Fusing optical and radar data to estimate sagebrush, herbaceous, and bare ground cover in Yellowstone

The arid and semi-arid sagebrush-grass ecosystem occupies a substantial portion of rangelands in the western United States. Using remote sensing techniques to map the percent of sagebrush, grass/forb, and bare ground components is necessary for forage production estimation and natural resource management over large areas. However optical data have significant deficiencies in these ecosystems because of exposed bright soil, spectrally-indeterminate vegetation, and a large dead vegetation component. Radar data also have deficiencies caused by factors such as antenna pattern calibration, local incidence angle (LIA), soil moisture, and surface roughness. With the complementary vegetation information gained from optical data and radar data, these two datasets were fused to estimate 10-m sagebrush, grass, and bare ground percent cover in the non-forested areas of Yellowstone National Park, which is a representative native western rangeland ecosystem of the US. The datasets were processed to resolve the issues of antenna pattern calibration and LIA effect. Peak green Landsat, late fall Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AirSAR) data were fused in this analysis. AVIRIS, Landsat, AirSAR and elevation data were used to segment the study area into two main subcategories of “pure grass” and “mixed sagebrush and grass”. Landsat Tasseled Cap Greenness (LTCG) was used to retrieve bare land and grass percentages in pure grass areas. In the areas with mixed grass and sagebrush, standardized LTCG and radar C_vv were used to derive the vegetation cover percentage, and the ratio of standardized LTCG and radar L_hv was further used to calculate the relative abundance of sagebrush and grass. Comparison between the field and remote sensing estimations shows the correlation coefficients were 0.838, 0.746, and 0.830 for bare land, grass, and sagebrush, respectively. When grouped into three discrete categories of “low”, “medium”, and “high”, the overall accuracies were 79.4%, 75.9%, and 77.6%, respectively. Our study shows the potential for application of global spaceborne C- and L-band radar and optical data fusion for large-area rangeland monitoring.     

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).     

Technical note A new technique for interpolating the reflectance red edge position

The point of maximum slope on the reflectance spectrum of vegetation between red and near-infrared wavelengths, termed the red edge position (REP), is correlated strongly with foliar chlorophyll content and provides a very sensitive indicator of, among other things, vegetation stress. The high spectral resolution of airborne imaging spectrometers now offers the potential for determining the REP of vegetation canopies at regional scales. However, the accurate estimation of the REP is dependent upon sensor band positions and widths. Various techniques have been developed to minimize the error in estimating the REP, such as linear interpolation or inverted Gaussian curve fitting in the region of the red edge which requires an a priori knowledge of the spectrum under investigation. This technical note presents a simple technique known as Lagrangian interpolation which is applied to the first-derivative transformation of the reflectance spectrum. The technique fits a second-order polynomial curve to three bands, which need not be equally spaced, centred around the maximum slope position. A second derivative is then performed on the Lagrangian equation to determine the maximum slope position.     

基于NOAA18的渤海烟台海域溢油遥感研究

概述了海洋溢油监测的国内外研究现状,并指出了研究的切入点和研究意义所在。研究采用的是NOAA18的数据,由于溢油区域与非溢油区域的比热容不同,从而引起海表面的辐射发生变化,反映在卫星图片上是灰度值不同。因此利用发生溢油区域在卫星图片上的灰度值不同而呈现出深色区域,进而在卫星图片上找出疑似溢油区域,再进行人工解译,进一步确定溢油的发生区域。以烟台“金玫瑰”号溢油事故为例,综合考虑烟台海域水文因素等,分析得出溢油发生的范围。     

Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing

Forest fires leave behind a changed ecosystem with a patchwork of surface cover that includes ash, charred organic matter, soils and soil minerals, and dead, damaged, and living vegetation. The distributions of these materials affect post-fire processes of erosion, nutrient cycling, and vegetation regrowth. We analyzed high spatial resolution (2.4 m pixel size) Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data collected over the Cerro Grande fire, to map post-fire surface cover into 10 classes, including ash, soil minerals, scorched conifer trees, and green vegetation. The Cerro Grande fire occurred near Los Alamos, New Mexico, in May 2000. The AVIRIS data were collected September 3, 2000. The surface cover map revealed complex patterns of ash, iron oxide minerals, and clay minerals in areas of complete combustion. Scorched conifer trees, which retained dry needles heated by the fire but not fully combusted by the flames, were found to cover much of the post-fire landscape. These scorched trees were found in narrow zones at the edges of completely burned areas. A surface cover map was also made using Landsat Enhanced Thematic Mapper plus (ETM+) data, collected September 5, 2000, and a maximum likelihood, supervised classification. When compared to AVIRIS, the Landsat classification grossly overestimated cover by dry conifer and ash classes and severely underestimated soil and green vegetation cover. In a comparison of AVIRIS surface cover to the Burned Area Emergency Rehabilitation (BAER) map of burn severity, the BAER high burn severity areas did not capture the variable patterns of post-fire surface cover by ash, soil, and scorched conifer trees seen in the AVIRIS map. The BAER map, derived from air photos, also did not capture the distribution of scorched trees that were observed in the AVIRIS map. Similarly, the moderate severity class of Landsat-derived burn severity maps generated from the differenced Normalized Burn Ratio (dNBR) calculation had low agreement with the AVIRIS classes of scorched conifer trees. Burn severity and surface cover images were found to contain complementary information, with the dNBR map presenting an image of degree of change caused by fire and the AVIRIS-derived map showing specific surface cover resulting from fire.     

Approaches to fractional land cover and continuous field mapping: A comparative assessment over the BOREAS study region

Subpixel land cover mapping involves the estimation of surface properties using sensors whose spatial sampling is coarse enough to produce mixtures of the properties within each pixel. This study evaluates five algorithms for mapping subpixel land cover fractions and continuous fields of vegetation properties within the BOREAS study area. The algorithms include a conventional “hard”, per-pixel classifier, a neural network, a clustering/look-up-table approach, multivariate regression, and linear least squares inversion. A land cover map prepared using a Landsat TM mosaic was adopted as the source of fine scale calibration and validation data. Coarse scale mixtures of five basic land cover classes and continuous vegetation fields, both corresponding to the field of view of SPOT-VEGETATION imagery (1.15-km pixel size), were synthesised from the TM mosaic using a modelled point spread function. Two measures of land cover distribution were used, fractions of fine scale land cover categories and continuous fields of vegetation structural characteristics. The subpixel algorithms were applied using both proximate (<100 km) and distant (>400 km) separation between training and validation regions. “Hard” classification performed poorly in estimating proportions or continuous fields. The neural network, look-up-table and multivariate regression algorithms produced good matches of spatial patterns and regional land cover composition for the proximate treatment. However, all three methods exhibited substantial biases with the distant treatment due to the characteristics of the training data. Linear least squares inversion offers a relatively unbiased but less precise alternative for subpixel proportion and fraction mapping as it avoids calibration to the a priori distribution of land cover in the training data. In general, a combination of multivariate regression for proximate training data and linear least squares inversion for distant training data resulted in woody fraction estimates within 20% of the Landsat TM classification-based estimates.     

Remote sensing for detection and monitoring of vegetation affected by oil spills

This study is aimed at demonstrating the application of vegetation spectral techniques for detection and monitoring of the impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G-NIR) and green-shortwave infrared (G-SWIR) from the spill sites (SS) and non-spill sites (NSS) in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G-NIR and G-SWIR indicated a certain level of significant difference between vegetation condition at the SS and the NSS in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G-NIR – p-value 0.01 and G-SWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G-NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post-spill analysis shows that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique may help with the real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in mangrove forests.     

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

The aim of this article is to investigate and test the influence of oil spill volume and time gap (number of days between oil spill events and image acquisition date) on normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). This was carried out to determine the effect of these factors on vegetation condition affected by the oil spill. Based on regression analysis, it was shown that increase in the volume of oil spill resulted in increased deterioration of vegetation condition (estimated using NDVI and NDWI) in the study site. The study also tested how the length of time gap between the oil spill and image acquisition date influences the detectability of impacts of oil spill on vegetation. The results showed that the length of time between image acquisition and oil spill influenced the detectability of impacts of oil spill on vegetation condition. The longer the time between the date of image acquisition and the oil spill event, the lower the detectability of impacts of oil spill on vegetation condition. The NDVI seemed to produce better results than the NDWI. In conclusion, time and volume of oil spill can be important factors influencing the detection of pollution using vegetation indices (VIs) in an oil-polluted environment.     

Nonlinear hierarchical models for predicting cover crop biomass using Normalized Difference Vegetation Index

Incorporating cover crops into agricultural systems can improve soil structural properties, increase nutrient availability, reduce erosion and loss of agrochemicals, and suppress weeds. These benefits are a function of the amount of cover crop biomass that enters the soil. The ability to easily and inexpensively quantify the spatial variability of cover crop biomass is needed to better understand and predict its potential as an input to agricultural systems. Here, we explore the use of Normalized Difference Vegetation Index (NDVI) as a source of information for improving accuracy and precision of cover crop biomass prediction. We focus on developing models that account for biomass variability within and among fields. These models are used to produce digital data layers of predicted biomass and associated uncertainty. We propose hierarchical nonlinear models with field random effects and a residual variance function to accommodate strong heteroscedasticity. These models are motivated using aboveground biomass of red clover (Trifolium pratense L.) measured on three different dates in five fields in southwest Michigan. Model adequacy was assessed using the Deviance Information Criterion. Given this criterion, the “best” fitting model included field effects and a polynomial function to account for non-constant residual variance. Importantly, we demonstrate that accounting for heteroscedasticity in the model fitting is critical for capturing uncertainty in subsequent biomass prediction.     

Technical Note: Crop stress detection using AVIRIS hyperspectral imagery and artificial neural networks

The objectives of this study were to compare the results of artificial neural network (ANN) and standard vegetation algorithm processing to distinguish nutrient stress from in-field controls, and determine whether nutrient stress might be distinguished from water stress in the same test field. The test site was the US Department of Agriculture's Variable Rate Application (VRAT) site, Shelton, Nebraska. The VRAT field was planted in corn with test plots that were differentially treated with nitrogen (N). The field contained four replicates, each with N treatments ranging from 0 kg ha^?1 to 200 kg ha^?1 in 50 kg ha^?1 increments. Low-altitude (3 m pixel) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery (224 bands) was collected over the site. Ground data were collected to support image interpretation. An ANN was applied to the AVIRIS image data for detection of crop and water stress. Known vegetation indices were used as a baseline for comparison against ANN-based stress detection. The resulting comparison found that ANN methods provided a heightened capability to separate stressed crops from in-field, non-stressed controls and was sensitive to differences in nutrient- and water-stressed field regions.     

Estimating the quantity and quality of coarse woody debris in Yellowstone post-fire forest ecosystem from fusion of SAR and optical data

The Coarse Woody Debris (CWD) quantity, defined as biomass per unit area (t/ha), and the quality, defined as the proportion of standing dead logs to the total CWD quantity, greatly contribute to many ecological processes such as forest nutrient cycling, tree regeneration, wildlife habitat, fire dynamics, and carbon dynamics. However, a cost-effective and time-saving method to determine CWD is not available. Very limited literature could be found that applies remote sensing technique to CWD inventory. In this paper, we fused the wall-to-wall multi-frequency and multi-polarization Airborne Synthetic Aperture Radar (AirSAR) and optical Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to estimate the quantity and quality of CWD in Yellowstone post-fire forest ecosystem, where the severe 1988 fire event resulted in high spatial heterogeneity of dead logs. To relate backscatter values to CWD metrics, we first reduced the terrain effect to remove the interference of topography on AirSAR backscatter. Secondly, we removed the influence of regenerating sapling by quadratic polynomial fitting between AVIRIS Enhanced Vegetation Index (EVI) and different channels backscatters. The quantity of CWD was derived from P_hh and P_hv, and the quality of CWD was derived from P_hh aided by the ratio of L_hv and P_hh. Two maps of Yellowstone post-fire CWD quantity and quality were produced. The calculated CWD quantity and quality were validated by extensive field surveys. Regarding CWD quantity, the correlation coefficient between measured and predicted CWD is only 0.54 with mean absolute error up to 29.1 t/ha. However, if the CWD quantity was discretely classified into three categories of “≤ 60”, “60-120”, and “≥ 120”, the overall accuracy is 65.6%; if classified into two categories of “≤ 90” and “≥ 90”, the overall accuracy is 73.1%; if classified into two categories of “≤ 60” and “≥ 60”, the overall accuracy is 84.9%. This indicates our attempt to map CWD quantity spatially and continuously achieved partial success; however, the general and discrete categories are reasonable. Regarding CWD quality, the overall accuracy of 5 types (Type 1—standing CWD ratio ≥ 40%; Type 2—15% ≤ standing CWD ratio < 40%; Type 3—7% ≤ standing CWD ratio< 15%; Type 4—3% ≤ standing CWD ratio < 7%; Type 5—standing CWD ratio < 3%) is only 40.32%. However, when type 1, 2, 3 are combined into one category and type 4 and 5 are combined into one category, the overall accuracy is 67.74%. This indicates the partial success of our initial results to map CWD quality into detailed categories, but the result is acceptable if solely very coarse CWD quality is considered. Bias can be attributed to the complex influence of many factors, such as field survey error, sapling compensation, terrain effect reduction, surface properties, and backscatter mechanism understanding.     

Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion

In this paper, we explored fusion of structural metrics from the Laser Vegetation Imaging Sensor (LVIS) and spectral characteristics from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) for biomass estimation in the Sierra Nevada. In addition, we combined the two sensors to map species-specific biomass and stress at landscape scale. Multiple endmember spectral mixture analysis (MESMA) was used to classify vegetation from AVIRIS images and obtain sub-pixel fractions of green vegetation, non-photosynthetic vegetation, soil, and shade. LVIS metrics, AVIRIS spectral indices, and MESMA fractions were compared with field measures of biomass using linear and stepwise regressions at stand (1 ha) level. AVIRIS metrics such as water band indices and shade fractions showed strong correlation with LVIS canopy height (r² = 0.69, RMSE = 5.2 m) and explained around 60% variability in biomass. LVIS variables were found to be consistently good predictors of total and species specific biomass (r² = 0.77, RMSE = 70.12 Mg/ha). Prediction by LVIS after species stratification of field data reduced errors by 12% (r² = 0.84, RMSE = 58.78 Mg/ha) over using LVIS metrics alone. Species-specific biomass maps and associated errors created from fusion were different from those produced without fusion, particularly for hardwoods and pines, although mean biomass differences between the two techniques were not statistically significant. A combined analysis of spatial maps from LVIS and AVIRIS showed increased water and chlorophyll stress in several high biomass stands in the study area. This study provides further evidence that lidar is better suited for biomass estimation, per se, while the best use of hyperspectral data may be to refine biomass predictions through a priori species stratification, while also providing information on canopy state, such as stress. Together, the two sensors have many potential applications in carbon dynamics, ecological and habitat studies.     

基于ENVISat的海洋污染性油膜雷达散射特征与扩散趋势分析

污染性海洋溢油一旦发生,快速获取油膜信息对有效控制溢油危害具有重要意义。以渤海湾一次溢油污染事件为例,利用ENVISat数据根据油膜雷达后向散射特征分析溢油的发生,并利用溢油期间的连续风场信息和连续SAR数据对比研究油膜的扩散趋势以及扩散过程中油膜尺度的变化。结果表明：污染性油膜在海上扩散的不同阶段具有不同的SAR图像特征,海上溢油雷达遥感检测分析方法与检测效果因SAR图像获取时油膜所处扩散阶段不同而有所不同,通过SAR连续观测结合辅助信息可以对污染油膜及其运动进行有效监控与预测。     

Characterization of shrub distribution using high spatial resolution remote sensing: Ecosystem implications for a former Chihuahuan Desert grassland

Patchiness is often considered a defining quality of ecosystems in arid and semiarid regions. The spatial distribution of vegetation patches and soil nutrients coupled with wind and water erosion as well as biotic processes are believed to have an influence on land degradation. A geostatistical measure of spatial “connectivity” is presented to directly measure the size of patches in the landscape from a raster data set. Connectivity is defined as the probability that adjacent pixels belong to the same type of patch. Connectivity allows the size distribution of erodible patches to be quantified from a remote sensing image or field measurement, or specified for the purposes of modeling.Applied to high-resolution remote sensing imagery in the Jornada del Muerto Basin in New Mexico, the spatial distribution of plants indicates the current state of grassland-to-shrubland transition in addition to processes of degradation in this former grassland. Shrub encroachment is clearly evident from decreased intershrub patch size in coppice dunes of 27.8 m relative to shrublands of 65.2 m and grassland spacing of 118.9 m. Shrub patches remain a consistent 2-4 m diameter regardless of the development of bush encroachment. A strong SW-NE duneland orientation correlates with the prevailing wind direction and suggests a strong aeolian control of surface geomorphology.With appropriate data sets and classification, potential applications of the connectivity method extend beyond vegetation dynamics, including mineralogy mapping, preserve planning, habitat fragmentation, pore spacing in surface hydrology, and microbial community dynamics.     

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

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

A facility location model for logistics systems including reverse flows: The case of remanufacturing activities

After a brief introduction to the basic concepts of reverse logistics, we present a two-level location problem with three types of facility to be located in a specific reverse logistics system, named a Remanufacturing Network (RMN). For this problem, we propose a 0–1 mixed integer programming model, in which we simultaneously consider “forward” and “reverse” flows and their mutual interactions. An algorithm based on Lagrangian heuristics is developed and the model is tested on data adapted from classical test problems.     

SAR影像海洋表面溢油检测方法与实现

海洋是地球的重要组成部分,它为人类提供了丰富的物质和宝贵的资源,每年海洋都承受着不同程度的侵害,其中油类污染是给海洋造成巨大危害的污染之一。而油类污染又主要来源于轮船破裂漏油以及油井平台或海底输油管道爆炸等。每次事故造成的直接经济损失达几百万至上千万不等,所以对海上溢油进行监测具有重要的意义。选用Envisat的ASAR数据进行海上溢油检测,介绍并分析了SAR图像溢油检测的一般步骤及其实现方法,通过采用单一阈值分割法、最大熵分割法和非监督分类法对影像进行目标检测,从而粗略地将影像区分为前景区域与背景区域,并结合影像的纹理特征进行分类。在纹理特征选取过程中,通过人工选取部分溢油区与非溢油区作为感兴趣区,在感兴趣区上分别统计SAR影像常用的纹理特征,并结合不同目标检测的结果以及原始影像进行基于BP神经网络的分类,得到了良好的效果。最后展望了SAR图像海洋溢油检测的发展方向。     

Topological shape optimization of geometrically nonlinear structures using level set method

Using the level set method, a topological shape optimization method is developed for geometrically nonlinear structures in total Lagrangian formulation. The structural boundaries are implicitly represented by the level set function, obtainable from “Hamilton-Jacobi type” equation with “up-wind scheme,” embedded into a fixed initial domain. The method minimizes the compliance through the variations of implicit boundary, satisfying an allowable volume requirement. The required velocity field to solve the Hamilton-Jacobi equation is determined by the descent direction of Lagrangian derived from an optimality condition. Since the homogeneous material property and implicit boundary are utilized, the convergence difficulty is significantly relieved.     

Linear spectral mixture analysis of Landsat TM data for monitoring invasive exotic plants in estuarine wetlands

This study assessed the feasibility of spectral mixture analysis (SMA) of Landsat thematic mapper (TM) data for monitoring estuarine vegetation at species level. SMA modelling was evaluated, using χ² test, by comparing SMA fraction images with a precisely classified QuickBird image that has a higher spatial resolution. To clearly understand the strengths and weaknesses of SMA, eight SMA models with different endmember combinations were assessed. When the TM data dimension for SMA and the endmember number required were balanced, a model with three endmembers representing water and two vegetation types was most accurate, whereas a model with five endmembers approximated the actual surface situation and generated a relatively accurate result. Our results indicate that an SMA model with appropriate endmembers had relatively satisfactory accuracy in monitoring vegetation. However, errors might occur in SMA fraction images, especially in models with an inappropriate endmember combination, and the errors were mainly distributed in areas filled with water or near water. Therefore, short vegetation usually submerged during high tide tended to be poorly predicted by SMA models. These results strongly suggest that tide water has a great influence on SMA modelling, especially for short vegetation.     

SAR图像在韩国溢油监测中的应用

利用SAR图像,对2007年12月发生在韩国的溢油事件进行监测。首先分析了SAR图像监测溢油的原理和限制,然后以ENVISAT\|ASAR数据为例,分析SAR数据处理与信息提取过程,其中包括:进行几何精校正、对图像进行增强处理、滤波、分类。最后根据SAR图像综合信息,解译勾画出溢油信息边界,利用GIS系统叠加风场、地形、其它重要要素数据,对溢油的分布、扩散以及对周边环境的影响进行分析评价,为决策者提供决策支持。