*** (0.030) | -0.368*** (0.045) | -0.209*** (0.030) | -0.356*** (0.040) | -0.210*** (0.029) | -0.401*** (0.045) | -0.219*** (0.031) | |||
ENN_MN | -0.134** (0.051) | -0.010 (0.025) | -0.154** (0.050) | 0.001 (0.024) | -0.147** (0.045) | -0.013 (0.024) | -0.179*** (0.047) | -0.022 (0.025) | |||
CONTAG | 0.070 (0.036) | 0.017 (0.018) | 0.081* (0.039) | 0.006 (0.020) | 0.040 (0.034) | 0.005 (0.020) | 0.055 (0.036) | 0.008 (0.021) | |||
COHESION | 0.772*** (0.166) | 0.606*** (0.103) | 0.708*** (0.159) | 0.599*** (0.101) | 0.585*** (0.146) | 0.586*** (0.102) | 0.668*** (0.161) | 0.669*** (0.108) | |||
SPLIT | 0.068 (0.042) | 0.072** (0.023) | 0.012 (0.041) | 0.059* (0.024) | -0.013 (0.037) | 0.046* (0.023) | 0.026 (0.038) | 0.061** (0.023) | |||
SHDI | -0.204*** (0.037) | -0.043 (0.022) | -0.190*** (0.036) | -0.042 (0.022) | -0.189*** (0.032) | -0.047* (0.021) | -0.217*** (0.035) | -0.063** (0.023) | |||
高程 | 0.156*** (0.022) | 0.184*** (0.043) | 0.152*** (0.021) | 0.140*** (0.041) | 0.134*** (0.019) | 0.124*** (0.035) | 0.136*** (0.021) | 0.131*** (0.040) | |||
常数 | -0.393(0.182) | -0.407*** (0.114) | -0.342 (0.178) | -0.372** (0.114) | -0.162 (0.164) | -0.350** (0.114) | -0.248 (0.179) | -0.448*** (0.119) | |||
Spatial lag term | — | 0.128*** (0.038) | — | 0.105** (0.038) | — | 0.143*** (0.035) | — | 0.149*** (0.035) | |||
Spatial error term | — | 0.865*** (0.024) | — | 0.855*** (0.025) | — | 0.817*** (0.029) | — | 0.825*** (0.028) | |||
Diagnostics for spatial dependence(空间依赖性诊断) | |||||||||||
Moran’s I(error) | 0.582*** | 0.587*** | 0.543*** | 0.564*** | |||||||
LM(lag) | 249.618*** | 258.007*** | 215.834*** | 234.835*** | |||||||
Robust LM(lag) | 43.022*** | 44.147*** | 45.481*** | 47.434*** | |||||||
LM(error) | 397.112*** | 404.705*** | 345.976*** | 373.690*** | |||||||
Robust LM(error) | 190.517*** | 190.845*** | 175.622*** | 186.289*** | |||||||
LM(SARMA) | 440.135*** | 448.852*** | 391.456*** | 421.124*** | |||||||
Breusch-Pagan test | 41.833 | 23.397 | 42.873 | 29.057 | |||||||
Koenker-Bassett test | 36.289 | 19.411 | 28.816 | 22.003 | |||||||
Measures of fit | |||||||||||
Log likelihood | 580.225 | 831.697 | 589.445 | 835.721 | 641.987 | 855.742 | 586.895 | 813.407 | |||
Akaike information criterion | -1134.450 | -1635.390 | -1152.890 | -1643.440 | -1257.970 | -1683.480 | -1147.790 | -1598.810 | |||
Schwarz criterion | -1080.720 | -1577.530 | -1099.160 | -1585.570 | -1204.240 | -1625.620 | -1094.050 | -1540.950 | |||
R2 | 0.857 | 0.961 | 0.856 | 0.960 | 0.887 | 0.963 | 0.881 | 0.963 |
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