基于RS和GIS的地面温度和土地利用/覆被关系研究进展

对地面温度和土地利用／覆被关系的研究，不仅能够深入理解土地利用／覆被变化下城市热环境变化的空间特征和动态变化，而且可为防暑降温、市政建设及土地合理规划和利用提供重要的科学依据。本文从遥感数据的获取、地面温度的反演、GIS技术的运用以及定量研究等方面详细论述了国内外此领域的研究进展。最后文章提出此研究领域存在的一些问题和今后工作所要继续努力的方向，如实现地面温度的精确反演，获取多时段的地面温度数据，加强RS和GIS技术的结合，进一步深入两者关系的定量研究。     

半干旱环境下的土地利用/覆被时空变化研究—以吉林省通榆县为例

通榆县作为松嫩沙地的重要组成部分,是半干旱环境下典型的土地生态环境脆弱区。以1979年、1992年和2001年3期Landsat影像为数据源,在RS、GIS技术和景观生态学理论的支持下,全面分析了通榆县22 a来土地利用/覆被类型的时序和空间景观变化特征。结果表明：研究区土地利用/覆被类型以耕地、草地、林地和重度盐碱地为主,并且在时序上呈现出草地迅速减少、重度盐碱地大面积增加的趋势;在空间景观上呈现出耕地、草地、林地高度破碎与重度盐碱地高度集聚的特征。土地利用程度指数还表明研究区土地正处于由衰退期向发展期过渡的转型阶段。
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洪泽湖区土地利用/覆盖变化分析

利用1973年、1984年和2006年3期Landsat卫星遥感图像,分别对校正处理后的3期图像进行缨帽变换,并利用亮度、绿度和湿度这3个分量的差值运算进行信息复合。然后利用变化向量分析法确定复合图像中的变化和非变化像元之间的变化强度阈值。最后利用阈值分割和掩膜技术,对3期图像进行土地利用/覆盖变化监测。研究表明,近33年来洪泽湖区土地利用结构发生根本性改变:天然湿地面积大幅度缩减,1973年～1984年间减少了200.55km2,1984年～2006年间减少了357.04km2,主要表现为敞水区和湿地植被面积的减少,养殖场、农田面积显著增加,城镇用地略有增加。本文研究表明基于缨帽变换并结合差值运算方法和变化向量分析法实现土地利用/覆盖变化监测的可行性。     

西安城区土地利用/覆被变化及生态效应测评研究

利用两期遥感影像,以ArcGIS9.0和ERDAS8.5为平台分析了西安市2000~2007年间土地利用时空变化过程;并对研究区2000~2007 年间土地利用变化引起的生态效应变化进行了测评;结果表明：7 a间西安城区土地利用/覆被变化显著,综合土地动态度为1.9%,建设用地面积逐年增加,年变化率为3.1%;耕地、未利用地、园地、水体面积有所减少,西安城区的生态系统服务价值从2000年的259.8 ×106元减少到2007年的233.6×106元,共减少了26.2×106元;经证实用遥感与地理信息系统技术研究因土地利用/覆被变化引起的生态响应是一种有效的方法。     

土地利用/土地覆被对江西陆地碳储量的影响

研究不同类型的土地利用与陆地生态系统的相关性,有助于探究土地利用对区域生态环境的保护与可持续发展的影响。鉴于长时间序列的碳储量监测研究较少,文章借助江西省1985—2020年的土地利用数据,利用InVEST模型对陆地生态系统碳储量变化特征进行量化评估。结果表明,1985—2020年间江西省的碳储量总体呈下降趋势,累计下降3.8×10⁷ t。1990—2000年江西省碳储量变化明显的主要因素是林地的流入流出。南昌市城市建设对耕地的挤占明显,赣州市建设用地面积增量最大,碳储量减少4.29×10⁶ t,鹰潭市碳储量减少,最少碳储量减少0.29×10⁶ t。江西省陆地生态系统的碳储量呈现明显的降低趋势,其中建设用地扩张是造成碳储量减少的主要因素。     

基于RS／GIS的冀北地区土地利用／覆盖变化研究

本文以冀北地区为研究区，以TM数据为基本信息源，在ArcGIS、Erdas等地理信息系统和遥感图像处理软件的支持下，设计了一套由“多源遥感数据融合、屏幕目视解译与自动分类结合、GPS定位与野外校核”构成的遥感地学分析方法。分别提取了1987年和2000年研究区土地利用／覆盖信息，并分析了1987-2000年间研究区土地利用／覆盖变化的基本特征，为这一地区以土地利用调整为核心的生态建设提供了基础依据。     

土地利用/覆盖链式遥感分类方法研究

针对目前大面积土地利用/覆盖变化应用中存在的当地面参考数据不足时分类精度不高和分类过程复杂的问题,本文介绍了一种新型的链式遥感分类方法。该方法利用有限的地面参考数据,根据相邻影像间重叠区域的信息即可完成大面积的土地分类。研究利用该方法对华北地区约1.0×105km2的区域分别进行了林地和非林地的简单分类和耕地、水域、居民地及林地的复杂分类试验,总体分类精度分别为95.0244%和92.0947%,并分别与传统的分类方法进行了对比,总体分类精度仅降低1.6773%和2.1569%。研究结果表明,链式分类精度损失不大,但时间效率大大提高。     

基于3S技术的土地利用/土地覆盖变化野外采样框架设计——以东北地区黑龙江省为例

土地利用/土地覆盖变化是全球环境变化的重要组成部分,随着3S技术的不断成熟和发展,运用RS、GPS和GIS技术进行土地利用/土地覆盖变化研究已成为一种越来越成熟的方式和手段。从空间抽样模型理论出发,以我国黑龙江省为例,运用RS、GPS和GIS技术,通过对黑龙江省道路网、土地利用区划、土地利用/土地覆盖类型、土地利用/土地覆盖1 km×1 km格网数据等空间信息分布的综合考虑、分析,设计了土地利用/土地覆盖变化的综合野外采样框架。框架主要包括采样区的布设、采样路线和采样点的选择等。由于以多层空间信息为采样依据,经实践检验,该采样框架具有经济实用等优点。     

基于RS和GIS的上海市植被覆盖变化特征研究

利用1997、2000和2002年的Landsat TM/ETM 遥感数据,计算了归一化差异植被指数(NDVI)并剔除植被伪变化信息,同时结合GIS空间分析功能较为详细地研究了近几年来上海城市植被覆盖的动态演变特征.结果表明:市区植被覆盖变化不大,面积总体呈现增加趋势;与市区相比,郊区的植被覆盖变化较为明显且减少面积较多;植被减少部分主要为耕地,主要对应城镇和工业用地的扩展区,增加部分主要是林地和草地;市区和浦东新区一些工矿用地和未利用地转化为草地在很大程度上增加了植被面积.     

基于遥感与GIS的青海湖地区土地利用变化及其对生态环境影响的研究

根据1986年和2000年TM影像资料,运用遥感信息解译与地理信息系统技术,结合数理统计知识,分析了青海湖地区1986~2000年土地利用类型在数量与空间结构上的变化以及土地利用变化对青海湖地区生态环境产生的影响。土地类型转换结果分析表明:耕地、水域和城乡居住建设用地分别增加了5 930.82 hm²、2 218.75 hm²和382.68 hm²。而林地、草地和未利用土地分别减少了7 542.64 hm²、894.34 hm²和95.28 hm²。在减少的7 542.64 hm²草地面积中,有6266.56 hm²草地被开垦成耕地,601.40hm²草地净转换为沙地。     

Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate

Drylands cover about 41% of the globe's surface and provide important ecosystem services, but land use and climate change exert considerable pressure on these ecosystems. Both of these drivers frequently result in gradual vegetation change and landscape-scale trend analysis based on yearly vegetation estimates can capture such changes. Such trend analyses based on high-resolution time series of satellite imagery have so far not widely been used and existing studies in drylands relied on different vegetation measures. Spectral mixture analysis (SMA) has been chosen due to its superiority to simpler vegetation estimates in quantifying vegetation cover in single-date studies, however SMA can be challenging to implement for large areas. Here, we quantify the trade-off involved when using simple vegetation estimates instead of SMA fractions for subsequent trend analyses. We calculated NDVI, SAVI and Tasseled Cap Greenness, as well as SMA green vegetation fractions for a time series of Landsat images from 1984-2005 for a study region in Crete. Linear trend analysis showed that trend coefficients and the spatial patterns of trends were similar across all vegetation estimates and the entire study region, especially for areas where vegetation changed gradually. On average, trends based on simple measures differed less than 5% from SMA-based trends with decreasing similarity in trend results from Tasseled Cap Greenness to SAVI and NDVI. Vegetation estimates differed markedly in their response to disturbance events such as fires. Trend analyses based on qualitative measures can easily be applied across very large areas and using multi-sensor time series based on high-resolution data. While the subtle differences between vegetation estimates may still be important for some applications, the robustness of trend analyses regarding the choice of vegetation estimate bears considerable promise to reconstruct fine-scale vegetation dynamics and land use histories and to assess climate change impacts on the world's drylands.     

张掖市土地利用/覆盖变化模拟

土地利用/覆盖变化是全球变化的主要原因,也是与可持续发展密切相关的课题。土地利用/覆盖变化模拟是预测未来土地利用/覆盖变化的重要方法。将国际上先进的CLUE-S模型应用到位于黑河中上游的张掖市,模拟该地区的土地利用/覆盖变化。模拟时段为2001～2020年。模型中将土地利用类型分为:①耕地;②林地;③草地;④水域;⑤城镇用地;⑥未利用地。用回归分析的方法,选择了对该地区土地利用/覆盖变化有重要贡献的7种驱动因子,分别为:与城市的距离、与河流的距离、与道路的距离、人口密度、海拔、坡度、坡向。模拟结果显示:到2020年,林地、草地、水域和城镇用地面积增加,耕地,未利用地面减少。     

基于GIS的城市土地利用分析与建模框架研究

本文针对城市土地利用数据的时空特性,依托地理信息系统(GIS)丰富的空间分析工具以及对海量空间数据的高性能计算优势,围绕城市土地利用研究有关数据的处理、分析、建模等方面问题设计了一个基于GIS的城市土地利用分析与建模框架;框架主体结构中有关城市土地利用变化的驱动力机制建模方法选取逻辑回归模型,对地理数据的空间自相关性处理则根据Getis自相关系数构建滤波模型;具体应用则结合深圳市国土资源局的"城市土地利用虚拟政策实验室"项目,取得良好效果     

Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product

Information related to land surface phenology is important for a variety of applications. For example, phenology is widely used as a diagnostic of ecosystem response to global change. In addition, phenology influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. Increasingly, the importance of phenology for studies of habitat and biodiversity is also being recognized. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, remote sensing provides the only way to observe and monitor phenology over large scales and at regular intervals. The MODIS Global Land Cover Dynamics Product was developed to support investigations that require regional to global scale information related to spatio-temporal dynamics in land surface phenology. Here we describe the Collection 5 version of this product, which represents a substantial refinement relative to the Collection 4 product. This new version provides information related to land surface phenology at higher spatial resolution than Collection 4 (500-m vs. 1-km), and is based on 8-day instead of 16-day input data. The paper presents a brief overview of the algorithm, followed by an assessment of the product. To this end, we present (1) a comparison of results from Collection 5 versus Collection 4 for selected MODIS tiles that span a range of climate and ecological conditions, (2) a characterization of interannual variation in Collections 4 and 5 data for North America from 2001 to 2006, and (3) a comparison of Collection 5 results against ground observations for two forest sites in the northeastern United States. Results show that the Collection 5 product is qualitatively similar to Collection 4. However, Collection 5 has fewer missing values outside of regions with persistent cloud cover and atmospheric aerosols. Interannual variability in Collection 5 is consistent with expected ranges of variance suggesting that the algorithm is reliable and robust, except in the tropics where some systematic differences are observed. Finally, comparisons with ground data suggest that the algorithm is performing well, but that end of season metrics associated with vegetation senescence and dormancy have higher uncertainties than start of season metrics.     

基于遥感和GIS的东亚土地覆盖年际变化研究

土地覆盖的年际变化是以土地覆盖的宏观分布模式为基准,在外界驱动因子的作用下发生的年与年之间的变化,因此,为揭示东亚地区土地覆盖的年际变化特征,首先选取东亚地区时相一致的不同空间分辨率（1km和8km)的NDVI影像进行了非监督分类,并总结了东亚土地覆盖的宏观分布模式,然后以时间序列的8kmAVHRRNDVI数字影像为基础,应用跨平百分率分析方法生成每年5-9月距平百分率分级影像,并以该影像为基础分析总结了东亚土地覆盖的年际变化特征,结果显示,该方法及其揭示的现象比较客观地反映了东亚土地覆盖年际变化特征。     

基于RS与GIS的野鸭湖湿地土地利用/土地覆盖动态变化研究

通过对野鸭湖湿地的Landsat—TM影像和印度的IRS影像进行融合处理，得到卫星影像分类图。结合实地调查，标定土地利用类型，运用ArcView的解译及数据统计功能，分析研究野鸭湖湿地6年来土地利用／土地覆盖的变化。研究结果表明：耕地、居民点及工矿用地面积增加，水域面积减少，湿地生态环境受到严重破坏。其主要是由自然条件、人口和经济增长所致。水域面积、植被覆盖率的减少，使栖息和越冬鸟类丧失了大量的栖息地。为保护湿地环境，应逐步退耕还草、还林；恢复芦苇、沼泽，确保区内生态平衡和系统生态质量不断优化。     

县(市)级土地利用现状数据库关键技术实现

结合自身建立土地利用现状数据库系统的实践,探讨了GIS基础上建立县(市)级土地利用现状数据库系统的技术流程、质量控制及数据转换等问题,提出了具体可行的技术方法。     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

North American vegetation dynamics observed with multi-resolution satellite data

We investigated normalized difference vegetation index data from the NOAA series of Advanced Very High Resolution Radiometers and found regions in North America that experienced marked increases in annual photosynthetic capacity at various times from 1982 to 2005. Inspection of these anomalous areas with multi-resolution data from Landsat, Ikonos, aerial photography, and ancillary data revealed a range of causes for the NDVI increases: climatic influences; severe drought and subsequent recovery; irrigated agriculture expansion; insect outbreaks followed by logging and subsequent regeneration; and forest fires with subsequent regeneration. Vegetation in areas in the high Northern Latitudes appear to be solely impacted by climatic influences. In other areas examined, the impact of anthropogenic effects is more direct. The pattern of NDVI anomalies over longer time periods appear to be driven by long-term climate change but most appear to be associated with climate variability on decadal and shorter time scales along with direct anthropogenic land cover conversions. The local variability of drivers of change demonstrates the difficulty in interpreting changes in NDVI and indicates the complex nature of changes in the carbon cycle within North America. Coarse scale analysis of changes could well fail to identify the important local scale drivers controlling the carbon cycle and to identify the relative roles of disturbance and climate change. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America.     

遥感用于土地利用变化动态监测中的若干问题探讨

利用遥感与地理信息系统技术进行土地资源调查和动态变化监测已为人们所共识。由于土地利用与土地覆盖的复杂性,利用遥感技术进行土地利用变化动态监测时,遇到了许多具体问题。从应用的角度出发,重点在土地利用与土地覆盖的关系、影像的选择与处理、动态监测方法的选定与综合及其它多方面知识的参与等几个方面进行了探讨。这些问题的解决,将会在很大程度上提高土地利用变化监测的精度。