基于SAILH模型的植被冠层NDVI二向性分析

NDVI是植被遥感中最为常用的一种植被指数,建立NDVI与其他冠层参数模型必须考虑其方向性问题.本文基于SAILH模型讨论了连续植被冠层NDVI的二向性特征,并分析了叶面积指数、叶倾角分布、热点参数以及太阳天顶角和相对方位角对NDVI的影响.研究表明冠层NDVI在主平面观测方向存在一个明显的负热点,前向散射方向的NDV...     

基于NDVI与LAI的水稻生长状况研究

在水稻反射光谱特性与水稻生物参数关系的支持下,以吉林省德惠市夏家店镇为研究区,探讨了一条基于TM遥感影像反演得到的归一化植被指数(NDVI)与地面观测数据叶面积指数(LAI)的水稻生长状况的研究途径,并利用NDVI和LAI对该区2000年和2001年的水稻生长状况进行了分析研究。     

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。     

基于模型模拟的植被NDVI与观测天顶角和LAI的关系

利用PROSPECT和SAIL模型模拟了不同叶绿素含量、不同LAI和不同观测天顶角下的植被冠层反射率,分析了NDVI随LAI、观测天顶角和叶绿素含量的变化规律。结果表明:叶绿素影响冠层反射率主要在可见光波段,冠层反射率随叶绿素含量的增加而下降;冠层反射率随观测天顶角的增加而增加,而LAI较高时,其受观测天顶角的影响较小。观测天顶角相同时,随叶绿素含量的增加NDVI呈上升趋势;叶绿素含量一定时,NDVI随LAI的增加而增加。LAI为1时,在不同叶绿素含量下,随观测天顶角的增加,NDVI呈先下降后上升的趋势,拐点在观测天顶角65°或70°处,而LAI为3、5和7时,NDVI呈现下降趋势。叶绿素含量较高时,NDVI受观测天顶角的影响较小。当LAI较大和叶绿素含量较低时,NDVI随观测天顶角的增加(>70°)下降较快。     

基于Chen NDVI模型的NDVI尺度转换分形特性分析北大核心CSCD

空间尺度问题是定量遥感重要而基础的问题之一,文章针对“分形方法是否适用于定量遥感地表参数的尺度转换研究”的问题进行实验验证。基于传统及改进的Chen NDVI尺度转换模型,获得NDVI不同空间升尺度影像,进而利用分形尺度转换模型分析NDVI尺度转换结果,以获取“分形方法在NDVI尺度转换研究中的适用性结论”。以厦门为研究区,利用上述分形模型进行实验,结果表明:(1)若NDVI输入类型为均值,NDVI尺度转换特性更符合双对数直角坐标系下的线性函数关系,即分形特性;若NDVI输入类型为方差、方差/均值,NDVI尺度转换特性更符合直角坐标系下的对数函数关系;(2)农田在两种Chen NDVI模型及两种空间直角坐标系下所得拟合模型皆表现出显著的线性规律,尤其是其NDVI尺度转换分形特性表现显著;(3)综合而言,NDVI尺度转换结果具有分形特性,但NDVI尺度转换具有更为显著的直角坐标系下对数函数关系特性。文章所提出的融入精细地类信息的“类NDVI”地表参数通用尺度转换模型及尺度转换分形特性研究方法具有一定的代表性,值得参考。     

青藏高原NDVI对ENSO事件的响应

以NCEP/NCAR所发布的1950~1979年全球海平面温度(SST)数据为基础,得到了1980~2006年ENSO事件的3个典型阶段,即冷阶段、中性阶段和暖阶段。通过分析1982~2005年的NOAA\|AVHRR NDVI影像数据,得到了在不同ENSO 阶段青藏高原生长季(5~9月)和冬季(12~2月)的NDVI平均值和离差图。结果表明:在生长季,冷阶段的NDVI高于其他两个阶段;在冬季,高原上的NDVI在正常阶段最好,其次为暖阶段,在冷阶段生长状况最低。此外还发现,不同阶段的青藏高原北部和南部植被变化也存在明显差异。     

基于OpenCL的NDVI算法

随着计算机技术的不断发展,软件的规模也越来越大。一张遥感图像可达到数G以上,处理起来有时候可能需要数个小时。因此,针对这些大数据量的系统来说,加速比提高一倍,就会使运行时间减少几个小时,这对于系统来说就是一种非常可观的现实,非常值得去实现。本文将以NDVI算法为例,主要介绍了NDVI算法、NDVI的应用和性质、OpenCL介绍。     

土壤指数在消除植被对土壤光谱影响的效果分析

本文对土壤指数的效果进行了分析,指出土壤指数中,比值指数与植被覆盖率呈非线性关系,且植被类型的影响很大;正则指数与植被覆盖率呈线性关系,且植被类型的影响很小,表明正则指数的效果很好,其中SLI指数的效果最好。遥感接收到的地物光谱一般是土壤和植被的组合光谱。裸露土壤上生长有植物时,受植物光谱的影响,组合光谱在红外波段由于叶绿素吸收,近红外波段由于叶肉反射而偏离原土壤光谱;受土壤亮度和类型的影响,组合光谱也偏离了植被光谱。近二十年来,利用植被光谱指数消除土壤对组合光谱的影响,减少计算工作量,提高分类精度,得到了充分的重视和研究,而对利用土壤指数消除植被的影响方面没有得到应有的重视。本文就各种土壤指数的效果进行了分析研究。     

波段位置和宽度对河口湿地4种植被NDVI的影响

研究不同波段位置和宽度对植被NDVI的影响,对于进一步认识NDVI指数具有重要的意义。采用ASD(Analytical Spectral Devices)地物光谱仪测定闽江河口互花米草(Spartina alterniflora)、秋茄(Kandelia candel)、芦苇(Phragmites australis)和短叶茳芏(Cyperus malaccensis)冠层光谱,利用ViewSpecPro和Oragin8.0软件对光谱数据进行分析和处理,探讨不同波段位置和波段宽度对河口湿地4种植被NDVI的影响。结果表明:①当红光波段固定,近红外波段以50 nm宽度移动时,4种湿地植被NDVI随近红外波段中心位置增加而迅速增加,之后趋于平稳,在925~1 050 nm出现一个小的谷值,互花米草和短叶茳芏的谷值要比其他两种植物更为明显;不同波段宽度影响表现为:除红边与970 nm附近区域对NDVI的影响较显著外,其他波段影响不显著;②当近红外波段固定,红光波段以10 nm宽度移动时,4种湿地植被NDVI随红光波段中心位置移动先略有增加或变化不大,然后迅速降低;不同波段宽度影响表现为:在650~700 nm波段宽度越宽,NDVI值越小,600~650 nm范围内波段宽度对NDVI的影响不大;③4种湿地植被红光波段宽度对NDVI的影响要大于近红外波段。     

四种土壤管理方式对李园土壤微生物和土壤酶的影响

通过2003~2004两年的试验发现,刈割覆盖、刈割压埋、畜粪还园三处理纤维分解菌和硅酸盐细菌的数量均明显高于清耕处理(对照),且两种微生物数量均以夏季最多,秋季次之,春季再次,冬季最少。前三处理蔗糖酶、脲酶、过氧化氢酶、纤维分解酶、多酚氧化酶的酶活性都较清耕有显著或极显著提高;各处理酶活性也以夏季最高、秋季次之,春季再次,冬季最低。2004年刈割覆盖、刈割压埋两处理蔗糖酶、脲酶、过氧化氢酶活性较2003年有显著或极显著增加,畜粪还园、清耕两处理三种酶活性两年间差异很小。经相关性分析还发现,纤维分解菌除与脲酶相关性不显著外,它与其余的酶均显著或极显著正相关;硅酸盐细菌与所有的酶均极显著正相关。     

Simulation and Analysis on the Influence of Soil Background of Wheat Farmland on NDVI under Different Vegetation Coverage

It is quite confusing to effectively monitor and precisely evaluate growing conditions of wheat by using normalized differential vegetation index (NDVI)which is based on pixel scale as they are significantly different when acquired by the same growth status wheat with different background of soil types.This paper selects 9 typical soil types in our country as background with the wheat canopy spectrum is fixed which means the NDVIc is a constant value to study the influence of different soil background types on NDVI of wheat and analyze the sensitivity of NDVI of wheat to the vegetation coverage simulated by diverse liner mixed ratio of wheat canopy and soil background.The results show that：(1)wheat NDVI of farmland increases along with the increase of vegetation coverage under the same of soil background type,and vice versa;(2)wheat NDVI of farmland vary greatly with different soil background types,and the difference decrease while the vegetation coverage exceed 25%;(3)NDVI sensitivity also shows a quite difference to vegetation coverage under the diverse soil background types.With the increase of vegetation coverage,NDVI sensitivity decreases with the lower\|reflectance soil background while it increases monotonously with the higher reflectance soil background.It provides the foundation for the times of calculating the remote sensing’s NDVI information of all wheat growing periods under different types of soil background.     

The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVI_o) and full vegetation NDVI (NDVI_∞). Usually it is assumed that NDVI_o is close to zero (NDVI_o ∼ 0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI = 0.2) and is highly variable (standard deviation = 0.1). We show that the underestimation of NDVI_o yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI_o and NDVI_∞ derived from global scenes yields overestimations of Fg that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2 < NDVI_pixel < 0.4. When using conterminous U.S. scenes to derive NDVI_o and NDVI_∞, the overestimation is less (0.10-0.17 for 0.2 < NDVI_pixel < 0.4). As a result, parts of the conterminous U.S. are affected at different times of the year depending on the local seasonal NDVI cycle. We propose using global databases of NDVI_o along with information on historical NDVI_pixel values to compute a statistically most-likely estimate of Fg. Using in situ measurements made at the Sevilleta LTER, we show that this approach yields better estimates of Fg than using global invariant NDVI_o values estimated from whole scenes. At the two studied sites, the Fg estimate was adjusted by 52% at the grassland and 86% at the shrubland. More significant advances will require information on spatial distribution of soil reflectance.     

Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.     

Correction of the Soil Adjusted Factors of Vegetation Indices for Monitoring Rice Leaf Index Zrea

The vegetation indices that take the soil adjustment factor into consideration can reduce the influence of soil background conditions and have been widely used in monitoring all kinds of vegetation.However,the rice has been planted in the soil covered by a certain thickness of layer of water,which is different with other various soil backgrounds.Therefore,in this paper,through two years of rice plot experiments,we obtained the rice canopy spectral data and the corresponding leaf area index (LAI) data,and then calculated a series of vegetation indices (EVI,SAVI,WDVI) by using different soil adjustment factors changing within a certain range.We compared the abilities of these vegetation indices for rice LAI estimation,and then determine the optimum soil adjustment factors of vegetation indices to adjust the background of rice.In the study,we found that the best soil adjustment factor L for EVI,L of SAVI,a of WDVI are 0.25,0.10 and 1.25 respectively,and we further compared the LAI estimation results of the best soil adjustment factor with those of the conventional soil adjustment factor.For the model taking EVI as an independent variable,the RMSE of LAI estimation using the best soil adjustment factor is 6.82 % lower than that using the conventional soil adjustment factor;In SAVI model,the RMSE using the best soil adjustment factor is 10.23% lower than that using the conventional soil adjustment factor .These results indicate that the corrected vegetation indices considering the background of rice can improve the accuracy of rice leaf area index using remotely sensed data.     

The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters

NDVI (Normalized Difference Vegetation Index) has been widely used to monitor vegetation changes since the early eighties. On the other hand, little use has been made of land surface temperatures (LST), due to their sensitivity to the orbital drift which affects the NOAA (National Oceanic and Atmospheric Administration) platforms flying AVHRR sensor. This study presents a new method for monitoring vegetation by using NDVI and LST data, based on an orbital drift corrected dataset derived from data provided by the GIMMS (Global Inventory Modeling and Mapping Studies) group. This method, named Yearly Land Cover Dynamics (YLCD), characterizes NDVI and LST behavior on a yearly basis, through the retrieval of 3 parameters obtained by linear regression between NDVI and normalized LST data. These 3 parameters are the angle between regression line and abscissa axis, the extent of the data projected on the regression line, and the regression coefficient. Such parameters characterize respectively the vegetation type, the annual vegetation cycle length and the difference between real vegetation and ideal cases. Worldwide repartition of these three parameters is shown, and a map integrating these 3 parameters is presented. This map differentiates vegetation in function of climatic constraints, and shows that the presented method has good potential for vegetation monitoring, under the condition of a good filtering of the outliers in the data.     

Assessing the Impact of Ozone Pollution on Summer NDVI\|based Vegetation Growth in North China

The severe summer ozone(O³) pollution in North China has attracted much attention.Combined summerO³ monitoring and NDVI data for 2015-2016,we firstly assessed the impact ofO³pollution on vegetation growth activities.Developed a simple linear simulation technique,then we detected and evaluatedO³’s impact on summer vegetation growth.The results show that summerO³pollution is closely related to vegetation growth in North China.In spatial-scale the stronger affected regions by O³,whereas associated with a highO³pollution intensity,and vice versa.In sum,summerO³ pollution affected on the vegetation growth activities in North China.In addition,in time\|scale,the impact of 2016 is very different from that of 2015.In 2016,a higher O³ pollution intensity than that of 2015,but a decrease in the impact with an approximately 2.75 %.In addition,O³pollution\|induced changes in vegetation growth activities during 2015-2016 were about 11.7 %.This study provides a method for remote sensing to monitor the effects ofO³pollution on terrestrial ecosystems.     

Hierarchical image segmentation based on similarity of NDVI time series

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover-landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.     

基于遥感数据的流域土壤侵蚀强度快速估测方法

以北京延庆县境内的妫水河流域为例, 提出了一种基于遥感数据的土壤侵蚀强度快速估测方法。首先, 利用遥感数据和植被指数模型提取流域内土地利用类型信息和植被覆盖度信息; 其次, 利用数字高程模型数据生成坡度图; 然后, 结合土壤侵蚀强度分级指标, 将坡度图与土地类型图、植被覆盖度图空间叠加, 判断和计算侵蚀强度等级, 结果获得了流域土壤侵蚀强度等级图; 最后, 计算了流域的年平均侵蚀模数。结果表明, 妫水河流域的土壤侵蚀以微度和轻度为主, 所占面积比例为74.88% , 极度和剧烈侵蚀很少, 不到总面积的2%。整个流域的年侵蚀模数估计为1 74611/ km ²·a。     

Quantifying intra-annual persistent behaviour in SPOT-VEGETATION NDVI data for Mediterranean ecosystems of southern Italy

Multi-temporal series of satellite SPOT-VEGETATION Normalized Difference of Vegetation Index (NDVI) data from 1998 to 2003 were exploited for studying persistence in Mediterranean ecosystems of southern Italy. We used Multiple Segmenting Method (MSM), which is well suited to analyze scaling behaviour in short time series, and the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. Our findings point out to the characterization of Mediterranean ecosystems as governed by persistent mechanisms.