Chihuahuan沙漠南缘沙漠化进展评价——以San Luis Potosi高原为例（英文）

The aim of this study is to establish if the San Luis Potosi Plateau(SLPP), which is part of the southern edge of the Chihuahuan Desert, is generating desertification processes, indicating a progression of the desert toward the central part of Mexico. Therefore, we analyzed the temporal evolution of four environmental indicators of desertification: Normalized Difference Vegetation Index(NDVI), Normalized Difference Water Index(NDWI), Iron Oxides Index(IO) and Surface Temperature(ST). Landsat TM images are used to cover a period from 1990 to 2011. A new equation of total balance is proposed to generate an image of the overall evolution of each factor which is applied to get a probability map of desertification. The evolution of NDVI, NDWI and IO shows a behavior almost stable over the time. In contrast, the ST shows a slight increase. The outcomes of this study confirm periods of vegetation re-greening and 8.80% of the SLPP has the highest probability to develop desertification. The most affected area is the portion west of the region, and the east and south are the least affected areas. The results suggest a slight advance of the desert, although most of the area doesn't have the necessary conditions to develop desertification.     

美国中西部沙山地区环境变化的遥感研究

文章选择了典型半干旱地区———美国中西部的沙山为研究区,利用陆地卫星TM、MSS图像数据,采用线性光谱混合模型(LSMM)、穗帽变换、指数提取等方法,提取反映环境变化的沙地、湿地、水域、草地等信息,并研究典型半干旱地区———Nebraska近20年来的环境变化及其与气候变化的关系。     

归一化水汽指数与地表温度的关系

城市热岛的遥感研究,传统上主要利用归一化植被指数作为指标来评估地表温度-植被之间的关系。本研究探讨了以归一化水汽指数作为评估其与地表温度关系的适应性,比较了归一化水汽指数和归一化植被指数在表达城市热岛效应方面的效力。利用"单窗算法"和3个不同时段的TM/ETM+数据获取了珠江三角洲核心区域的地表温度分布图,分析了两种指数与地表温度之间的关系。对局部区域逐像元和对区域总体分析的结果表明:不同时段的地表温度与归一化水汽指数之间均存在较强的线性关系,而地表温度与归一化植被指数之间的线性关系相对较弱。说明在城市化的环境下,与传统上常用归一化植被指数作为指标来定量分析地表温度相比,归一化水汽指数提供了一种互补的、甚至更加有效的衡量指标。     

Albedo-NDVI特征空间及沙漠化遥感监测指数研究

利用遥感数据和野外调查数据分析了沙漠化与地表定量参数之间的关系,提出了Albedo-NDVI特征空间的概念以及基于Albedo-NDVI特征空间的沙漠化遥感监测模型,即沙漠化遥感监测差值指数模型(DDI)。这个模型充分利用了多维遥感信息,指标反映了沙漠化土地地表覆盖、水热组合及其变化,具有明确的生物物理意义。而且指标简单、易于获取,有利于沙漠化的定量分析与监测。     

基于谱间特征和归一化指数分析的城市建筑用地信息提取

以福州市ETM+影像为例,研究了城市建筑用地信息快速准确提取的原理和方法。通过对归一化差异型指数构成原理的分析以及对同名异义和异名同义现象的甄别,选取了归一化差异建筑指数(NDBI)、修正归一化差异水体指数(MNDWI)和土壤调节植被指数(SAVI)来代表城市建成区的三种最主要的土地利用类型--建筑用地、水体和植被。在此基础上进一步对这三个新的指数波段进行谱间特征分析,最后利用基于规则的逻辑判别运算将城市建筑用地信息提取出来。研究表明这一方法可以使繁杂的多波段谱间分析得以简化, 是一种快速准确、未经人工干预的建筑用地信息提取方法。本文还探讨了在城市建成区的研究中采用SAVI指数替代NDVI指数的优点。     

人类活动和气候变化对红碱淖植被覆盖变化的影响

利用GIMMS NDVI、SPOT VGT两种归一化植被指数(NDVI)数据和气候资料对红碱淖地区植被覆盖变化、气候变化进行了研究, 从气候变化和人类活动的角度分析了植被覆盖变化的原因。结果表明,1957—2007年期间红碱淖地区温度上升趋势显著,降水经历了由少许的增加转向减少的过程,进入了一个相对干旱气候态。1982—2007年期间红碱淖地区植被覆盖变化趋势是在波动中逐渐增加,大致经历了4个阶段：①1982—1988年植被覆盖持续增加;②1989—1998年小幅波动,相对稳定;③1999年植被覆盖迅速下降,1999—2001年维持较低值;④2002年快速增加到较高水平,2007年达最大值。气候变暖使春季生长季节提前、秋季生长期延长。春季的降水量对春季的植被覆盖影响明显,春夏之交降水量对NDVI的影响存在一个月的滞后现象。生长季的降水量变化趋势与植被覆盖的变化趋势相一致。夏季温度上升加速了地表蒸散发过程,同时降水具有减少的趋势,干旱对植被生长有抑制作用,夏季的植被覆盖却在显著增加。     

基于无人机遥感的高寒草原沙化模型及等级划分

高寒草原是青藏高原草地生态系统的主要组成，在防风固沙、野生动物保育等方面具有重要作用。近年来，在全球气候变化和人为干扰加剧的背景下，高寒草原沙化加剧，基于时空尺度监测范围及程度是防治高寒草地沙化的前提。以青海三江源区玛多县的高寒草原为研究区，结合大疆“精灵3”和“经纬M100”旋翼无人机和地面调查，探讨基于无人机遥测的植被指数在草地沙化调查方面的适宜性，以此为基础制定了高寒草原沙化模型及等级划分标准。结果显示：（1）通过对VDVI（Visible-Band Difference Vegetation Index）、ENDVI（Enhanced Normalized Difference Vegetation Index）和NGRDI（Normalized Green-Red Difference Index）指数与草地沙化指数GDI（Grassland Desertification Index）的相关分析，选取出高寒草地沙化研究最优植被指数为VDVI（R=0.9055）；（2）GDI与VDVI的关系模型为VDVI=0.3024GDI²-0.0335GDI+0.0119（R²=0.9326）。模型相对误差为1.779%（RMSE=0.165，R²=0.7447），拟合精度较高；（3）基于无人机遥感植被指数的聚类分析，将研究区高寒草原沙化划分为5个等级，即无明显沙化（VDVI>0.2247）、轻度沙化（0.1493 < VDVI < 0.2246）、中度沙化（0.0924 < VDVI < 0.1492）、重度沙化（0.0692 < VDVI < 0.0923）和极度沙化（VDVI<0.0692）。     

生态建设政策对沙漠化影响的定量分析

本文从分析沙漠化的物理机制入手,将生态建设政策对沙漠化的作用以政策指数（PIX ）来表示,并将其分解为空间广度（PSS）和时间强度（PEF）两个变量,PIX=（PSS+PEF）/2。以宁夏盐池县退耕还林和草地禁牧两种政策为例,在政策实施区分别选取40个样本点,提取沙漠化变化、植被指数变化、政策指数等数据。计算表明：退耕还林和草地禁牧政策与沙漠化变化相关系数分别为-0.664和-0.746,两种政策都对沙漠化面积的减少起到了显著的作用。政策广度和政策强度都与沙漠化的逆转显著相关,说明构建的政策指数可以更准确地反映政策因素对沙漠化的作用。政策因素对沙漠化作用的定量分析,对沙漠化治理政策的绩效评价以及新制度的设计,都有重要意义。     

区域植被覆盖的多尺度空间变异性——以贵州喀斯特高原为例

地理格局尺度依赖性的客观存在,要求在连续尺度序列上对区域植被覆盖空间变异性进行考察,以真实反映植被覆盖空间特征。以贵州喀斯特高原为例,借助地统计学和GIS 软件,揭示了研究区NDVI的空间变异特征,并进行了空间变异与空间尺度的耦合研究。结论如下：① NDVI空间变异程度表现出明显的尺度依存性,空间尺度的粗粒化对NDVI的平滑作用非常显著,但两种重采样方法对原始数据的粗粒化作用机制不同;② 基于不同遥感数据源获得的NDVI数据之间空间格局差异明显,而且传统统计结果与地统计学结果明显不同,说明空间信息对数据间的差异性统计影响显著;③ NDVI空间变异性呈现显著的各向异性,并表现出对遥感数据源的敏感性。     

天山山区草地变化与气候要素的时滞效应分析

通过选取新疆天山山区作为研究区,分析该地区气候参数(降水、温度、光照)对草地季节变化影响的滞后性特征.利用研究区内各气象站点的气温、降水和日照时数的逐句数据、SPOTVGT时序数据和土地利用覆盖数据,运用时滞相关分析和GIS空间分析方法.根据13个滞后期(0～12旬)和13个时间尺度(1～13旬)分析了植被NDVI与同...     

干旱地区植被指数(VI)的适宜性研究

以位于典型干旱区的甘肃河西地区石羊河下游的民勤绿洲为例,对NDVI、SAVI、MSAVI和GEMI等4种VI(植被指数)受土壤背景光谱影响的程度和探测低盖度植被的能力进行了对比研究。通过分析VI提取植被信息时植被土壤噪音比的变化发现,植被覆盖较低条件下VI提取植被信息总体受土壤背景光谱影响程度较低,相比而言,GEMI提取植被信息中受土壤背景影响最小,其他3种VI的区别不太明显。通过分析不同VI随植被覆盖度增加的反映速率变化及不同覆盖条件下不同VI的取值范围的变化发现,NDVI探测低盖度植被的能力最强,GEMI次之。GEMI可能是干旱地区植被探测较适宜的植被指数。     

Analyzing vegetation dynamic trend on the Mongolian Plateau based on the Hurst exponent and influencing factors from 1982–2013

This study analyzed the spatial and temporal variations in the Normalized Difference Vegetation Index (NDVI) on the Mongolian Plateau from 1982–2013 using Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data and explored the effects of climate factors and human activities on vegetation. The results indicate that NDVI has slight upward trend in the Mongolian Plateau over the last 32 years. The area in which NDVI increased was much larger than that in which it decreased. Increased NDVI was primarily distributed in the southern part of the plateau, especially in the agro-pastoral ecotone of Inner Mongolia. Improvement in the vegetative cover is predicted for a larger area compared to that in which degradation is predicted based on Hurst exponent analysis. The NDVI-indicated vegetation growth in the Mongolian Plateau is a combined result of climate variations and human activities. Specifically, the precipitation has been the dominant factor and the recent human effort in protecting the ecological environments has left readily detectable imprints in the NDVI data series.     

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

There is a need for improved and up-to-date land use/land cover (LULC) data sets over an intensively changing area in the Amur River Basin (ARB) in support of science and policy applications focused on understanding of the role and response of the LULC to environmental change issues. The main goal of this study was to map LULC in the ARB using MODIS 250-m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI), and reflectance time series data for 2001 and 2007. Another goal was to test the consistency of the classification results using relatively coarse resolution MODIS imagery data in order to develop a methodology for rapid production of an up-to-date LULC data set. The results on MODIS land cover were evaluated using existing land use/cover data as derived from Landsat TM data. It was found that the MODIS 250-m NDVI data sets featured sufficient spatial, spectral and temporal resolution to detect unique multi-temporal signatures for the region’s major land cover types. It turned out that MODIS 250 NDVI time series data have high potential for large-basin land use/land cover monitoring and information updating for purposes of environmental basin research and management.     

半干旱典型风沙区植被覆盖度演变与气候变化的关系及其对生态建设的意义

中国北方四大沙地(毛乌素沙地、浑善达克沙地、科尔沁沙地、呼伦贝尔沙地)是半干旱地区荒漠化发展的典型地区和荒漠化防治的重点区域.分析四大沙地植被覆盖度的时空演变规律及其对气候要素的响应机制,对保护北方生态脆弱区的生物多样性与生态安全具有重要意义.基于1998-2018年SPOT-VEGETATION的植被覆盖度指数(ND...     

基于遥感影像的哈尔滨市城市热环境变化

以Landsat TM/ETM+为基本数据源,利用遥感和GIS技术定量反演了哈尔滨市1989和2000年的地面亮温和植被指数(NDVI),对标准化地面亮温分级数据进行热力景观指数计算,研究城市热场分布的空间格局、时空变化规律和不同植被指数(NDVI)等级下热环境的变化特征.结果表明:1989和2000年哈尔滨市建成区均存在显著的热岛效应,2000年建成区热岛效应增强,哈尔滨市总体地面亮温升高;热力景观日趋破碎,各景观类型受干扰程度较大,处于很不稳定的状态.NDVI与地面亮温的负相关性随着植被覆盖度的升高而增大;植被覆盖度与地面亮温的空间变异呈负相关.     

锡林郭勒草原植被生长对降水响应的滞后性研究

植被生长对气候因子变化的响应一直是全球气候变化研究的热点.NDVI作为植被生长的有效指示因子广泛的应用在植被遥感中.多数滞后性响应的研究基于月尺度或季节尺度的NDVI,容易夸大或缩小滞后效应,不利于滞后时间的确定.为了揭示降水对草原植被生长的有效作用尺度,本研究以2001年至2007年MODIS反射率数据和气象站点的降水为数据源,分析了时间间隔8天的内蒙古锡林郭勒盟草原生长季NDVI的变化特征以及NDVI与不同尺度的累积降水的相关关系.结果表明,年内生长期草原的NDVI变化呈单峰型,荒漠草原的NDVI变化没有草甸草原和典型草原明显.草原生长对降水存在明显的滞后,一般在50～60天,不同类型的草原滞后时间不同,但均无明显差异.同时,分析指出4～9月草原植被在不同时间段对降水响应的滞后时间分别为64、80、40、40、56和64天,特别是6月和7月份草原植被生长对降水变化的响应最为敏感.这为NDVI预测干旱提供参考.     

Relationships between Normalized Difference Vegetation Index (NDVI) and carbon fluxes of biologic soil crusts assessed by ground measurements

The aim of this research was to study the relationships between the biological soil crusts (BSC), spectral reflectance and photosynthetic activity. Twenty field campaigns, each lasting several days, were conducted during the 2002–2003 rainy season at sand dune and loess environments in the north-western Negev desert of Israel. Simultaneous measurements of CO₂ net exchange and spectral reflectance were carried out for several types of BSC. The Normalized Difference Vegetation Index (NDVI) was derived from the BSC reflectance and correlated with their CO₂ exchange data. The relationship between NDVI and CO₂ exchange is discussed in detail with respect to environmental factors, such as soil water content, air temperature, and light intensity. Fairly good correlations were found in the rainy season. The NDVI was useful in indicating the potential magnitude and capacity of the BSC assimilation activity. Furthermore, the index corresponded well with different rates of photosynthetic activity of the different types of microphytes. The results demonstrate that spectral reflectances of the BSC can be related to photosynthetic activities and posseses the potential to assess the amount of carbon sequestration by these microphytes on an areal scale using satellite images.     

气候和土地利用变化对塔里木河干流区植被覆盖变化的影响

基于遥感和地理信息系统技术,利用1998—2008年SPOT-VEGETATION归一化植被指数（NDVI）数据对塔里木河干流区1998—2007年植被覆盖的时空变化进行了监测,并从气候变化和土地利用变化双重角度分析了植被覆盖变化的原因。研究表明,塔里木河干流区植被覆盖变化经历了两个阶段:1998—2001年植被覆盖严重退化时期;2002—2007年植被覆盖度由急剧上升到缓慢下降再到持续升高时期,NDVI明显高于20世纪末期水平。塔里木河干流区植被覆盖变化存在显著的空间差异,绿洲农业灌溉区和退耕还林还草生态恢复区的植被覆盖度显著提高,天然草地植被区的植被退化严重。塔里木河干流区植被覆盖变化是气候和土地利用变化共同作用的结果。温度对植被覆盖变化的影响表现为对植被生长年内韵律的控制和秋季植被生长期的延长,年降水量的波动式上升是导致塔里木河干流区植被覆盖变化两个阶段呈现差异的主导因素。     

Mesoscale Satellite Bioclimatology

This paper examines the strength of relationships between the Normalized Difference Vegetation Index (NDVI) and climatic data, when examined at the mesoscale. Mean monthly AVHRR NDVI data for 1988-1996 for the months of April through October for State of Kansas, its nine climatic divisions (CDs), and dominant land cover types within each CD were used. Corresponding climatic and water budget data were obtained or derived from National Climatic Data Center data. Temperature, precipitation, and NDVI deviations from normal were determined. Statistical analysis revealed significant relationships between NDVI and climatic variables, although strengths of the associations were modest. The highest correlation coefficient (r) for the state as a whole was 0.53, between NDVI and estimated actual evapotranspiration. When examined by climatic division or major land cover type, relationships between NDVI and a drought index were statistically significant in most cases and ranged from 0.30 to 0.56.     

The temporal and spatial relationship between NDVI and climatological parameters in Colorado

This paper describes the spatial and temporal relationship between AVHRR/NDVI (Normalized Difference Vegetation Index) and climatological parameters (temperature and precipitation), which, in some sense, is influenced by topographical factors and land-cover types in Colorado. The correlation coefficients and partial correlation coefficients have been computed pixel by pixel over Colorado in order to analyze the relationship. The temporal variation and correlation of AVHRR/NDVI, temperature and precipitation were analyzed with a sampling method. The study reveals that there exists a close correspondence between monthly NDVI and temperature, which has strong impact from temperature on the changes of NDVI in Colorado. The spatial changes of NDVI are not influenced obviously by the precipitation since these two variables are different from each other in time series in Colorado. The study clearly revealed the spatial variation and its distribution patterns of relationship between NDVI and climatic parameters (temperature and precipitation) in Colorado.