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基于多源遥感数据的土地整治生态环境质量动态监测
引用本文:单薇,金晓斌,孟宪素,杨晓艳,徐志刚,顾铮鸣,周寅康.基于多源遥感数据的土地整治生态环境质量动态监测[J].农业工程学报,2019,35(1):234-242.
作者姓名:单薇  金晓斌  孟宪素  杨晓艳  徐志刚  顾铮鸣  周寅康
作者单位:1. 南京大学地理与海洋科学学院,南京 210023;,1. 南京大学地理与海洋科学学院,南京 210023; 2. 国土资源部海岸带开发与保护重点实验室,南京 210023;,3. 国土资源部土地整治中心,北京 100035;,3. 国土资源部土地整治中心,北京 100035;,1. 南京大学地理与海洋科学学院,南京 210023;,1. 南京大学地理与海洋科学学院,南京 210023;,1. 南京大学地理与海洋科学学院,南京 210023; 2. 国土资源部海岸带开发与保护重点实验室,南京 210023;
基金项目:国家科技支撑计划课题(2015BAD06B02)
摘    要:土地整治生态转型是土地整治发展的必然趋势,在项目区尺度进行科学合理、客观直接、长期全面的生态环境质量监测评估具有重要意义。该研究基于多源遥感数据,选取典型土地整治项目,运用主成分分析法构建RSEI(remote sensing ecological index)模型,反演得到湿度、绿度、热度、干度指标以及RSEI指数,实现对项目区整治过程中生态环境质量变化的监测与分析。研究结果表明:1)湿度和绿度指标对项目区生态环境质量具有正向作用,而热度和干度指标起负向作用,且干度指标的影响最大;2)RSEI总均值在整治前、中、后分别为0.652、0.572和0.605;RSEI等级中的优良等级在整治前、中、后所占比例分别为78.73%、39.55%和63.29%;RSEI变差、不变和变好的比例分比为42.55%、46.25%和11.20%;3)项目区生态环境质量呈现"先下降-后上升-整体下降"的态势,表现为"整治期变差-恢复期变好-全过程变差"的总体特征,土地整治对项目区生态环境的扰动具有持续性,区域生态环境恢复与改善存在滞后期,在项目竣工5年后项目区的生态环境质量水平仍低于整治前。

关 键 词:土地整治  生态  遥感  环境质量  动态监测  RSEI指数
收稿时间:2018/5/17 0:00:00
修稿时间:2018/10/15 0:00:00

Dynamical monitoring of ecological environment quality of land consolidation based on multi-source remote sensing data
Shan Wei,Jin Xiaobin,Meng Xiansu,Yang Xiaoyan,Xu Zhigang,Gu Zhengming and Zhou Yinkang.Dynamical monitoring of ecological environment quality of land consolidation based on multi-source remote sensing data[J].Transactions of the Chinese Society of Agricultural Engineering,2019,35(1):234-242.
Authors:Shan Wei  Jin Xiaobin  Meng Xiansu  Yang Xiaoyan  Xu Zhigang  Gu Zhengming and Zhou Yinkang
Institution:1. College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China;,1. College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; 2. Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing 210023, China;,3. Land Consolidation and Rehabilitation Center, Ministry of Land and Resources, Beijing 100035, China,3. Land Consolidation and Rehabilitation Center, Ministry of Land and Resources, Beijing 100035, China,1. College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China;,1. College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; and 1. College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; 2. Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing 210023, China;
Abstract:Abstract: Land consolidation is one of the important means to safeguard national food security, support rural revitalization strategy, optimize the allocation of land resources, and promote the construction of ecological civilization. Ecological transformation is the inevitable trend for the development of land consolidation. It is quite important to carry out scientific and reasonable, objective and direct, and long-term comprehensive monitoring and evaluation of ecological environmental quality at project area scale. Based on remote sensing technology, RSEI (remote sensing ecological index) model can quickly, objectively and quantitatively evaluate regional ecological environment quality, and realize visual representation, time-space analysis, simulation and prediction of regional ecological environment quality changes. Applying multi-source remote sensing data and taking 6 years before and after land consolidation as study period, this research constructed the RSEI model with principal component analysis, and retrieved values of the wetness, greenness, heat, and dryness indicators and RSEI index to monitor and analyze ecological environmental quality in a typical land consolidation project. The standard values in the research are: 1) RSEI ranges from 0 to 1, and the closer to 1 the value, the better the ecological environment quality. 2) The RSEI basic level is divided into 5 levels from small to large, i.e. "poor, inferior, medium, good, and excellent". 3) Based on the RSEI basic level, the RSEI level is divided into 9 subdivision levels (from (4 to 4) and 3 classes (worse, unchanged and better). All negative subdivision levels are classified as "worse", 0-value is classified as "unchanged", and all positive subdivision levels are classified as "better". The result shows: 1) The wetness and greenness indicators have a positive effect on promoting the ecological environment quality of the region, while the heat and dryness indicators have a restraining effect on the regional ecological environment quality, and the dryness indicator is more significant than the other 3 indicators. 2) Before, during, and after the land consolidation, the mean values of RSEI are 0.652, 0.572, and 0.605, respectively, and the proportion of excellent plus good RSEI class accounts for 78.73%, 39.55%, and 63.29% respectively in 3 periods. Meanwhile, the degenerated, unchanged, and improved RSEI classes are 42.55%, 46.25%, and 11.20% of the total area, respectively. 3) In this project area, the ecological environment quality presents a trend of decreasing firstly and increasing later, with an overall trend of decreasing, which are the characteristics of "turning worse during the consolidation period, getting better during the recovery period, and becoming worse in the overall process". Land consolidation causes persistent disturbance to the ecological environment, and there is a lag to restore and improve regional ecological environment. Five years after the project completion, the ecological environment quality level is still lower than before. The project area is the direct target of land consolidation and the carrier of benefit manifestation. This study can provide certain theoretical guidance and method reference for the continuous monitoring and dynamic assessment of ecological environment quality at the project area scale. Furthermore, it can provide certain method reference and data support to improve the local ecological environment, ensure the quality of cultivated land, improve agricultural production conditions, and promote regional sustainable development in some degree.
Keywords:land consolidation  ecology  remote sensing  environment quality  dynamically monitoring  RSEI index
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