基于空间自相关的区域农地变化驱动力研究——以珠三角地区为例

doi:10.18402/resci.2016.04.13

摘要/Abstract

摘要：

近年来,随着社会经济的快速发展,农地保护工作压力逐渐增大,掌握区域农地变化规律及其驱动力是制定可持续土地利用政策的基础。本文以珠三角地区为例,利用2000年和2010年土地利用遥感监测数据、基础地理空间数据和统计年鉴数据,分析了该区农地变化特征,然后在构建传统Logistic模型基础上引入空间自相关因子,采用AutoLogistic模型从自然、社会经济、空间距离和土地利用特征的空间自相关性等方面探讨了区域尺度农地变化驱动力。结果表明:①2000-2010年该区耕地、林地等农地以净减少为主,景观破碎度有所提高,耕地成为建设用地扩张的主要来源;②农地属性变化和土地开发强度的空间自相关性均为农地变化的重要驱动力,其他驱动力中耕地变化主要受地均GDP变化、到最近铁路的距离、总人口密度变化、年日照时数倾向率等因子影响,林地变化主要受地均GDP变化、总人口密度变化、坡度、到最近道路等因子影响;③同传统Logistic模型相比,采用能反映土地利用特征空间自相关性的AutoLogistic模型更适用于区域农地变化驱动力研究。

关键词: 农地变化, 驱动力, 空间自相关, Logistic模型, AutoLogistic模型, 珠三角地区

Abstract:

Land use/cover change（LUCC）is at the core of global change and agricultural land change is important in the study of LUCC. With socio-economic development,pressure on agri-cultural land protection has been increasing in China. To develop sustainable land use policy, we need to understand regional agricultural land changes and driving forces. Using land use monitoring data,basic geographic spatial data and statistical yearbooks,we analyzed characteristic of agricultural land change in the Pearl River Delta Area,China. Traditional Logistic modeling and AutoLogistic modeling which bring in spatial autocorrelation were compared to investigate the drivers of agricultural land change at regional scale from the perspectives of natural,socio-economic,spatial distance and spatial autocorrelation of land use characteristics. We found that cultivated land and forest in this region decreased from 2000 to 2010,leading to further fragmentation. Cultivated land has become a main source of expansion of construction land. And,both spatial autocorrelation of agricultural land property and land development intensity are important driving forces of agricultural land change. As for other driving forces,cultivated land change was mainly affected by factors such as ‘change of per kilometer GDP',‘distance to nearest railway',‘change of total population density',‘tendency rate of annual sunshine hours'. Forest changes were mainly affected by ‘change of per kilometer GDP',‘change of total population density',‘slope' and ‘distance to nearest road'. Compared with traditional Logistic modeling,the AutoLogistic model is more suited to study driving forces of regional agricultural land change.

Key words: agricultural land change, driving forces, spatial autocorrelation, Logistic model, Autologistic model, Pearl River Delta Area

曹祺文, 吴健生, 仝德, 张晓娜, 卢志强, 司梦林. 基于空间自相关的区域农地变化驱动力研究——以珠三角地区为例[J]. 资源科学, 2016, 38(4): 714-727.

CAO Qiwen,WU Jiansheng,TONG De,ZHANG Xiaona,LU Zhiqiang,SI Menglin. Drivers of regional agricultural land changes based on spatial autocorrelation in the Pearl River Delta,China[J]. Resources Science, 2016, 38(4): 714-727.

图/表 7

图1

表1

表2

表3

表4

表5

珠三角地区耕地变化驱动力回归模型对比"

解释变量	Logistic					AutoLogistic
解释变量	参数β值估计	标准误差 SE	检验统计量 Wald χ²	Pr>χ²	发生比率	参数β值估计	标准误差 SE	检验统计量 Wald χ²	Pr>χ²	发生比率
θ_preciptation	-0.000 14	0.000 6	0.063	0.802	0.999 8	0.001 02	0.000 6	2.541	0.111	1.001 0
θ_temperature	-2.332 41**	0.862 8	7.307	0.007	0.097 1	-0.754 10	0.919 1	0.673	0.412	0.470 4
θ_sunshine	-0.003 26*	0.001 3	6.072	0.014	0.9967	-0.003 46*	0.001 4	5.739	0.017	0.996 5
Elevation	-0.003 18	0.001 6	3.720	0.054	0.996 8	-0.000 71	0.001 5	0.212	0.645	0.999 3
Slope	-0.007 75	0.015 4	0.253	0.615	0.992 3	-0.019 39	0.015 9	1.489	0.222	0.980 8
Aspect	0.104 53	0.057 0	3.366	0.067	1.110 2	0.131 29*	0.060 3	4.744	0.029	1.140 3
OM	-0.002 39*	0.001 0	5.610	0.018	0.997 6	-0.001 23	0.001 0	1.463	0.226	0.998 8
Pop_density	0.000 60	0.000 3	3.442	0.064	1.000 1	0.000 09*	0.000 0	6.265	0.012	1.000 1
Rural_pop_density	0.000 29	0.000 2	2.793	0.095	1.000 3	0.000 21	0.000 2	1.260	0.262	1.000 2
Invest	-0.000 10**	2.79E-8	7.320	0.007	0.999 9	-0.000 01*	2.88E-8	5.274	0.022	0.999 9
Power	-0.000 10	0.51E-8	2.941	0.086	0.999 0	0.000 01	0.58E-8	1.933	0.164	1.000 1
GDP	0.000 08**	0.04E-8	362.849	0.000	1.001 0	0.000 06**	0.05E-8	159.412	0.000	1.000 1
DIS2center	0.000 04	0.000 1	3.189	0.074	1.000 1	0.000 04	0.000 1	2.282	0.131	1.000 1
DIS2road	-0.000 90**	0.000 2	9.095	0.003	0.999 0	-0.000 04	0.000 1	2.003	0.157	0.999 5
DIS2railway	-0.000 40**	0.05E-8	63.836	0.000	0.999 9	-0.000 02**	0.05E-8	21.539	0.000	0.999 9
DIS2residential	0.000 01	0.000 1	0.447	0.504	1.000 1	0.000 02	0.000 1	1.229	0.268	1.000 1
DIS2river	-0.000 01	0.000 1	0.721	0.396	0.999 9	0.000 01	0.000 1	0.612	0.434	1.000 1
Lag_crop						2.361 23**	0.158 9	220.566	0.000	10.604 0
Lag_light						0.008 39*	0.003 9	4.435	0.035	1.008 4
常数Constant	-1.702 33**	0.409 7	17.267	0.000	0.182 2	-2.739 45**	0.439 7	38.815	0.000	0.064 6
模型参数	LR χ²（17）=1 473.39 P=0.000 ROC=0.841 0 预测正确率PCP=78.72%					LR χ²（19）=1 785.98 P=0.000 ROC=0.8 693 预测正确率PCP=80.30%

表5

表6

珠三角地区林地变化驱动力回归模型对比"

解释变量	Logistic					AutoLogistic
解释变量	参数β值估计	标准误差 SE	检验统计量 Wald χ²	Pr>χ²	发生比率	参数β值估计	标准误差 SE	检验统计量 Wald χ²	Pr>χ²	发生比率
θ_preciptation	-0.001 04	0.000 9	1.233	0.267	0.998 9	-0.000 27	0.001 1	0.064	0.801	0.999 7
θ_temperature	-2.335 57	1.447 0	2.607	0.106	0.096 8	-1.134 94	1.507 7	0.567	0.452	0.321 4
θ_sunshine	-0.000 57	0.001 9	0.094	0.760	0.999 4	0.000 16	0.001 9	0.007	0.935	1.000 2
Elevation	-0.001 37	0.001 2	1.284	0.257	0.998 6	-0.001 62	0.001 2	1.705	0.192	0.998 4
Slope	-0.070 50**	0.016 7	17.778	0.000	0.931 9	-0.063 08**	0.017 2	13.497	0.000	0.938 9
Aspect	-0.039 22	0.114 4	0.118	0.732	0.961 5	-0.080 62	0.120 4	0.448	0.503	0.922 5
OM	-0.003 00	0.001 6	3.394	0.065	0.997 0	-0.002 55	0.001 6	2.390	0.122	0.997 5
Pop_density	0.000 40**	0.000 1	16.222	0.000	1.000 4	0.000 37**	0.000 1	13.079	0.000	1.000 3
Rural_pop_density	0.000 20	0.005 0	0.163	0.686	1.000 2	0.000 28	0.000 5	0.265	0.607	1.000 2
Invest	-8.83E-8	5.69E-8	2.409	0.121	0.999 9	-9.75E-8	5.64E-8	2.985	0.084	0.999 9
Power	-0.04E-8**	0.01E-8	9.064	0.003	0.999 9	-0.01E-8	0.01E-8	1.057	0.304	0.999 9
GDP	0.000 11**	0.000 1	113.823	0.000	1.000 1	0.000 10**	0.000 1	86.566	0.000	1.000 1
DIS2center	-0.08E-8	0.000 1	0.033	0.855	0.999 9	-0.000 03	0.000 1	0.484	0.487	0.999 9
DIS2road	-0.000 15**	0.000 1	8.119	0.004	0.999 8	-0.000 11*	0.000 1	4.085	0.043	0.999 8
DIS2railway	-0.000 03**	-0.08E-8	12.458	0.000	0.999 9	-0.000 02*	0.08E-8	3.954	0.047	0.999 9
DIS2residential	-0.000 03	0.000 1	1.052	0.305	0.999 9	0.000 01	0.000 0	0.179	0.672	1.000 1
DIS2river	-0.000 02	0.000 1	0.726	0.394	0.999 9	-0.09E-8	0.000 1	0.091	0.763	0.999 9
Lag_forest						3.046 55**	0.344 2	78.340	0.000	21.042 6
Lag_light						-0.005 87*	0.002 6	4.957	0.026	0.994 1
常数Constant	-0.436 20	0.624 6	0.488	0.485	0.646 5	-1.172 19	0.657 0	3.183	0.074	0.309 7
模型参数	LR χ²（17）=1 190.91;P=0.000 ROC=0.937 1;预测正确率PCP=90.20%					LR χ²（19）=1 273.22;P=0.000 ROC=0.949 5;预测正确率PCP=91.00%

表6

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	变量	变量名称	单位或描述
因变量	2000-2010年耕地变化	Culti_land_change	0,1
	2000-2010年林地变化	Forest_change	0,1
自然驱动力	年降水量倾向率	θ_preciptation	mm/10a
	年均温倾向率	θ_temperature	℃/10a
	年日照时数倾向率	θ_sunshine	h/10a
	高程	Elevation	m
	坡度	Slope	°
	坡向	Aspect	[-1,1]
	土壤有机质含量	OM	%
社会经济驱动力	2000-2010年总人口密度变化	Pop_density	人/km²
	2000-2010年农村人口密度变化	Rural_pop_density	人/km²
	2000-2010年固定资产投资变化	Invest	万元
	2000-2010年农机总动力变化	Power	kW
	2000-2010年地均GDP变化	GDP	万元/km²
空间距离驱动力	到最近乡镇中心的距离	DIS2center	km
	到最近道路的距离	DIS2road	km
	到最近铁路的距离	DIS2railway	km
	到最近农村居民点的距离	DIS2residential	km
	到最近河流的距离	DIS2river	km
土地利用特征的空间自相关性	耕地属性变化空间滞后	Lag_crop
	林地属性变化空间滞后	Lag_forest
	土地开发强度空间滞后	Lag_light

	总面积/km²		转出面积 /m²	转入面积 /m²	净变化 /km²	土地利用动态度 /%
	2000年	2010年	转出面积 /m²	转入面积 /m²	净变化 /km²	土地利用动态度 /%
耕地	14 458	12 698	1 779	19	-1 760	-1.10
林地	29 779	29 241	690	152	-538	-0.16

	平均斑块面积/km²		平均斑块周长/km		破碎度		面积加权平均斑块分维数
	2000年	2010年	2000年	2010年	2000年	2010年	2000年	2010年
耕地	5.382 7	4.457 8	10.383 4	9.244 2	0.165 8	0.186 8	1.193 6	1.158 6
林地	22.995 3	23.132 8	20.261 0	20.126 4	0.071 7	0.074 3	1.221 3	1.208 5