Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data

The monitoring of earth surface dynamic processes requires global observations of the structure and the functioning of vegetation. Moderate resolution sensors (with pixel size ranging from 250 m to 7 km) provide frequent estimates of biophysical variables to characterize vegetation such as the leaf area index (LAI). However, the computation of LAI from moderate resolution remote sensing data induces a scaling bias on the LAI estimate if the moderate resolution pixel is heterogeneous and if the transfer function that relates remote sensing data to LAI is non-linear.This study provides a model to evaluate and correct the scaling bias. The model is built first for a univariate semi-empirical transfer function relating LAI directly to NDVI. The scaling bias is a function of (i) the degree of non-linearity of the transfer function quantified by its second derivative and (ii) the spatial heterogeneity of the moderate resolution pixel quantified by the variogram of the high spatial resolution (20 m) NDVI image. Then, the model is extended to a bivariate transfer function where LAI is related to red and near infrared reflectances. The scaling bias depends on (i) the Hessian matrix of the transfer function and (ii) the variograms and cross variogram of the red and near infrared reflectances.The scaling bias is investigated on several distinct landscapes from the VALERI database. Adjusting for scaling bias is critical on crop sites which are the most heterogeneous sites at the landscape level. Regarding the univariate transfer function, the magnitude of the scaling bias increases rapidly with pixel size until this size is larger than the typical spatial scale of the data. For the bivariate transfer function, it results from the addition of several components that may add up or cancel each other out. It is thus more difficult to analyze.The accuracy of the model to estimate the scaling bias is discussed. It depends mainly on the ability of the variograms and cross variogram to represent the local dispersion variances and covariance within the moderate resolution pixel. The model is generally highly accurate at 1000 m spatial resolution for the univariate transfer function and less accurate for the bivariate transfer function.     

Land Cover Extraction in the Ejina Oasis by Hyperspectral Remote Sensing

The extraction of land surface coverage is the basis of ecological environment evaluation,vegetation change analysis and regional ecological and hydrological processes.Aerial hyperspectral remote sensing has great advantage in land surface coverage extraction,such as flexible,wide coverage,high spatial resolution and high spectral resolution.Research area has landscape characteristics of vegetation,landscape fragmentation and heterogeneity in Ejina Poplar Forest National Nature Reserve.Comparison and analysis of two methods of dimension reduction based on minimum noise transform and principal component analysis,three supervised classification methods based on maximum likelihood method,support vector machine and object\|oriented classification.Land surface coverage is extracted by NDVI threshold segmentation,minimum noise transform dimensionality reduction method and maximum likelihood classification method according to the characteristics of landscape fragmentation,heterogeneity and high redundancy of hyperspectral data based on the Airborne Hyperspectral Data of Ejina oasis in the lower reaches of Heihe.The land surface coverage results overall accuracy and Kappa coefficient are 87.95% and 0.885 by random sampling based on airborne remote sensing data.The results show that the classification results of high accuracy can provide effective parameters for ecological research.     

河谷型城市土地利用类型及格局的热环境效应遥感分析——以兰州市为例

以河谷型城市兰州为例,采用Landsat ETM+遥感影像为基本数据源,定量反演了地表温度（LST）和植被指数（NDVI）,利用GIS空间分析方法,分析了LST和NDVI 在不同土地利用类型之间的差异以及二者之间的定量关系,并引入多样性和聚集度指数,讨论了在不同土地利用的空间组合下,LST和NDVI 的空间差异及相互关系。结果显示:LST和NDVI具有明显的相关性,中心城区LST表现出热岛效应,而NDVI则为低谷效应;土地利用斑块和类型两种尺度水平上LST和NDVI均具有明显负相关的线性关系,城市内部不同土地类型所产生的热环境效应不同;土地利用多样性越丰富、聚集度越小的区域,其温度对地表植被覆盖的敏感性越弱。     

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.     

Spectral modelling of multicomponent landscapes in the Sahel

Abstract

Certain landscapes in the Sahel and elsewhere consist of a ‘checkerboard’ arrangement of vegetated and non-vegetated areas in which there may be several spectrally distinct vegetation and bare ground components. When individual components form large spatially coherent patches, and the vertical dimension of the vegetation is small, spectral interactions between components are negligible. The influence of any one component on the average reflectance of the landscape can then be described by its spectral properties and relative area using simple additive mixture models. These models can be extended to the vegetation indices. The spatial average normalized difference vegetation index (NDVI) is a function of the brightness (red plus near-infrared reflectances), the NDVI and the fractional cover of the components. In landscapes where soil and vegetation can be considered the only components, the NDVI-brightness model can be inverted to obtain the NDVI of the vegetation. Aerial photoradiometer data from Mali, West Africa were used to determine the red and near-infrared component reflectances of soil and vegetation. The derived soil component reflectances were well correlated with ground measurements. The relationship between the vegetation component NDVI and plant cover was better than between the NDVI of the entire landscape and plant cover. The usefulness of this modelling approach depends on the existence of clearly distinguishable landscape components. The method resolves the spectral properties of individual components, but the vegetation component, while free of the effect of bare ground components, is still affected by the underlying soil.     

Analysis on the spatial ecological distribution model of landscape plant community

Because the spatial pattern of plant communities in garden landscapes usually exhibits highly non-random distribution characteristics, the accuracy of the analysis results is not high. For this purpose, a spatial ecological distribution model of landscape landform plant communities is designed. Based on the remote sensing images of landscape plant communities, the study area was obtained. The grassland communities in this area were classified and calculated by geostatistics, and the semi variogram values of typical samples of different vegetation types were obtained. According to the calculation results, the spatial terrain factors of landscape plant community are extracted, and the annual NDVI value is taken as the ecological vitality index of landscape green vegetation, the NDVI level is divided, the spatial ecological distribution model of landscape plant community is constructed, and the evaluation index system is generated, so as to complete the spatial ecological distribution model analysis of landscape plant community. The results show that the method of this study accurately analyzes the area of coniferous and broad-leaved mixed forest, mountain meadow, road, construction land, water area land and slope grade, which is slightly different from the actual value, indicating that the method has certain reliability.     

Seasonality of finely-resolved spatial structure of NDVI and its component reflectances in tallgrass prairie

Grass canopies exhibit distinct seasonality in reflectance and spatial patterns in reflectance result from landscape heterogeneity. To investigate the spatial structure of NDVI and its two component reflectances at fine resolution in burned and unburned tallgrass prairie, we use range and relative nugget effect two indicators of spatial structure derived from semivariograms. Results show that spatial dependence of the three spectral measures are similar in the early season when the litter layer has been removed by burning and again in the later season when the canopy is senescent. However, as the canopy develops, the spatial dependence of NDVI deviates from that of its component reflectances. In the unburned canopy, red reflectance appears to strongly influence the spatial pattern of NDVI. In the burned canopy, neither component reflectance strictly determines the spatial pattern of NDVI. Maximum divergence in spatial pattern between NDVI and its components coincides with minimum available moisture, suggesting a relation between moisture stress and spatio-spectral heterogeneity.     

Monitoring change in the spatial heterogeneity of vegetation cover in an African savanna

The extent to which a new intensity‐dominant scale approach to characterizing spatial heterogeneity from remote sensing imagery can be used to monitor two‐dimensional changes (i.e. variability and patch size) in the spatial heterogeneity of vegetation cover (estimated from a Landsat Thematic Mapper (TM)‐derived Normalized Difference Vegetation Index (NDVI)) was tested in the Sebungwe region in north‐western Zimbabwe between 1984 and 1992. Intensity of spatial heterogeneity (i.e. the maximum variance obtained when a spatially distributed landscape property is measured with a successively increasing window size) was used to measure variability in vegetation cover. Dominant scale of spatial heterogeneity (i.e. the window size at which the maximum variance in the landscape property is measured) was used to measure the dominant patch dimension of vegetation cover. This approach was validated by testing whether the observed change in the dominant scale and intensity of spatial heterogeneity of vegetation cover between 1984 and 1992 was related to changes in the proportion of arable fields. The results also indicated that there was a significant relationship (p<0.05) between changes in the proportion of agricultural fields and changes in the intensity and the product of intensity and dominant scale of spatial heterogeneity (intensity×dominant scale), suggesting that the new approach captures observable changes in the landscape, and is not an artefact of the data. The results imply that the intensity‐dominant scale approach to quantifying spatial heterogeneity in remote sensing imagery can be used for a comprehensive characterization and monitoring of changes in landscape condition.     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

Detecting land cover change at the Jornada Experimental Range, New Mexico with ASTER emissivities

Multispectral thermal infrared remote sensing of surface emissivities can detect and monitor long term land vegetation cover changes over arid regions. The technique is based on the link between spectral emissivities within the 8.5-9.5 μm interval and density of sparsely covered terrains. The link exists regardless of plant color, which means that it is often possible to distinguish bare soils from senescent and non-green vegetation. This capability is typically not feasible with vegetation indices. The method is demonstrated and verified using ASTER remote sensing observations between 2001 and 2003 over the Jornada Experimental Range, a semi-arid site in southern New Mexico, USA. A compilation of 27 nearly cloud-free, multispectral thermal infrared scenes revealed spatially coherent patterns of spectral emissivities decreasing at rates on the order of 3% per year with R² values of ∼ 0.82. These patterns are interpreted as regions of decreased vegetation densities, a view supported by ground-based leaf area index transect data. The multi-year trend revealed by ASTER's 90-m resolution data are independently confirmed by 1-km data from Terra MODIS. Comparable NDVI images do not detect the long-term spatially coherent changes in vegetation. These results show that multispectral thermal infrared data, used in conjunction with visible and near infrared data, could be particularly valuable for monitoring land cover changes.     

Land degradation and erosion risk mapping by fusion of spectrally-based information and digital geomorphometric attributes

The Guadalentin basin, located in the SE of Spain, has a semiarid climate and presents typical characteristics of Mediterranean landscapes vulnerable to land degradation processes and desertification risks. In such an environment, when the vegetation cover is low, the signal received by satellites is dominated by the spectral properties of soils. Changes in these properties can be interpreted in terms of varying soil surface conditions. These optical changes underline the major modifications affecting soil surface under land degradation processes. The present research uses remote sensing techniques to characterise land degradation based on two approaches: spectral mixture analysis and a set of indices describing the spectrum shape. It also presents an integrated approach for evaluating ecosystem vulnerability to land degradation, through the combined analysis of spectrally-derived land units and geomorphometric units. Specific objectives consist of evaluating the potential of extending the indices describing the spectrum shape to the short-wave infrared region, and of identifying landscape units according to their sensitivity to land degradation. Our results demonstrate that the spatial distribution of regional patterns of land degradation can be reliably mapped by using both indices describing the spectrum shape and spectral unmixing. The latter holds great potential for operational mapping of soil conditions and erosion features from optical images. Moreover, landscape-unit analysis shows that DEM (Digital Elevation Model) variables combined with spectral information are very useful for land degradation assessment. This approach allowed us to segment the landscape into different units according to their lithology and vegetation characteristics, as well as their susceptibility to water erosion.     

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

基于多时相Landsat8 OLI影像的作物种植结构提取

针对基于多时相遥感影像、多种特征量提取多种作物种植结构在我国研究较少的现状,利用多时相Landsat8OLI影像数据,根据温宿县不同作物的农事历,通过分析主要地物的光谱特征和归一化植被指数的时间变化信息,构建不同作物种植结构提取的决策树模型,实现了对温宿县多种作物种植结构信息的提取。结果表明:1水稻的最佳识别依据是5月20日影像的近红外波段和7月23日影像的NDVI值;棉花和春玉米的最佳识别依据是5月20日~9月9日影像的NDVI变化值;冬小麦—夏玉米和林果的最佳识别依据是5月20日~7月23日影像的NDVI变化值;2与单时相监督分类相比,多时相决策树法对多种作物种植结构的提取效果更理想,总体精度提高了7.90%,Kappa系数提高了0.10;3Landsat8OLI影像数据分辨率高、成本低、获取方便,是农作物遥感的良好数据源。     

基于蚁群优化算法的遥感器波段设置探究

卫星载荷研制发射后其光谱和空间观测模式固定,无法根据复杂地表的多样化需求进行实时灵活调整,且目前遥感器波段设置尚不完善还存在优化空间.引进基于蚁群优化算法的波段选择方法(AntColonyOptimization basedBandSelection,ACOBS),结合北美区域３３景AVIRIS航空高光谱图像,开展了不同区域、不同地表覆盖类型的高光谱波段优选研究,发现各地表类型优选波段组合存在一定差异,其中４波段组合中红光、近红外波段为２个共同入选波段,６波段组合中绿光、红光、短波红外波段为３个共有波段,８波段组合中紫光、绿光、红光、红边、近红外１、近红外２、短波红外１、短波红外２为８个共有入选波段,其他入选波段与地表覆盖类型有关.在此基础上,进一步开展了多光谱卫星波段设置评价研究,发现:４波段优化方案中,绿光、红光、近红外波段１ (７７０~８９５nm)、近红外波段２(９００~１３５０nm)为最优波段组合;６波段优化方案中,绿、红、红边、近红外１(７７０~８９５nm)、近红外２(９００~１３５０nm)、短波红外１(１５６０~１６６０nm)为最优波段组合;８波段优化方案中,蓝、绿、红、红边、近红外１(７７０~８９５nm)、近红外２(９００~１３５０nm)、短波红外１(１５６０~１６６０nm)和短波红外２(２１００~２３００nm)为最优波段组合.研究结果表明Land satTM OLI、SPOT等陆地资源遥感器波段设置还存在一定优化调整空间,特别是红边波段在目前传感器波段设置中没有得到足够重视.     

Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland

Remote sensing, in combination with multivariate geostatistical methods, has the potential to improve the prediction of soil properties at landscape scales. In the Everglades region, and particularly in Water Conservation Area 2A (WCA-2A), phosphorus enrichment has drawn a lot of attention and has led to an extensive documentation of different aspects of the degradation of the system. This study presents a hybrid geospatial modeling approach to predict soil total phosphorus (TP) using remotely-sensed data and ancillary landscape properties as supporting variables. Two remote sensors, Landsat 7 Enhanced Thematic Mapper (ETM)+ and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), were used to investigate relationships between spectral data and indices and soil TP. A variation of a vegetation index (Normalized Difference Vegetation Index - NDVI green) was found to be the most effective in predicting floc TP values, due to its capacity to capture small variations in chlorophyll a that are associated to TP levels in periphyton, especially in aquatic/non-impacted areas. On the other hand, NDVI, a more traditionally used vegetation index, was still a good indicator of TP variability, particularly in the soil surface layer, due to its stronger relationship with impacted areas dominated by cattail (Typha domingensis Pers.).Findings from this study indicate that: a) remote sensing can play an important role in optimizing monitoring of environmental variables, particularly below-ground properties of floc and soils; b) because of limitations about the numbers and frequency of soil samples that can be taken, the combination of remote sensing and geostatistics could represent a non-invasive and cost-effective method to monitor soil nutrient status in complex wetland systems, and c) variations of traditional remote sensing indices such as NDVI can be used to better capture the spatial variability associated with soil and periphyton TP.     

Variability of biome reflectance directional signatures as seen by POLDER

Reflectance measurements acquired with the spaceborne POLDER instrument are used to analyze the variability of land surface directional signatures as a function of vegetation cover type. The reflectance directional signatures are quantified by the three parameters of a modified version of the Ross-Li reflectance model. The variability of the estimated parameters with respect to the seven MODIS biome classes was found to be higher within the classes than between classes, with the exception of the desert targets that show more isotropic reflectances. A limited number of standard BRDFs (typically 5 in the red and near infrared) capture most of the variability of the directional reflectance measurements, supporting the idea that different land surfaces have similar directional signatures. Over vegetation targets, they are characterized by a strong increase toward backscattering and much smaller variations in forward directions. The results express the diversity in structural situations within a given biome class and indicate that, at the resolution of the POLDER sensor, i.e. a few kilometers, the BRDF contains little information on the dominant vegetation type. We show that standard directional signatures may be used to correct the reflectance measurements for directional effects with an RMS error on the order of 0.011 in the red and 0.015 in the near infrared.     

Evaluation of a useful method to identify snow‐covered areas under vegetation – comparisons among a newly proposed snow index,normalized difference snow index,and visible reflectance

Objective methods of monitoring snow‐covered areas by optical remote sensing were evaluated using synchronous observations conducted with the passage of the Landsat‐7 satellite over the plains of Niigata prefecture, one of the snowiest regions in Japan. The observations were conducted in the springs of 2002 and 2003. Snow‐covered areas were identified using three methods: (1) visible (red) reflectance, (2) Normalized Difference Snow Index (NDSI) which uses visible and shortwave‐infrared reflectances, and (3) a newly proposed snow index called S3 which uses visible, near‐infrared and shortwave‐infrared reflectances. The Snow‐Cover Ratio (SCR) was defined as the ratio of the number of pixels in snow‐covered areas to the total number of pixels in an image. The threshold value for the three indices used to identify snow‐covered areas was defined as 50% of SCR, which converged to nearly the same value regardless of the images analysed. Under clear conditions, visible (red) reflectance can identify snow‐covered areas accurately if no vegetation is present. NDSI can distinguish snow‐covered areas from mixels (mixed pixels) of snow and vegetation by referring to the Normalized Difference Vegetation Index (NDVI). S3 can distinguish snow‐covered areas from mixels of snow and vegetation without any reference data. S3 is, therefore, more useful than NDSI because it automatically distinguishes snow‐covered areas from mixels of snow and vegetation.     

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.