基于Logistic回归模型的陕西秦巴山区林地变化驱动力分析

doi:10.12302/j.issn.1000-2006.202009045

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (1): 106-114.doi: 10.12302/j.issn.1000-2006.202009045

基于Logistic回归模型的陕西秦巴山区林地变化驱动力分析

邓元杰¹(), 侯孟阳¹, 张晓¹, 贾磊¹, 李园园¹, 姚顺波¹^,^*(), 龚直文¹, 刘广全²

1.西北农林科技大学经济管理学院,西北农林科技大学资源经济与环境管理研究中心,陕西杨凌 712100
2.中国水利水电科学研究院,北京 100038

收稿日期:2020-09-21 接受日期:2020-12-02 出版日期:2022-01-30 发布日期:2022-02-09
通讯作者: 姚顺波
基金资助:
国家自然科学基金项目(71773091);国家重点研发计划(2016YFC0501602);西北农林科技大学经济管理学院研究生科技创新项目(JGYJSCXXM202002);陕西省社会科学基金项目(2020R053)

Drivers of forestland change in the Qinba Mountain region of Shaanxi based on the Logistic regression model

DENG Yuanjie¹(), HOU Mengyang¹, ZHANG Xiao¹, JIA Lei¹, LI Yuanyuan¹, YAO Shunbo¹^,^*(), GONG Zhiwen¹, LIU Guangquan²

1. College of Economics and Management, Northwest A & F University Centre for Resource Economic and Environmental Management, Northwest A & F University, Yangling 712100, China
2. China Institute of Water Resources and Hydropower Research, Beijing 100038, China

Received:2020-09-21 Accepted:2020-12-02 Online:2022-01-30 Published:2022-02-09
Contact: YAO Shunbo

摘要/Abstract

摘要：

【目的】探究陕西秦巴山区林地时空变化及其驱动因素,为林地保护和林地资源可持续利用提供依据。【方法】基于土地利用遥感监测数据,分析秦巴山区2000—2018年林地变化及其景观格局的时空特征;选择自然、社会经济和地理区位3类共12个影响因子,运用Logistic回归模型分别对秦巴山区林地增加和减少驱动因素进行分析。【结果】2000—2018年秦巴山区林地以净增加为主,林地净增加39 951.72 hm²,增加主要来源于21.83万hm²耕地转为林地,占林地转入总面积的94%。自然和地理区位因素是陕西秦巴山区林地转化的最主要驱动因素。海拔、到城镇的距离、年均气温倾向率、土壤有机质含量、坡度≥25°、到农村居民点的距离等是林地增加的主要驱动因子。海拔、坡度≥15°~25°、到城镇的距离、到道路的距离等则是林地减少的主要驱动因子。【结论】退耕还林等生态修复工程的有效实施是秦巴山区林地增加的重要原因,而林地减少则是在社会经济快速发展背景下,自然和地理区位因素共同作用的结果。

关键词: 林地变化, 驱动力, Logistic模型, 陕西秦巴山区

Abstract:

【Objective】 This study sought to clarify the spatio-temporal changes in forest land and its drivers in the Qinba Mountain region of Shaanxi Province, China, and provide basis for forest land protection and sustainable utilization of forest land resource. 【Method】 Using remote-sensing and monitoring data of land use, this study first analyzed the spatio-temporal characteristics of forest land changes and landscape patterns in the Qinba Mountain region from 2000 to 2018. Then, we selected three categories (i.e., natural, socio-economic and geographic) including 12 influencing factors to conduct driving force analysis of increase and decrease to forestland in the Qinba Mountain region via Logistic regression model. 【Result】 From 2000 to 2018, the forested land area primarily experienced a net increase, where the total increase was 39 951.72 hm²; this was mainly converted from cultivated land. Specifically, the conversion of cultivated land to forested land was 218 300 hm², accounting for 94% of the total forested land area. Natural and geographical drivers were the most important forces driving forested land change in the Qinba Mountain region. The dominant drivers of increase in forest land included factors such as altitude, distance to town, slope-temperature, soil organic matter content, slope ≥25°, and distance to rural residential areas. The dominant drivers for decreased forested land included factors such as altitude, slope of 15°-25°, distance to town, and distance to road. 【Conclusion】 Effective implementation of ecological restoration program such as the Grain for Green program is an important reason for the increase of forested land in the Qinba Mountain region. The decrease in forested land was caused by the combination of natural and geographic drivers in the context of a rapidly developing society and economy.

Key words: forestland change, drivers, Logistic model, Qinba Mountain region of Shaanxi Province

中图分类号:

邓元杰,侯孟阳,张晓,等. 基于Logistic回归模型的陕西秦巴山区林地变化驱动力分析[J]. 南京林业大学学报（自然科学版）, 2022, 46(1): 106-114.

DENG Yuanjie, HOU Mengyang, ZHANG Xiao, JIA Lei, LI Yuanyuan, YAO Shunbo, GONG Zhiwen, LIU Guangquan. Drivers of forestland change in the Qinba Mountain region of Shaanxi based on the Logistic regression model[J].Journal of Nanjing Forestry University (Natural Science Edition), 2022, 46(1): 106-114.DOI: 10.12302/j.issn.1000-2006.202009045.

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指标 driving force index	变量 variables	变量名称 variable name	类型 types of variables	单位或描述 units
被解释变量 dependent variable	年林地增加	inc-forest land	二分类	0,1
被解释变量 dependent variable	年林地减少	dec-forest land	二分类	0,1
自然驱动力 natural drivers	海拔	altitude	连续型	m
	坡度	slope	多分类	Ⅰ~Ⅴ
	土壤有机质含量	som	连续型	%
	年降雨倾向率	slope-precipitation	连续型	mm/a
	年均气温倾向率	slope-temperature	连续型	℃/a
社会经济驱动力 socio-economic drivers	年总人口密度变化	pop-density	连续型	人/km²
社会经济驱动力 socio-economic drivers	年地均GDP变化	per-GDP	连续型	万元/km²
地理区位驱动力 geographical location drivers	到道路的距离	dis-road	连续型	m
	到铁路的距离	dis-railway	连续型	m
	到城镇的距离	dis-city	连续型	m
	到农村居民点的距离	dis-residential	连续型	m
	到河流的距离	dis-river	连续型	m

驱动因子 driving factors	参数估计 parameter estimation (β)	标准误差 standard error	Wald χ²统计量 test statistic Wald χ²	显著性 sig.	发生比率 odds ratio (exp β)
常量constant	3.719	0.409	82.706	0.000^***	41.204
海拔altitude	-0.002	0.000	289.620	0.000^***	0.998
坡度slope	—	—	34.929	0.000	—
坡度 Ⅱ slopeⅡ	1.389	0.660	4.433	0.035^**	4.009
坡度 Ⅲ slopeⅢ	0.437	0.233	3.517	0.061^**	1.549
坡度 Ⅳ slope Ⅳ	0.230	0.099	5.389	0.020^**	1.259
坡度 Ⅴ slopeⅤ	0.318	0.076	17.698	0.000^***	1.375
土壤有机质含量soil organic matter content	0.045	0.014	10.184	0.001^***	1.046
年均气温倾向率slope-temperature	-30.825	4.885	39.813	0.000^***	0.000
年降雨倾向率 slope-precipitation	0.002	0.041	0.002	0.966	1.002
人口密度 pop-density	-51.493	29.630	3.020	0.082	0.000
地均GDP per-GDP	-0.000	0.000	0.061	0.805	1.000
到农村居民点的距离 dis-residential	0.000	0.000	12.195	0.000^***	1.000
到铁路的距离 dis-railway	-0.000	0.000	6.219	0.013^**	1.000
到河流的距离 dis-river	0.000	0.000	0.031	0.861	1.000
到道路的距离 dis-road	-0.000	0.000	10.376	0.001^***	1.000
到城镇的距离 dis-city	-0.000	0.000	48.052	0.000^***	1.000

驱动因子 driving factors	参数估计 parameter estimation (β)	标准误差 standard error	Wald χ²统计量 test statistic Wald χ²	显著性 sig.	发生比率 odds ratio (exp β)
常量constant	3.161	0.400	62.506	0.000	23.600
海拔altitude	-0.001	0.000	154.744	0.000^***	0.999
坡度slope	—	—	261.115	0.000^***	—
坡度 Ⅱ slopeⅡ	2.463	0.623	15.638	0.000^***	11.744
坡度 Ⅲ slopeⅢ	1.063	0.197	29.269	0.000^***	2.896
坡度 Ⅳ slopeⅣ	0.929	0.090	106.903	0.000^***	2.532
坡度 Ⅴ slopeⅤ	0.702	0.076	85.055	0.000^***	2.019
土壤有机质含量soil organic matter content	-0.025	0.015	2.734	0.098	0.976
年均气温倾向率slope-temperature	-15.320	5.167	8.791	0.003^**	0.000
年降雨倾向率slope-precipitation	-0.049	0.040	1.446	0.229	0.953
人口密度pop-density	-19.559	31.130	0.395	0.530	0.000
地均GDP per-GDP	-0.000	0.000	0.169	0.681	1.000
到农村居民点的距离dis-residential	0.000	0.000	7.870	0.005^**	1.000
到铁路的距离 dis-railway	0.000	0.000	2.664	0.103	1.000
到河流的距离 dis-river	-0.000	0.000	0.759	0.384	1.000
到道路的距离 dis-road	-0.000	0.000	9.419	0.002^**	1.000
到城镇的距离 dis-city	-0.000	0.000	31.302	0.000^***	1.000

基于Logistic回归模型的陕西秦巴山区林地变化驱动力分析

Drivers of forestland change in the Qinba Mountain region of Shaanxi based on the Logistic regression model

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