基于NDVI的大同煤田植被覆盖度动态变化研究

利用大同煤田地区1987年9月10日、2000年9月2日和2010年9月6日的TM遥感影像数据,采用基于NDVI的像元二分模型法反演了植被覆盖度,获取了该地区3个时期的植被覆盖度等级图,定量分析了该地区在大规模煤炭开采影响下的植被覆盖度的动态变化情况.研究结果表明1987年～2010年近24年以来,大同煤田地区植被覆盖度呈较明显的下降趋势,植被退化较严重.     

基于RS和GIS的青海湖流域植被覆盖度变化与驱动因子研究

基于2001—2010年MOD13Q1数据,采用像元二分模型估算近年青海湖流域的植被覆盖度,分析其时空变化规律,探讨植被覆盖度与气象因子、地形因子之间的关系。结果表明:在青海湖流域,年平均植被覆盖度呈中部高、西北低的态势,中高覆盖度面积最大,占流域总面积的60%以上;近10年流域平均植被覆盖度呈波动上升趋势,最大值出现在2010年;NDVI在年际尺度上与气温的相关性并不显著,但与降雨量呈显著正相关关系(R=0.79),降雨量与植被覆盖度年际变化总体趋势较为相似;植被覆盖度随高程和坡度的增大均先增大后减小,最大值出现在海拔3500—4000m、坡度2°—6°的缓平地上;阳坡的植被覆盖度大于阴坡,而平地最小。     

Sentinel 2A影像去云下的丘陵地区植被覆盖度反演

光学遥感影像作为一种重要的数据源,被广泛应用于植被覆盖度反演,但南方丘陵地区常年多云雾,受云雾污染,像元质量下降。受丘陵地形的限制,地块破碎化严重,加之南方丘陵植被类型多,植被光谱变化较大,这些因素造成了植被覆盖度提取的准确度降低。为了提高丘陵地区植被覆盖度反演的精度,提出一种整合光学与SAR影像的反演框架。以Sentinel1A和Sentinel 2A影像作为遥感数据源,首先利用Sentinel 1A的光谱特征去除Sentinel 2A影像上的云像元,再使用光谱归一化减弱植被的光谱变化,在此基础上将扩展线性光谱混合模型用于反演植被覆盖度。结果表明:该框架能有效恢复云污染的像元,端元和影像归一化后,降低光谱异质性,反演丘陵地区植被覆盖度的精度较高,均方根误差为0. 14,相关系数为0.95,接近地表植被覆盖的真实情况。     

山区县域植被覆盖度遥感估算研究——以沂源县为例

为分析典型山区县域城市——沂源县植被覆盖的时空变化情况,通过获取Landsat8_OLI数据计算NDVI指数,进一步利用像元二分模型实现对地表植被覆盖度的估算。结果表明:(1) NDVI能够有效准确地获取地表植物群落的生长发展状况;(2)将求取的植被覆盖度和地表实际数据通过统计描述特征进行相互验证,总体精度约为80%,验证了像元二分模型的可行性;(3)沂源县植被覆盖度呈现东高西低的总体趋势,植被覆盖度与人类活动成整体负相关关系。     

基于SPOT VGT/NDVI的陕西省关中地区冬小麦遥感估产

冬小麦是关中地区的主要粮食作物之一。对冬小麦产量估算的研究可使国家有关部门及时掌握粮食生产状况,对国家制定粮食政策和经济计划,及时进行宏观调控有着重要的意义。以陕西省关中地区为研究区域,利用1999—2007年的SPOT VGT/NDVI遥感数据对冬小麦产量进行预测。首先,选取冬小麦关键生长期内0.2—0.8范围内的旬NDVI数据,利用最大值合成法合成后的月NDVI数据建立与冬小麦产量之间的关系;其次,使用相关分析方法筛选出冬小麦关键期内与产量相关性较好的月份和旬,分别建立这些月份、旬与冬小麦产量之间的回归关系模型,最后对模型进行精度验证。结果显示,NDVI与产量之间有很好的相关性,其中4月上旬的相关性最好,其相关系数R2为0.605,即利用4月份上旬的数据预测冬小麦产量最好。在预测产量与实际产量对比的基础上,得出估产的相对误差为-5.2%—-7.9%,表明估产精度良好,可运用于冬小麦的实际估产。     

高分辨率遥感影像在城市绿地信息提取中的应用研究

高分辨率卫星遥感图像数据量大、空间分辨率高、结构信息复杂、地物同物异谱现象更为突出等特征给专题信息提取技术带来了新的挑战。基于植被的光谱特征,利用监督分类、植被指数分类和目视解译等方法对QuickBird高分辨率卫星遥感影像的绿地信息进行提取,并对分类精度作了比对分析。研究结果表明,监督分类方法不能得到令人满意的结果,运用植被指数分类方法则有明显改善,其中归一化植被指数(NDVI)精度最高,因此NDVI能有效地对植被进行分类与识别。     

西宁大气气溶胶特征分析

利用2005～2015年的MODIS气溶胶光学厚度产品AOT和相关气象资料,分析了西宁大气气溶胶的特征,研究得出:(1)西宁AOT的时空分布差异性显著。空间上,植被覆盖度小的城区AOT高于植被覆盖度大的林区的AOT;时间上,夏季的AOT相对于冬季值较大;从2005～2015年西宁林区和城区的AOT都呈现下降趋势。(2)西宁AOT的空间分布与下垫面条件密切相关。时间变化特征与气候条件(包括太阳辐射、温度、相对湿度、降水和风向等)、下垫面状况和MODIS遥感反演的气溶胶产品本身的误差等因素有关。(3)西宁的AOT与PM10浓度在时间变化上表现为负相关关系。     

植被覆盖度对居民区温度影响的分析

选取长沙市不同植被覆盖度的3个小区,分析植被覆盖度对居民区环境温度的影响与两者间的定量关系。结果表明:(1)植被覆盖度与周边温度之间存在明显的负相关关系,且不同居民区的树冠遮蔽处、阳光直射处、构筑物阴影处也存在类似关系;(2)当居民区植被覆盖度超过40.22%时,植被调节能力有所下降;(3)秋季长沙市居民区内植被覆盖度每增加10%,温度调节能力约0.2℃,植被对居民区内高温、低温均有调控作用。     

用环境小卫星遥感数据分析攀枝花市苏保区植被覆盖变化情况

利用环境小卫星遥感数据,可以计算特定区域的归一化植被指数,进而定量分析其植被覆盖情况。以"四川攀枝花苏铁国家级自然保护区"为研究对象,选取下载了相应区域2009年和2015年的环境小卫星遥感数据,经过辐射定标、图像校准等预处理后,计算得到归一化植被指数和植被覆盖度,通过对植被覆盖度数据进行统计和分析比较,结果表明该自然保护区及周边区域2015年植被覆盖度比2009年有明显增加。     

低温雪灾后莽山保护区森林植被恢复遥感评价

以2008年1月的低温雪灾为背景,选择反映植被长势的植被覆盖度为研究对象,以受人类活动干扰最少、最能体现植被自然恢复的莽山保护区为研究区,以像元二分模型为基础,通过计算样区植被遥感直方图的拐点来确定NDYI(soil)和NDVI(veg),通过植被覆盖的变化情况来衡量低温雨雪后森林植被的恢复状况,并对不同地形条件下、不同林龄和树种的森林植被恢复情况进行了分析,以期为整个莽山乃至南方受灾地区的受灾程度评价提供方法支持,为灾后重建决策提供数据参考.     

Combining remote sensing imagery and forest age inventory for biomass mapping

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM(+) images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM(+) reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R(2)=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R(2)=0.792). AGB of mixed forests was predicted well by LAI and SR (R(2)=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha(-1).     

Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images

Spatial distributions of the leaf area index (LAI) needed for carbon cycle modeling in Xingguo County, China were estimated based on correlations between the field-measurements and vegetation indices (VIs). After making geometric and atmospheric corrections to two Landsat ETM+ images, one in January 2000 and the other in May 2003, three VIs (SR, NDVI, and RSR) were derived, and their separate correlations with ground LAI measurements were established. The correlation with RSR was the highest among the three VIs. The retrieved LAI values for January 2000 were lower than those for May 2003 because of a small seasonal variation in the coniferous forests (predominantly masson pine) and the decrease in the understorey vegetation during winter.     

Preliminary comparison of landscape pattern-normalized difference vegetation index (NDVI) relationships to Central Plains stream conditions

We explored relationships of water quality parameters with landscape pattern metrics (LPMs), land use-land cover (LULC) proportions, and the advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) or NDVI-derived metrics. Stream sites (271) in Nebraska, Kansas, and Missouri were sampled for water quality parameters, the index of biotic integrity, and a habitat index in either 1994 or 1995. Although a combination of LPMs (interspersion and juxtaposition index, patch density, and percent forest) within Ozark Highlands watersheds explained >60% of the variation in levels of nitrite-nitrate nitrogen and conductivity, in most cases the LPMs were not significantly correlated with the stream data. Several problems using landscape pattern metrics were noted: small watersheds having only one or two patches, collinearity with LULC data, and counterintuitive or inconsistent results that resulted from basic differences in land use-land cover patterns among ecoregions or from other factors determining water quality. The amount of variation explained in water quality parameters using multiple regression models that combined LULC and LPMs was generally lower than that from NDVI or vegetation phenology metrics derived from time-series NDVI data. A comparison of LPMs and NDVI indicated that NDVI had greater promise for monitoring landscapes for stream conditions within the study area.     

LAI inversion algorithm based on directional reflectance kernels

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.     

Satellite-based indices in the analysis of land cover for municipalities in the province of Siena, Italy

Territorial indicators based on the satellite measured reflected or emitted energy can provide valuable information on the spatial evolution of a territory. Information regarding land use, biomass coverage and radiant temperature were obtained from remotely obtained measurements for each municipality in the Province of Siena. Remotely sensed data were combined into different indices to compare characteristics of land cover between territories and create an information base for continued study. A classification was performed on a municipal level and land use classes were grouped together and a general index of use pressure was created. A vegetation index was used to compare biomass densities. A radiant temperature index was calculated using measure thermal infrared emissions. The results of these analyses allowed researchers to examine the intra and inter-municipal spatial heterogeneity within the Province. A comparison between the results of each index was made and the problems related to the use of such indices were examined.     

PRIESTLEY‐TAYLOR ALPHA COEFFICIENT: VARIABILITY AND RELATIONSHIP TO NDVI IN ARCTIC TUNDRA LANDSCAPES1

ABSTRACT: Average daily values of the Priestley‐Taylor coefficient (a) were calculated for two eddy covariance (flux) tower sites with contrasting vegetation, soil moisture, and temperature characteristics on the North Slope of Alaska over the 1994 and 1995 growing seasons. Because variations in a have been shown to be associated with changes in vegetation, soil moisture, and meteorological conditions in Arctic ecosystems, we hypothesized that a values would be significantly different between sites. Since variations in the normalized difference vegetation index (NDVI) follow patterns of vegetation community composition and state that are largely controlled by moisture and temperature gradients on the North Slope of Alaska, we hypothesized that temporal variations in a respond to these same conditions and thus co‐vary with NDVI. Significant differences in a values were found between the two sites in 1994 under average precipitation conditions. However, in 1995, when precipitation conditions were above average, no significant difference was found. Overall, the variations in a over the two growing seasons showed little relationship to the seasonal progression of the regional NDVI. The only significant relationship was found at the drier, upland study site.     

Spatial scaling between leaf area index maps of different resolutions

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.     

Preliminary Estimation of the Realistic Optimum Temperature for Vegetation Growth in China

The estimation of optimum temperature of vegetation growth is very useful for a wide range of applications such as agriculture and climate change studies. Thermal conditions substantially affect vegetation growth. In this study, the normalized difference vegetation index (NDVI) and daily temperature data set from 1982 to 2006 for China were used to examine optimum temperature of vegetation growth. Based on a simple analysis of ecological amplitude and Shelford’s law of tolerance, a scientific framework for calculating the optimum temperature was constructed. The optimum temperature range and referenced optimum temperature (ROT) of terrestrial vegetation were obtained and explored over different eco-geographical regions of China. The results showed that the relationship between NDVI and air temperature was significant over almost all of China, indicating that terrestrial vegetation growth was closely related to thermal conditions. ROTs were different in various regions. The lowest ROT, about 7.0 °C, occurred in the Qinghai-Tibet Plateau, while the highest ROT, more than 22.0 °C, occurred in the middle and lower reaches of the Yangtze River and the Southern China region.