西部地区生态状况变化及生态系统服务权衡与协同

西部大开发战略实施20年以来,一系列重大生态建设工程使得西部地区的生态状况和生态服务发生了重要变化,本文分析西部地区生态状况变化及其生态服务时空变化特征,并运用相关分析法研究其服务功能权衡协同关系。研究发现西部地区2000—2019年间：① 聚落和湿地生态系统面积显著增加,草地有逐年下降趋势。② 植被覆盖度呈波动上升趋势,存在年际差异且空间差异较大。③ 水源涵养服务有轻微下降趋势,下降区域主要分布在藏东南地区、三江源西部地区、喀斯特石漠化地区等;土壤保持服务波动中呈上升趋势,上升区域主要黄土高原地区、川滇西部地区、藏西北地区、藏东南地区等;防风固沙服务呈下降趋势,大幅下降区域主要位于内蒙古中西部地区、西藏和北疆部分地区。④ 生态系统供给与水源涵养、土壤保持主要为协同关系,与防风固沙主要为权衡关系且分布在农牧交错带地区。⑤ 生态工程区内生态系统服务协同程度大于非工程区。定量评估生态系统服务变化及其权衡协同关系有助于进行科学化生态管理,最大化生态效益。     

珠三角城市群地区都市农业功能演变及其协同-权衡关系

以珠三角城市群地区的县（区）为研究单元,从生产供给、经济发展、社会保障和生态保育四个维度构建都市农业功能评价指标体系,利用熵值法、时空差异诊断模型、Spearman秩相关系数法和双变量局部空间自相关模型等对2005年、2012年和2019年珠三角地区都市农业多维功能演变及协同-权衡关系进行测度。结果表明：① 2005—2019年,珠三角地区都市农业的生产供给、经济发展和社会保障功能均值先下降后上升而生态保育功能反之;生产供给和生态保育功能总体呈现稳定的外部较强而中心偏弱的空间格局,经济发展和社会保障功能的空间分布格局变化较大。② 时间上,2005年珠三角都市农业经济发展功能较其他功能相对独立发展,2012年功能之间权衡关系显现,2019年各功能之间以协同关系为主;空间上,珠三角地区都市农业生态保育功能与其他三项功能的协同-权衡关系较为稳定,社会保障功能与生产供给、经济发展功能的协同-权衡关系较为稳定。③ 都市农业功能演变及协同-权衡关系发展具有阶段性特征,大致呈现“相互独立/低位协同-相互权衡-高位协同”的演化规律。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.