An assessment of angular variations of red and NIR reflectances in multi-date IRS-1D Wide Field Sensor data

A study has been carried out to assess angular variations in red and near infrared (NIR) reflectance of different features of the Earth's surface in a common overlap area of accumulated four-date Indian Remote Sensing Satellite (IRS-1D) Wide Field Sensor (WiFS) data from the first fortnight of October 2003. An improved dark object subtraction (DOS) method has been used to perform image based atmospheric corrections. Red and NIR reflectance variations of four structurally different classes—dense vegetation (shrub), sparse crop (pearl millet/maize), wasteland and forest with Sun-target-sensor geometry were analysed. A linearly constrained least squares technique was used to estimate red and NIR model coefficients of the linear Ross Thick-Li Sparse (RTLS) semi- empirical Bidirectional Reflectance Distribution Function (BRDF) model and compared with Moderate Resolution Imaging Spectrometer (MODIS) BRDF product coefficients. The relative reflectance difference between two dates as well as anisotropic factors for red and NIR for all classes and dates were also computed. Red and NIR reflectance of the four land cover classes at different locations with different observation geometry were estimated using both WiFS derived and MODIS BRDF product RTLS model coefficients and compared with WiFS observed reflectance. It was found that the mean relative difference in red and NIR reflectances between consecutive dates varied between 4–11% and 6–8%, respectively, while the computed mean anisotropy factors varied between 3–10% in the red and 8–11% in the NIR. A small anisotropy in the Normalized Difference Vegetation Index (NDVI) as a function of the scattering angle was observed for the four land cover classes. This may imply that angular effects in WiFS are relatively small and do not exceed 10–11 % for the land cover classes considered here.     

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x? = 0.086, σ = 0.088, broadleaf x? = 0.019, σ = 0.079, coniferous x? = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r² = 0.76 for the NIR band; r² = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.     

Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.     

Land Surface Temperature Data Fusion of Landsat ETM and MODISby Combining TsHARP and STITFM Model

Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere,and LST has widely used in the fields of urban heat island effect,soil moisture and surface radiative flux.Currently,no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution,which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image,a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm,defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting,the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method,the fusion results in the selected areas show some regularity with the increasing of the window.In general,a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.     

Assessment of multispectral, -temporal and -angular MODIS data for tree cover mapping in the tundra–taiga transition zone

The latitudinal tree cover gradient is an important characteristic of the tundra–taiga transition zone stretching around the northern hemisphere. Accurately mapped continuous tree cover fields would enable the depiction of forest extent over this ecotone, which is sensitive to climate change, natural disturbances and human activities. The objective of this study was to assess the explanatory power of multispectral, -temporal and -angular MODIS data to estimate tree cover at the regional scale in northernmost Finland. The standard MODIS BRDF/Albedo (MOD43B) data products at approximately 1 km resolution were used. The tree cover was estimated using generalized linear models (GLM), which were calibrated and evaluated by high resolution biotope inventory data. The benefit of coupling the multispectral, -temporal and -angular variables was assessed by variation partitioning. The predicted tree cover fields were also used for the forest–non-forest classification over a larger region and compared with the forest extent of Finnish CORINE land cover 2000 data set. The results show that multitemporal and -angular variables can increase the accuracy of the tree cover estimates and mapping of the forest extent in comparison to the peak of the growing season nadir-view multispectral data. The season of the data acquisition also affect the model performance, the late-spring and early-summer data being superior to mid- and late-summer data. Although the pure effect of the multiangular variables i.e. the parameters of the MODIS BRDF model and selected multiangular indices were relatively small in the models, the inclusion of these data increased the accuracy of the tree cover estimates in the mires in comparison to the peak of the growing season nadir-view multispectral data and multitemporal variables.     

Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil

The paper investigated the application of MODIS data for mapping regional land cover at moderate resolutions (250 and 500 m), for regional conservation purposes. Land cover maps were generated for two major conservation areas (Greater Yellowstone Ecosystem—GYE, USA and the Pará State, Brazil) using MODIS data and decision tree classifications. The MODIS land cover products were evaluated using existing Landsat TM land cover maps as reference data. The Landsat TM land cover maps were processed to their fractional composition at the MODIS resolution (250 and 500 m). In GYE, the MODIS land cover was very successful at mapping extensive cover types (e.g. coniferous forest and grasslands) and far less successful at mapping smaller habitats (e.g. wetlands, deciduous tree cover) that typically occur in patches that are smaller than the MODIS pixels, but are reported to be very important to biodiversity conservation. The MODIS classification for Pará State was successful at producing a regional forest/non-forest product which is useful for monitoring the extreme human impacts such as deforestation. The ability of MODIS data to map secondary forest remains to be tested, since regrowth typically harbors reduced levels of biodiversity. The two case studies showed the value of using multi-date 250 m data with only two spectral bands, as well as single day 500 m data with seven spectral bands, thus illustrating the versatile use of MODIS data in two contrasting environments. MODIS data provide new options for regional land cover mapping that are less labor-intensive than Landsat and have higher resolution than previous 1 km AVHRR or the current 1 km global land cover product. The usefulness of the MODIS data in addressing biodiversity conservation questions will ultimately depend upon the patch sizes of important habitats and the land cover transformations that threaten them.     

基于时序Landsat数据的三江平原植被地表类型变化遥感探测研究

Landsat 卫星遥感数据具有分辨率较高,数据积累时间长的特点,在探测地表覆盖变化和地物分类中得到广泛应用。首先,对获取的Landsat TM/ETM+时间序列数据进行了定量化处理,获取了三江平原七台河市1989~2012年时间序列Landsat地表反射率图像。其次,设计了林地指数和湿地指数,提取了三江平原七台河区域地物光谱和时序特征,同时设计构建了地表覆盖分类和植被地表类型变化探测的决策树算法,实现了1989~2012年七台河区域的植被地表覆盖变化的动态监测,提取了森林覆盖变化的空间分布与变化时间。最后,对七台河区域地表覆盖与植被地表类型变化进行了精度检验,分类总体精度达到90.04%,Kappa系数达0.88。研究结果表明:基于定量化的Landsat时间序列数据的分类算法能克服单时相影像分类的缺陷,实现区域地物自动分类和地表覆盖变化的动态监测。
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A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.     

Cropland distributions from temporal unmixing of MODIS data

Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R² ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km². The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.     

Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales

Land surface albedo is dependent on atmospheric state and hence is difficult to validate. Over the UK persistent cloud cover and land cover heterogeneity at moderate (km-scale) spatial resolution can also complicate comparison of field-measured albedo with that derived from instruments such as the Moderate Resolution Imaging Spectrometer (MODIS). A practical method of comparing moderate resolution satellite-derived albedo with ground-based measurements over an agricultural site in the UK is presented. Point measurements of albedo made on the ground are scaled up to the MODIS resolution (1 km) through reflectance data obtained at a range of spatial scales. The point measurements of albedo agreed in magnitude with MODIS values over the test site to within a few per cent, despite problems such as persistent cloud cover and the difficulties of comparing measurements made during different years. Albedo values derived from airborne and field-measured data were generally lower than the corresponding satellite-derived values. This is thought to be due to assumptions made regarding the ratio of direct to diffuse illumination used when calculating albedo from reflectance. Measurements of albedo calculated for specific times fitted closely to the trajectories of temporal albedo derived from both Systeme pour l'Observation de la Terre (SPOT) Vegetation (VGT) and MODIS instruments.     

Characterizing vegetation cover in global savannas with an annual foliage clumping index derived from the MODIS BRDF product

The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator.     

A consistency analysis of surface reflectance and leaf area index retrieval from overlapping clear-sky Landsat ETM+ imagery

In this study, the consistency of systematic retrievals of surface reflectance and leaf area index was assessed using overlap regions in adjacent Landsat Enhanced Thematic Mapper-Plus (ETM+) scenes. Adjacent scenes were acquired within 7-25 days apart to minimize variations in the land surface reflectance between acquisition dates. Each Landsat ETM+ scene was independently geo-referenced and atmospherically corrected using a variety of standard approaches. Leaf area index (LAI) models were then applied to the surface reflectance data and the difference in LAI between overlapping scenes was evaluated. The results from this analysis show that systematic LAI retrieval from Landsat ETM+ imagery using a baseline atmospheric correction approach that assumes a constant aerosol optical depth equal to 0.06 is consistent to within ±0.61 LAI units. The average absolute difference in LAI retrieval over all 10 image pairs was 26% for a mean LAI of 2.05 and the maximum absolute difference over any one pair was 61% for a mean LAI of 1.13. When no atmospheric correction was performed on the data, the consistency in LAI retrieval was improved by 1%. When a scene-based dense, dark vegetation atmospheric correction algorithm was used, the LAI retrieval differences increased to 28% for a mean LAI of 2.32. This implies that a scene-based atmospheric correction procedure may improve the absolute accuracy of LAI retrieval without having a major impact on retrieval consistency. Such consistency trials provide insight into the current limits concerning surface reflectance and LAI retrieval from fine spatial resolution remote sensing imagery with respect to the variability in clear-sky atmospheric conditions.     

Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain,China

A method is presented for bi‐directional reflectance distribution function (BRDF) parametrization for topographic correction and surface reflectance estimation from Landsat Thematic Mapper (TM) over rugged terrain. Following this reflectance, albedo is calculated accurately. BRDF is parametrized using a land‐cover map and Landsat TM to build a BRDF factor to remove the variation of relative solar incident angle and relative sensor viewing angle per pixel. Based on the BRDF factor and radiative transfer model, solar direct radiance correction, sky diffuse radiance and adjacent terrain reflected radiance correction were introduced into the atmospheric‐topographic correction method. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance, as well as atmospheric transmittance and path radiance, are analysed in detail and calculated per pixel using a look‐up table (LUT) with a digital elevation model (DEM). The method is applied to Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF gives better surface reflectance compared with sole atmospheric correction and two other useful atmospheric‐topographic correction methods. Finally, surface albedo is calculated based on this topography‐corrected reflectance and shows a reasonable accuracy in albedo estimation.     

An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data

Long term observations of global vegetation from multiple satellites require much effort to ensure continuity and compatibility due to differences in sensor characteristics and product generation algorithms. In this study, we focused on the bandpass filter differences and empirically investigated cross-sensor relationships of the normalized difference vegetation index (NDVI) and reflectance. The specific objectives were: 1) to understand the systematic trends in cross-sensor relationships of the NDVI and reflectance as a function of spectral bandpasses, 2) to examine/identify the relative importance of the spectral features (i.e., the green peak, red edge, and leaf liquid water absorption regions) in and the mechanism(s) of causing the observed systematic trends, and 3) to evaluate the performance of several empirical cross-calibration methods in modeling the observed systematic trends. A Level 1A Hyperion hyperspectral image acquired over a tropical forest—savanna transitional region in Brazil was processed to simulate atmospherically corrected reflectances and NDVI for various bandpasses, including Terra Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA-14 Advanced Very High Resolution Radiometer (AVHRR), and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). Data were extracted from various land cover types typically found in tropical forest and savanna biomes and used for analyses. Both NDVI and reflectance relationships among the sensors were neither linear nor unique and were found to exhibit complex patterns and bandpass dependencies. The reflectance relationships showed strong land cover dependencies. The NDVI relationships, in contrast, did not show land cover dependencies, but resulted in nonlinear forms. From sensitivity analyses, the green peak (∼550 nm) and red-NIR transitional (680-780 nm) features were identified as the key factors in producing the observed land cover dependencies and nonlinearity in cross-sensor relationships. In particular, differences in the extents to which the red and/or NIR bandpasses included these features significantly influenced the forms and degrees of nonlinearity in the relationships. Translation of MODIS NDVI to “AVHRR-like” NDVI using a weighted average of MODIS green and red bands performed very poorly, resulting in no reduction of overall discrepancy between MODIS and AVHRR NDVI. Cross-calibration of NDVI and reflectance using NDVI-based quadratic functions performed well, reducing their differences to ± .025 units for the NDVI and ± .01 units for the reflectances; however, many of the translation results suffered from bias errors. The present results suggest that distinct translation equations and coefficients need to be developed for every sensor pairs and that land cover-dependency need to be explicitly accounted for to reduce bias errors.     

Quality assessment of Landsat surface reflectance products using MODIS data

Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.     

Possibility investigation of tree diversity mapping using Landsat ETM+ data in the Hyrcanian forests of Iran

Lack of data often limits understanding and management of biodiversity in forested areas. Remote sensing imagery has considerable potential to aid in the monitoring and prediction of biodiversity across many spatial and temporal scales. In this paper, we explored the possibility of defining relationships between species diversity indices and Landsat ETM+ reflectance values for Hyrcanian forests in Golestan province of Iran. We used the COST model for atmospheric correction of the imagery. Linear regression models were implemented to predict measures of biodiversity (species richness and reciprocal of Simpson indices) using various combinations of Landsat spectral data. Species richness was modeled using the band set ETM5, ETM7, DVI, wetness and variances of ETM1, ETM2 and ETM5 (adjusted R² = 0.59, RMSE = 1.51). Reciprocal of Simpson index was modeled using the band set NDVI, brightness, greenness, variances of ETM2, ETM5 and ETM7 (adjusted R² = 0.459 RMSE = 1.15). The results demonstrated that spectral reflectance from Landsat can be used to effectively model tree species diversity. Predictive map derived from the presented methodology can help evaluate spatial aspects and monitor tree species diversity of the studied forest. The methodology also facilitates the evaluation of forest management and conservation strategies in northern Iran.     

MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data

The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF‐Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16‐day composites. To provide a standard for MODIS tasselled cap axes, we recommend an orientation in MODIS spectral band space as similar as possible to the orientation of the Landsat Thematic Mapper (TM) tasselled cap axes. To achieve this we first transformed our global sample of MODIS NBAR reflectance values to TM tasselled cap values using the existing TM transformation, then used an existing algorithm (Procrustes) to compute the transformation that minimizes the mean square difference between the TM transformed NBAR values and NBAR PCA values. This transformation can then be used as a standard to rotate the MODIS NBAR PCA axes into a new MODIS Kauth–Thomas (KT) orientation. Global land cover patterns in tasselled cap space are demonstrated graphically by linking the global sample with several other products, including the MODIS Land Cover product (MOD12) and the MODIS Vegetation Continuous Fields product (MOD44). Patterns seen at this global scale agree with previous explorations of TM tasselled cap space, but are shown here in greater detail with a globally representative sample. Temporal trends of eight smaller‐scale BigFoot Project (www.fsl.orst.edu/larse/bigfoot) sites were also examined, confirming the spectral shifts in tasselled cap space related to phenology.     

Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data

This article describes a series of fundamental analyses designed to test and compare the utility of various MODIS data and products for detecting land cover change over a large area of the tropics. The approach for estimating proportional forest cover change as a continuous variable was based on a reduced major axis regression model. The model relates multispectral and multi-temporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for several study sites across the Central American region. Three MODIS data sets with diverse attributes were evaluated on model consistency, prediction accuracy and practical utility in estimating change in forest cover over multiple time intervals and spatial extents.A spectral index based on short-wave infrared information (normalized difference moisture index), calculated from half-kilometer Calibrated Radiances data sets, generally showed the best relationships with the reference data and the lowest model prediction errors at individual study areas and time intervals. However, spectral indices based on atmospherically corrected surface reflectance data, as with the Vegetation Indices and Nadir Bidirectional Reflectance Distribution Function - Adjusted Reflectance (NBAR) data sets, produced consistent model parameters and accurate forest cover change estimates when modeling over multiple time intervals. Models based on anniversary date acquisitions of the one-kilometer resolution NBAR product proved to be the most consistent and practical to implement. Linear regression models based on spectral indices that correlate with change in the brightness, greenness and wetness spectral domains of these data estimated proportional change in forest cover with less than 10% prediction error over the full spatial and temporal extent of this study.     

Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Land‐cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land‐cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet‐merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey‐level co‐occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum‐likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land‐cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8–6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land‐cover classification accuracies.     

The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally

The U.S. Landsat satellite series provide the longest dedicated land remote sensing data record with a balance between requirements for localized high spatial resolution studies and global monitoring. As with any other optical wavelength satellite sensor, cloud contamination greatly compromises image usability for land surface studies. Additionally, selective scene acquisition due to payload, ground station and mission cost constraints further reduces Landsat image availability. Since the 1999 launch of the Landsat Enhanced Thematic Mapper Plus (ETM+) a Long-term Acquisition Plan (LTAP) has been used to anticipate user requests with the goal of annually refreshing a global daytime archive of cloud-free ETM+ data. This research evaluates the availability of cloud-free Landsat ETM+ data over the conterminous U.S. and globally using 3 years of ETM+ cloud fraction metadata archived by the U.S. Landsat project. Landsat application requirements including obtaining at least one cloud-free observation in a year, a season, and two different seasons, or at least a pair of cloud-free observations occurring no more than 16, 32, 48, 64, and 80 days apart within a year and season are considered. Probabilistic analyses indicate that over the conterminous U.S., land applications requiring at least one cloud-free observation in a year, a season, two different seasons, or at least two cloud-free observations occurring within any period of the year, are on average largely unaffected by cloud cover, except for certain Winter applications and cloudy scenes near the U.S.-Canada border and the Great Lakes. Cloud becomes a constraint when at least two cloud-free observations are required from the same season over the conterminous U.S., especially when the separation between observations is restricted to short time intervals. Global applications requiring at least one cloud-free observation in a season, in two different seasons, and applications requiring at least two cloud-free observations in a year, are all severely affected by cloud and data availability constraints; and globally it is generally not practical to consider land applications that require at least two cloud-free observations in any season. Globally, only land applications requiring at least one cloud-free observation per year are largely unaffected by cloud cover and the reduced global ETM+ data availability. These results are specific only to the U.S. Landsat ETM+ archive; they suggest the need for an increased global Landsat acquisition rate for the current and future Landsat missions and/or the development of new approaches to mitigating cloud contamination in the U.S. global Landsat ETM+ archive.