基于Landsat 8影像的额济纳荒漠绿洲植被覆盖度估算方法对比研究

荒漠绿洲是维持当地人类生存和社会发展的主要依托,但其地表植被稀疏,生态系统极其脆弱,而植被覆盖度是反映荒漠生态环境信息的重要指标之一。以黑河下游额济纳荒漠绿洲为例,基于Landsat 8影像和野外实测植被覆盖度数据,对比和分析现有的适宜于干旱荒漠区的3类植被覆盖度提取方法(经验模型法、像元二分法和三波段梯度差法)在该区域的应用效果,并尝试利用基于转换型土壤调整植被指数(TSAVI)的像元二分模型法和修正的三波段梯度差法(MTGDVI)进行植被覆盖度估算,以期找到计算额济纳荒漠绿洲植被覆盖度的最佳模型。研究结果表明:用TSAVI像元二分模型法的反演精度高而且能够较好地估算额济纳荒漠区域和绿洲区域的植被覆盖度,适用于估算额济纳荒漠绿洲的植被覆盖度。     

遥感技术估算非光合植被覆盖度研究综述

定量地估算光合植被覆盖度(fPV)和非光合植被覆盖度(fNPV)对陆地生态系统碳储存、植被生产力、土壤侵蚀和火灾监测具有重要的意义。非光合植被在温带草原、热带稀树大草原、森林、沙地、农田等生态系统中扮演着重要的角色,是衡量地表植被覆盖状况的重要指标。综述了目前利用高光谱和多光谱遥感估算fNPV的研究进展,讨论了PV、NPV和BS光谱特征的理论基础,总结了目前估算fNPV的两种主要方法:光谱指数法和光谱混合分析法,并分析了高光谱和多光谱两种主要遥感数据源实际应用。最后对fNPV估算研究中存在的问题以及发展趋势进行了分析,以期为今后的fNPV估算提供借鉴和参考。     

基于Landsat８影像的额济纳荒漠绿洲植被覆盖度估算方法对比研究

荒漠绿洲是维持当地人类生存和社会发展的主要依托,但其地表植被稀疏,生态系统极其脆弱,而植被覆盖度是反映荒漠生态环境信息的重要指标之一.以黑河下游额济纳荒漠绿洲为例,基于Landsat８影像和野外实测植被覆盖度数据,对比和分析现有的适宜于干旱荒漠区的３类植被覆盖度提取方法(经验模型法、像元二分法和三波段梯度差法)在该区域的应用效果,并尝试利用基于转换型土壤调整植被指数(TSAVI)的像元二分模型法和修正的三波段梯度差法(MTGDVI)进行植被覆盖度估算,以期找到计算额济纳荒漠绿洲植被覆盖度的最佳模型. 研究结果表明:用TSAVI像元二分模型法的反演精度高而且能够较好地估算额济纳荒漠区域和绿洲区域的植被覆
盖度,适用于估算额济纳荒漠绿洲的植被覆盖度.     

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。     

基于直方图与像元分解模型的森林覆盖度遥感估算

森林覆盖度是能够勾描出林分边界的森林覆盖率,定量化的覆盖度信息可体现其水平尺度的时空分异特性。像元分解模型在覆盖度遥感估算中得到了广泛应用,但仍然有很多问题,如很难找到一种树冠覆盖度的纯光谱端元,从而难以高精度地估算树冠覆盖度。为此,基于像元分解模型,结合使用土地利用和土壤类型数据,提出利用直方图法确定模型中不同类型植被——土壤端元参数,对区域尺度森林覆盖度进行估算,并利用三峡库区的历史野外161个样点的实测覆盖度数据进行验证,发现R~2达到0.74～0.85,计算结果比较满意。该方法将为区域尺度高分辨率森林覆盖度的遥感估算提供借鉴。     

城市异质植被的覆盖度估算模型比较研究

植被覆盖度是城市生态环境评价的一个重要指标。针对亚热带城市异质植被覆盖特征,选择像元尺度的植被指数(NDVI)转换模型、亚像元尺度的植被—土壤两端元模型(V-S Model)和植被—高—低反射率三端元模型(V-H-L Model)在TM影像上估算植被覆盖度,并结合野外实地调查对比验证3种模型的估算精度及其适用性。结果表明模型尺度和背景亮度对植被覆盖度估算有着不同程度的影响。NDVI转换模型整体高估覆盖度为27%,V-S模型和V-H-L模型整体低估覆盖度分别为23%和5%;验证结果证明:NDVI转换模型对高密度(60%)植被的估算结果最好,低估4%;V-H-L模型对中密度(40%~60%)和低密度(40%)植被的估算结果最优,仅低估2%,并受背景亮度的影响最小。因此,NDVI转换模型适用于高密度植被覆盖度的估算,亚像元尺度下的V-S模型和V-H-L模型适用于低、中密度植被覆盖度的估算,并以V-H-L模型估算较为准确。     

基于遥感的植被覆盖度估算方法述评

随着遥感技术的发展,作为描述植被覆盖及生长状况的度量参数--“植被覆盖度”在农业、环境、生态等领域应用越来越广泛。介绍了植被覆盖度的重要作用及研究意义,叙述了其遥感监测方法的研究现状及常用遥感数据源,主要从统计、物理、FCD等模型入手讨论植被覆盖度的遥感估算方法及其优缺点,并对今后研究方向及发展趋势进行了展望。
     

从数码照片中快速提取植被覆盖度的方法研究

植被覆盖度是反映植被基本情况的指标,是农学、生态学等所关心的一个重要参数。获取地表数码照片并进一步提取植被覆盖度已成为一种最具潜力的对植被覆盖度进行地面测量的手段,而如何快速、准确地从数码照片中提取植被覆盖度信息尚缺乏成熟的方法。通过利用NDI法对数码照片的处理,实现了植被覆盖度的快速提取,同时用监督分类法提取相同数码照片的植被覆盖。通过对两种方法及其计算结果进行精度评价和比较表明,用NDI法和监督分类法估计的植被覆盖度都能够达到较高的准确性,结果可信度高,但NDI法要比监督分类法更自动化和快速,在精准农业作业系统等方面极具实用价值。     

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。     

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。     

A Kind of Vegetation Cover Fraction Retrieval Algorithm based on Spectral Normalization Frame

Vegetation cover fraction is an important control factor in the process of simulating surface vegetation transpiration，soil water evaporation and vegetation photosynthesis.Based on the TM image data of two different types of vegetation cover，a collaborative sparse regression algorithm based on the spectra normalization framework is proposed to retrieve the vegetation cover fraction，which solves the problems such as the error of the endmember variability and the efficient of the algorithm arisen from many spectral mixture analysis algorithms used to retrieving vegetation cover fraction.And also by contrast to the dimidiate pixel algorithm，the accuracy of the algorithm is indicated.The experimental results show that the normalization of the image and endmenbers can effectively reduce their heterogeneity and improve the retrieval precision and the algorithm has higher accuracy than the dimidiate pixel algorithm.     

Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico

On June 1, 2000, an oil spill accident occurred along transportation pipeline located in the Jornada Experimental Range (USDA), Jornada, New Mexico, a long-term ecological research (LTER). In order to detect potential vegetation stress caused by the accident, two AVIRIS data sets of the oil spill area, before and after the oil release, are analyzed and the reliability of several techniques in the detection of vegetation stress is examined.The polynomial fitting and Lagrangian interpolation, and spectral mixture analysis (SMA) are applied to the AVIRIS data sets. The first two methods are applied for the detection of the “red-edge” shift in vegetation reflectance spectra, and the last for the detection of change in vegetation fraction. The results indicate that the polynomial fitting and Lagrangian interpolation both are able to detect a red-shift of the vegetation “red-edge”, but the latter's performance depends on the band combination used and is sensitive to data noise. The polynomial fitting results are inconsistent in detection of “the red-edge” shift, while Lagrangian interpolation is not. Within the oil spill area, the fraction estimates of vegetation resulting from SMA demonstrate a decrease (10-30%) of the vegetation fraction after the accident, indicating stressed vegetation and cover change. The result also indicates that areas of extremely large decrease (>40%) in plant cover outside of the oil spill area is due to the response of grasses due to the water stress in 2000, and that the integration of some auxiliary data on ecological and climatological data with the analysis of remotely sensed data is thus very important to the interpretation of the detection results. A sensitivity analysis indicates that the detected vegetation cover change is insensitive to the noise introduced by the radiometric normalization.     

植被指数遥感定量研究--以民勤绿洲为例

研究以我国西北干旱区的代表性绿洲一民勤绿洲为例，使用法国CE313光谱仪，对典型样区植被反射率进行了野外测定，计算常用的6种植被指数，通过对降低土壤背景影响的效果和不同植被指数提取植被信息的能力进行分析，遴选出适宜于干旱区民勤绿洲的植被指数估算模型。定量研究了民勤绿洲近20年来植被覆盖空间变化过程，对预测生态环境的变化和防治绿洲沙漠化具有重要意义。     

面向对象的黑河下游河岸林植被覆盖信息分类!

地表植被覆盖是描述区域生态系统的基础数据,也是全球及区域陆面过程、生态与水文众多模型中所需的重要地表参数。对于黑河下游额济纳绿洲,以Landsat 30m分辨率为主的遥感影像难以真实提取下游绿洲河岸林植被覆盖信息,而高分辨率影像目标地物轮廓清晰、空间细节信息丰富,有利于干旱背景下景观破碎、异质性强的植被覆盖信息分类。基于黑河下游额济纳绿洲QuickBird影像,通过面向对象的分类方法提取耕地、胡杨、柽柳、草地和裸地等主要植被覆盖类型,分类总体精度和Kappa系数分别为84.71%和0.7986。结果表明:利用面向对象分类方法对高分辨率影像进行植被覆盖信息分类,分类结果较好,能够满足精度要求。     

Vegetation and surface roughness effects on AMSR-E land observations

Characteristics of the land surface including soil moisture, vegetation cover, and soil roughness among others influence the microwave emissivity and brightness temperature of the surface as observed from space. Knowledge of the variability of microwave signatures of vegetation and soil roughness is necessary to separate these influences from those of soil moisture for remote sensing applications to global hydrology and climate. We describe here a characterization of vegetation and soil roughness at the frequencies and spatial resolution of the EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E). A single parameter has been used to approximate the combined effects of vegetation and roughness. AMSR-E data have been analyzed to determine the frequency dependence of this parameter and to generate a global vegetation/roughness map and an estimate of seasonal variability. A physical model is used for the analysis with approximations appropriate to the AMSR-E footprint scale and coefficients calibrated empirically against the AMSR-E data. The spatial variabilities of roughness and vegetation cannot be estimated independently using this approach, but their temporal dynamics allow separation of predominantly static roughness effects from time-varying vegetation effects using multitemporal analysis. Global signals of time-varying vegetation water content derived from this analysis of AMSR-E data are consistent with time-varying biomass estimates obtained by optical/infrared remote sensing techniques.     

Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling

Methods for using airborne laser scanning (also called airborne LIDAR) to retrieve forest parameters that are critical for fire behavior modeling are presented. A model for the automatic extraction of forest information is demonstrated to provide spatial coverage of the study area, making it possible to produce 3-D inputs to improve fire behavior models.The Toposys I airborne laser system recorded the last return of each footprint (0.30-0.38 m) over a 2000 m by 190 m flight line. Raw data were transformed into height above the surface, eliminating the effect of terrain on vegetation height and allowing separation of ground surface and crown heights. Data were defined as ground elevation if heights were less than 0.6 m. A cluster analysis was used to discriminate crown base height, allowing identification of both tree and understory canopy heights. Tree height was defined as the 99 percentile of the tree crown height group, while crown base height was the 1 percentile of the tree crown height group. Tree cover (TC) was estimated from the fraction of total tree laser hits relative to the total number of laser hits. Surface canopy (SC) height was computed as the 99 percentile of the surface canopy group. Surface canopy cover is equal to the fraction of total surface canopy hits relative to the total number of hits, once the canopy height profile (CHP) was corrected. Crown bulk density (CBD) was obtained from foliage biomass (FB) estimate and crown volume (CV), using an empirical equation for foliage biomass. Crown volume was estimated as the crown area times the crown height after a correction for mean canopy cover.     

A new GLM-based method for mapping tree cover continuous fields using regional MODIS reflectance data

Knowledge about land cover and its change is an important input for the monitoring and modeling of ecological and environmental processes from the regional to the global scale. Considerable efforts have been made to develop global continuous fields for different land cover types at large spatial scales based on NOAA-AVHRR and TERRA-MODIS data and a range of techniques have been applied to depict the sub-pixel fraction of land cover types from these data. In this study, a new methodology is described for deriving and optimizing continuous fields of tree cover for complex topography at the regional scale of the European Alps using generalized linear models (GLM). MODIS data (MOD09) at a spatial resolution of 500 m were used to calibrate the models against regional training data of fractional tree cover. For evaluating the method we test the GLM model output to a regression tree model (using the same data structure). Further we test the resulting GLM-based tree cover continuous fields against two different, independent test data sets; one of which is spatially separated and the other is from within the calibration area. Finally, we compare the GLM model output with two available global data sets at spatial resolutions of 1 km and 3 km: (1) TERRA-MODIS Vegetation Continuous Fields product (MOD44), and (2) the NOAA-AVHRR vegetation continuous fields. Our GLM-based method results in high accuracy (MAE=9.1%) and low bias (−1.2%) across the combined evaluation and calibration area, and with small differences only between the calibration and the spatially separated evaluation area (1.3%). Compared to the regression tree model the results from the GLM model for all analyses are significantly better. Thus we conclude that generalized linear models are appropriate for deriving continuous fields of fractional tree cover for complex topography at the regional scale. GLMs can handle nonlinear relationships present in the training data set well, and the method is robust with respect to sample size and the number of months used for calibration. Regional calibrations of vegetation continuous fields may offer significantly improved predictions compared to globally calibrated models. Such regionally calibrated and optimized models may serve as valuable tools for regional monitoring of land cover pattern and its temporal change.     

The effects of vegetation on the ability to map soils using imaging spectrometer data

Remote sensing applied to tasks of mapping soil and rock surfaces must address the problem of vegetation cover in all but the most arid terrain. Masking out pixels with a high proportion of vegetation using a threshold on the near-infrared/red ratio is a popular strategy for live vegetation. The important effects of dead vegetation on the SWIR reflectance is usually ignored. Data gathered by the GER-II imaging spectrometer over a semi-arid area near Almaden, south central Spain were used to test the sensitivity of thematic soil mapping to variable cover of live and dead vegetation. After calibration to reflectance a least-squares unmixing analysis was performed using image end-members and proportions maps of vegetation and soil/rock components generated. Despite a low signal-to-noise ratio, three soil/rock and four vegetation endmembers were successfully mapped and validated from field estimates. A quantitative assessment was made of the effects of live and dead vegetation on the ability of the unmixing analysis to distinguish between granite and shale soils using synthetically mixed spectra gathered using field spectroradiometry and statistical analysis of the imaging spectrometer data. Dead vegetation was shown to have a greater impact on soil spectra than live vegetation. The ability to distinguish between the soils was lost at 50-60 per cent vegetation cover.