1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form ...1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form a large reservoir. Its impacts on environment have attracted wide attention. Entrusted by National Scientific-Technical Commission, the Chinese Academy of Sciences (CAS) was in charge of a research project on this issuse from 1984 to 1989. Tho use of remote sensing played an important role in the project considering the study area is mountainous and not convenientlv located, which makes it difficult to conduct the research onlv using conventional means.展开更多
The satellite-based vegetation condition index(VCI) and temperature condition index(TCI) have been used extensively for drought detection and tracking, the assessment of weather impacts on vegetation and evaluation of...The satellite-based vegetation condition index(VCI) and temperature condition index(TCI) have been used extensively for drought detection and tracking, the assessment of weather impacts on vegetation and evaluation of the health and productivity of vegetation. In this study, in order to detect and monitor the growth condition of vegetation, we have collected data on vegetation indices and land surface temperature derived from MODIS(2001-2012) and defined a vegetation health index(VHI) based on VCI and TCI for assessing vegetation health condition in the Three Gorges Area, China(TGA). The results of the study show that temporal and spatial characteristics of vegetation health condition can be detected, tracked and mapped by the VHI index. In most parts of the TGA, the vegetation health condition showed an overall increasing trend during the study period, especially in Wulong, Fengdu, Shizhu and other regions located in the midstream sections of the Three Gorges Reservoir. In addition, the four studied vegetation types all showed clear increasing trends during the study period. The increasing trend in the vegetation health condition shows a strong positive correlation with topographical slope and altitude(below 500 m). Over the seasons, this trend is strongest in autumn, followed by spring. However, the correlations between vegetation health condition and climatic factors are more frequently significant in summer and winter than in autumn and spring. The vegetation health condition has been low in 2006 and 2011. This finding is consistent with the extreme weather conditions in those two years. However, only in the summer is vegetation health condition significantly correlated with three climatic factors in most of the study area. This result implies that vegetation growth may show a lagged response to climatic factors and may also be affected by human activities, including agricultural activities, industrial activities and other economic activities.展开更多
在“双碳”目标背景下,探究重庆三峡库区农业碳排放特征及其驱动因素,可为库区低碳农业发展提供科学依据。采用联合国政府间气候变化专门委员会(IPCC)的因子法测算2015—2022年重庆三峡库区农业碳排放量,系统分析库区农业碳排放量和强...在“双碳”目标背景下,探究重庆三峡库区农业碳排放特征及其驱动因素,可为库区低碳农业发展提供科学依据。采用联合国政府间气候变化专门委员会(IPCC)的因子法测算2015—2022年重庆三峡库区农业碳排放量,系统分析库区农业碳排放量和强度时空分异特征,利用Tapio脱钩模型分析库区农业碳排放量与农业经济增长的脱钩关系,并进一步运用LMDI(logarithmic mean divisia index)模型解析库区农业碳排放驱动因素。结果表明:重庆三峡库区农业碳排放总量整体呈波动降低趋势,农业碳排放总量从2015年的645.89万t降至2022年的620.74万t,库区农业碳排放主要来源为农田土壤碳排放和畜禽养殖碳排放。库区农业碳排放强度总体呈下降趋势,各区县间碳排放强度差距逐渐缩小。2015—2022年,库区农业经济与农业碳排放量整体上呈脱钩关系。随着农业生产的恢复与发展,农业产值增长,农业碳排放量增加。脱钩关系以2019年为节点表现为由强脱钩向弱脱钩转变。农业生产效率、农业人口规模、农业产业结构对库区农业碳排放量的增长具有抑制作用,而农业经济规模对农业碳排放量的增长则具有促进作用。基于以上结果,本文提出减少禽畜养殖业碳排放量、控制农田土壤利用碳排放量和发挥农业碳排放驱动因素抑制作用等相关建议,以期为库区低碳农业发展提供理论依据。展开更多
传统滑坡地表形变监测手段存在着监测范围小、复杂地形信息获取难度高、经济成本投入量大等缺点,且大型复杂滑坡变形时间序列的非线性、不确定性变化特征也一直是滑坡形变监测及预测研究中亟待解决的难题。以三峡库区范家坪滑坡为研究对...传统滑坡地表形变监测手段存在着监测范围小、复杂地形信息获取难度高、经济成本投入量大等缺点,且大型复杂滑坡变形时间序列的非线性、不确定性变化特征也一直是滑坡形变监测及预测研究中亟待解决的难题。以三峡库区范家坪滑坡为研究对象,利用差分干涉测量短基线集时序分析技术(small baseline subset InSAR,SBAS-InSAR),结合地表GPS监测数据进行滑坡形变监测,基于SBAS-InSAR时间序列数据及长短时记忆网络(long short term memory,LSTM)开展滑坡形变预测研究。结果表明:研究时段内,范家坪滑坡SBAS-InSAR形变监测结果与地表GPS监测数据所反映出的形变区域及形变量级基本保持一致,与现场调查情况相吻合;范家坪滑坡的位移变形与坡体的高程分布及库水位条件密切相关,当库水位高于160 m时,滑坡前缘阻滑段主要受“浮托减重”效应影响,当库水位低于160 m时,渗流压力占主导作用,水位下降阶段的位移变形总体明显大于水位上升阶段,库水位下降速率对范家坪滑坡的位移变形产生重要影响,且木鱼包滑坡区相较于谭家河滑坡区对库水位下降速率的变形响应更为强烈;将LSTM神经网络模型与传统神经网络模型的预测结果进行效果对比、置信区间估计及相关性检验,结果显示,LSTM神经网络模型的预测结果始终保持较高的预测精度,验证了InSAR与神经网络结合的滑坡监测与预测方法能够为三峡库区地质灾害防治提供重要的数据参考和信息支撑。展开更多
文摘1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form a large reservoir. Its impacts on environment have attracted wide attention. Entrusted by National Scientific-Technical Commission, the Chinese Academy of Sciences (CAS) was in charge of a research project on this issuse from 1984 to 1989. Tho use of remote sensing played an important role in the project considering the study area is mountainous and not convenientlv located, which makes it difficult to conduct the research onlv using conventional means.
基金Natural Science Foundation Project of CQ CSTC(CSTC2011jj A00025)
文摘The satellite-based vegetation condition index(VCI) and temperature condition index(TCI) have been used extensively for drought detection and tracking, the assessment of weather impacts on vegetation and evaluation of the health and productivity of vegetation. In this study, in order to detect and monitor the growth condition of vegetation, we have collected data on vegetation indices and land surface temperature derived from MODIS(2001-2012) and defined a vegetation health index(VHI) based on VCI and TCI for assessing vegetation health condition in the Three Gorges Area, China(TGA). The results of the study show that temporal and spatial characteristics of vegetation health condition can be detected, tracked and mapped by the VHI index. In most parts of the TGA, the vegetation health condition showed an overall increasing trend during the study period, especially in Wulong, Fengdu, Shizhu and other regions located in the midstream sections of the Three Gorges Reservoir. In addition, the four studied vegetation types all showed clear increasing trends during the study period. The increasing trend in the vegetation health condition shows a strong positive correlation with topographical slope and altitude(below 500 m). Over the seasons, this trend is strongest in autumn, followed by spring. However, the correlations between vegetation health condition and climatic factors are more frequently significant in summer and winter than in autumn and spring. The vegetation health condition has been low in 2006 and 2011. This finding is consistent with the extreme weather conditions in those two years. However, only in the summer is vegetation health condition significantly correlated with three climatic factors in most of the study area. This result implies that vegetation growth may show a lagged response to climatic factors and may also be affected by human activities, including agricultural activities, industrial activities and other economic activities.
文摘在“双碳”目标背景下,探究重庆三峡库区农业碳排放特征及其驱动因素,可为库区低碳农业发展提供科学依据。采用联合国政府间气候变化专门委员会(IPCC)的因子法测算2015—2022年重庆三峡库区农业碳排放量,系统分析库区农业碳排放量和强度时空分异特征,利用Tapio脱钩模型分析库区农业碳排放量与农业经济增长的脱钩关系,并进一步运用LMDI(logarithmic mean divisia index)模型解析库区农业碳排放驱动因素。结果表明:重庆三峡库区农业碳排放总量整体呈波动降低趋势,农业碳排放总量从2015年的645.89万t降至2022年的620.74万t,库区农业碳排放主要来源为农田土壤碳排放和畜禽养殖碳排放。库区农业碳排放强度总体呈下降趋势,各区县间碳排放强度差距逐渐缩小。2015—2022年,库区农业经济与农业碳排放量整体上呈脱钩关系。随着农业生产的恢复与发展,农业产值增长,农业碳排放量增加。脱钩关系以2019年为节点表现为由强脱钩向弱脱钩转变。农业生产效率、农业人口规模、农业产业结构对库区农业碳排放量的增长具有抑制作用,而农业经济规模对农业碳排放量的增长则具有促进作用。基于以上结果,本文提出减少禽畜养殖业碳排放量、控制农田土壤利用碳排放量和发挥农业碳排放驱动因素抑制作用等相关建议,以期为库区低碳农业发展提供理论依据。
文摘传统滑坡地表形变监测手段存在着监测范围小、复杂地形信息获取难度高、经济成本投入量大等缺点,且大型复杂滑坡变形时间序列的非线性、不确定性变化特征也一直是滑坡形变监测及预测研究中亟待解决的难题。以三峡库区范家坪滑坡为研究对象,利用差分干涉测量短基线集时序分析技术(small baseline subset InSAR,SBAS-InSAR),结合地表GPS监测数据进行滑坡形变监测,基于SBAS-InSAR时间序列数据及长短时记忆网络(long short term memory,LSTM)开展滑坡形变预测研究。结果表明:研究时段内,范家坪滑坡SBAS-InSAR形变监测结果与地表GPS监测数据所反映出的形变区域及形变量级基本保持一致,与现场调查情况相吻合;范家坪滑坡的位移变形与坡体的高程分布及库水位条件密切相关,当库水位高于160 m时,滑坡前缘阻滑段主要受“浮托减重”效应影响,当库水位低于160 m时,渗流压力占主导作用,水位下降阶段的位移变形总体明显大于水位上升阶段,库水位下降速率对范家坪滑坡的位移变形产生重要影响,且木鱼包滑坡区相较于谭家河滑坡区对库水位下降速率的变形响应更为强烈;将LSTM神经网络模型与传统神经网络模型的预测结果进行效果对比、置信区间估计及相关性检验,结果显示,LSTM神经网络模型的预测结果始终保持较高的预测精度,验证了InSAR与神经网络结合的滑坡监测与预测方法能够为三峡库区地质灾害防治提供重要的数据参考和信息支撑。
