基于MODIS NDVI的吉林省植被覆盖度动态遥感监测

植被覆盖度是植物群落覆盖地表状况的一个综合量化指标,植被覆盖及其变化是区域环境变化的重要指示,对于区域水文及生态状况、全球变化的区域响应等都具有重要意义。以MODIS NDVI为数据源,采用像元二分模型,提取2000~2007年吉林省植被覆盖度,获取不同时期的植被覆盖度图,并进一步分析了植被覆盖度变化的原因。结果表明：吉林省植被覆盖度由东部到西部逐渐降低,其中白山地区植被覆盖情况最好。过去8 a间,吉林省植被覆盖度总体呈上升趋势,2007年植被覆盖度达到最高,为83.04%。在此期间,中部地区和西部地区植被覆盖增加了 797.52 km²,占总面积变化的74.79%。生态恢复工程、降水和气温等是影响植被覆盖度变化的主要因素。     

基于Landsat TM数据的潮白河流域植被覆盖变化研究

使用经严格配准的同一时间(1991年和2002年)Landsat TM图像数据，编制归一化植被指数(NDVI)图，进而计算生成植被覆盖度图像。通过掩膜技术和变化检测等提取了北京潮白河流域中上游地区从1991～2002年的植被覆盖变化信息。研究结果表明，北京潮白河流域中上游地区11年间植被退化的总面积为1635．3km^2，占该区域总面积的30．6％；其中植被覆盖度为40％～50％的类型退化的面积最多，为411．74km^2，变化率为66．0％，覆盖度为90％～100％的类型退化的面积最少，为14km^2，变化率为4．4％；覆盖度为30-40％的类型变化率最大，为100％，覆盖度为90％～100％的类型的变化率最小。为4．4％；从植被覆盖度变化的趋势来看，随着植被覆盖度的增加，变化率在逐渐降低；流域中游、密云水库北部和东北部以及上游的河谷地带由于受人类活动干扰的强度较大，植被退化较严重；而上游的山地区域由于人类活动干扰较少，再加上近年来采取封山育林、植树造林等措施，植被覆盖程度有所改善。     

基于TM影像的长乐市植被覆盖度变化研究

基于长乐市2006年、2009年两景Landsat TM影像,采用像元二分模型法获取二者的植被覆盖度,并结合该地区的土地覆盖结果,对长乐市2006年～2009年间植被覆盖度的变化及其原因开展分析研究.研究结果表明,长乐市植被覆盖总体良好,2006年该地区中高度、高度植被覆盖区占总面积的61.36％,2009年占总面积的58.81％.但2006年～2009年期间长乐市植被覆盖度整体呈现下降趋势,其中高植被覆盖区由2006年的31.46％降到2009年的28.49％.人口激增和经济的快速发展侵占和破坏了大量的耕地和林地,这是造成长乐市植被覆盖度变化的根本原因.     

辽宁抚顺近10年来市域热场与植被变化分析

利用1999年和2010年的TM卫星遥感影像,定量反演了抚顺市域的热场和植被指数,并对其变化进行了分析。结果表明,11 a全市的平均热场温度升高了1.53 ℃,城市热岛主要集中在抚顺市的城市建成区以及苏子河河谷和黑大线沿线地带,但强热岛和极强热岛的空间分布范围2010年较1999年压缩幅度空前。从植被盖度总体情况来看,高覆盖度植被覆盖面积均在60%以上,而全市低覆盖度等级以下的植被面积比例很小,其面积比例都在1.5％以下。从植被盖度的变化看,高覆盖度和较高覆盖度的植被面积比例分别下降了3.22％和2.31％;而中覆盖度的植被面积比例增加了4.94％,其变化最大的区域在抚顺市区,该区域变化的比率是全市变化的3~5倍。从热场与植被的变化原因来看,首先是受植物生长季节气候的暖干化变化趋势的影响,其次还与土地利用类型中耕地和草地的减少以及建设用地的快速增加有关,此外,抚顺市生态建设工作对其也有一定程度的影响。     

石家庄1995～2015年植被覆盖变化监测及预测

为揭示石家庄1995~2015年植被覆盖变化状况,掌握植被覆盖的变化趋势,该文基于1995、2001、2007、2009、2012和2015年Landsat TM/OLI遥感影像数据,通过像元二分模型求得石家庄6个时期的植被覆盖度,借助变异系数模型和Slope模型分析该地区20年内植被覆盖的空间变化特点和变化趋势,最后利用元胞自动机-马尔可夫模型对石家庄2018年各级植被覆盖状况进行预测。结果表明:从1995到2015年,石家庄植被覆盖度均值增加了3.71%;全市平均变异系数为0.211,人为因素是植被覆盖波动变化的主要因素;植被覆盖变化趋势呈基本不变和三类变好区域共占全市面积的82.22%,三类变差区域多分布在城市建设和经济发展的活跃地区;到2018年,石家庄高植被覆盖和中高植被覆盖面积都有下降,中植被覆盖和低植被覆盖面积有所提高,极低植被覆盖面积基本不变。     

基于遥感的海南本岛植被覆盖度时空变化及其地形效应研究北大核心CSCD

植被覆盖在维持生态系统结构稳定和防治水土流失等方面发挥着重要的作用,海南自1988年建省以来迅速发展,导致海南岛植被覆盖也产生了巨大的变化.为揭示海南本岛地形因子对植被覆盖度的影响以及为海南本岛进一步制定合理的生态环保策略提供依据,基于1988、1998、2008、2017和2020年Landsat-TM/OLI多光谱影像,以海南本岛为研究区域,采用归一化植被指数和像元二分模型进行植被覆盖度提取,通过线性趋势分析海南本岛近30a植被覆盖变化特征.并结合30 m_DEM获取的海、坡度和坡向数据,来进一步探讨海南本岛植被覆盖度在不同地形因子条件下的空间分布特征.结果表明:①1988～2020年海南岛平均植被覆盖度介于0.58～0.88之间,整体呈先下降后上升趋势;②海南岛高植被覆盖主要分布于海南岛中部、西南部和东南部地区,低植被覆盖主要出现在居住区、沿海地区等人为干扰因素较高的地区;③海南岛各等级植被覆盖均随海拔的增加而不断降低,在海拔小于100 m的区域分布面积最大;坡度为0～5°时植被覆盖面积达到最大值,随着坡度的增加,植被覆盖面积呈减少趋势;各等级植被覆盖在阴坡和阳坡的分布面积变化差异不大,主要以高植被覆盖类型为主.     

南方丘陵区植被覆盖度遥感估算与时空变化研究——以福建省永定县为例

植被覆盖度是生态环境监测的重要指标,而复杂地形因素影响对山地植被遥感信息准确提取。基于Landsat-8OLI遥感数据,分别采用像元二分模型和线性混合光谱分解法,在对比分析植被覆盖度的地形敏感性基础上,选择山地植被指数(NDMVI)估算了1992、2002和2014年永定县的植被覆盖度,并分析其变化。结果表明:1基于山地植被指数(NDMVI)的覆盖度估算模型的地形敏感性最弱,更适合于南方丘陵山地的植被覆盖度遥感反演;2永定县总体植被覆盖度较高,平均植被覆盖度达77.99%以上,高覆盖度区占59.73%以上,22年内植被覆盖度经历了先提高再下降的过程;3在空间上,高坎抚、金丰和西部片区的植被覆盖度较低,动态变化较明显。永定县金丰片区植被覆盖度明显提高;而近12年内高坎抚片区因矿业开采活动对生态环境的破坏,植被覆盖度降低幅度大,且变化面积较大。     

内蒙古浑善达克沙地南部草地盖度探测及其变化分析

陆地生态系统植被覆盖程度是评价区域生态环境变化的重要因子。以内蒙古浑善达克沙地南部(锡林郭勒盟正蓝旗北部地区)为研究区,应用中国环境与灾害监测预报小卫星数据HJ-1A CCD及美国陆地卫星数据Landsat TM,分别基于像元二分模型和三波段梯度差模型、使用NDVI和RDVI等参数,对研究区草地植被覆盖度进行了探测,并对比了不同模型方法和参数所得研究区草地植被盖度成果的分类精度。研究结果表明,基于像元二分模型和RDVI参数探测植被盖度的方法表现最好;以此为基础,进一步分析了研究区2000~2009年区域植被覆盖度动态变化,发现本地区在2000年之后草地覆盖改善区面积超过草地盖度下降区面积,浑善达克沙地南缘植被恢复状况总体较好。     

基于Landsat影像的北京植被覆盖度变化趋势分析

植被覆盖状况是决定大城市地区生态环境质量的重要因素之一,但在快速城市化进程下城市内部及周边地区植被覆盖的动态变化状况尚不清晰,需结合遥感数据进行分析。以北京市为研究区,基于Landsat影像获取植被覆盖度的空间分布,计算移动窗口内植被覆盖度的均值和标准差,将其分别作为表征局部植被覆盖水平和植被覆盖度异质性的指标,采用Mann-Kendall检验识别均值和标准差具有显著变化趋势的窗口,并使用Sen’s Slope估算变化梯度,进而分析北京植被覆盖度变化趋势。结果表明在1984~2014年间：①植被覆盖水平呈显著上升趋势的区域主要分布在市中心与西部和北部山区,而在市中心外“东北、东、东南、南、西南”方向的近郊分布有大量植被覆盖水平显著下降的区域;②植被覆盖度异质性呈显著上升趋势的区域主要分布在平原区,呈显著下降趋势的区域主要集中在北部山区。     

用于土壤流失量遥感监测的植被因子算式的初步研究

基于标桩法实测到国内较大量的土壤年流失量及有关参数资料,建立了山丘植被区较为可靠的植被因子算式。它比 USLE 查表法定C值较便于应用在土壤流失量的遥感监测中。按照植被因子算式,C 值与植被覆盖度成反比,且其变化范围随植被覆盖度大于78.8%或76.1%～0%而变为0～0.45。经与反算 C 值和实测流失量验证,应用植被因子算式监测流失量的精度平均为77.4%或82.1%,多数在80%以上。     

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

Interannual vineyard crop variability in the Castilla–La Mancha region during the period 1991–1996 with Landsat Thematic Mapper images

The vineyard crop is considered an indicator of vegetation cover processes in the Castilla–La Mancha region, as the crop has undergone far-reaching changes in the last ten years: abandonment, removal of vineyards and replacement with other crops such as cereal. The so-called ‘Change detection’ is a process that allows identification of differences in the state of the vineyard by observing it at different times. Essentially, it involves the ability to quantify temporal effects using multi-temporal datasets.

The aim of this study is to analyse the vineyard variability during the period 1991–1996 using different Landsat-5 Thematic Mapper (TM) images belonging to an identified period that highlights differences in the vegetation phenology changes. The images were firstly georeferenced and radiometrically normalized for the change detection purpose. The study area is located in central Spain and involves five councils belonging to the Castilla–La Mancha region: Mota del Cuervo, Las Mesas, Tomelloso, Socuellamos and Villarrobledo, with a surface area of 174?040?ha. This area is especially cultivated by the vineyard crop. Moreover, the study area was classified with the images acquired in 1991, using the multi-temporal masking techniques for the vineyard inventory.

The changes experimented by the vineyard have been grouped in three categories: cereal (CE), abandoned vineyard (AB) and bare soil (BS). Therefore, two methods of Normalized Difference Vegetation Index (NDVI) image difference and NDVI Change Vector Analysis (CVA) were used to map these categories of change. The results indicate that 28.4% of vineyard area undergoes changes during the period 1991–1996. The results have been checked with the ground data and all information is highly conformed.     

Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia

Satellite observations play an important role in characterization of the interannual variation of vegetation. Here, we report anomalies of two vegetation indices for Northern Asia (40°N-75°N, and 45°E-179°E), using images from the SPOT-4 VEGETATION (VGT) sensor over the period of April 1, 1998 to November 20, 2001. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), which are correlated to a number of vegetation properties (e.g., net primary production, leaf area index), were compared. The results show that there is a large disagreement between NDVI and EVI anomalies in 1998 and 1999 for Northern Asia. The NDVI anomaly in 1998 was largely affected by atmospheric contamination, predominantly aerosols from extensive forest fires in that year. The EVI anomaly in 1998 was less sensitive to residual atmospheric contamination, as it is designed to be, and thus EVI is a useful alternative vegetation index for the large-scale study of vegetation. The EVI anomaly also suggests that potential vegetation productivity in Northern Asia was highest in 1998 but declined substantially in 2001, consistent with precipitation data from 1998-2001.     

基于遥感的植被覆盖变化分区研究——以黄土高原马莲河流域为例

为了研究黄土高原1977~2010年近33 a的植被覆盖变化过程,以黄土高原典型植被区马莲河流域为例,将其划分为董志塬农业区、子午岭林业区和北部半农半牧区3个生态功能区进行研究。利用1977、1987、2000和2010年共4期陆地卫星影像,采用监督分类和非监督分类相结合的方法获得植被分类结果。利用转移矩阵分析植被覆盖的变化过程,并计算植被覆盖年度变化率。结果表明:1977~1987年董志塬农业区的植被恢复程度大于子午岭林业区,而北部半农半牧区除草原大面积恢复,其他植被均退化;1987~2000年是植被覆盖变化的一个转折点,各分区植被均以退化为主,董志塬农业区最为典型;2000~2010年各分区均以植被恢复为主,董志塬农业区和北部半农半牧区植被恢复相对较快。整个马莲河流域植被覆盖呈现出退化-恶化-恢复的变化趋势。     

利用MODIS EVI时间序列数据分析福建省植被变化(2000~2017年)

以全国森林覆盖率最高的福建省为研究对象,利用2000~2017年夏季的MODIS EVI植被指数数据和气象与非气象因子进行协同分析,以揭示近17年福建植被的时空变化及其影响因子。结果表明:研究期内福建的EVI均值整体上升,从2000年的0.454上升至2017年的0.505,17 a间上升了11.2%,表明福建省的植被整体处于变好的状态,且在中部和西南部的变化最明显。相关分析表明,在研究期内,气象因子(气温和降水)对EVI变化的影响不显著,植被的变好主要为非气象因子的作用。EVI的提高主要得益于2003年福建省建设生态省后森林覆盖率的提高,并和2012年开始的水土流失治理有明显关系,这说明人类活动的积极作用对福建植被的变好起到了关键的作用。     

Analysis of Vegetation Changes in Fujian Province Using MODIS EVI Time Series Data (2000~2017)

Fujian province has the highest forest coverage rate in China for decades, which has played an important role in maintaining a good ecosystem quality in southeastern China. This study conducted an investigate aiming to find out the spatial and temporal changes of the vegetation status in Fujian and the impact factor involving in the vegetation growth during the period from 2000 to 2017, using the summer data of MODIS Enhanced Vegetation Index (EVI) product, associated with meteorological and non-meteorological data. The results showed that the mean EVI of Fujian rose as a whole during the 17 study years, from 0.454 in 2000 to 0.505 in 2017, increased by 11.2% in the period. This indicates that the overall vegetation status in Fujian has been improved, especially in, south and west parts of the province, while eastern coastal areas have shown decrease in vegetation coverage. Correlation analysis showed that during the study period, meteorological factors (temperature and precipitation) had no significant impact on the provincial EVI change, and the improvement of the vegetation status mainly due to non-meteorological factors. Both the construction of the ecological province in Fujian starting in 2003 and the soil erosion treatment starting in 2012 have strong relationships with vegetation increase. The increase of forest coverage rate and the decrease of soil erosion area have contributed significantly to the enhancement of Fujian’s EVI in the past 17 years.