基于遥感的植被覆盖变化景观分析——以北京海淀区为例

提出了基于土地利用分类的植被覆盖度计算的亚象元模型,针对不同的植被覆盖类型,综合利用“等密度模型“和“变密度模型“计算植被覆盖度,使其能有效地从遥感数据中提取植被覆盖信息。在GIS的支持下,应用该模型对北京海淀区1975年、1991年和1999年5月份植被覆盖度进行计算,并着重对其景观变化过程进行了分析。研究结果表明：1975至1999年来,海淀区植被覆盖整体表现为增加趋势,但空间格局分布不均衡,全植被覆盖区和高植被覆盖区过分集中在西北部狭长地带,东南部则多为低植被覆盖区,反映出北京海淀区土地利用的结构特征。     

基于MODIS和ENVISAT数据的湖北省四湖地区土地覆盖分类

依据ENVISAT ASAR数据中后向散射系数的差异对湖北四湖地区的城镇、水域、植被覆盖区进行划分;根据区域种植制度及物候特点,以4月下旬至5月上旬MODIS-NDVI值区别植被覆盖区中的作物植被与非作物植被;并采用基于高分辨率ETM数据的土地分类结果对基于上述规则分类的结果进行验证.结果表明：借助DEM高程数据,可将研究区非作物植被划分为林地与滩地;利用月份NDVI平均值的差异,可将作物划分为中稻、棉花和晚稻,并获得水田和旱地的区分;采用低空间分辨率的MODIS数据获得的土地覆盖分类结果,与采用高分辨率ETM数据分类的结果具有一定程度的相似性,以ETM数据分类结果为标准的总误差率为13.15%;利用上述判别流程进行大尺度土地覆盖分类与制图可以实现对区域土地覆盖变化的快速跟踪.     

利用GIS对吉林针阔混交林TM遥感图像分类方法的初探

为提高林区TM遥感图像自动分类识别精度,在GIS技术辅助下,以吉林省汪清林业局针阔混交林TM遥感图像为例,对研究区DEM、坡向等地理因子和土壤类型等环境因子与森林植被分布之间的内在规律进行了定量分析,并结合对遥感图像预分类的定性分析,形成分类知识库,建立了适用于针阔混交林的自动分类识别专家系统.分类试验证明,该系统能比较明显地削弱混合像元和地形阴影的影响,分类精度较无监督分类法提高了14.22%,Kappa指数为0.7556,达到区别森林类型的分类目的.将GIS数据引入专家系统,应用先验知识建立推理机制,可以解决遥感图像中云区和云阴影区由于不能接收到正确的光谱值而无法进行分类的问题.     

云南干热河谷植被与环境研究进展

特殊的地理位置和独特的地形地貌特征组合形成了典型的干热气候环境, 在云南省亚热带高原山地河谷下部发育了一类特有的植被类型, 即干热河谷植被。干热河谷植被具有非地带性和稀有性, 以及由土地利用变化为主的人为活动干扰导致的脆弱性。本文回顾了干热河谷植被的研究历史, 分别从干热河谷的植物群落学和植物区系学、干热河谷植被与土地的关系以及干热河谷植被保护与恢复三个方面进行了总结。植物群落与区系研究主要集中于群落分类、植被分类、群落特征、人为干扰影响、区系特征、性质和起源; 植被与土地关系研究侧重于土壤特性、土地利用/覆盖变化、土地退化及水土流失状况; 植被保护与恢复的热点在植被恢复目标、植被恢复功能区划、植被恢复引种及筛选及植被恢复效益评价研究。未来在这些区域应注重自然灾害及预防、水电工程建设对植被的影响及其响应等方面的研究, 深入开展大尺度植被时空格局的监测和动态服务功能分析。     

植被格局对侵蚀产沙影响的研究评述

土壤侵蚀是人类可持续发展的重要挑战,植被重建则是防控侵蚀产沙的有效手段。在当前水土资源日益稀缺的新形势下,优化植被布局将成为黄土高原等生态脆弱地区未来阻蚀减沙、改善环境的重要途径,有关植被格局对侵蚀产沙影响的科技需求更加急迫、意义更加重要。为此,从坡面与流域两个尺度,总结评述了侵蚀产沙研究中的植被格局表征、侵蚀产沙对植被格局的变化响应两个议题的研究进展。分析认为,目前主要存在缺乏反映侵蚀产沙过程的植被格局表征指数、较少考虑植被与地形叠置格局对侵蚀产沙的耦合影响、尚未建立侵蚀产沙对植被格局的直观响应关系等问题。为促进有关研究,今后在格局指数和耦合关系的构建中应重视植被和地形对侵蚀产沙过程的耦合影响,并选用具有良好应用精度的分布式物理成因模型,以获得反映植被格局影响的流域侵蚀产沙结果。同时,可通过采用GIS空间分析技术,设计获得更多的典型植被格局及对应情景的侵蚀产沙,以丰富基础资料,减少信息干扰。     

遥感和GIS支持下的城市植被制图及其特征分析

 以上海城市植被为研究对象,探索城市植被的分类理论和方法,提出一套适用于城市植被分类与制图的方法。根据城市植被信息获取的方式以及生态-外貌分类原则,建立了基于植物生活型的、可用图形表示的城市植被分类体系,分天然与半天然植被和人工植被两个大类,共有9个植被型和34个植被亚型。在遥感和地理信息系统(GIS)技术的支持下,建立研究区的城市植被信息数据库,并在此基础上对城市植被类型的特征以及与土地利用的关系进行分析比较,以期掌握城市植被类型的数量特征及其空间分布,同时按不同分类级别用计算机进行城市植被制图。城市植被     

北京地区不同地形条件下的土地覆盖动态

利用MSS/TM影像和1∶250 000 高程数据分析了1978~2001年北京地区土地覆盖的变化。为研究人类活动对土地覆盖类型及植被变化的影响,根据植被的分布规律和人类活动影响,利用数字高程模型(DEM)将研究区遥感影像分为不同海拔段,分别建立标志进行解译。通过转移概率矩阵计算、不同地形因子影响下的土地覆盖类型分布的GIS分析,得到如下结果:1) 1978~2001年间北京市土地覆盖变化主要发生在平原和低海拔、小坡度的平缓地区,表现为农业用地向城镇用地的转变;高海拔地区主要为天然植被所覆盖,土地覆盖变化相对较小,主要是灌丛向林地的转变。2) 地形因子显著影响土地覆盖类型的分布及变化。随着海拔的升高和坡度的增大,农业用地和城镇用地减少,林地和灌丛逐渐增加。坡向对植被的分布也有较大影响。     

基于NDVI_Ts特征空间的中国土地覆盖分类研究

 归一化植被指数（NDVI）与地表温度（Ts）是描述地表覆盖特征的两个重要参数, 其构成的NDVI_Ts特征空间具有丰富的地学和生态学内涵。该文在NOAA/AVHRR连续时间序列数据反演Ts的基础上,通过主成分分析、非监督分类和基于DEM的分类后处理等方法,以Ts/NDVI为指标对中国土地覆盖进行分类。结果表明,Ts/NDVI对中国较大尺度上不同土地覆盖类型的差异具有较强的敏感性,其对中国土地覆盖分类结果的野外抽样检验精度比传统的单独利用NDVI时间序列进行非监督分类提高了3.3%,Kappa系数提高了0.020 2;在综合其它反映植被特征及其环境的指标（如气候、地形等）的基础上,利用Ts/NDVI将有可能较为准确 地提取中国植被或土地覆盖的信息,有利于对其进行分类和变化监测,具有深远的研究潜力 和应用价值。     

基于SNIC-CNN-SVM模型的京津风沙源二期工程区土地利用/土地覆盖遥感识别研究

稀疏植被覆盖（草地、沙地、戈壁）演变能够直接表征区域生态环境和人类活动的动态影响变化。但由于大尺度稀疏植被区一般都具有地理跨度大,景观结构复杂多样,破碎化程度高,现有地表覆盖分类产品针对性不足等问题,使得该区域内林草沙的遥感提取难度较大,精度普遍偏低,直接制约生态效应评价模型的应用效果。因此,以典型大尺度稀疏植被区--京津风沙源治理二期工程区为研究区,研建了SNIC-CNN-SVM （SCS）模型,实现了大尺度稀疏植被区林草沙典型要素的信息自动提取和主要土地利用/土地覆盖类型识别。研究结果表明：1）引入惩罚性机制优化后的SNIC分割算法,有效提升了稀疏植被区与沙地区的边界区分度,有助于分类精度的提升;2）基于改进SNIC-CNN-SVM模型方案的研究区总体分类精度达89.41%,较优化前提高了11.17%,特别是乔、灌、草、沙地和戈壁的分类识别精度显著提升,表明该优化方案在以研究区为代表的稀疏植被区域分类中具有较好的应用效果和推广价值;3）分类结果显示,2020年工程区草地面积最大,占到了一半以上（51.52%）,沙地占比11.96%,稀疏植被覆盖（草地、沙地、戈壁）区域占比68.68%,表明工程区处在林地-稀疏植被-沙地的过渡地带,生态环境保护压力与防沙治沙形势依然严峻;4）近20年来,乔灌草等植被增加面积约占工程区20.64%,主要由沙化土地转化,沙化土地减少面积约占工程区的4.58%,表明研究区植被状况不断改善,实施的各项生态工程作用显著,能够更有效地服务于多维度生态系统服务功能评价。该研究以期能够为京津风沙源二期工程区的生态系统演变规律研究及生态工程评价等工作提供重要科学支撑。     

祁连山区典型草地生态系统土壤抗冲性影响因子

为探索祁连山区典型草地生态系统土壤抗冲性的影响因子及其效应,采用野外实地放水冲刷法,以一定体积的冲刷水流含沙量为指标,对土壤抗冲性进行了研究,并调查了海拔、坡度、植被高度、植被覆盖度、地上生物量、根系密度、生物多样性、土壤质地等因子,利用通径分析研究了各因子与土壤抗冲性的关系。结果表明:(1)土壤抗冲性与海拔、植被覆盖度、根系密度和土壤砂粒体积分数呈极显著正相关(P0.01),而与坡度和土壤粉粒体积分数呈极显著负相关(P0.01),与地上生物量和物种丰富度呈显著正相关(P0.05),与植被高度不具备显著相关性;(2)通径分析结果显示,植被覆盖度和根系密度是影响祁连山区典型草地生态系统土壤抗冲性的主导因素,植被覆盖度对土壤抗冲性的影响主要表现为强烈的直接作用(0.660),而根系密度对土壤抗冲性的直接作用相对较小(0.286),有较大一部分影响表现为间接作用(0.174);(3)径流含沙量随植被覆盖度和根系密度的增加明显减少,植被覆盖度与径流含沙量间的关系可用指数或对数形式表达,根系密度与径流含沙量间的关系可用指数形式表达。研究显示,在祁连山区典型草地生态系统,与海拔、坡度、地上生物量、植物多样性、土壤质地等因素相比,植被覆盖度和根系密度对土壤抗冲性的影响作用更突出,提高植被覆盖度与根系密度能够有效增强土壤抗冲性。该研究可为祁连山区的土壤侵蚀规律研究及效益评价提供依据。     

浙江西门岛湿地景观格局与人为干扰度动态变化

基于西门岛湿地2007和 2010年两期SPOT 5影像(空间分辨率均为5 m)数据,结合西门岛湿地土地利用现状,参考《全国土地利用分类》（试行）、海域使用分类海洋行业标准以及综合考虑遥感数据特性,建立西门岛湿地人为干扰类型分类系统;应用景观格局指数法、GIS空间分析法,得到景观指数和人为干扰度指数,探讨了西门岛湿地景观格局与人为干扰度动态变化.结果表明: 研究期间,西门岛湿地景观异质性、破碎度和优势度降低,景观形状复杂度降低.西门岛湿地人为干扰中心由分散发展到集中;干扰中心主要贡献景观类型是裸地和居民点;干扰总程度由海域向陆地逐渐加重,居民点、码头、交通用地干扰总程度最高.滩涂养殖、泥滩地、浮筏养殖景观的人为干扰度跳跃性大.水陆交错带出现干扰总程度较低但干扰极不平稳的现象.无干扰、半干扰和全干扰型景观的斑块数量下降.无干扰、半干扰景观的平均斑块面积增加,全干扰景观的平均斑块面积减少.无干扰景观的平均形状指数下降,半干扰型和全干扰型景观形状有复杂化趋势.     

Mapping land cover and forest density in Zagros forests of Khuzestan province in Iran: A study based on Sentinel-2, Google Earth and field data

Zagros forests in western Iran have widely been destroyed because of various reasons. This study was performed to provide the land cover and forest density maps in Zagros forests of Khuzestan province using Sentinel-2, Google Earth and field data. The forest boundary in Khuzestan province was digitized in Google Earth. Sentinel-2 satellite images were provided for the study area. One 1:25000 index sheet of Iranian Mapping Organization (IMO) was selected as pilot area in the province. Sentinel-2 image of the pilot area was classified using different supervised classification algorithms to select the best algorithm for land cover mapping in Khuzestan province. In addition, to evaluate the accuracy of Google Earth data, field sampling was performed using random plots in different land covers. Field data of forest plots were applied to investigate tree canopy cover percent (forest density), as well. Classification of Sentinel-2 image in Zagros area of Khuzestan province was done using the best algorithm and the land cover was obtained. The forest density map was also obtained using a linear regression model between tree canopy cover percent (obtained from field plots) and normalized difference vegetation index (NDVI) (obtained from NDVI map). Finally, the accuracy of land cover map was assessed by some square plots on Google Earth. Results demonstrated that support vector machine (SVM) algorithm had the highest accuracy for land cover mapping. Results also showed that Google Earth images had a good accuracy in the Zagros forests of Khuzestan province. Results demonstrated that NDVI has been a good predicator to estimate tree canopy cover in the study area. Based on results, an area of 443,091.22 ha is covered by Zagros forests in Khuzestan province. Results of accuracy assessment of the land cover map showed the good accuracy of this map in Khuzestan province (overall accuracy: 91% and kappa index: 0.83). For optimum management of Zagros forests, it is suggested that the land cover and forest density mapping will be performed using SVM algorithm, NDVI, and Sentinel-2 satellite images in Zagros forests of Khuzestan province in the certain periods.     

A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery

Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.     

A retrospective analysis of land cover change using a polygon shape index

Aim This study tests the hypothesis that the propensity of land cover patches to change is related to their shape and geometric complexity. Location The analysis is based on a 1000‐km² area of the Cairngorms in Scotland, incorporating part of Speyside and the high plateau area within the Grampian Mountains. Methods A combined dataset was created by intersecting 1964 land cover data (derived from archive aerial photography) and 1988 land cover data (from the Land Cover of Scotland dataset). A shape index was calculated for each land cover polygon inside a GIS. Information on land cover change was analysed with reference to land cover class and the polygon shape index using a regression analysis. Results For upland seminatural land cover classes, subject to low levels of management, change is related to polygon shape, such that the more complex patches were found to be more susceptible to change. This relationship breaks down where classes are more intensively managed or have been aggregated into mosaic classes. Conclusions Propensity to change was related to shape index for seminatural land cover classes. This implies that at least some landscape processes, such as anthropogenic disturbance of seminatural land covers, can be linked to ecological theory via measurements of spatial pattern. The study also highlighted some of the cartographic issues involved in estimating changes between land cover classes: there are advantages in replacing the ‘cartographic paradigm’ of comparing two derived datasets (in this case land cover maps) with direct comparison of the digital data — air photographs or satellite imagery. Such a direct approach avoids the compounding of errors introduced by the approximation of each successive air photo as a thematic map.     

基于地形限制特征的泾河流域遥感地表覆被分类

由于在分类方法和空间分辨率等方面存在局限性,基于粗分辨率遥感数据的传统非监督分类结果在不同地物过渡带内往往误差较大。该文提出了基于地形限制特征的分类方法,在非监督分类的基础上,将非监督分类结果按照像元进行细分,并运用地形限制条件对细分后的像元进行二次判别分类。结果表明,分类精度明显提高,其中,农田和居民点分类精度的提高最为明显。这一方法使得完全同质的单元可以进行属性的变更,改善了像元空间分辨率差造成的误差;而地形限制特征的引入减少了传统非监督分类的不确定性,使模糊区域的分类有了较为明确的区分特征,提高了分类的精度。     

Mapping and monitoring of coastal wetlands of Çukurova Delta in the Eastern Mediterranean region

The aim of this research was to link vegetation characteristics, such as spatial and temporal distribution, and environmental variables, with land cover information derived from remotely sensed satellite images of the Eastern Mediterranean coastal wetlands of Turkey. The research method was based on (i) recording land cover characteristics by means of a vegetation indicator, and (ii) classifying and mapping coastal wetlands utilizing a Landsat Thematic Mapper (TM) image of Çukurova Deltas in Turkey. Vegetation characteristics of various habitats, such as sand dunes, salt marshes, salty plains and afforestation areas, were identified by field surveys. A Landsat TM image of 4 July 1993 was pre-processed and then classified using the Maximum Likelihood (ML) algorithm and Artificial Neural Networks (ANN). As a result of this supervised classification, the land cover types were classified with a largest accuracy of 90.2% by ANN. The classified satellite sensor imagery was linked to vegetation and bird census data, which were available through literature in a Geographical Information System (GIS) environment to determine the spatial distribution of plant and bird biodiversity in this coastal wetland. The resulting data provide an important baseline for further investigations such as monitoring, change detections and designing conservation policies in this coastal ecosystem.     

遥感主体图的准确度对景观生态学研究的影响

用各种案例系统地解释了遥感数据分类误差对景观指数误差的必然影响。一方面 ,遥感数据在各种时间和空间尺度上为景观生态学研究提供必需的土地类型数据 ;另一方面 ,遥感技术的灵活性和复杂性可以产生出各种质量的土地类型数据。但景观生态学方面的用户对土地类型数据基本上是没有选择地使用 ,甚至是不知好坏地使用 ,所以景观生态学的发现和结论具有不可避免的任意性。总结了在各种情况下景观指数的变动区间 ,指出了现实较低的遥感数据的分类准确度会引起更低的景观指数的准确度 ,当进行景观变化分析时 ,这种误差的放大效应将更加明显。当前 ,人们对除面积以外的景观指数的误差仍然束手无策 ,尽可能地提高遥感数据的分类准确度是唯一力所能及的办法。     

Comparing socioeconomic vulnerability index and land cover indices: Application of fuzzy TOPSIS model and geographic information system

Vulnerability Assessment (VA) has numerous management implications, particularly for locating the most vulnerable areas affected by land cover degradation. This study aims to assess and compare the relationship between socioeconomic vulnerability (SEV) and land cover indices (LCIs) in summer rangelands (Natanz county) and winter rangelands (Aran-V-Bidgol county) in Isfahan Province, Iran. To assess the socioeconomic vulnerability index (SEVI), a survey was conducted. Based on the SEVI, summer and winter rangelands were classified and mapped using a combination of fuzzy TOPSIS model and geographic information system (GIS) techniques. Field research was conducted to estimate LCIs, including plant cover, plant yield, litter (dead biomass), sand and gravel, bare soil, and forage production. Finally, the correlations between SEVI and LCIs were verified, and the SEV of winter rangelands and summer rangelands was examined. Results showed the rangelands of Chah Robat, Bidhend, and Tar were the most vulnerable summer rangelands, while the rangelands of Chah Zard, Cheshmeh Sefid, and Kandeh Matin were the most vulnerable winter rangelands. Findings further revealed more LCIs, including total ground cover, the yield of shrubs, and total yield had a significant relationship with SEVI in summer rangelands compared to winter rangelands (yield of annual forbs and total yield). Moreover, there was a significant difference between SEVI, determined by the fuzzy TOPSIS model, in summer and winter rangelands. It can be concluded that while the fuzzy TOPSIS model and GIS can be used to gain a better understanding of VA, the type of rangeland has an impact on how well these techniques can assess the SEV of rangelands.     

1980-2010年鄱阳湖地区土地覆盖与景观格局变化

以1980、2005和2010年鄱阳湖地区的土地覆盖数据为基础,采用GIS、RS及景观生态学的方法,分析1980-2010年该区土地覆盖及景观格局变化情况,并定量分析了土地覆盖类型变化度、斑块面积指数、斑块形状指数、边缘密度指数以及多样性指数等指标.结果表明：1980-2010年,研究区水田、内陆水体、常绿阔叶林和城镇建设用地不仅是该区域的主要土地覆盖类型,而且变化最强烈.总体上,内陆水体、城镇建设用地面积显著增加,水田和旱地面积有所减少.从景观格局变化来看,虽然鄱阳湖地区受到人口增长和经济发展的影响,其景观破碎度变化和景观多样性指数略呈下降趋势,但变化并不大,这反映出前期环境管理对于区域环境保护已经发挥重要作用,但仍然面临长期挑战.