Mapping forest background reflectivity over North America with Multi-angle Imaging SpectroRadiometer (MISR) data

The spatial and temporal patterns of the forest background optical properties are critically important in retrieving the biophysical parameters of the forest canopy (overstory) and in ecosystem modeling. In this paper we carry out background reflectivity mapping over conterminous United States, Canada, Mexico, and the Caribbean land mass using Multi-angle Imaging SpectroRadiometer (MISR) data at 1.1 km resolution. The refined methodology uses the nadir and 45° forward directions of the MISR camera images. The background reflectivity is shown to vary between coniferous and deciduous stands, particularly in the near-infrared band, and with the overall amount of overstory vegetation. The largest seasonal differences were observed over a boreal region. The main drawback is a high amount of missing MISR data due to the presence of clouds and other atmospheric effects. The paper also contains a demonstration of the effect on LAI estimates when the dynamic background reflectivity information is inserted into a global LAI algorithm. Multi-angular remote sensing is thus shown to enable us to effectively map yet another forest structure parameter over large areas, which was not possible using mono-angle data.     

Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model

A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multi-angle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and a hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semi-arid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li-Ross BRDF model; diffuse brightness (ρ0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li-Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km² area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r² = 0.78, RMSE = 0.013, N = 3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r² = 0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets.     

Effect of foliage spatial heterogeneity in the MODIS LAI and FPAR algorithm over broadleaf forests

This paper presents the analysis of radiative transfer assumptions underlying moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) algorithm for the case of spatially heterogeneous broadleaf forests. Data collected by a Boston University research group during the July 2000 field campaign at the Earth Observing System (EOS) core validation site, Harvard Forest, MA, were used for this purpose. The analysis covers three themes. First, the assumption of wavelength independence of spectral invariants of transport equation, central to the parameterization of the MODIS LAI and FPAR algorithm, is evaluated. The physical interpretation of those parameters is given and an approach to minimize the uncertainties in its retrievals is proposed. Second, the theoretical basis of the algorithm was refined by introducing stochastic concepts which account for the effect of foliage clumping and discontinuities on LAI retrievals. Third, the effect of spatial heterogeneity in FPAR was analyzed and compared to FPAR variation due to diurnal changes in solar zenith angle (SZA) to asses the validity of its static approximation.     

Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r²_NDVI = 0.90; r²_CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.     

Retrieval of leaf area index in different vegetation types using high resolution satellite data

With the successful launch of the IKONOS satellite, very high geometric resolution imagery is within reach of civilian users. In the 1-m spatial resolution images acquired by the IKONOS satellite, details of buildings, individual trees, and vegetation structural variations are detectable. The visibility of such details opens up many new applications, which require the use of geometrical information contained in the images. This paper presents an application in which spectral and textural information is used for mapping the leaf area index (LAI) of different vegetation types. This study includes the estimation of LAI by different spectral vegetation indices (SVIs) combined with image textural information and geostatistical parameters derived from high resolution satellite data. It is shown that the relationships between spectral vegetation indices and biophysical parameters should be developed separately for each vegetation type, and that the combination of the texture indices and vegetation indices results in an improved fit of the regression equation for most vegetation types when compared with one derived from SVIs alone. High within-field spatial variability was found in LAI, suggesting that high resolution mapping of LAI may be relevant to the introduction of precision farming techniques in the agricultural management strategies of the investigated area.     

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ_o and σ_c. The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002-2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.     

Fast damage mapping in case of earthquakes using multitemporal SAR data

This work shows that earthquake damages in urban areas can be determined with an acceptable accuracy through the exploitation of multitemporal SAR data and ancillary information defining urban blocks. In this article, two different methodologies are presented: an unsupervised statistical analysis of the parameters of the models representing backscatterer intensity or coherence values for each block of the urban area under analysis, and a supervised approach which involves a multi-band/multi-temporal classification, performed using a Markov Random Field (MRF) classifier or a spatial Fuzzy ARTMAP (FA) classifier. The two procedures are compared by using ERS images acquired before and after the earthquake of Turkey in 1999.

Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans

Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage.     

极轨气象卫星有效载荷航空校飞数据地理定位

航空校飞数据的地理定位方法用来确定数据中每一个像素的地理经纬度,它是整个数据处理过程的基础。介绍了一种用于航空校飞试验数据的地理定位方法。首先给出了问题的描述;其次建立了航迹和姿态模型;然后给出了地理定位方法的算法;最后给出计算结果及误差分析,验证了方法的可行性以及高精度。地理定位方法不仅仅可以用于极轨气象卫星的校飞试验数据处理,同时也适用于其他遥感卫星的校飞试验数据处理,具有非常重要的意义。     

基于无人机观测的水稻冠层样方多角度反射特点分析

无人机平台成本低和灵活性高的优点可弥补传统遥感平台的缺陷,为农业遥感近地表数据获取提供有效途径.任何型号无人机搭载传感器进行数据采集时均有一定的观测几何,但无人机观测几何诱发的方向反射差异及其在后续应用中的潜在误差仍需深入分析.利用无人机采集典型扬花期水稻田样方多角度观测,探讨其样方级别的方向反射及可见光植被指数的方向...     

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。     

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.     

森林叶面积指数遥感反演与空间尺度转换研究

以贵州省黎平县为研究区,着重研究森林叶面积指数(LAI)的ETM遥感信息反演和向1km空间尺度转换算法.通过LAI-2000的针叶林和阔叶林等植被类型的LAI实地观测,建立实测LAI与ETM影像归一化植被指数(NDVI)的相关关系并进行LAI遥感制图,并在陆地覆盖类型遥感分类信息提取的基础上,发展了针叶林、混交林和空旷地三种地表类型LAI的向上空间尺度转换算法,以对粗分辨MODIS遥感数据的LAI产品实现LAI算法的转换与校正,并通过示例应用显示了本研究空间尺度转换算法的有效性.     

A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies

Leaf area index (LAI) is an important variable needed by various land surface process models. It has been produced operationally from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a look-up table (LUT) method, but the inversion accuracy still needs significant improvements. We propose an alternative method in this study that integrates both the radiative transfer (RT) simulation and nonparametric regression methods. Two nonparametric regression methods (i.e., the neural network [NN] and the projection pursuit regression [PPR]) were examined. An integrated database was constructed from radiative transfer simulations tuned for two broad biome categories (broadleaf and needleleaf vegetations). A new soil reflectance index (SRI) and analytically simulated leaf optical properties were used in the parameterization process. This algorithm was tested in two sites, one at Maryland, USA, a middle latitude temperate agricultural area, and the other at Canada, a boreal forest site, and LAI was accurately estimated. The derived LAI maps were also compared with those from MODIS science team and ETM+ data. The MODIS standard LAI products were found consistent with our results for broadleaf crops, needleleaf forest, and other cover types, but overestimated broadleaf forest by 2.0-3.0 due to the complex biome types.     

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables (n=40). Ratio-based and soil-line-related broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R²=0.67, RMSE=0.69 m² m⁻² (21% of the mean); after removal of one forest stand subjected to clearing measures: R²=0.77, RMSE=0.54 m² m⁻² (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) (R²=0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved.     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

Retrieving photometric properties of desert surfaces in China using the Hapke model and MISR data

The retrieval of photometric properties of desert surfaces is an important first step in the parameterization of land surface components of regional dust emission and global radiation models and in Earth system modeling. In this study, the values of Hapke's photometric parameters (ω, h, b, c, B0, and θ?) were retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) instrument at locations in China's deserts. Four pixels represented the typical surface characteristics of the Taklimakan Desert, sand dunes of Kumtag Desert, relatively smooth areas of the Kumtag Desert and the aeolian sandy soil of Loulan. In contrast to earlier studies, we found that the retrieved parameter values were largely affected by the initial value. To combat this problem we used a Monte Carlo method with physical constraints and a conformity indicator to ensure physically meaningful inversion.The results showed that the angular domain of MISR observations was sufficiently large to determine confidently the values of Hapke's photometric parameters with the exception of the opposition effect width (h). Retrieved values for the single scattering albedo (ω) and macroscopic roughness (θ?) were consistent with qualitative observations about the structure and composition of the surface material and the nature of the dune forms, respectively. At Loulan, where the surface was smoother than other sites, retrieved values exhibited the strongest backward scattering. These results indicated that at the sensor scale, a rough surface (e.g., dunes) does not necessarily mean more backward scattering than a smooth surface. This finding has significant implications for empirical methods (e.g., using the normalized index of backward-scattered radiance minus forward-scattered radiance as an indicator to indicate surface roughness) which should be used carefully for analyzing surface roughness from remote sensing data. Future research is needed to 1) understand how surface roughness at the sub-pixel scale modifies the angular characteristics of reflectance and to 2) find practical methods for rapid whole image processing for mapping the photometric parameters.     

Glacial cover mapping (1987-1996) of the Cordillera Blanca (Peru) using satellite imagery

Multitemporal glacier area mapping is a key element in accurately determining fresh water reserves, as well as providing an indicator of climate change.In Peru, the first glacier inventory was based on visual interpretation of aerial photos, requiring several years of effort. Landsat Thematic Mapper satellite imagery, on the other hand, provides an increasingly employed alternative for the monitoring of changes in glacier area and in other glaciological parameters.By means of Normalized Difference Snow Index (NDSI) computations on TM images, an estimate of the glacierized area in Cordillera Blanca (Peru) was carried out for 1987 (643±63 km²) and 1996 (600±61 km²). Compared to an estimate of 721 km² in 1970, it can be concluded that the glacier area has retreated in this massif by more than 15% in 25 years.     

TM遥感与地块内冬小麦产量变异

卫星遥感可以为农作物的准确管理提供必要,及时并具有空间连续性的信息,但高成本一直是限制该项技术在农业上深入发展的主要障碍,利用价格相对较为低廉的TM卫星影像作为信息源来评价其对估测小区域内作物产量空间变异并为规划管理单元提供必要信息的可行性做了初步的研究,结果表明,利用TM图像所获得的植被指数能较好地反映小麦各生育时期的基本特点,两种植被指数（NDVI及RVI）都表现出一定程序的空间,而且都以小麦抽穗后期的变异程度为最大,而且,小麦生长发育的三个重要时期（分蘖期,抽穗期及拔节期）的两种植被指数之间具有极显著相关关系,两个试验地块小麦11月8日的归一化植被指数都与产量表现出了良好的相关关系,另外,两种植被指数在表现作物千粒重和亩穗数等产量指标信息方面,也有一定的效果。     

Determination of spring onset and growing season leaf development using satellite measurements

An integrated approach to retrieve microwave emissivity difference vegetation index (EDVI) over land regions has been developed from combined multi-platform/multi-sensor satellite measurements, including SSM/I measurements. A possible relationship of the remotely sensed EDVI and the leaf physiology of canopy is explored at the Harvard Forest site for two growing seasons. This study finds that the EDVI is sensitive to leaf development through vegetation water content of the crown layer of the forest canopy, and has demonstrated that the spring onset and growing season duration can be determined accurately from the time series of satellite estimated EDVI within uncertainties of approximately 3 and 7 days for spring onset and growing season duration, respectively, compared to in situ observations. The leaf growing stage can also be monitored by a normalized EDVI. EDVI retrievals from satellite generally are possible during both daytime and nighttime when it is not raining. The EDVI technique studied here may provide higher temporal resolution observations for monitoring the onset of spring, the duration of growing season, and leaf development stage compared to current operational satellite methods.