基于HJ-1星CCD数据的溢油遥感特性分析与信息提取

基于HJ-1星CCD多光谱数据,以2010年美国墨西哥湾深海油井溢油污染海域为研究区,对溢油迁移转化过程进行分析,认为油水混合物是该次事故的主要污染类型之一.研究HJ-1星CCD数据多种目标的影像特征与光谱响应特征,指出油水混合物能明显改变海水对入射光的后向散射性能,可以被有效地识别与判定.利用决策树分类方法能有效地提取墨西哥湾溢油污染中的油水混合物;对误判信息进行分块合并处理后,可进一步提高油水混合物的提取精度.研究表明,HJ-1星CCD多光谱数据能有效提取海洋溢油污染信息,具备了开展海洋溢油污染遥感监测的能力.     

一种卫星遥感影像村庄区域提取算法

卫星遥感影像提取村庄区域在地理和气象领域均有十分重要的意义.针对卫星遥感影像的特点,提出了一种村庄区域提取方法.利用改进的去雾算法对卫星遥感影像进行预处理,通过遥感卫星影像的颜色特征实现分割,结合村庄区域分布特点进行去噪处理,实现卫星遥感影像村庄区域的提取.实验结果表明:该算法能够对卫星遥感图像中不同类型村庄区域进行提取,且提取准确率高,可以应用于地理以及气象等领域.     

Technique of large-scale soil mapping in basaltic terrain using satellite remote sensing data

The present paper discusses the technique of large-scale soil mapping using remote sensing data. IRS-1C PAN merged data of two seasons, namely late Kharif (monsoon) and Rabi (post rainy season), were interpreted visually in conjunction with Survey of India (SOI) toposheet (1:50?000 scale) and available ground data to prepare the physiography–land use (PLU) map. The PLU delineation explained a three-tier approach comprising landform, slope and land use characteristics of a given parcel of land. The first letter in the PLU legend indicates the landform, the second letter denotes slope percentage and the third letter indicates the land use characteristics. Soils occurring in different PLU units were examined and a PLU–soil relationship was developed. The soil map depicting phases of soil series was prepared using ILWIS software.     

Quantifying light-fishing for Dosidicus gigas in the eastern Pacific using satellite remote sensing

The distribution and abundance of the fleet targeting Jumbo flying squid (Dosidicus gigas) in the Eastern Pacific is examined during the 1999 fishery season. The commercial fishery consists of a multinational jigging fleet, which fish at night using powerful lights to attract squid. The emission of light from these vessels can be observed using satellite-derived imagery obtained by the United States Defence Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). In order to quantify fishing effort using lights, data on the distribution and abundance of vessels were obtained via satellite tracking using the ARGOS system. The distribution of the fishery as derived from light signatures was found to closely resemble that derived from ship location data. By using ARGOS data to calibrate DMSP-OLS images, we are able to estimate fishing effort in terms of the ‘area illuminated’ by the fishing fleet. Light signatures derived from DMSP-OLS were successfully used to quantify fishing effort, estimating the number of vessels fishing to within ±2 in 85 out of 103 satellite images (83%). High seas fishing was also quantified, with light signatures corresponding to a single fishing vessel observed in 11 out of 103 satellite passes during the fishery season (July-December 1999). This study examines how much light (in terms of area) is emitted by a single squid fishing vessel, and may prove to be a valuable tool in assessing and policing fisheries using satellite remote sensing.     

Wildlife habitat analysis for ‘sambar’ (Cervus unicolor) in Kanha National Park using remote sensing

In the present study habitat suitability analysis has been done in the census sectors of two ranges of Kanha National Park. Each sector was evaluated with respect to habitat parameters, viz., water, cover types (food and shelter values) and terrain. Cover type information was obtained from maps generated by visual interpretation of Landsat Thematic Mapper (TM) false colour composite on a 1:50 000 scale, and understorey information as supplemented from cover maps generated by using aerial photographs on a 1:10 000 scale and ground sampling. Waterbodies were taken from field records and terrain information from Survey of India toposheets. Four suitability ratings were assigned to each sector using habitat parameters. The results revealed that among 16 sectors in the Kisli range 3 sectors each were found to be in a very good, good and moderately suitable category and 7 sectors were rated under low suitability for sambar. In the Kanha range, out of 28 sectors, 12 were found to be very good, 6 were ‘good’ and 5 sectors each in the category of moderate and low suitability classes.     

Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.     

Semi-supervised fuzzy neighborhood preserving analysis for feature extraction in hyperspectral remote sensing images

Neural Computing and Applications - Semi-supervised feature extraction methods are an important focus of interest in data mining and machine learning areas. These methods are improved methods based...     

High-precision extraction of nearshore green tides using satellite remote sensing data of the Yellow Sea,China

Since 2007, a massive floating green tide has occurred repeatedly in the Yellow Sea. The work involved in monitoring and clearing such a phenomenon requires highly precise spatial information. Satellite remote sensing data can provide such detail but they can be limited by long revisit periods and small-scale observational coverage. Therefore, in operational monitoring of green tides, Moderate Resolution Imaging Spectroradiometer (MODIS) data have an important role because of their short revisit period (twice daily) and large-scale observational coverage (viewing swath width: 2330 km). However, errors exist in MODIS-derived areal detection of green tides because its coarse spatial resolution (250–1000 m) can lead to mixed-pixel problems (i.e. in many cases, the limit of spatial resolution results in single pixels covering multiple ground objects). In this study, a method for spectral unmixing was developed for remote green tide detection. This method is based on mathematical morphology, and it integrates both the spatial and spectral characteristics of the image in the process of endmember extraction. Experiments using simulated and actual MODIS images were undertaken to illustrate the advantages of the proposed method. Compared with traditional linear unmixing methods, the abundance and area of green tides extracted by the proposed method were closer to the ‘real’ condition. The average relative deviation of green tide area decreased from 84.0% to 21.0%. The results illustrate that the proposed method is able to work efficiently and that it can provide improved information on green tide abundance and area.     

Detection limits of coral reef bleaching by satellite remote sensing: Simulation and data analysis

Monitoring of coral reef bleaching has hitherto been based on regional-scale, in situ data. Larger-scale trends, however, must be determined using satellite-based observations. Using both a radiative transfer simulation and an analysis of multitemporal Landsat TM images, the ability of satellite remote sensing to detect and monitor coral reef bleaching is examined. The radiative transfer simulation indicates that the blue and green bands of Landsat TM can detect bleaching if at least 23% of the coral surface in a pixel has been bleached, assuming a Landsat TM pixel with a resolution of 30×30 m on shallow (less than 3 m deep) reef flats at Ishigaki Island, Japan. Assuming an area with an initial coral coverage of 100% and in which all corals became completely bleached, the bleaching could be detected at a depth of up to 17 m. The difference in reflectance of shallow sand and corals is compared by examining multitemporal Landsat TM images at Ishigaki Island, after normalizing for variations in atmospheric conditions, incident light, water depth, and the sensor's reaction to the radiance received. After the normalization, a severe bleaching event when 25-55% of coral coverage was bleached was detected, but a slight bleaching event when 15% of coral coverage was bleached was not detected. The simulation and data analysis agreed well with each other, and identified reliable limits for satellite remote sensing for detecting coral reef bleaching. Sensitivity analysis on solar zenith angle, aerosol (visibility) and water quality (Chl a concentration) quantified the effect of these factors on bleaching detection, and thus served as general guidelines for detecting coral reef bleaching. Spatial misregistration resulted in a high degree of uncertainty in the detection of changes at the edges of coral patches mainly because of the low (∼30 m) spatial resolution of Landsat TM, indicating that detection of coral reef bleaching by Landsat TM is limited to extremely severe cases on a large homogeneous coral patch and shallow water depths. Satellite remote sensing of coral reef bleaching should be encouraged, however, because the development and deployment of advanced satellite sensors with high spatial resolution continue to progress.     

Using satellite remote sensing analysis to evaluate a socio-economic and ecological model of deforestation in Rondônia,Brazil

The effectiveness of an integrated socio-economic and ecological simulation model for estimating patterns and rates of deforestation in Rondônia, Brazil is evaluated using Landsat data and landscape pattern metrics. The Percent Cleared, Contagion, and Fractal Dimension of image classifications are compared to those determined from model outputs. Results indicate that rates and spatial patterns of deforestation are similar between model outputs and Landsat image analysis. Differences in clearing patterns between the model and Landsat data are due in part to topography, localized farming obstacles and the patchiness of clearings. The effects of varying spatial resolution on the metrics is also examined.     

Cyanobacteria blooms in three eutrophic basins of the Great Lakes: a comparative analysis using satellite remote sensing

Blooms of harmful cyanobacteria (cyanoHABs) were mapped for three eutrophic basins (western basin of Lake Erie, WBLE; Green Bay, Lake Michigan, GB; and Saginaw Bay, Lake Huron, SB) in the Great Lakes from 2002 to 2013 using Moderate Resolution Imaging Spectroradiometer (MODIS) ocean colour imagery. These blooms were examined in relationship to basic meteorological and environmental parameters. Annual cyanoHAB extent trends were generated using two modified remote-sensing approaches. The first approach was a modified bio-optical chlorophyll retrieval algorithm enhanced with empirical relationships to estimate water column cyanoHABs (MCH), whereas the second approach uses near-infrared (NIR) reflectance to quantify the surface scums of cyanoHABs (SSI). The development and application of the SSI are unique products in the Great Lakes and may have generic application to ecological and public health issues. Satellite-derived cyanoHAB estimates agreed well with in situ observations (89% accuracy). The annual cyanoHAB trends (MCH and SSI) for WBLE, SB, and GB were not similar for the 2002–2013 analysis period. A recent trend of increasing cyanoHABs was noted in WBLE but not in GB or SB. Moreover, extensive and persistent surface scums were observed in WBLE but not in GB or SB. Meteorological parameters were similar among the basins; however, significant differences in spring discharge of the dominant river were observed among basins. Spring discharge was a significant predictor of cyanoHAB occurrence in WBLE but not in GB and SB. Wind-induced sediment re-suspension events were common during the bloom period in WBLE but not in GB or SB and these events were highly correlated with cyanoHAB occurrence. The differences among basins in the role of riverine discharge and re-suspension suggest local factors are more important than regional factors in controlling cyanoHAB dynamics within these three basins in the Great Lakes.     

遥感卫星地面接收系统业务能力评估

随着航天事业的快速发展,遥感卫星的发射数量越来越多,遥感卫星地面接收系统面临着快速、稳定的接收新发射卫星下传数据的迫切需求。在此背景下提出了一种遥感卫星地面接收系统业务能力的评估方法,建立基于AHP的业务能力评估指标体系。结合业务特点,阐述了系统单机状态评估模型、系统业务能力评估模型并确定了各级因素的影响权重。实例分析结果表明,此评估方法能够快速确定遥感卫星地面接收系统对于某遥感卫星数据接收业务能力状态,给出系统最小化改进指引,对系统资源的利用及优化提供了可行性建议。     

Evaluating the complementarity of acoustic and satellite remote sensing for seagrass landscape mapping

Accurate maps representing seagrass spatial distribution are essential components for effective monitoring and management of coastal vegetated habitats. Satellite and acoustic remote sensing provide valuable spatial data for seagrass mapping, though few studies have evaluated the complementarity of these methods. In this study, the complementarity of seagrass mapping was assessed through comparison of acoustic and satellite remote-sensing data sets. QuickBird^® satellite imagery representing the seagrass landscape of the Richibucto estuary, New Brunswick, Canada, was classified through an object-based procedure and evaluated against a single-beam sonar data set. Acoustic percentage cover values were classified into binary presence/absence format through the application of a decision threshold, allowing comparison with satellite data using the error matrix and derived metrics. Though the binary satellite classification resulted in relatively high accuracy compared with independent ground reference data, agreement between satellite and acoustic data sets was limited. Local differences in seagrass prevalence and patchiness affected classification accuracy, highlighting the potential for under- or overestimating seagrass cover when applying bay-scale classification to areas with different landscape structure. These results emphasize the importance of landscape context in seagrass mapping. Satellite and acoustic remote sensing were seen to fundamentally differ in their depiction of the landscape. Comparison of multiple remote-sensing methods allowed for assessment of complementarity as well as ecologically relevant insight to seagrass spatial dynamics, with implications for mapping and monitoring of seagrass habitats.     

Multispectral satellite remote sensing of an oceanic cyclone in the northwestern Mediterranean

Radiance data in the visible waveband from the Nimbus-7 Coastal Zone Color Scanner (CZCS) has enabled us to identify mesoscale activity in the Gulf of Lions in 1979. After correction for atmospheric effects, chlorophyll-like pigment concentrations were derived from subsurface upwelling radiances by a previously tested algorithm. The mapping of oceanic phytoplankton distribution revealed a cyclonic eddy offshore from Marseilles, on 13 April 1979. It was centred at 42°52'N-5° 12'E and had a diameter of approximately 50 km. Our results seem to confirm the results of numerous hydrological cruises carried out in this area.     

一种遥感卫星动量轮容错控制的方法研究

针对低轨遥感卫星动量轮故障频发影响姿态稳定的情况,对以动量轮为执行器的低轨遥感卫星进行容错控制研究,提出一种结合卡尔曼滤波及模糊PID控制率的容错控制方法。首先建立低轨遥感卫星动量轮故障模型,应用卡尔曼滤波算法进行噪声滤除,然后根据卡尔曼滤波的结果,设计模糊PID容错控制器,并与传统PID容错控制器进行仿真比对实验,在闭环姿态控制系统的容错控制领域验证了该方法的较强自适应性和较强鲁棒性的优点。     

High-resolution satellite remote sensing: a new frontier for biodiversity exploration in Indian Himalayan forests

Satellite imagery with fine spectral and spatial resolution and high temporal resolution provides a unique opportunity to map and monitor biodiversity. The aim of this article is (i) to review the state of the use of high-resolution remote sensing for ecological studies in the Indian Himalayan Region (IHR) and (ii) to suggest further potential avenues of research in the region using this technology. It has been recognized that the recent improvements in remote sensing have enabled researchers to categorize and spatially map species and communities and to detect vegetation types based on ecological gradients and environmental drivers. Across the globe, the use of current high-resolution imagery in vegetation studies is increasing due to their improved efficiency, as fine-scale vegetation information is needed for both theoretical understanding of the processes involved and conservation towards the maintenance of ecological functions of natural ecosystems. Remote sensing is particularly useful in the IHR, where field-based mapping of forest vegetation and land cover using conventional techniques is a cost- and time-intensive effort. Mapping fine-scale patterns of tree species using high-resolution remote-sensing data has important implications for the understanding and monitoring of ecosystem function and biodiversity patterns in forests.     

Software system for satellite data processing of applied tasks in remote sensing of the Earth

This paper presents a software system for satellite data processing developed at the Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch, Russian Academy of Sciences, together with the research center Planeta of Rosgidromet RF. It also describes how this system is used for applied tasks in remote sensing of the Earth.     

Using remote sensing for monitoring of autumn tillage in Norway

Management systems on agricultural land in Norway are causing concern because of increasing area wide soil erosion, consequent increase in sediment loads and nutrient losses to waters, and their consequent pollution. Monitoring of areas in autumn is important because this is the start of a season when much of the erosion occurs. This is often caused by management systems in autumn, but also because climatic conditions in this season are important for erosion processes.

We have investigated several sources of remote sensing data, with a view to assembling the best overall monitoring system for this geoscience problem. The sources were digitised CIR (colour infrared pseudocolour) aerial photography, SPOT and ERS-1 SAR. CIR photography proved more accurate than SPOT data, especially with regard to exposed soils as result of CIR's continuous spectral sensitivity. We found that SPOT is clearly limited as a monitoring tool for soils and there is a need for better sensors in range 680 to 790 nm wavelengths.

ERS-1 SAR provides information when the optical sources cannot, but is dependant on good digital elevation models. We found that the most effective monitoring is done using a combination of microwave and optical sources.     

Improvement of tropical vegetation mapping using a remote sensing technique: A case of Khao Yai National Park,Thailand

The forest ecosystems of Thailand are characterized by a diverse and complex vegetation structure. Classification of vegetation types of such forest ecosystems has been experienced as a difficult task, even with large-scale aerial photography. Satellite remote sensing, the digital technique in particular, has not been widely used for vegetation mapping in Thailand until now. The objective of this study was to explore the potential of digital image processing over the existing technique of visual interpretation of Landsat Thematic Mapper (TM) false colour composite (BGR-2, 3, 4) to produce forest cover maps in Thailand. Supervised and unsupervised classification methods were employed with different band combinations to discriminate vegetation types in the Khao Yai National Park using Landsat TM data. The results indicated that thematic classes derived from supervised classification produced higher overall accuracy than unsupervised classification. In addition, the combination of ratio bands R4/3, R5/2, R5/4 and R5/7 ranked the highest in terms of accuracy (65% for unsupervised and 79% for supervised) and the combination of bands 2, 3 and 4 gave the lowest (56% for both methods). Finally, it was concluded that, even within the limit of spectral information available in the image, the digital classification can improve the result of visual interpretation.     

AIR-PV: a benchmark dataset for photovoltaic panel extraction in optical remote sensing imagery

<正>The energy crisis has been a hot topic of global concern.Photovoltaic devices, a typical new energy source, have progressed rapidly and become among the main sources of power generation in the world [1]. Most photovoltaic fields are often constructed in large areas, making it difficult to monitor photovoltaic panel situations. Combining remote sensing(RS) and deep learning [2],