城市化背景下小微湿地景观动态变化及其驱动因素

小微湿地是指自然界在长期演变过程中形成的小型湿地。城市发展导致小微湿地大量消失,了解小微湿地景观动态变化特征及驱动因素是保护与管理小微湿地的重要基础。以合肥市包河区为研究区域,选取2006-2018年4期遥感影像,利用空间分析方法确定2006-2010年、2010-2014年、2014-2018年、2006-2018年4个时期小微湿地景观动态变化,基于300m×300m的网格单元,结合增强回归树和地理加权逻辑回归模型,分析13个预测变量与小微湿地损失之间的关系。结果表明：2006-2018年,小微湿地在整个研究区大范围减少,总面积下降了60.8%,斑块数量减少了60.5%,同时,小微湿地边缘复杂度降低,小微湿地间的空间距离增加,聚集程度降低。增强回归树模型显示,城市发展初期(2006-2010年),周边用地类型(建设用地、旱地、林地和草地)变化是导致小微湿地损失的主要因素,中后期(2010-2018年)各类型土地利用变化的相对影响有不同程度的下降,斑块面积和坡度对小微湿地损失的驱动作用逐渐凸显。2006-2018年,建设用地变化(14.4%)、斑块面积(13.5%)、旱地变化(11.1%)、坡度(10.1%)、林地变化(8.5%)、草地变化(7.0%)是导致小微湿地损失的高重要性变量。地理加权逻辑回归模型揭示了高重要性变量对小微湿地损失影响的空间非平稳性特征,结果显示,除斑块面积系数的空间可视化无解释意义,其余高重要性变量对小微湿地损失的影响随地点的变化,贡献的大小和方向也有所不同。研究方法和结果可以为城市快速发展地区小微湿地的保护与管理提供理论支持。

Dynamic changes in small wetland landscapes and their driving factors under the background of urbanization

The term "small wetland" refers to wetlands of small scale, which can stabilize ecological systems in the process of long-term development. Against the backdrop of rapid urbanization, many small wetlands have been disappearing, often in groups in cities. Understanding the dynamic changes and their driving factors in small wetlands provides an important basis for the effective protection and management of small wetlands. In this study, we took Baohe District in Hefei City as the research area and analyzed the landscape pattern characteristics by interpreting remote sensing data through time (2006, 2010, 2014, and 2018). The spatial analysis method was used to determine the dynamic changes in small wetland landscapes over four study periods of 2006-2010, 2010-2014, 2014-2018, and 2006-2018. Based on a 300 m×300 m grid unit, boosted regression tree and geographically weighted logistic regression models were employed to identify the relationships between 13 predictive variables and the loss of small wetlands. The results showed that the total area of small wetlands decreased by 60.8% and the number of patches decreased by 60.5% during the period from 2006 to 2018. At the same time, the extent and complexity of the perimeter of the small wetlands decreased, the spatial distance between the small wetlands increased, and the distribution tended towards more discrete. The boosted regression tree model quantified the relative influences of the predictive variables and determined high-importance variables to further analyze the nonlinear relationships between the variables and the decline in small wetlands. In the early stage of urbanization, changes in the surrounding land-use type were the major driving factors in the loss of small wetlands. In the middle and late stages of urbanization, both the trend in large-scale urban sprawl slowed down, and the relative importance of other land-use type changes also declined to varying extents. The driving factors of patch area and slope on the loss of small wetlands gradually increased in importance. Construction land changes (14.4%), patch area of small wetlands (13.5%), dryland changes (11.1%), slope (10.1%), forestland changes (8.5%), and grassland changes (7.0%) were high-importance variables of small wetland losses from 2006 to 2018. The local and spatial influences of these high-importance variables were analyzed further by geographically weighted logistic regression using coefficients determined at each sample point. With the exception that the spatial visualization of the small wetland patch areas had no explanatory significance, the influences of the remaining variables varied with location, and their contributions also differed in magnitude and direction. This study provides a reference for the protection and management of small wetlands in rapidly developing urban areas.