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MODIS植被指数估算青海湖流域植被覆盖度研究

将MODIS数据合成的4种植被指数作为输入参数,采用像元二分模型对研究区的植被覆盖度进行估算,利用2006年的TM数据解译结果和2011年8月的野外实测数据对反演结果进行验证。结果显示:采用ND-VI估算的植被覆盖度比较符合研究区实地状况,样点估算精度达到87.13%;其他3种植被指数估算的植被覆盖度值比实际值低,尤其是对该区域典型植被草原草甸的覆盖度估算结果明显偏低。研究表明:2011年8月青海湖流域植被覆盖度以中高覆盖度为主,占整个流域面积的57%以上;植被覆盖度在空间上呈中部高、西北低的分布特点。     

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.     

一种基于光谱归一化下的植被覆盖度反演算法

针对常规混合像元分解算法在植被覆盖度遥感反演中存在的端元变化误差及运算效率的问题,以两个不同类型植被覆盖下地区的TM影像数据为基础,提出了一种基于光谱归一化框架下的协同稀疏回归的植被覆盖度反演算法,并针对多种地表类型下的植被覆盖度反演试验,与常用的像元二分法模型进行对比分析。试验结果表明:对影像与端元组进行归一化后,有效地降低了它们的异质性,从而提高了反演精度,同时,该算法获取的植被覆盖度相对像元二分法具有更高的精度。     

基于HJ-1高光谱数据的植被覆盖度估测方法研究

植被覆盖度是衡量地表植被状况的一个重要参数,在水文、生态等方面有重要意义,同时,也是影响土壤侵蚀与水土流失的主要因子,是评价土地荒漠化最有效的指标。以环境一号(HJ-1)小卫星上搭载的新型传感器HSI获取的高光谱数据为数据源,通过选择合适的植被指数建立了植被覆盖度反演模型——像元二分模型。然后运用该模型提取了新疆石河子地区的植被覆盖度信息。通过与地面样方数据进行交互比较,对HJ-1/HSI数据反演植被覆盖度的精度进行了评价。研究结果表明,HJ-1/HSI数据能够得到较高精度的植被覆盖度反演结果,在植被动态及全球变化研究领域具有潜在应用价值。     

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。     

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。     

基于一种改进的多端元混合像元分解方法的三江源中东部地区植被盖度信息的提取

三江源地处青海省南部,近年来生态环境不断恶化、草地退化严重。植被盖度是研究草地退化的重要指标之一,如何快速、准确、大面积地获取植被信息对三江源的生态环境变化监测尤为重要。传统的固定端元混合像元分解(TSMA)不适用于地域辽阔、地物类型复杂多样的三江源区,而多端元混合像元分解(MESMA)允许端元的类型和数量随像元的不同而变化,更符合三江源的实际情况。基于已有相关研究提出一种改进的多端元混合像元分解(IMESMA),即加入端元组分合理性最优端元模型判断规则,分两步对影像进行分解并获得像元的植被盖度信息。第一步利用多端元模型探测出含有植被信息和完全不含植被信息的像元,并对非植被像元进行掩膜,以提高分解精度;第二步仅对含有植被信息的像元进行分解,获得最终的三江源中东部植被盖度信息。通过多端元分布图、混淆矩阵和与实测数据的均方根误差(RMSE)对比表明,相比TSMA,IMESMA考虑了同物异谱现象,并且其分解精度高、分解结果可信,更适合三江源区的植被信息提取。本方法也可用于其他复杂环境地表组分信息的提取。     

内蒙古大兴安岭根河植被覆盖度动态变化及影响因素的分析

研究植被覆盖度（Fractional Vegetation Cover,FVC）动态变化,可增强了解森林群落的抵抗力和恢复力,为森林生态系统定量评价提供科学依据。基于像元二分模型、Landsat-5 TM（2006、2010）及高分一号（GF-1,2016）数据估算了3个时期的根河市FVC,引入变化率和动态度2个指标评价其动态变化情况,并且分析了多因素对该变化的影响。实验结果显示：中度以上等级占总面积80%以上,2016年低、较低、中度、较高、高等级FVC分别为1 645.02、1 655.97、3 536.59、5 556.87、7507.15 km²。采用0.2 m航空CCD影像进行植被/非植被点提取后,针对2016年的FVC估算结果进行交叉验证的精度为0.92。变化分析结果显示：除部分地区外（敖鲁古雅）,2006~2016 年间FVC变化整体上呈增加态,尤其是高等级增加了1 668.78 km²。综合来看,根河市植被覆盖良好,多重因素共同影响其动态变化,局部FVC对火灾干扰的变化极为敏感,低海拔和平坡FVC明显降低,与人类生产生活密切相关。     

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。     

多云雾地区高时空分辨率植被覆盖度构建方法研究

针对多云雾地区高时空分辨率数据缺乏现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法.首先,通过时空适应反射率融合模型(STARFM)有效地将TM 的较高空间分辨率与MODIS的高时间分辨率融合在一起,构建了研究区植被生长峰值阶段的NDVI数据;然后,以植被生长峰值阶段的NDVI为输入,基于地表覆被类型,综合应用等密度和非密度亚像元模型对研究区的植被覆盖度进行估算.结果表明:①即使数据源存在大量的云雾,且存在一定的时相差异,研究区植被覆盖度的估算结果过渡自然,不存在明显的不接边效应;②以植被生长峰值阶段的NDVI数据为输入进行植被覆盖度估算,有效拉开了同一地表覆被类型不同覆盖度像元的NDVI梯度,提高了亚像元估算模型对输入数据的抗扰动性;③基于地表覆被类型,应用亚像元混合模型,能够提高植被覆盖度的估算精度.经野外实测数据验证,总体约８５％的估算精度表明,针对高时空分辨率遥感数据缺乏的多云雾区域,本研究提出的方法能够实现区域尺度植被覆盖度数据的构建.     

Julian dates and introduced temporal error in remote sensing vegetation phenology studies

Remote‐sensing‐based vegetation phenology studies are commonly used to study agriculture, forestry, species distributions, and the effect of climate change on vegetation. These studies utilize annual time series of NDVI data to characterize seasonal growth patterns. The NDVI data for most of these studies have been pre‐processed using a maximum value compositing process to minimize contamination from clouds. A side effect of this process is a degradation of temporal data, since NDVI values are assigned to multiday periods rather than the specific date of image capture. In this study, the compositing process is examined to determine if there is a reliable pattern to pixel selection. Also, the magnitude of the introduced error is estimated by comparing vegetation phenology metrics calculated using the temporally degraded data and metrics calculated using the actual date of each pixel. The root mean square errors between these datasets ranged from 9.4 to 10.9 days, much larger than is acceptable for most phenology studies. We conclude that vegetation phenology studies must make use of accurate temporal data to characterize changes in vegetation seasonality.     

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。     

Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model

Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. The simplest and most widely used model for the estimation of FVC is the dimidiate pixel model. The normalized difference vegetation index (NDVI) is commonly used as a vegetation index (VI) in this model. A range of VIs is possible alternative to the use of NDVI in the dimidiate pixel model. In this article, six VI-based dimidiate pixel models were compared using in situ measurements and canopy reflectances simulated by the PROSAIL model over nine different soil backgrounds. A comparison with in situ measurements showed that the Gutman–Ignatov method overestimated FVC, with a mean root mean square error (RMSE) of 0.14. The mean RMSE had an intermediate value of 0.08 in the Carlson–Ripley method and was further reduced to 0.05 in the method proposed by Baret et al. The use of both modified soil-adjusted vegetation index (MSAVI) and a mixture of NDVI and the ratio vegetation index (RVI) to replace NDVI in the Gutman–Ignatov model reduced the RMSE to 0.06. The mean RMSE in the difference vegetation index (DVI)-based model was 0.08. The simulated results indicated that soil backgrounds have significant effects on these VI-based models. The sensitivity of the first three models and the NDVI plus RVI-based model to soil backgrounds decreased with an increase in soil reflectance. In contrast, the DVI-based model is sensitive to soil backgrounds with high reflectances. MSAVI, which is less sensitive to soil backgrounds, represents a feasible alternative to the use of NDVI in the Gutman–Ignatov model.     

MODIS NDVI与MODIS EVI的比较分析

MODIS NDVI与MODIS EVI是目前应用比较广泛的植被指数,MODIS EVI是对NDVI的发展和延续,从植被指数计算公式和合成方法两方面做了改进。具体表现在:避免了MODIS NDVI在植被高覆盖区易饱和的问题,考虑了土壤背景对植被指数的影响,对气溶胶等残留做了进一步校正,采用BRDF/CV-MVC合成方法保证了合成采用最佳像元。EVI时间序列相较于NDVI时间序列季节性更明显,能够更好地反映高植被覆盖区的季节性变化特征,并且很少有突降现象,时间序列曲线较平滑。EVI的这些优势为高覆盖植被物候特征的季节性变化监测提供了新的思路。     

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。     

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub‐pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub‐pixel bare soil reflectance are major difficulties in sub‐pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub‐pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub‐pixel soil reflectance from vegetation reflectance. Without sub‐pixel reflectance contamination, results demonstrate the true linkage between the growth of sub‐pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub‐pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near‐infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band‐based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.     

Global analysis of empirical relations between annual climate and seasonality of NDVI

This paper describes the use of satellite data to calibrate a new climate vegetation greenness relation for global change studies. We examined statistical relations between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our understanding of intra-annual patterns and global controls on natural vegetation dynamics. Multiple linear regression results using global 1 gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80% of the geographical variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from 1 grid cells mapped as greater than 25% inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.     

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVI's response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a region's FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.