基于Fragstats4的景观格局指数与地表温度的相关性——以石家庄市为例

以石家庄市城市景观为研究对象,选取1987年、2004年和2019年Landsat遥感影像数据,采用监督分类方法将研究区分为绿地、水体、不透水地表和未利用地四类景观,采用单窗算法和劈窗算法反演地表温度(LST)。从景观生态学角度出发,利用Fragstats4.2计算4种类型的景观格局指数,并对景观粒度和移动窗口的尺度选择进行探讨分析;利用ArcGIS空间分析方法和统计分析方法,分析4种类型的景观格局指数与LST的相关性。结果表明:1987—2019年绿地斑块类型面积(CA)、最大斑块面积指数(LPI)和聚集度指数(AI)逐渐下降,不透水地表CA、LPI和AI逐渐增加,随着城市化进程的加快,绿地面积逐渐减少和裂化,绿地景观优势在不断下降,不透水地表面积在逐渐增加和聚合,不透水地表景观优势在不断加强,逐渐形成优势景观。斑块百分比指数(PLAND)、LPI和AI与LST表现出一致的极显著相关关系,绿地和水体为负相关,不透水地表和未利用地为正相关;斑块破碎化指数(SPLIT)则相反,绿地和水体为正相关,不透水地表和未利用地为负相关。LST与PLAND和LPI的相关系数明显高于LST与AI和SPLIT的相关系数,说明一个优势景观对地表温度的影响效果明显大于几个比较分散或破碎的景观。

A Fragstats4-Based Case Study of Correlation between Landscape Pattern Index and Surface Temperature in Shijiazhuang

Taking the urban landscape of Shijiazhuang as the research object, the Landsat remote sensing image data of 1987, 2004 and 2019 are selected, and the supervised classification method is used to distinguish the studied area into four types of landscapes: green land, water body, impervious surface, and unused land. The window algorithm and split window algorithm are used to invert land surface temperature (LST). From the perspective of landscape ecology, Fragstats4.2 is used to calculate the four types of landscape pattern indexes, and explore and analyze the landscape granularity and mobile window scale selection, using the ArcGIS spatial analysis method and statistical analysis method to analyze the four types of landscape correlation between pattern index and LST. The results show that, from 1987 to 2019, the green patch type area (CA), maximum patch area index (LPI) and aggregation index (AI) gradually decreased, and the CA, LPI and AI of impervious surface gradually increased. With the urbanization process, the area of green land gradually reduced and cracked, the advantage of green landscape was declining, the surface area of impervious land was gradually increasing and converging, and the advantage of impervious surface landscape was constantly strengthening, gradually forming an advantageous landscape. The Plaque percentage index (PLAND), LPI, AI and LST show a consistent and extremely significant correlation; green land and water are negatively correlated; impervious surface and unused land were positively correlated. The SPLIT index was the opposite, green lands and water bodies are positively correlated, and impervious surface and unused land are negatively correlated. The correlation coefficient of LST with PLAND and LPI is significantly higher than that with AI and SPLIT, indicating that the effect of a dominant landscape on the surface temperature is significantly greater than that of several more scattered or broken landscapes.