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安徽省土地利用/覆被时空变化及其驱动因素分析
李长爱, 刘玲, 邱冰, 聂存明, 宁丽丽, 李亚亮, 王慧, 刘星宇, 杨素慧
南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (5) : 213-223.
PDF(2821 KB)
PDF(2821 KB)
安徽省土地利用/覆被时空变化及其驱动因素分析
Spatial-temporal change and driving factors of land use/cover in Anhui Province
【目的】 研究安徽省土地利用/覆被变化(LUCC)及其驱动因素,为安徽省国土资源利用与管理提供理论参考。【方法】 基于安徽省1995—2020年6期30 m分辨率的Landsat系列遥感影像土地利用/覆被分类数据,利用土地利用/覆被类型结构与空间分布、转移矩阵、动态变化及土地利用程度综合指数和其聚类,分析安徽省土地利用/覆被类型时空变化特征;选取13个影响LUCC的驱动因子,采用主成分分析法(PCA)进行定量分析。【结果】 1995—2020年安徽省土地利用/覆被类型面积耕地排首位,其次是林地、建设用地;25年间耕地、林地、草地分别减少了3 826.6、164.7和78.5 km2,建设用地、水域、未利用地分别增加了3 895.9、160.9、16.2 km2,耕地与建设用地相互转移面积最大。2005—2015年耕地、林地、草地减少与建设用地增加的趋势最明显,2015—2020年建设用地转移为耕地的面积大增。从土地利用程度综合指数变化率可知,25年间经济发展较快的合肥市土地利用综合指数变化率(2.18%)、芜湖市(1.60%)及矿业城市铜陵市(3.83%)、淮北市(1.60%)等高于以山区、丘陵地形为主的黄山市(0.42%)、六安市(0.66%)等。2020年各市土地利用程度综合指数聚类结果分为3类:亳州市、阜阳市等土地开发利用程度最高,为第1类;滁州市、马鞍山市等中等,为第2类;池州市、黄山市等最低,为第3类。单一与综合LUCC动态度显示的趋势为2005年前较小,2005—2015年较大,2015年后又变小。除城镇化速度、经济发展、人民生活水平提高外,2015年以后的政策因素也是安徽省LUCC的主要驱动力。【结论】 随着安徽省经济、社会的发展,LUCC急剧增大,2005—2015年间有2 926.1 km2耕地被建设用地侵占。2015年后,得益于相关土地利用与生态政策的推行,LUCC综合动态度减小至0.05%,但仍存在耕地减少、建设用地增加的情况。因此,今后应根据安徽省LUCC特征及经济、社会、自然资源具体情况,深入实施基本农田保护及耕地占补平衡等合理利用开发土地资源的政策,促进安徽省可持续发展。
【Objective】The study aims to determine land-use/cover changes (LUCC) and their driving factors in Anhui Province, and provide theoretical reference data for the use and management of land and resources in Anhui Province. 【Method】Based on the classification data of Landsat series remote sensing images with a resolution of 30 m in Anhui Province, the spatial and temporal changes of land use/cover types in Anhui Province in a 25 year period from 1995 to 2020 were analyzed using the structure, spatial distribution, transfer matrix, dynamic change of land use/cover types, and comprehensive index of land use degrees, as well as its cluster analysis. The principal component analysis(PCA) was used to analyze the 13 factors that affect LUCC. 【Result】The cultivated land ranked first, followed by forest land and construction land in Anhui Province from 1995 to 2020. Cultivated land, forest land and grassland decreased by 3 826.6, 164.7 and 78.5 km2, respectively, while the construction land, water area and unutilized land increased by 3 895.9, 160.9 and 16.2 km2, respectively. The transfer area between the cultivated and constructed land was the largest. From 2005 to 2015, the decrease in the cultivated land, forest land and grassland and the increase in construction land showed the most obvious trends. From 2015 to 2020, the area of construction land converted to the cultivated land increased significantly. The change rates of the comprehensive index of land use degrees over 25 years in Hefei (2.18%) and Wuhu (1.60%), and the mining cities of Tongling (3.83%) and Huaibei (1.60%) were higher than those in Huangshan (0.42%) and Lu’an (0.66%), which feature mainly mountainous and hilly terrains. The results of the comprehensive index clustering analysis of the degree of land use in each city in 2020 were divided into three categories. The first category was Bozhou and Fuyang, which had the highest degrees of land development and utilization. The second category was Chuzhou, Ma’anshan, and other medium. The third category was Chizhou, Huangshan, and so on, which was the lowest. The dynamic attitude of the single and comprehensive LUCC was small before 2005, large from 2005 to 2015, and then decreased again after 2015. The results showed that the urbanization speed, economic development, and improvement in living standards and policy factors after 2015 were also the main driving forces of the LUCC in Anhui Province. 【Conclusion】With the economic and social development of Anhui Province from 2005 to 2015, LUCC increased sharply, and 2 926.1 km2 of the cultivated land was occupied by the construction land. After 2015, the implementation of relevant land use and ecological policies resulted in the reduction of the integrated dynamic degree of the LUCC to 0.05%. However, there was still a decrease in the cultivated land and an increase in the construction land. Therefore, in the future, according to the LUCC characteristics and the specific situation of the economy, society, and natural resources of Anhui Province, policies for the rational utilization and development of land resources, such as the basic farmland protection and the balance of farmland occupation and compensation, should be implemented to promote the sustainable development of Anhui Province.
land use/cover change (LUCC) / transfer matrix / driving factor / Anhui Province
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以甘肃省为例，在基于Google Earth Engine (GEE)平台实现1995年、2000年、2005年、2010年、2015年和2020年土地变化监测的基础上，利用贝叶斯层次时空模型(BHM)分析土地利用程度的时空变化特征。结果表明：① 研究期间内甘肃省土地利用程度呈增长趋势，其中1995―2000年和2010―2015年增长速度较明显；② 土地利用程度空间格局“东高西低”，热点区域主要分布在陇中、陇东和陇南地区；③ 土地利用程度局部变化呈现明显区域差异，整体表现为“东弱西强”，局部变化热点区域主要分布在河西地区；④ 影响土地利用程度变化的主要因素是经济规模和产业结构，其中经济因素影响程度最高。
Land use change has always been an important content of global change research. An in-depth understanding of the temporal and spatial characteristics of land use change can not only provide a direct decision-making basis for the optimal allocation of land resources, but also provide important data support for regulating ecosystem management and improving human social well-being. However, previous studies on land use change lack the analysis of the spatio-temporal coupling process. Taking Gansu Province as an example, based on the Google Earth Engine (GEE) platform to achieve land change monitoring in 1995, 2000, 2005, 2010, 2015 and 2020, and uses Bayesian hierarchical spatio?temporal model (BHM) to analyze the characteristics of temporal and spatial changes of land use degree. The results show that: 1) During the study period, the land use degree of Gansu Province showed an increasing trend, among which the growth rate was obvious from 1995 to 2000 and 2010 to 2015; 2) The spatial pattern of land use degree is “high in the east and low in the west”, hot spots mainly distributed in Longzhong, Longdong and Longnan regions; 3) The local changes of land use degree show obvious regional differences, and the overall performance is “weak in the east and strong in the west”. The hot spots of local changes are mainly distributed in the Hexi region; 4) The main factors affecting changes of land use degree are economic scale and industrial structure, among which economic factors have the highest degree of influence. |
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钟凯文, 孙彩歌, 解靓. 基于GIS的广州市土地利用遥感动态监测与变化分析[J]. 地球信息科学学报, 2009, 11(1):111-116.
以1997、2003年广州市的Landsat TM影像为基本数据源,在遥感和地理信息系统处理软件的支持下,经过几何纠正、图像裁剪和图像增强处理后,采用最大似然比法结合人工目视解译对影像进行了识别分类,获得了广州市两个时相的土地利用分类数据,并进行了分类精度评价.然后建立了土地利用转移矩阵,并利用数值统计的方法,从不同角度分析了1997～2003年广州市土地利用变化的过程,包括土地利用变化的数量、速度、空间转移及其区域差异等.另外,经济发展、人口的增长、城市化建设等因素是广州市土地利用变化的主要驱动力.
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陈亚军, 付保红, 邱玥. 基于RS-GIS的昆明市土地利用变化及驱动力分析[J]. 云南地理环境研究, 2018, 30(6):65-71.
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赵慧, 汪云甲. 影响ETM影像土地利用/覆盖分类精度因素的研究[J]. 遥感技术与应用, 2012, 27(4):600-608.
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