吉林省西部土地利用变化对生态系统服务价值的影响

以1993年和2003年两期Landsat TM/ETM影像解译数据为基础,分析吉林省西部地区10年来的土地利用/土地覆被变化,并计算由此导致的生态系统服务价值(ESV)变化.结果表明:10年来,研究区水田、林地、未利用土地和建筑用地面积大幅增加,草地、湿地、水域和旱田面积大幅减少;ESV由1993年的988.45亿元减少到2003年的921.12亿元,年均递减率达0.68%;生态系统单项服务功能价值中,气体调节、土壤形成与保护、食物生产、原材料等服务价值在增加,气候调节、水源涵养、娱乐休闲、废物处理、生物多样性保护等服务价值在减少.     

Down-scaling LUCC based on the histo-variogram

In remote sensing applications, accurate extraction of land type area after classification is very important. But for images of land use/cover change (LUCC) obtained from the special spatial resolution remote sensing data, it will be of great significance to obtain the land type area information with higher resolution by making use of spatial distribution characteristcs information of the land type itself first and further scaling-down in a given scale threshold on the basis of the existing spatial resolution data. An explicit expression of the relationship between the measurement scale, global fractal dimension and the land type area corresponding to different measurement scales is obtained on the research basis of the authors’ histo-variogram using the standardized area index (SAI). A good attempt has been made to obtain the land type area information with higher resolution by merely using the spatial distribution characteristcs information of the land type in the image itself and further scaling-down in a given scale threshold on the basis of the existing spatial resolution data. Supported by the National Natural Science Foundation of China (Grant No. 40601068), the National Basic Research Program of China (“973” Project) (Grant No. 2007CB714402) and the Key Science and Technology R&D Program of Qinghai Province (Grant No. 2006-6-160-01)     

云南沿边境地带小流域LUCC遥感监测试验研究

通过对1990年和2004年两期Landsat TM影像LUCC信息提取方法的处理,揭示了14年间畹町河流域土地利用/土地覆盖的动态变化情况,并对其数量变化和空间特征进行了分析。研究表明,1990-2004年间流域内林地和水域面积显著减少,城镇沿公路大量向外扩张,耕地和未利用土地面积有所增加,草地面积相对持平。     

渭河流域1980—2000年间LUCC变化分析

基于渭河流域1980年、1990年和2000年的土地利用/覆被变化数据,对渭河流域近20年来的土地利用/覆被规律进行了研究。结果表明:渭河流域1980—2000年的土地利用/覆被变化发生了较大改变,且其时空格局具有明显的区域特征;渭河流域的人类活动极大地影响并改变了流域下垫面的土地利用覆被变化条件,进而影响了流域的水沙过程。     

基于RS与GIS的典型地区土地利用/覆盖变化研究——以三江源生态环境重点保护区玛多县为例

典型地区和脆弱区的土地利用/覆盖变化是区域性研究的重要内容。三江源地区是国家级自然保护区,生态系统异常敏感和脆弱。以典型地区玛多县为案例,基于RS和GIS技术,系统研究了该地区土地利用/覆盖变化及其对景观格局的影响,探讨了生态环境演化过程,可为更广泛的区域性综合提供基础。研究发现:①10 a间,该地区湿地、水体和天然草地明显减少,湿地减少比例达到36.63%,大于6 hm²的湖泊减少近半数;沙地,裸地和盐碱地等明显增加,草地退化、沙漠化严重;②天然草地、裸地、沙地、水体、湿地和盐碱地之间的类型转化较为明显,且与水资源又有密切的关系。③多样性、均匀性指数和景观异质程度提高,土地趋于多样化和均匀化;裸地、沙地优势度增加,湿地、水体和天然草地优势度减少;整体破碎化程度呈缓慢减少趋势,而湿地、沙地破碎度增加。④综合土地利用动态度为0.98,天然草地、裸地、湿地、沙地动态面积变化较大。⑤研究区的土地利用/覆盖变化将深刻影响该地区的生态过程,同时还将影响三江源地区的水源涵养和供给。     

基于时序特征的黄河三角洲土地覆盖动态及驱动力分析

综合不同土地利用类型的Landsat影像时序特征和面向对象分割方法,分析1985-2018年现代黄河三角洲土地覆盖时空特征,探讨土地覆盖变化的驱动机制。结果表明:1985-2018年间研究区天然湿地面积大幅减少,共减少9.9847×104 hm²,其中沼泽和草甸湿地面积减少尤为明显,而人工湿地面积则逐年增加,年平均增加率达54.66%,主要是盐田养殖场和水稻种植面积的增大;社会经济因素对研究区土地覆盖的影响程度高于水文因素;输沙量是影响研究区土地覆盖变化的主要水文因素,实测径流量的减少进一步导致天然湿地向旱地类型转化;人口和GDP变化对人工湿地面积增加的正相关作用比较显著,而天然湿地面积的减少则与该两个指标的负相关关系比较明显。     

Urban growth modeling of Kathmandu metropolitan region, Nepal

The complexity of urban system requires integrated tools and techniques to understand the spatial process of urban development and project the future scenarios. This research aims to simulate urban growth patterns in Kathmandu metropolitan region in Nepal. The region, surrounded by complex mountainous terrain, has very limited land resources for new developments. As similar to many cities of the developing world, it has been facing rapid population growth and daunting environmental problems. Three time series land use maps in a fine-scale (30 m resolution), derived from satellite remote sensing, for the last three decades of the 20th century were used to clarify the spatial process of urbanization. Based on the historical experiences of the land use transitions, we adopted weight of evidence method integrated in cellular automata framework for predicting the future spatial patterns of urban growth. We extrapolated urban development patterns to 2010 and 2020 under the current scenario across the metropolitan region. Depending on local characteristics and land cover transition rates, this model produced noticeable spatial pattern of changes in the region. Based on the extrapolated spatial patterns, the urban development in the Kathmandu valley will continue through both in-filling in existing urban areas and outward rapid expansion toward the east and south directions. Overall development will be greatly affected by the existing urban space, transportation network, and topographic complexity.     

洱海流域用地变化的智能体建模与非点源污染响应模拟

洱海流域水环境污染具有显著的人类活动-土地利用/覆被变化-非点源污染链式驱动效应,有必要分析其驱动机制,模拟和预测流域的用地变化及其非点源污染响应过程。本文利用智能体模型模拟人地交互作用过程,预测洱海流域未来土地利用/覆被动态变化的结果,进而利用SWAT模型计算不同用地情景下流域主要非点源污染指标(氮和磷)的产出量和时空分布,最后,利用统计学分析方法与GIS技术,对综合模拟结果进行了精度验证、敏感性检验以及空间分析与可视化表达。结果表明:(1)流域过去和未来10年的土地利用/覆被变化表现为耕地的持续性减少(-19.28%)与建设用地的不断增长(35.27%);(2)氮和磷在时间与空间上具有相对一致性,说明流域土地利用/覆被变化对氮、磷具有相似的水环境效应;(3)未来10年流域非点源污染主要来自于农耕区,建议在保护耕地的同时,优化农耕区的种植结构和生产模式,以减少农业生产的非点源污染贡献率;(4)未来10年流域耕地面积持续性缩减,农业非点源污染呈现逐年降低趋势;同时随着建设用地不断增加,不透水表面的快速增长可能导致的城市非点源污染风险需要引起足够的重视。     

页岩气开发面临的环保挑战及建议

以合肥市区为研究区、以2002和2007年两个时相的TM影像为数据源,分别选择5种端元,利用Matlab的非负最小二乘算法,进行线性波谱分离,经综合后进行差值运算得到植被、水体和不透水面丰度变化数据,经与高分辨率图像勾绘结果进行对比,证明该方法精度较高,在植被覆盖度方面精度高于原来的NDVI二分法,该方法可用于城市LUCC研究。     

Importance of spatially distributed hydrologic variables for land use change modeling

Land use changes have a pronounced impact on hydrology. Vice versa, hydrologic changes affect land use patterns. The objective of this study is to test whether hydrologic variables can explain land use change. We employ a set of spatially distributed hydrologic variables and compare it against a set of commonly used explanatory variables for land use change. The explanatory power of these variables is assessed by using a logistic regression approach to model the spatial distribution of land use changes in a meso-scale Indian catchment. When hydrologic variables are additionally included, the accuracies of the logistic regression models improve, which is indicated by a change in the relative operating characteristic statistic (ROC) by up to 11%. This is mostly due to the complementarity of the two datasets that is reflected in the use of 44% commonly used variables and 56% hydrologic variables in the best models for land use change.