环巢湖地区多水塘景观时空格局演变特征及其驱动因素

快速城市化背景下,小型水体(人工、自然、半自然)景观正在大量消失,以环巢湖地区多水塘景观(水塘面积小于10 hm2)为例,基于遥感影像数据,综合运用RS/GIS技术和Fragstats 3.3软件对1989年、2000年和2016年3个年份环巢湖地区多水塘景观格局时空动态进行分析,运用地理探测器深入探讨多水塘景观面积变化驱动要素,对帮助理解多水塘景观演变带来的景观格局——过程关系及多水塘景观保护、利用和恢复等具有重要的现实意义。研究结果表明:(1) 1989—2016年间,农田景观面积比例呈下降趋势,表现的更加破碎,建设用地景观面积比例大幅度增加,森林绿地景观持续破碎化,水体景观面积比例下降,多水塘景观斑块数量、面积、斑块形状指数、最大斑块指数均呈下降趋势;(2)基于3 km×3 km网格单元多水塘景观时空演变特征分析表明,巢湖北岸多水塘景观集中在烔炀镇、黄麓镇,巢湖南岸多集中在白山镇、盛桥镇和槐林镇,这些地区也是多水塘景观格局变化最剧烈的地方;(3)基于地理探测器,揭示环巢湖地区多水塘景观用地变化的主要影响因子。因子探测结果表明,自然环境条件中的坡度因子q值最大,为0.545,其次为建设用地变化量、农田变化量、人口密度变化量和林地变化量等,交互探测结果表明,多水塘景观面积变化各因子交互作用后,对多水塘景观面积变化的影响显著增强,由此表现出多水塘景观变化影响要素的多样性和复杂性。

Spatial-temporal characteristics of multi-pond landscape change and their driving factors in the Chaohu Basin, China

Recently, the decrease in small pond landscapes due to rapid urbanization has been a concerning issue in China. In this paper, an integrated approach of remote sensing (RS), geographic information system (GIS) techniques, and statistical methods was employed to characterize the spatial-temporal dynamics of multi-pond landscape changes in the Chaohu Basin, an area that has seen rapid urbanization since the late 1980s. Landscape metrics were calculated to analyze the multi-pond landscape change associated with increased urbanization. The geographical detector method was also used to investigate the driving factors of changes in multi-pond landscapes. The results showed that the proportions of urbanized areas and forest landscape fragmentation increased rapidly in the study area, and the proportions of farmland and water bodies decreased. Significantly, the research revealed the overall decreases in multi-pond landscape numbers and pond densities, but also increasing multi-pond landscape fragmentation. Based on a 3 km×3 km grid, spatial unit analyses showed that the multi-pond landscapes were concentrated in the towns of Tongyang and Huanglu on the north bank of Chaohu Lake, and the towns of Baishan, Shengqiao, and Huai Lin on the south bank of Chaohu Lake. Results from factor detectors showed that the slope factor had the greatest effect on the change in multi-pond landscapes, followed by changes in the construction land factor, farmland factor, population density factor, and forest land factor. Results from the ecological detector disclosed that there were significant differences in the formation mechanisms of the multi-pond landscape change, and after the interaction of each factor, the change in multi-pond landscapes was significantly enhanced, which showed driving factors were various and complex in their contributions toward the change in multi-pond landscapes.