频谱激电法的发展与展望

从频谱激电法的发展概况﹑仪器系统、数学模型、反演方法及应用等方面,介绍了频谱激电法的研究现状。介绍了频谱激电法目前常用的仪器系统:SIP-FUCHSII和V8,数据模型主要有:Cole-Cole、常相位角模型CPA、普通化的Cole-Cole模型和理论模型SNP。反演方法简要介绍了几种常用算法,反演算法也由一维、二维发展到三维。列举了频谱激电法近年来在矿产资源、水资源调查等多个领域的应用概况,展望了频谱激电法的发展方向:(1)同时考虑激电效应和电磁效应的三维电磁场正演计算技术是研究的前沿和热点;(2)频谱激电法对有机污染的探查成为未来应用研究的新领域。     

Landsat 8数据地表温度反演算法对比

随着卫星遥感技术的发展,利用遥感反演地表温度的方法不断出现,如劈窗法、双角度法和单通道算法等。Landsat系列卫星的遥感数据是地表温度反演的重要数据之一。本文选择无锡周边区域为研究区,利用Landsat 8卫星遥感数据,对两种劈窗算法(Juan C.Jiménez-Muoz劈窗算法和Offer Rozenstein劈窗算法)和两种单窗算法(Juan C.Jiménez-Muoz单通道算法和覃志豪单窗算法)的地表温度反演精度进行了对比和敏感性分析。采用太湖16个浮标站的实测数据来验证了4种算法的反演精度。结果表明:两种劈窗算法的精度较高且较为接近,误差为0.7 K左右;覃志豪单窗算法和Juan C.Jiménez-Muoz单通道算法精度较低,误差分别为1.3 K和1.4 K左右。Juan C.Jiménez-Muoz劈窗算法对参数的敏感性最低,Juan C.Jiménez-Muoz单通道算法次之,覃志豪单窗算法和Offer Rozenstein劈窗算法敏感性相对最高。其中Juan C.Jiménez-Muoz单通道算法只适用于一定的水汽含量范围,有一定的局限性。     

资源一号02C与Landsat8影像融合方法对比分析

针对以往关于资源一号02C和Landsat8卫星影像数据融合的研究不足的问题,该文利用前者在空间分辨率上高于后者、后者具有前者所不具有的光谱信息这一特性,选取主成分变换法、比值变换法、色彩变换法、高通滤波法和超分辨率贝叶斯法5种融合方法,分别对两种数据本身及数据间进行融合,并利用定性与定量的方法对融合结果进行评价,得出:资源一号02C星全色波段与多光谱波段数据融合结果中高通滤波法与超分辨率贝叶斯法效果较好,Landsat8OLI全色波段与多光谱数据融合结果中高通滤波法效果最好,资源一号02C星全色波段与Landsat8OLI多光谱数据融合结果中高通滤波法效果最好。     

Monitoring land changes in an urban area using satellite imagery,GIS and landscape metrics

Monitoring land changes is an important activity in landscape planning and resource management. In this study, we analyze urban land changes in Atlanta metropolitan area through the combined use of satellite imagery, geographic information systems (GIS), and landscape metrics. The study site is a fast-growing large metropolis in the United States, which contains a mosaic of complex landscape types. Our method consisted of two major components: remote sensing-based land classification and GIS-based land change analysis. Specifically, we adopted a stratified image classification strategy combined with a GIS-based spatial reclassification procedure to map land classes from Landsat Thematic Mapper (TM) scenes acquired in two different years. Then, we analyzed the spatial variation and expansion of urban land changes across the entire metropolitan area through post classification change detection and a variety of GIS-based operations. We further examined the size, pattern, and nature of land changes using landscape metrics to examine the size, pattern, and nature of land changes. This study has demonstrated the usefulness of integrating remote sensing with GIS and landscape metrics in land change analysis that allows the characterization of spatial patterns and helps reveal the underlying processes of urban land changes. Our results indicate a transition of urbanization patterns in the study site with a limited outward expansion despite the dominant suburbanization process.     

Detection of foreclosure-related landscape management changes using Landsat

The volume of properties affected by foreclosure over the past decade suggests the potential for dramatic change in vegetation cover due to changes in management. Yet, the specific pathology of each foreclosure, the temporal asynchrony among foreclosures, and differences in the area available for vegetation growth across properties presents challenges to observing and measuring change. This paper develops and tests a difference in deviations approach that compares the parcel NDVI to a neighborhood norm before and after foreclosure. The difference in deviations approach addresses the challenges of separating parcel-level change corresponding to foreclosure and identifies changes on both small and large parcels. The method relies on a time series of Landsat Normalized Difference Vegetation Index (NDVI) data, individual home foreclosure records and property tax assessment data for Maricopa County, Arizona from 2002 to 2012. To establish the level of difference associated with observable landscape change, we use a probit regression model, coding Google Earth images for properties across the range of observed deviations of difference. The basic assumption underlying the approach is that if foreclosure coincides with a change in management, it will lead to changes in vegetation structure and thus, NDVI values. We estimate that 13% of home foreclosures in Maricopa County over the period from 2002 to 2012 resulted in declines in vegetation whereas 6.5% resulted in vegetation increases. Future uses of this method for understanding landscape management in residential landscapes are discussed.     

基于Landsat 8劈窗算法与混合光谱分解的城市热岛空间格局分析——以兰州市中心城区为例

在ENVI和GIS支持下,提出了基于Landsat 8遥感影像的地温反演劈窗算法,提取兰州市中心城区地表温度。利用FNEA和混合光谱分解法确定了兰州市中心城区的城市热岛中心、不透水面和植被盖度,分析了城市热岛空间分布格局以及地表温度与下垫面之间的关系。结果显示:基于Landsat 8数据地温反演的劈窗算法是可行的。兰州中心城区的高温区分布较集中,地表温度与植被呈较强的负相关,与不透水面呈不显著的正相关,与其他非光合物质呈正相关。     

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field,India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.     

Multitemporal settlement and population mapping from Landsat using Google Earth Engine

As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.     

Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data — where do the severely degraded areas occur?

The Tibetan Plateau in Western China is the world’s largest alpine landscape, sheltering a rich diversity of native flora and fauna. In the past few decades, the Tibetan Plateau was found to suffer from grassland degradation processes. Grassland degradation is assumed to not only endanger biodiversity but also to increase the risk for natural hazards in other parts of the country which are ecologically and hydrologically connected to the area. However, the mechanisms behind the degradation processes remain poorly understood due to scarce baseline data and insufficient scientific research.We argue that remote sensing data can help to better understand degradation processes and patterns by: (1) identifying the distribution of severely degraded areas and (2) comparing the patterns of key spatial attributes of the identified areas (altitude above sea level, aspect, slope, administrative districts) with existing theories on degradation drivers. Therefore, we applied four Landsat 8 images covering large portions of the three counties Jigzhi, Baima and Darlag in the Eastern Tibetan Plateau. The dates of the Landsat scenes were selected to cover differing phenological stages of the ecosystem. Reference data were collected with a remotely piloted aircraft and a standard consumer RGB camera. To exploit the phenological information in the Landsat data as well as deal with the problem of cloud cover in multiple images, we developed a straightforward PCA-based procedure to merge the Landsat scenes. The merged Landsat data served as input to a supervised support vector machine classification which was validated with an iterative bootstrap procedure and an additional independent validation set. The considered classes were “high-cover grassland”, “grassland (including several stages of grassland vitality)”, “(severely) degraded grassland”, “green shrubland”, “grey shrubland”, “urban areas” and “water bodies”. Kappa accuracies ranged between 0.84 and 0.93 in the iterative procedure, while the independent validation led to a kappa accuracy of 0.76. Mean producer’s and user’s accuracies for all classes were higher than 80%, and confusion mainly occurred between the two shrubland classes and between the three grassland classes.Analysis of the slope, aspect and altitude values of the vegetation classes revealed that the degraded areas mostly occurred at the higher altitudes of the study area (4300–4600 m), with no strong connection to any specific slope or aspect. High-cover grassland was mostly located on sunny slopes at lower altitudes (less than 4300 m), while shrubland preferred shady, relatively steep slopes across all altitudes. These observations proved to be stable across the examined counties, while the proportions of land-cover classes differed between the examined regions. Most counties showed 5–7% severely degraded land cover. Darlag, the county located at the edge of the permafrost zone, and featuring the highest average altitude and lowest annual temperature and precipitation, was found to suffer from larger areas of severe degradation (14%).Therefore, our findings support a strong connection between degradation patterns and climatic as well as altitudinal gradients, with an increased degradation risk for high altitude areas and areas in colder and drier climatic zones. This is relevant information for pastoral management to avoid further degradation of high altitude pastures.     

Fusion of TerraSAR-x and Landsat ETM+ data for protected area mapping in Uganda

TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda.