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1.
Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat's differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R2 = 0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBRMT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBRMT estimates resulted in a moderate-high coefficient of determination R2 = 0.54. dNBRMT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBRMT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS's repeated temporal sampling, the dNBRMT accounts for both first- and second-order fire effects.  相似文献   

2.
基于高分一号影像,选用归一化差异水体指数(NDWI)阈值法、LBV变换法、"全域-局部"水体自动分割法,分别提取了布若错、赛布错和雅根错的边界。分析对比3种方法,结果显示NDWI阈值法与LBV变换法从整张影像中提取湖泊边界,影像中的非湖泊范围的区域如阴影区等,会误判为湖泊的像元;"全域-局部"水体自动分割法将提取范围从全域转为局部,减少了非湖泊范围内地物信息的干扰,并对每个湖泊的NDWI阈值独立判断,实现了湖泊边界的精细提取。  相似文献   

3.
Users of geographic information systems (GIS) usually render terrain using a point light source defined by an illumination vector. A terrain shaded from a single point provides good perceptual cues to surface orientation. This type of hill shading, however, does not include any visual cues to the relative height of surface elements. We propose shading the terrain under uniform diffuse illumination, where light arrives equally from all directions of a theoretical sky surrounding the terrain. Surface elements at lower elevations tend to have more of the sky obscured from view and are thus shaded darker. This tinting approach has the advantage that it provides more detailed renderings than point source illumination. We describe two techniques of computing terrain shading under uniform diffuse illumination. One technique uses a GIS–based hill-shading and shadowing tool to combine many point source renderings into one approximating the terrain under uniform diffuse illumination. The second technique uses a C++ computer algorithm for computing the inclination to the horizon in all azimuth directions at all points of the terrain. These virtual horizons are used to map sky brightness to the rendering of the terrain. To evaluate our techniques, we use two Digital Elevation Models (DEMs)—of the Schell Creek Range of eastern Nevada and a portion of downtown Houston, Texas, developed from Light Detection and Ranging (lidar) data. Renderings based on the uniform diffuse illumination model show more detailed changes in shading than renderings based on a point source illumination model.  相似文献   

4.
Mapping of vegetation in mountain areas based on remote sensing is obstructed by atmospheric and topographic distortions. A variety of atmospheric and topographic correction methods has been proposed to minimize atmospheric and topographic effects and should in principle lead to a better land cover classification. Only a limited number of atmospheric and topographic combinations has been tested and the effect on class accuracy and on different illumination conditions is not yet researched extensively. The purpose of this study was to evaluate the effect of coupled correction methods on land cover classification accuracy. Therefore, all combinations of three atmospheric (no atmospheric correction, dark object subtraction and correction based on transmittance functions) and five topographic corrections (no topographic correction, band ratioing, cosine correction, pixel-based Minnaert and pixel-based C-correction) were applied on two acquisitions (2009 and 2010) of a Landsat image in the Romanian Carpathian mountains. The accuracies of the fifteen resulting land cover maps were evaluated statistically based on two validation sets: a random validation set and a validation subset containing pixels present in the difference area between the uncorrected classification and one of the fourteen corrected classifications. New insights into the differences in classification accuracy were obtained. First, results showed that all corrected images resulted in higher overall classification accuracies than the uncorrected images. The highest accuracy for the full validation set was achieved after combination of an atmospheric correction based on transmittance functions and a pixel-based Minnaert topographic correction. Secondly, class accuracies of especially the coniferous and mixed forest classes were enhanced after correction. There was only a minor improvement for the other land cover classes (broadleaved forest, bare soil, grass and water). This was explained by the position of different land cover types in the landscape. Finally, coupled correction methods showed most efficient on weakly illuminated slopes. After correction, accuracies in the low illumination zone (cos β ≤ 0.65) were improved more than in the moderate and high illumination zones. Considering all results, best overall classification results were achieved after combination of the transmittance function correction with pixel-based Minnaert or pixel-based C-topographic correction. Furthermore, results of this bi-temporal study indicated that the topographic component had a higher influence on classification accuracy than the atmospheric component and that it is worthwhile to invest in both atmospheric and topographic corrections in a multi-temporal study.  相似文献   

5.
We investigate the effect of broken clouds on the satellite-based retrieval of columnar water vapor using a near-infrared radiance ratio technique. A typical difference between the retrieval using only pixels directly illuminated by the Sun and pixels with mixed illumination containing direct sunlight as well as cloud shadows is found to be within 3%.  相似文献   

6.
Scree Representation on Topographic Maps   总被引：1，自引：0，他引：1  
Abstract

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Scree patterns are an important element of mountain maps in Swiss style. The size and density of scree dots vary with the exposition towards a source of illumination, which makes the dots extremely labour intensive to map without specialized algorithms. This paper identifies design principles for the symbolisation of scree fields on mountain slopes and presents a digital method for the quick placement of dot symbols requiring only minimal interventions by a cartographer. When digitally produced scree is combined with a shaded relief and a rock drawing, the terrain appears as a continuous three-dimensional surface to the reader. The described method is implemented in Scree Painter, a specialized free open-source software application. Scree patterns produced with Scree Painter match the quality standards of manually generated scree representations.  相似文献   

7.
In Africa, food security early warning systems use satellite-derived data concerning crop conditions and agricultural production. Such systems can be improved if they are provided with a more reliable estimation of the cultivated area at national scale. This paper evaluates the potential of using time series from the MODerate resolution Imaging Spectroradiometer MOD13Q1 (16-day composite of normalized difference vegetation index at 250 m resolution) to extract cultivated areas in the fragmented rural landscapes of Mali. To this end, we first stratified Southern Mali into 13 rural landscapes based on the spatio-temporal variability of NDVI and textural indices, using an object-oriented classification scheme.The accuracy of the resulting map (MODIScrop) and how it compares with existing coarse-resolution global land products (GLC2000 Africa, GLOBCOVER, MODIS V05 and ECOCLIMAP-II), was then assessed against six crop/non-crop maps derived from SPOT 2.5 m resolution images used as references. For crop areal coverage, the MODIScrop cultivated map was successful in assessing the overall cultivated area at five out of the six validation sites (less than 6% of the absolute difference), while in terms of crop spatial distribution, the producer accuracy was between 33.1% and 80.8%. This accuracy was linearly correlated with the mean patch size index calculated on the SPOT crop maps (r2 = 0.8). Using the Pareto boundary as an accuracy assessment method at the study sites, we showed that (i) 20-40% of the classification crop error was due to the spatial resolution of the MODIS sensor (250 m), and that (ii) compared to MODIS V05, which otherwise performed better than the other existing products, MODIScrop generally minimized omission-commission errors. A spatial validation of the different products was carried out using SPOT image classifications as reference. In the corresponding error matrices, the fraction of correctly classified pixels for our product was 70%, compared to 58% for MODIS V05, while it ranged between 40% and 51% for the GLC2000, the ECOCLIMAP-II and the GLOBCOVER.  相似文献   

8.
Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and ecological responses at different forest layers. In this study, using field measured fire severity across five forest strata of dominant tree, intermediate-sized tree, shrub, herb, substrate layers, and the aggregated measure of CBI as response variables, we fit statistical models with predictors of Landsat TM bands, Landsat derived NBR or dNBR, image differencing, and image ratioing data. We model multi-strata forest fire in the historical recorded largest wildfire in California, the Big Sur Basin Complex fire. We explore the potential contributions of the post-fire Landsat bands, image differencing, image ratioing to fire severity modeling and compare with the widely used NBR and dNBR. Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing, and image ratioing. We predict and map multi-strata forest fire severity across the whole Big Sur fire areas, and find that the overall measure CBI is not optimal to represent multi-strata forest fire severity.  相似文献   

9.
基于影像的Landsat TM/ETM+数据正规化技术   总被引：7，自引：0，他引：7  
阐述了基于影像的LandsatTM／ETM^＋的数据正规化技术及其发展。该技术通过将Landsat影像的亮度值转换成传感器处的辐射值和反射率采对影像进行辐射校正。实例表明，使用正规化技术处理后的影像可以明显削弱日照和大气的影响，去除它们产生的噪声；其所书的传感器处的反射率与地面实测反射率的RMS值非常小。  相似文献   

10.
Shadow is an inevitable problem in high-resolution remote sensing images. There are need and significance in extracting information from shadow-covered areas, such as in land-cover mapping. Although the illumination energy of shadow pixels is low, hyperspectral image can provides rich enough band information to differentiate various urban targets/materials and to classify them. This study firstly analyzes the spectra difference between shadow and non-shadow classes so as to detect shadow-pixel. To classify the shadow pixels, Spectral Angle Mapper (SAM) method was adopted to classify urban land-cover mapping, because it can reduce the influence resulted from different illumination intensity. Then, training samples were collected among different classes from the shadow pixels, and their Jeffries–Matusita (J–M) distance were computed to validate the spectral separability among classes, with the square distances of J–M among classes all bigger than 1.9. Finally, Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) classifier were used to classify all the shadow pixels as different land-cover types. The results showed MLC and SVM outperform the SAM in classifying similar classes. The classification result in SVM was validated to find having conformity with ground truth.  相似文献   

11.
光照模型是地貌晕渲图能够在2维平面获得地貌3维立体形态的主要原因。在计算机图形学的基础上,推导了基于数字高程模型的晕渲光照模型计算公式;通过对相关文献公式的分析,指出了数字地貌晕渲采用的光照模型与传统地貌晕渲几何光学原理的关系;并对该模型下地表灰度值的计算与变化进行了分析研究。  相似文献   

12.
Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun–canopy–sensor (SCS) model significantly improved over those based on the sun–terrain–sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun–crown–sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS + C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS + W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun–crown–sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor Wt also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model comparison on the red and near infrared bands. The advantages of SCnS + C and SCnS + W on both bands are expected to facilitate forest classification and change detection applications.  相似文献   

13.
Bundle adjustment is a method for simultaneously calculating both the interior and exterior orientation parameters of a set of images, and the object-space coordinates of the observed points. In the case of long focal length lenses and narrow field-of-view (FOV) imaging situations, collinearity based (perspective projection) algorithms may result in linear dependencies between parameters that cause solution instability. The use of a scaled orthographic projection model based on linear algebraic formulations was therefore adopted to reduce this risk. Using quaternions, a new mathematical model is derived that includes the partial derivatives as well as the inner constraint equations for a scaled orthographic bundle adjustment. The model was then tested using two image sets of a single, small vessel (about 6 m length) with a cube target of known dimensions at two distinct ranges; perspective solutions were also calculated for comparison. RMS residual errors of 0.74-0.78 pixels associated with the new method compare favorably to a residual error range of 0.59-0.74 pixels using a perspective bundle adjustment of the same target points. Relative precisions (as a ratio of target size) of between 1:1650 and 1:750 have been achieved at ranges of 375 m and 850 m, respectively, given comparisons with the known cube dimensions. A third image dataset consisting of a network of 16 images was solved with a 1:2200 relative precision showing the new method can successfully handle high redundancy. For the experiments that were conducted, the new method was found to produce less precise results than the perspective bundle solution for a FOV of 0.50-0.65° where the object fills 5-8% of the image. However, it was found to match the precision of the perspective model (with an uncalibrated camera) for a FOV of 0.20-0.30° where the object of interest fills only 1-2% of the full image.  相似文献   

14.
The main objective of this study was to improve the long-term land use change detection by improving classification accuracy of previous generation satellite image using a recent super-resolution technique. The study also analysed the change in land cover over a period of 41 years in a coal mining area. A dual-tree complex wavelet transform-based image super-resolution technique was used to enhance Landsat images of 1975 and 2016. Separating pixels with similar spectral response is an enigmatical task, especially when those pixel represent different ground features. Therefore, an advanced neural net supervised classifier was used to minimize classification errors. Accuracy of the classified images (both super-resolved and original) were measured using confusion matrices and kappa coefficients. A significant improvement of more than 10% was observed in the overall classification accuracy for the image of 1975, highlighting that the classification accuracy of earlier generation satellite data can be improved substantially.  相似文献   

15.
Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 multispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differentiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30 m × 30 m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r = 0.75, p < 0.01), the BA and the entropy of blue band (r = 0.73, p < 0.01), and the GC and the contrast of blue band (r = 0.71, p < 0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determination coefficient (R2) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56 ha−1 for the NT; 0.54 and 1.79 m2 ha−1 for the BA; 0.42 and 27.18 m3 ha−1 for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R2 > 0.50). In conclusion, cross-validated statistics confirmed that the structural parameters including the BA, SDDBH, and GC can be predicted and mapped with a reasonable accuracy using the texture features extracted from the spectral bands of WorldView-2 image.  相似文献   

16.
To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency, this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area, China, using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005. The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM. Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance. It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is <2.68 m to achieve blending accuracy with a mean average absolute difference of 4% between the observed and blended reflectance in all spectral bands.  相似文献   

17.
遥感影像融合的自适应变化检测   总被引：3，自引：1，他引：2  
提出一种基于影像融合和自适应阈值选择的遥感影像变化检测方法。首先利用经过改进的融合技术对原 始数据的差值影像和比值影像进行处理, 构造融合影像, 在该融合影像的基础上进行自适应迭代运算得到初步变 化阈值范围, 然后通过分析阈值范围两侧影像像元的离散程度, 求解最终的阈值范围, 从而得到更优变化阈值, 提 取变化区域。实验结果表明, 本文方法的检测精度优于传统的变化检测方法, 同时具有一定的稳定性和智能性。  相似文献   

18.
Spatial structure in imagery depends on a complicated interaction between the observational regime and the types and arrangements of entities within the scene that the image portrays. Although block averaging of pixels has commonly been used to simulate coarser resolution imagery, relatively little attention has been focused on the effects of simple rescaling on spatial structure and the explanation and a possible solution to the problem. Yet, if there are significant differences in spatial variance between rescaled and observed images, it may affect the reliability of retrieved biogeophysical quantities. To investigate these issues, a nested series of high spatial resolution digital imagery was collected at a research site in eastern Nebraska in 2001. An airborne Kodak DCS420IR camera acquired imagery at three altitudes, yielding nominal spatial resolutions ranging from 0.187 m to 1 m. The red and near infrared (NIR) bands of the co-registered image series were normalized using pseudo-invariant features, and the normalized difference vegetation index (NDVI) was calculated. Plots of grain sorghum planted in orthogonal crop row orientations were extracted from the image series. The finest spatial resolution data were then rescaled by averaging blocks of pixels to produce a rescaled image series that closely matched the spatial resolution of the observed image series. Spatial structures of the observed and rescaled image series were characterized using semivariogram analysis. Results for NDVI and its component bands show, as expected, that decreasing spatial resolution leads to decreasing spatial variability and increasing spatial dependence. However, compared to the observed data, the rescaled images contain more persistent spatial structure that exhibits limited variation in both spatial dependence and spatial heterogeneity. Rescaling via simple block averaging fails to consider the effect of scene object shape and extent on spatial information. As the features portrayed by pixels are equally weighted regardless of the shape and extent of the underlying scene objects, the rescaled image retains more of the original spatial information than would occur through direct observation at a coarser sensor spatial resolution. In contrast, for the observed images, due to the effect of the modulation transfer function (MTF) of the imaging system, high frequency features like edges are blurred or lost as the pixel size increases, resulting in greater variation in spatial structure. Successive applications of a low-pass spatial convolution filter are shown to mimic a MTF. Accordingly, it is recommended that such a procedure be applied prior to rescaling by simple block averaging, if insufficient image metadata exist to replicate the net MTF of the imaging system, as might be expected in land cover change analysis studies using historical imagery.  相似文献   

19.
Road network extraction from high resolution satellite images is one of the most important aspects. In the present paper, research experimentation is carried out in order to extract the roads from the high resolution satellite image using image segmentation methods. The segmentation technique is implemented using adaptive global thresholding and morphological operations. Global thresholding segments the image to fix the boundaries. To compute the appropriate threshold values several problems are also analyzed, for instance, the illumination conditions, the different type of pavement material, the presence of objects such as vegetation, vehicles, buildings etc. Image segmentation is performed using morphological approach implemented through dilation of similar boundaries and erosion of dissimilar and irrelevant boundaries decided on the basis of pixel characteristics. The roads are clearly identifiable in the final processed image, which is obtained by superimposing the segmented image over the original enhanced image. The experimental results proved that proposed approach can be used in reliable way for automatic detection of roads from high resolution satellite image. The results can be used in automated map preparation, detection of network in trajectory planning for unmanned aerial vehicles. It also has wide applications in navigation, computer vision as a predictor-corrector algorithm for estimating the road position to simulate dynamic process of road extraction. Although an expert can label road pixels from a given satellite image but this operation is prone to errors. Therefore, an automated system is required to detect the road network in a high resolution satellite image in a robust manner.  相似文献   

20.
本文提出一种新的半经验地形校正模型SCEDIL(Simple topographic Correction using Estimation of Diffuse Light),该模型通过结合DEM与光学影像数据寻找局部区域内完全光照和阴影的水平像元,并以光照、阴影水平像元的平均反射率值估算局部区域散射辐射比,提高了陡峭山区影像的地形校正精度。以高分一号卫星和Landsat ETM+影像为例,从目视判读和定量分析两个方面,比较分析该算法与传统半经验地形校正算法(C、SCS+C)的校正结果。结果表明:(1)对较为平坦的地形,SCEDIL和C、SCS+C校正都有较好的目视结果;对地面起伏较大的陡峭地形,C、SCS+C校正后,原阴影区域易呈现破碎化特征,SCEDIL校正后,原阴影区域过渡较为平滑。(2)SCEDIL校正后,各波段反射率的均值和标准差优于C、SCS+C校正,SCEDIL校正后,影像总分类精度与同类地物光谱信息均一性均优于C和SCS+C校正。SCEDIL半经验地形校正方法能有效地去除影像中的地形干扰,尤其对陡峭地形的校正效果,优于常规地形校正模型。  相似文献   

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