Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification

Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 × 3 pixels and 11 × 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy = 78.95%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2-19%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellite image data, providing an additional tool to ongoing aerial photograph interpretation.     

A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery

Sudden Oak Death is a new and virulent disease affecting hardwood forests in coastal California. The spatial-temporal dynamics of oak mortality at the landscape scale are crucial indicators of disease progression. Modeling disease spread requires accurate mapping of the dynamic pattern of oak mortality in time through multi-temporal image analysis. Traditional mapping approaches using per-pixel, single-date image classifications have not generated consistently satisfactory results. Incorporation of spatial-temporal contextual information can improve these results. In this paper, we propose a spatial-temporally explicit algorithm to classify individual images using the spectral and spatial-temporal information derived from multiple co-registered images. This algorithm is initialized by a spectral classification using Support Vector Machines (SVM) for each individual image. Then, a Markov Random Fields (MRF) model accounting for ecological compatibility is used to model the spatial-temporal contextual prior probabilities of images. Finally, an iterative algorithm, Iterative Conditional Mode (ICM), is used to update the classification based on the combination of the initial SVM spectral classifications and MRF spatial-temporal contextual model. The algorithm was applied to two-year (2000, 2001) ADAR (Airborne Data Acquisition and Registration) images, from which three classes (bare, dead, forest) are detected. The results showed that the proposed algorithm achieved significantly better results (Year 2000: Kappa = 0.92; Year 2001: Kappa = 0.91), compared to traditional pixel-based single-date approaches (Year 2000: Kappa = 0.67; Year 2001: Kappa = 0.66). The improvement from the contributions of spatial-temporal contextual information indicated the importance of spatial-temporal modeling in multi-temporal remote sensing in general and forest disease modeling in particular.     

Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation

High spatial resolution remotely sensed data has the potential to complement existing forest health programs for both strategic planning over large areas, as well as for detailed and precise identification of tree crowns subject to stress and infestation. The area impacted by the current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has increased 40-fold over the previous 5 years, with approximately 8.5 million ha of forest infested in 2005. As a result of the spatial extent and intensity of the outbreak, new technologies are being assessed to help detect, map, and monitor the damage caused by the beetle, and to inform mitigation of future beetle outbreaks. In this paper, we evaluate the capacity of high spatial resolution QuickBird multi-spectral imagery to detect mountain pine beetle red attack damage. ANOVA testing of individual spectral bands, as well as the Normalized Difference Vegetation Index (NDVI) and a ratio of red to green reflectance (Red-Green Index or RGI), indicated that the RGI was the most successful (p < 0.001) at separating non-attack crowns from red attack crowns. Based on this result, the RGI was subsequently used to develop a binary classification of red attack and non-attack pixels. The total number of QuickBird pixels classified as having red attack damage within a 50 m buffer of a known forest health survey point were compared to the number of red attack trees recorded at the time of the forest health survey. The relationship between the number of red attack pixels and observed red attack crowns was assessed using independent validation data and was found to be significant (r² = 0.48, p < 0.001, standard error = 2.8 crowns). A comparison of the number of QuickBird pixels classified as red attack, and a broader scale index of mountain pine beetle red attack damage (Enhanced Wetness Difference Index, calculated from a time series of Landsat imagery), was significant (r² = 0.61, p < 0.001, standard error = 1.3 crowns). These results suggest that high spatial resolution imagery, in particular QuickBird satellite imagery, has a valuable role to play in identifying tree crowns with red attack damage. This information could subsequently be used to augment existing detailed forest health surveys, calibrate synoptic estimates of red attack damage generated from overview surveys and/or coarse scale remotely sensed data, and facilitate the generation of value-added information products, such as estimates of timber volume impacts at the forest stand level.     

A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250 m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu.     

Multivariate forest structure modelling and mapping using high resolution airborne imagery and topographic information

Remote sensing has been widely used for modelling and mapping individual forest structural attributes, such as LAI and stem density, however the development and evaluation of methods for simultaneously modelling and mapping multivariate aspects of forest structure are comparatively limited. Multivariate representation of forest structure can be used as a means to infer other environmental attributes such as biodiversity and habitat, which have often been shown to be enhanced in more structurally diverse or complex forests. Image-based modelling of multivariate forest structure is useful in developing an understanding of the associations between different aspects of vertical and horizontal structure and image characteristics. Models can also be applied spatially to all image pixels to produce maps of multivariate forest structure as an alternative to sample-based field assessment. This research used high spatial resolution multispectral airborne imagery to provide spectral, spatial, and object-based information in the development of a model of multivariate forest structure as represented by twenty-four field variables measured in plots within a temperate hardwood forest in southern Quebec, Canada. Redundancy Analysis (RDA) was used to develop a model that explained a statistically significant proportion of the variance of these structural attributes. The resulting model included image variables representing mostly within-crown and within-shadow brightness variance (texture) as well as elevation, taken from a DEM of the study area. It was applied spatially across the entire study area to produce a continuous map of predicted multivariate forest structure. Bootstrapping validation of the model provided an RMSE of 19.9%, while independent field validation of mapped areas of complex and simple structure showed accuracies of 89% and 69%, respectively. Multiscale testing using resampled imagery suggested that the methods could possibly be used with current pan-sharpened, or future sub-metre resolution, multispectral satellite imagery, which would provide much greater spatial coverage and reduced image processing compared to airborne imagery.     

Geostatistical and local cluster analysis of high resolution hyperspectral imagery for detection of anomalies

This paper describes a new methodology to detect small anomalies in high resolution hyperspectral imagery, which involves successively: (1) a multivariate statistical analysis (principal component analysis, PCA) of all spectral bands; (2) a geostatistical filtering of noise and regional background in the first principal components using factorial kriging; and finally (3) the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values and anomalies. The approach is illustrated using 1 m resolution data collected in and near northeastern Yellowstone National Park. Ground validation data for tarps and for disturbed soils on mine tailings demonstrate the ability of the filtering procedure to reduce the proportion of false alarms (i.e., pixels wrongly classified as target), and its robustness under low signal to noise ratios. In almost all scenarios, the proposed approach outperforms traditional anomaly detectors (i.e., RX detector which computes the Mahalanobis distance between the vector of spectral values and the vector of global means), and fewer false alarms are obtained when using a novel statistic S₂ (average absolute deviation of p-values from 0.5 through all spectral bands) to summarize information across bands. Image degradation through addition of noise or reduction of spectral resolution tends to blur the detection of anomalies, increasing false alarms, in particular for the identification of the least pure pixels. Results from a mine tailings site demonstrate the approach performs reasonably well for highly complex landscape with multiple targets of various sizes and shapes. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.     

Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R² of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.     

Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per-pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.     

Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery

Exotic plant invasion is a major environmental and ecological concern and is a particular issue for Mediterranean-type ecosystems. Early detection of invasive plants is crucial for effective weed management. Several studies have explored hyperspectral imagery for mapping invasive plants with promising results. However, only a few extensive or comparative studies about image processing techniques for invasive plant detection have been reported, and even fewer studies have involved very high spatial and spectral resolution imagery. The primary goal of this study was to investigate the utility of very high spatial (0.5 m) and spectral (4 nm) resolution imagery and several classification approaches for detecting tamarisk (Tamarix spp.) infestations, the most problematic invasive plant species in the riparian habitats of southern California.Hierarchical clustering was a particularly effective and efficient statistical method for identifying wavebands and spectral transforms having the greatest discriminatory power. Products resulting from the classification of airborne hyperspectral image data varied by scene, input data type, classifier, and minimum patch size. Overall accuracy of image classification accuracy of products co-varied with commission error rates, such that products having strong agreement with reference data also had a high number of false detections. Integrating the findings from qualitative map analysis, areal proportion statistics, and object-based accuracy assessment indicates that the parallelepiped classifier with several narrow wavebands selected through hierarchical clustering yielded the most accurate and reliable tamarisk classification products.     

A method for detecting large-scale forest cover change using coarse spatial resolution imagery

Many large countries, including Canada, rely on earth observation as a practical and cost-effective means of monitoring their vast inland ecosystems. A potentially efficient approach is one that detects vegetation changes over a hierarchy of spatial scales ranging from coarse to fine. This paper presents a Change Screening Analysis Technique (Change-SAT) designed as a coarse filter to identify the location and timing of large (>5-10 km²) forest cover changes caused by anthropogenic and natural disturbances at an annual, continental scale. The method uses change metrics derived from 1-km multi-temporal SPOT VEGETATION and NOAA AVHRR imagery (reflectance, temperature, and texture information) and ancillary spatial variables (proximity to active fires, roads, and forest tenures) in combination with logistic regression and decision tree classifiers. Major forest changes of interest include wildfires, insect defoliation, forest harvesting, and flooding. Change-SAT was tested for 1998-2000 using an independent sample of change and no-change sites over Canada. Overall accuracy was 94% and commission error, especially critical for large-area change applications, was less than 1%. Regions identified as having major or widespread changes could be targeted for more detailed investigation and mapping using field visits, aerial survey, or fine resolution EO methods, such as those being applied under Canadian monitoring programs. This multi-resolution approach could be used as part of a forest monitoring system to report on carbon stocks and forest stewardship.     

高分辨率遥感影像土地利用动态监测技术研究

近年来,利用高分辨率遥感影像进行土地利用的动态监测在我国已到关键技术和系统研制阶段。本文以某一示范区的土地利用动态监测系统的研究为例,详细阐述了其变化图斑获取子系统实现的关键技术和过程。论文首先简要介绍了系统的主要组成部分;接着详细阐述了变化图斑获取子系统的区域变化检测技术、分类技术、变化区域和属性信息的获取技术;最后对系统作业的工艺流程和主要功能作了介绍,并对系统的应用前景和进一步改进的问题进行了分析。     

Evaluation of annual forest disturbance monitoring using a static decision tree approach and 250 m MODIS data

Research on change detected has largely focused on method development and evaluation in a temporally dependent manner where training and validation data are from the same temporal period. Monitoring over several change periods needs to account for increased variability resulting from possible combinations of atmosphere, sensor, and surface conditions. Training a change method for each monitoring period (i.e. a dynamic approach) is an option, but can be costly to develop the needed training datasets and may not be warranted if sufficient accuracy can be obtained without retraining (i.e. a static approach). In this research the potential of change detection using a static approach suitable for near-real time annual monitoring was evaluated. The research assessed the influence of feature set size, radiometric normalization, incorporation of temporal information, and change object size and sub-pixel fraction on accuracy. The static approach was based on a decision tree developed using 250 m MODIS data from 2005 to 2006 and applied annually for the period 2001-2005. Change results between years were combined and compared to reference data representing change from 2001 to 2005 to evaluate monitoring performance. Results revealed high accuracy for the decision tree change model development from 2005 to 2006 (bootstrap cross-validation KAPPA = 0.91), with lower accuracy (KAPPA = 0.80) when applied for monitoring from 2001 to 2005. Radiometric normalization increased monitoring accuracy (KAPPA = 0.86). Further improvement was achieved with the incorporation of temporal contextual tests to combine the 2001-2005 inter-annual change maps (KAPPA = 0.90), but required a time lag of 1 year. An alternative temporal test that was not restricted by the 1 year time lag produced slightly lower accuracy (KAPPA = 0.88). Evaluation of the effect of object size on detection accuracy showed that accuracy for objects less than 7 pixels was strongly related to object size, with objects less than 3 pixels having low detection rates. The effect of sub-pixel change fraction was found to be dependent on object size with larger objects reducing detection error across the range of fractions evaluated.     

A change detection model based on neighborhood correlation image analysis and decision tree classification

This study introduces a change detection model based on Neighborhood Correlation Image (NCI) logic. It is based on the fact that the same geographic area (e.g., a 3 × 3 pixel window) on two dates of imagery will tend to be highly correlated if little change has occurred, and uncorrelated when change occurs. Computing the piecewise correlation between two data sets provides valuable information regarding the location and numeric change value derived using contextual information within the specified neighborhood. Various neighborhood configurations (i.e., multi-level NCIs) were explored in the study using high spatial resolution multispectral imagery: smaller neighborhood sizes provided some detailed change information (such as a new patios added to an existing building) at the cost of introducing some noise (such as changes in shadows). Larger neighborhood sizes were useful for removing this noise but introduced some inaccurate change information (such as removing some linear feature changes). When combined with image classification using a machine learning decision tree (C5.0), classifications based on multi-level NCIs yielded superior results (e.g., using a 3-pixel circular radius neighborhood had a Kappa of 0.94), compared to the classification that did not incorporate NCIs (Kappa = 0.86).     

A miniature shielded-loop active H-field probe design with high spatial resolution for near-field measurement

A miniature practical active magnetic field (H-field) probe with 0.5 mm × 0.15 mm loop size is designed for electromagnetic interference analysis in electronic systems from 150 kHz to 12 GHz. This probe is fabricated in a four-layer printed circuit board using high-performance and low-loss Rogers material (RO4350B). A low noise amplifier with 14 dB-gain is applied to amplify the radio frequency detect signal. The spatial resolution of the proposed probe is verified under the microstrip with different widths (1.55 and 0.24 mm). In addition, the verification results indicate that the proposed small loop active shielded H-field probe can obtain the better spatial resolution of 422 μm with liftoff = 100 μm. Regarding to the sensitivity of the probe, the proposed probe realizes 16.7 dB μA at 3 GHz with the liftoff = 100 μm. compared with other commercial probes and a reference probe, the proposed probe has better spatial resolution at 150 kHz–12 GHz and sensitivity at 1.5–12 GHz.     

High spatial resolution sea surface climatology from Landsat thermal infrared data

High spatial and temporal resolution maps of sea surface temperature (SST) have numerous applications in coastal and estuarine systems. A climatology map, tracking SST as a function of year-day, was produced at Southern New England using 53 Landsat TM and ETM+ thermal infrared data. A recursive curve-fitting algorithm was used to fit these data and eliminate cloud contamination, resulting in an average daily temperature at every 60-m pixel. The climatology was validated against long-term in situ records that were analyzed with the same techniques. The results show, as expected, that isolated and shallow water bodies undergo more extreme temperature variation (−2 to 25 °C) than deeper, well-connected embayments (1 to 21 °C) or the coastal ocean (4 to 18 °C). The coastal ocean is shown to lag insolation and shallow lakes by up to 44 days, with embayments showing a gradation between these extremes. Despite the subtle temperature range variation, there is rich detail in the spatial patterns which are relevant to the applied sciences of coastal and estuarine systems. The spatial pattern of the climatology reveals anomalous patterns, such as occur where anthropogenic forcing alters climatological patterns. The heat budget of Mount Hope Bay in northeast Narragansett Bay has anthropogenic thermal input from a large power plant, and this input is reflected in the climatology. From the results, it is seen that Narragansett Bay has, on average, a mean annual temperature of 11.86±0.41 °C, while the Mount Hope Bay system is consistently warmer at 12.30±0.21 °C and shows a delayed response to autumn cooling. The long history of Landsat data acquisition can be used to create a climatology of coastal and estuarine scale dynamics at an order of magnitude finer scale resolution than AVHRR climatologies.     

一种提高CCD空间分辨力的方法研究

基于多次移位成像的原理,提出了一种提高红外电荷耦合器件(CCD)空间分辨力的方法.在不改变CCD像元尺寸的前提下,使各相邻像元对应的地面目标进行局部多次采样,提高采样频率,达到将CCD空间分辨力提高多倍的目的.仿真试验显示了其良好的性能.     

高分辨卫星图像卷积神经网络分类模型

目的 卫星图像往往目标、背景复杂而且带有噪声,因此使用人工选取的特征进行卫星图像的分类就变得十分困难。提出一种新的使用卷积神经网络进行卫星图像分类的方案。使用卷积神经网络可以提取卫星图像的高层特征,进而提高卫星图像分类的识别率。方法 首先,提出一个包含六类图像的新的卫星图像数据集来解决卷积神经网络的有标签训练样本不足的问题。其次,使用了一种直接训练卷积神经网络模型和3种预训练卷积神经网络模型来进行卫星图像分类。直接训练模型直接在文章提出的数据集上进行训练,预训练模型先在ILSVRC（the ImageNet large scale visual recognition challenge）-2012数据集上进行预训练,然后在提出的卫星图像数据集上进行微调训练。完成微调的模型用于卫星图像分类。结果 提出的微调预训练卷积神经网络深层模型具有最高的分类正确率。在提出的数据集上,深层卷积神经网络模型达到了99.50%的识别率。在数据集UC Merced Land Use上,深层卷积神经网络模型达到了96.44%的识别率。结论 本文提出的数据集具有一般性和代表性,使用的深层卷积神经网络模型具有很强的特征提取能力和分类能力,且是一种端到端的分类模型,不需要堆叠其他模型或分类器。在高分辨卫星图像的分类上,本文模型和对比模型相比取得了更有说服力的结果。     

A vegetation index based technique for spatial sharpening of thermal imagery

High spatial resolution (∼ 100 m) thermal infrared band imagery has utility in a variety of applications in environmental monitoring. However, currently such data have limited availability and only at low temporal resolution, while coarser resolution thermal data (∼ 1000 m) are routinely available, but not as useful for identifying environmental features for many landscapes. An algorithm for sharpening thermal imagery (TsHARP) to higher resolutions typically associated with the shorter wavebands (visible and near-infrared) used to compute vegetation indices is examined over an extensive corn/soybean production area in central Iowa during a period of rapid crop growth. This algorithm is based on the assumption that a unique relationship between radiometric surface temperature (T_R) relationship and vegetation index (VI) exists at multiple resolutions. Four different methods for defining a VI − T_R basis function for sharpening were examined, and an optimal form involving a transformation to fractional vegetation cover was identified. The accuracy of the high-resolution temperature retrieval was evaluated using aircraft and Landsat thermal imagery, aggregated to simulate native and target resolutions associated with Landsat, MODIS, and GOES short- and longwave datasets. Applying TsHARP to simulated MODIS thermal maps at 1-km resolution and sharpening down to ∼ 250 m (MODIS VI resolution) yielded root-mean-square errors (RMSE) of 0.67-1.35 °C compared to the ‘observed’ temperature fields, directly aggregated to 250 m. Sharpening simulated Landsat thermal maps (60 and 120 m) to Landsat VI resolution (30 m) yielded errors of 1.8-2.4 °C, while sharpening simulated GOES thermal maps from 5 km to 1 km and 250 m yielded RMSEs of 0.98 and 1.97, respectively. These results demonstrate the potential for improving the spatial resolution of thermal-band satellite imagery over this type of rainfed agricultural region. By combining GOES thermal data with shortwave VI data from polar orbiters, thermal imagery with 250-m spatial resolution and 15-min temporal resolution can be generated with reasonable accuracy. Further research is required to examine the performance of TsHARP over regions with different climatic and land-use characteristics at local and regional scales.     

Correction of atmospheric and topographic effects for high spatial resolution satellite imagery

A method for the combined correction of atmospheric and topographic effects has been developed. It accounts for horizontally varying atmospheric conditions and also includes the height dependence of the atmospheric radiance and transmittance functions to simulate the simplified properties of a threedimensional atmosphere. A Digital Elevation Model (DEM) is used to obtain information about surface elevation, slope, and orientation. Based on the Lambertian assumption the surface reflectance in rugged terrain is calculated. The method is restricted to high spatial resolution satellite sensors like Landsat TM and SPOT HRV, since some simplifying assumptions are being made to reduce the required image processing time. The possibilities and limitations of the method are critically discussed.