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。     

不同空间分辨率热红外数据在近海核电厂温排水监测一致性研究

针对近年来我国核电厂大力发展而引起的核电温排水体污染的问题,该文利用相同日期的环境一号B星(HJ-1B)红外相机与Landsat ETM+热红外波段数据,均采用辐射传输模型法反演出大亚湾区域海表温度,通过同日期MODIS海温产品作为基准,比较HJ-1B数据、Landsat ETM+数据温度反演的差别,在此基础上,基于同一基准温度提取方法,开展HJ-1B数据、Landsat ETM+数据及MODIS反演结果3种不同空间分辨率数据在近海核电站温排水监测的一致性进行了分析与评价。研究表明:ETM+数据温度反演精度高于HJ-1B结果,且更能体现高温升区监测精度;基于劈窗算法的MODIS海温反演精度更高,但不能反映温排水温升分布细节;综合考虑具有较高时间分布率和较大幅宽的HJ-1BIRS数据更能满足业务需求。     

基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究北大核心CSCD

近年来湿地的植被退化一直是全球关注的问题,对湿地植被覆盖度进行反演并研究其时空分布特征显得尤为重要。而为了解决植被反演中存在的混合像元问题,提出了基于面向对象的多端元光谱混合分析方法。以扎龙湿地保护区为研究对象,中高分辨率Landsat影像为数据源,从时间尺度和植被覆盖度等级变化层面,研究湿地植被时空变化特征。面向对象多端元混解模型,有效地减少了计算量和混合像元的端元变化,且反演值与检验值相关性较高,均方根误差较小,优于传统多端元混解模型方法,提高了植被覆盖度反演精度。扎龙湿地多年植被覆盖度整体呈现退化趋势,2001～2017年的平均变化速率高于1985～2000年,对于提高全球气候变化情景下植被转移预测精度具有重要理论意义。     

基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究

近年来湿地的植被退化一直是全球关注的问题,对湿地植被覆盖度进行反演并研究其时空分布特征显得尤为重要。而为了解决植被反演中存在的混合像元问题,提出了基于面向对象的多端元光谱混合分析方法。以扎龙湿地保护区为研究对象,中高分辨率Landsat影像为数据源,从时间尺度和植被覆盖度等级变化层面,研究湿地植被时空变化特征。面向对象多端元混解模型,有效地减少了计算量和混合像元的端元变化,且反演值与检验值相关性较高,均方根误差较小,优于传统多端元混解模型方法,提高了植被覆盖度反演精度。扎龙湿地多年植被覆盖度整体呈现退化趋势,2001～2017年的平均变化速率高于1985～2000年,对于提高全球气候变化情景下植被转移预测精度具有重要理论意义。     

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。     

基于多源遥感数据协同反演森林地表土壤水分研究

土壤水分在土壤监测中是一项重要的指标,对于农业生产、生态环境以及水资源管理有着重要的影响。随着遥感建模与反演理论的不断成熟,其逐渐成为分析土壤指标的重要技术与手段。因此,利用光学影像与雷达影像数据,以大兴安岭地区漠河市为研究区域,分别建立以Landsat 8为数据源的土壤水分反演模型和由Landsat 8影像数据与GF-3卫星数据协同反演的土壤水分反演模型,将反演结果与实际测得数据进行对比验证,并评价所建立的反演模型。结果表明：①对研究区地温进行反演,利用地表温度（Ts）与归一化差异湿度指数NDMI构建Ts-NDMI特征空间,结合实测数据可以发现Ts-NDMI特征空间土壤水分反演模型的反演结果与实测土壤含水量为负相关性;②协同GF-3卫星数据和Landsat 8遥感影像数据所建立的土壤水分反演模型能得到质量较高的反演结果,且在高植被覆盖度地区,利用该协同反演模型得到的反演结果比利用单一光学数据源所建模型得到的反演结果精度高,为今后高植被覆盖度地区土壤湿度的研究提供了新途径。     

HJ-1A/B星地表反照率生成算法与检验

提出了适合环境与灾害监测预报小卫星-A、B星(简称HJ-1A/B星)CCD相机的大气订正算法,并基于不同地表特性和大气条件下的辐射传输模拟数据,建立HJ-1A/B星的窄波段向宽波段反照率转换的模型.利用多级灰阶靶标实测数据、敦煌检验场实测数据验证了大气订正算法以及转换模型的可靠性和精度,并将HJ-1A/B星影像数据计算的反照率产品与同时相的MODIS反照率产品进行对比分析.结果表明:文章提出的HJ-1A/B星CCD相机大气订正算法可有效校正大气影响;窄波段向宽波段反照率转换模型反演的反照率精度可靠;基于研究成果生成的HJ-1A/B星地表反照率与MODIS反照率产品一致性较好,满足后续遥感数据定量化模型研究的精度需要.     

基于Sentinel-1与FY-3C数据反演植被覆盖地表土壤水分

基于新一代的Sentinel-1SAR数据与FY-3C的MWRI数据,研究植被覆盖地表土壤湿度反演方法。为消除植被对土壤湿度反演影响,首先利用FY-3C/MWRI的微波极化差异指数MPDI,建立植被含水量反演模型;然后,结合植被含水量反演模型和水—云模型,发展一种主被动微波联合反演植被覆盖地表土壤含水量模型;最后,在江淮地区开展反演试验,利用观测的土壤湿度数据进行反演结果的精度验证。结果表明:(1)对于植被覆盖地表土壤湿度反演,由FY3C/MWRI提取的MPDI对于去除植被影响效果较好;(2)相比于VH极化哨兵1号卫星数据,VV极化数据更适用于土壤含水量的反演,能够得到更高的土壤湿度反演精度;(3)哨兵1号卫星数据能够获得较高精度的土壤含水量反演结果,试验反演的土壤湿度值与实测值相关系数为0.561 2,均方根误差为0.044cm~3/cm~3。     

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HJ-1A高光谱数据的藏北高原草地分类方法对比

环境减灾星星座A星(HJ-1A)携带的超光谱仪填补了我国星载高光谱影像采集领域的空白,但目前国内关于该高光谱数据的应用较少.本文基于HJ-1A高光谱(HSI)数据预处理技术,以申扎县北部为研究区,采用SPCA-MLC和HSI-SAM分类方法,结合野外实测样本,将研究区分为沼泽草甸、高寒草甸、高寒草原、荒漠化草原和裸地5种类型,并结合分类精度和分类图对2种分类方法进行了对比分析,可得基于HJ-1A高光谱数据的藏北高原草地分类方法中SPCA-MLC法优于HSI-SAM法.2种方法的分类精度皆大于80％,证明了HJ-1A的HSI数据在实现藏北草地高精度分类方面的巨大潜力.     

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.     

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.     

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.     

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。     

The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

ABSTRACT

Land surface temperature (LST) plays a significant role in surface water circulation and energy balance at both global and regional scales. Thermal disaggregation technique, which relies on vegetation indices, has been widely used due to its advantage in producing relatively high resolution LST data. However, the spatial enhancement of satellite LST using soil moisture delineated vegetation indices has not gained enough attention. Here we compared the performances of temperature vegetation dryness index (TVDI), normalized difference vegetation index (NDVI), and fractional vegetation coverage (FVC), in disaggregating LST over the humid agriculture region. The random forest (RF) regression was used to depict the relationship between LST and vegetation indices in implementing thermal disaggregating. To improve the model performance, we used the thin plate spline (TPS) approach to calibrate the RF residual estimation. Results suggested that the models based on TVDI performed better than those based on NDVI and FVC, with a reduced average root mean square error and mean absolute error of 0.20 K and 0.16 K, respectively. Moreover, based on the surface energy balance model, we found the surface evapotranspiration (ET) derived with the TVDI disaggregated LST as inputs achieved higher accuracy than those derived with NDVI and FVC disaggregated LST. It is indicated that TVDI, a soil moisture delineated vegetation indices, can improve the performance of LST enhancement and ET estimation over the humid agriculture region, when combining random forest regression and TPS calibration. This work is valuable for terrestrial hydrology related research.     

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。     

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。     

Fractional vegetation coverage response to climatic factors based on grey relational analysis during the 2000-2017 growing season in Sichuan Province,China

ABSTRACT

Sichuan Province, China, is a typical ecologically fragile area that is sensitive to global climate change. Studies regarding the spatial-temporal variations and driving factors of FVC (Fractional Vegetation Coverage) in Sichuan Province’s vegetation ecosystem are of important theoretical and practical significance for revealing the relationship between global climate change and vegetation ecosystems. These studies are also important theoretical and practical significance for the evaluation of environmental quality and service function adjustment of terrestrial ecosystems. In existing studies, there is a lack of detailed depictions of the FVC response to climatic factors in the context of different vegetation types and different landform features in Sichuan Province. In this study, the spatial-temporal patterns and change trends for the FVC of the growing seasons during the 2000–2017 period for Sichuan Province were analysed based on the FVCs that were inversely determined from MODIS (MODerate-resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) remote sensing data, and they were combined with air temperature, relative humidity and precipitation data. Moreover, the GRA (Grey Relational Analysis) method was used to study the response of the FVC to climate changes. Based on the results of the GRA, the zoning of climatic factors as driving forces for the FVC was performed, and the differences in the spatial-temporal characteristics of the FVC response to different climatic factors were presented in quantitative form. Here, we found that the vegetation coverage in Sichuan province showed a slight degradation trend, and that the medium to low altitude woody plants were significantly degraded. The proportion of regions in which relative humidity (17.3%) and precipitation (17.4) were strong drivers for FVC changes, was much greater than the regions in which air temperature (1.8%) and other co-drivers were the force.     

Estimating ground fractional vegetation cover using the double-exposure method

Fractional vegetation cover (FVC) is a key parameter in ecological models. It is important to determine the ground FVC quickly and accurately in studies of soil erosion, surface energy balance, and carbon cycling. As one of the FVC ground measurement methods, the photographic method is easy to operate with relatively high precision. However, its classification result showed poor accuracy when an image of a high-contrast scene contained a shadow region where a low signal-to-noise ratio (SNR) existed, because the single-exposure image in the photographic method did not contain sufficient surface information about both the illuminated and shadowed parts. This article presents application of a double-exposure photographic method to determine vegetation cover in the shadow region of an image. It consists of two measurements used in acquiring images (normal and over-exposure) and one image-processing part to handle the obtained images. Illuminated vegetation and soil, as well as the shadow region, was classified with the normally exposed image in the intensity, hue, and saturation (IHS) colour space, and the shadow region was further classified as shadowed vegetation and shadowed soil using the over-exposed image. The results indicate that the over-exposed image reduced the average bias of the FVC in the shadow region from 15.40% to ?4.14% and the root mean square error (RMSE) from 0.174 to 0.066. The RMSE of the entire scene was 0.055 in the over-exposed image and 0.092 in the single-exposed image. The double-exposure method also showed a better classification result than the high dynamic range method in deep shadow regions. This study shows that this method is capable of distinguishing vegetation and soil in the shadow region and thus it is an effective and accurate method for ground FVC measurement.     

Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area

The fraction of vegetation cover (FVC) and the leaf area index (LAI) are important parameters for many agronomic, ecological and meteorological applications. Several in‐situ and remote sensing techniques for estimating FVC and LAI have been developed in recent years. In this paper, the uncertainty of in‐situ FVC and LAI measurements was evaluated by comparing estimates from LAI‐2000 and digital hemispherical photography (DHP). The accuracy achieved with a spectral mixture analysis algorithm and two vegetation indices‐based methods was assessed using atmospherically corrected Landsat Thematic Mapper (TM) data over the Barrax cropland area where the European Space Agency (ESA) SENtinel‐2 and FLuorescence EXperiment (SEN2FLEX) field campaign was carried out in July 2005. The results indicate that LAI‐2000 and DHP performances are comparable, with uncertainties of 5% for FVC and 15% for effective LAI. The selected remote sensing methods are shown to be consistent, with a notable overall accuracy (root mean square error, RMSE) of 0.07 (10% in relative terms) for FVC and 0.8 (30%) for LAI. Similar bounds were found on upscaling in‐situ measurements with empirical transfer functions (TFs). These results suggest that the pragmatic methods considered applied at high resolution with minimum calibration data could be useful for mapping FVC and LAI in the study area, reducing in‐situ labour‐intensive characterization necessities for validation studies.