全球及区域气候变化和人类活动是导致荒漠化的主要原因，土地荒漠化已成为人类必须面对的重要生态环境问题，它严重制约着区域生态平衡及人类经济社会稳定。我国北方地区荒漠化过程明显，在全球气候变暖大背景下开展该区域荒漠化遥感动态监测及其驱动因素分析，对于科学认识我国北方荒漠化过程及其时空格局和调整防治政策与措施具有重要的理论与现实意义。

    本文以中国北方为研究区域，以2000~2020年MODIS 09A1数据为基础，分析了不同土地荒漠化遥感监测模型对研究区的适用性，寻找最佳监测模型对我国北方地区土地荒漠化时空变化和驱动力进行研究；此外，基于GIS地理统计分析方法，从时间和空间维度综合分析土地荒漠化的变化特征、空间分布格局与重心演变规律；最后，结合气候、自然地理和社会经济三个方面11种因素，应用Pearson相关系数法、地理探测器和地理加权回归模型探讨影响土地荒漠化时空变化的关键因素及作用效果。本文的主要研究结论如下：

（1）基于Thornthwaite公式结合地面气象站点数据确定了我国北方地区荒漠化的潜在发生范围，为后续荒漠化监测体系的建立提供理论依据。本文根据计算的湿润指数进行荒漠化气候区划发现，2000~2020年间，各气候类型面积占比均呈现减少趋势，其中以半干旱区面积减少较为显著，其平均增长率为-0.302/a；在各气候区划类型中，亚湿润干旱区面积占比最小，其次是干旱区，半干旱区面积占比最大；截至到2020年，我国北方荒漠化潜在发生区面积占我国北方地区总面积的34.42%。

（2）采用主成分分析法构建综合荒漠化指数（Remote Sensing-based Desertification Index，RSDI），并将该指数与现有的像元二分模型、Albedo-NDVI特征空间（线对线和点对点）和Albedo-MSAVI特征空间（线对线和点对点）提取结果进行全域和区域尺度精度对比分析。在我国北方地区，基于RSDI不仅弥补了现有荒漠化监测模型中的不足，而且模型精度最高，其总体精度为87.11%。

（3）在时间变化趋势上，我国北方地区土地荒漠化呈现下降趋势，其平均增长率为-0.0007/a；在空间上，我国北方地区荒漠化的空间变化趋势主要以逐步改善趋势为主，区域占比48.79%，主要分布在我国生态修复工程实施的重点区域。基于Hurst指数的未来趋势分析中，发现未来荒漠化的反向持续性强于正向持续性，也说明我国北方地区未来荒漠化程度呈现逐步改善趋势。各荒漠化类型重心以中度荒漠化类型迁移距离最大（553.86km），重度荒漠化类型迁移距离最小（36.93km），且极度、重度、轻度和非荒漠化影响范围在逐步缩小，中度荒漠化影响范围在逐步扩大。

（4）利用Pearson相关分析法定性探究不同驱动因子与RSDI时空相关性，平均风速、平均气温和日照时数呈现正相关趋势，降水量和平均相对湿度呈现负相关趋势。运用地理探测器分别从因子和交互探测两个方面，进一步从全局尺度定量研究了不同驱动因子对RSDI的影响。自然地理因素对我国北方地区荒漠化的影响力无明显变化，而气候因素和社会经济因素对荒漠化的影响越来越强，并且总降水和平均相对湿度是影响我国北方荒漠化的主要因子。运用地理加权回归模型，进一步从局部尺度定量研究了不同驱动因子对RSDI的影响。不同因素对不同时期、不同空间RSDI的影响呈现差异性，总降水表现出正向促进作用，平均气温、日照时数和高程既存在正向作用，又存在负向作用。

Global and regional climate change and human activities are the main causes of desertification, and land desertification has become an important ecological and environmental problem that human beings must face, which seriously restricts the regional ecological balance and human economic and social stability. In the context of global warming, remote sensing dynamic monitoring of desertification and its driving factors are of great theoretical and practical significance for scientific understanding of the desertification process and its spatial and temporal patterns in northern China and adjusting the policies and measures for prevention and control.

This paper takes northern China as the study area, based on MODIS 09A1 data from 2000 to 2020, analyzes the applicability of different land desertification remote sensing monitoring models to the study area, and searches for the best monitoring model to study the spatial and temporal changes and driving forces of land desertification in northern China; in addition, based on GIS geostatistical analysis method, the change characteristics of land desertification, spatial distribution pattern and evolution law of center of gravity are comprehensively analyzed from time and space dimensions; finally, Pearson correlation coefficient method, geographic probe and geographic weighted regression model are applied to explore the key factors affecting spatial and temporal changes of land desertification by combining 11 factors in climate, physical geography and socio-economic aspects and The main findings of this paper are as follows. The main research findings of this paper are as follows:

(1) Based on Thornthwaite's formula combined with the data from ground meteorological stations, the potential occurrence range of desertification in the northern region of China was determined, providing a theoretical basis for the establishment of the subsequent desertification monitoring system. In this paper, the climatic zoning of desertification based on the calculated wetness index finds that the area share of each climatic type shows a decreasing trend between 2000 and 2020, among which the area share of semi-arid zone decreases more significantly, and its average growth rate is -0.302/a; among the climatic zoning types, the area share of sub-humid arid zone is the smallest, followed by arid zone, and the area share of semi-arid zone is the largest; up to 2020, the area of potential occurrence of desertification in northern China accounts for 34.42% of the total area of northern regions in China.

(2) In this study, the Remote Sensing-based Desertification Index (RSDI) was constructed using principal component analysis, and the index was compared with the existing image dichotomous model, Albedo-NDVI feature space (line-to-line and point-to-point) and Albedo-MSAVI feature space (line-to-line and point-to-point). point-to-point) extraction results for a comparative analysis of full-area and regional scale accuracy. In the northern region of China, the RSDI-based model not only compensates for the deficiencies in the existing desertification monitoring models, but also has the highest model accuracy, with an overall accuracy of 87.11%.

(3) In the trend of temporal change, the land desertification in the northern regions of China shows a decreasing trend, and its average growth rate is -0.0007/a. Spatially, the spatial change trend of desertification in the northern regions of China is mainly based on the trend of gradual improvement, and the regional proportion is 48.79%, which is mainly distributed in the key areas where ecological restoration projects are implemented in China. In the future trend analysis based on Hurst index, it is found that the reverse persistence of desertification in the future is stronger than the positive persistence, which also indicates that the future desertification degree in the northern regions of China shows a gradual improvement trend. The center of gravity of each desertification type has the largest migration distance (553.86km) for the moderate desertification type and the smallest migration distance (36.93km) for the severe desertification type, and the influence range of extreme, severe, mild and non-desertification is gradually decreasing, while the influence range of moderate desertification is gradually expanding.

(4) Using Pearson correlation analysis to qualitatively explore the spatial and temporal correlations between different driving factors and RSDI, the average wind speed, average temperature and sunshine hours showed positive correlation trends, and the precipitation and average relative humidity showed negative correlation trends. The influence of different driving factors on RSDI was further quantitatively investigated from the global scale by using the geographic probe model in terms of factors and interaction detection, respectively. There was no significant change in the influence of natural geographical detector on desertification in northern China, while climatic and socio-economic factors had an increasingly strong influence on desertification, and total precipitation and mean relative humidity were the main factors affecting desertification in northern China. Using a geographically weighted regression model, the influence of different driving factors on RSDI was further studied quantitatively at the local scale. The effects of different factors on RSDI in different periods and spaces showed variability, with total precipitation showing a positive contribution, and average temperature, sunshine hours and elevation having both positive and negative effects.

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