The environmental analysis and site selection of mussel and large yellow croaker aquaculture areas based on high resolution remote sensing

Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys.A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas.This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors.The fishery ranching index(FRI1,FRI2)was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan,using Gaofen-1(GF-1)and Gaofen-6(GF-6)satellite data with a special resolution of 2 m.In the second step,the environmental factors such as sea surface temperature(SST),chlorophyll a(Chl-a)concentration,current and tide,suspended sediment concentration(SSC)in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail.The results show the following three points.(1)For the extraction of the mussel aquaculture area,FRI1 and FRI2 are complementary,and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area.As for the large yellow croaker aquaculture area extraction,FRI2 is suitable.(2)Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters.The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined.(3)This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future.the fishery ranching index(FRI1,FRI2)in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.

Generation of high resolution sea surface temperature using multi-satellite data for operational oceanography

In the present article, we introduce a high resolution sea surface temperature （SST） product generated daily by Korea Institute of Ocean Science and Technology （KIOST）. The SST product is comprised of four sets of data including eight-hour and daily average SST data of I km resolution, and is based on the four infrared （IR） satellite SST data acquired by advanced very high resolution radiometer （AVHRR）, Moderate Resolution Imaging Spectroradiometer （MODIS）, Multifunctional Transport Satellites-2 （MTSAT-2） Imager and Meteorological Imager （MI）, two microwave radiometer SSTs acquired by Advanced Microwave Scanning Radiometer 2 （AMSR2）, and WindSAT with in-situ temperature data. These input satellite and in-situ SST data are merged by using the optimal interpolation （OI） algorithm. The root-mean-square-errors （RMSEs） of satellite and in-situ data are used as a weighting value in the OI algorithm. As a pilot product, four SST data sets were generated daily from Ianuary to December 2013. In the comparison between the SSTs measured by moored buoys and the daily mean KIOST SSTs, the estimated RMSE was 0.71~C and the bias value was -0.08~C. The largest RMSE and bias were 0.86 and -0.26~C respectively, observed at a buoy site in the boundary region of warm and cold waters with increased physical variability in the Sea of Iapan/East Sea. Other site near the coasts shows a lower RMSE value of 0.60~C than those at the open waters. To investigate the spatial distributions of SST, the Group for High Resolution Sea Surface Temperature （GHRSST） product was used in the comparison of temperature gradients, and it was shown that the KIOST SST product represents well the water mass structures around the Korean Peninsula. The KIOST SST product generated from both satellite and buoy data is expected to make substantial contribution to the Korea Operational Oceanographic System （KOOS） as an input parameter for data assimilation.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.

Level study on fractal characteristics of tidal creeks and information of seashell habitats in the Gaizhou Beach based on high-resolution satellite images

The fractal characteristics of tidal creeks in the Gaizhou Beach are analyzed based on high-resolution images fusionof Landsat TM and ERS2, and then the graphic models and characteristics of converse information tree of tidalcreeks in the Gaizhou Beach are established. A calculation model is established based on the above results, and at thesame time, quantitative calculation of the evolution characteristics and the diversity between the northern and thesouthern parts of the Gaizhou Beach is carried out. By the supervised classification of these images, distribution andareas of high tidal flats, middle tidal flats and low tidal flats in the Gaizhou Beach are studied quantitatively, and imagecharactistics of seashell habitats in the Gaizhou Beach and the correlation between mudflat distribution and seashellhabitats are studied. At last, the engineering problems in the Gaizhou Beach are discussed.

Geometric Calibration and Image Quality Assessment of High Resolution Dual-Camera Satellite

The evaluation of geometric calibration accuracy of high resolution satellite images has been increasingly recognized in recent years.In order to evaluate geometric accuracy for dual-camera satellite images based on the ground control points(GCP),a rigorous geometric imaging model,which was based on the collinear equation of the probe directional angle and the optimized tri-axial attitude determination(TRIAD)algorithm,is presented.Two reliable test fields in Tianjin and Jinan(China)were utilized for geometric accuracy validation of Pakistan Remote Sensing Satellite-1.The experimental results demonstrate a certain deviation of the on-orbit calibration result from the initial design values of the calibration parameters.Therefore,on-orbit geometric calibration is necessary for optical satellite imagery.Within this research,the geometrical performances including positioning accuracy without/with GCP and band registration of the dual-camera satellite were analyzed in detail,and the results of geometric image quality are assessed and discussed.As a result,it is feasible and necessary to establish such a geometric calibration model to evaluate the geometric quality of dual-camera satellite.

Intelligent High Resolution Satellite/Aerial Imagery

High resolution satellite images are rich source of geospatial information. Nowadays, these images contain finest spectral and spatial information of ground realities in different electromagnetic spectrum. Many image processing softwares, algorithms and techniques are available to extract such information from these images. Multi spectral as well as panchromatic (PAN) high resolution satellite images are missing, one important information, regarding ground features and realities that information is attribute information which is not directly available in high resolution satellite images. From very first day, this information used to be collected through indirect ways using GPS, digitizing, geo-coding, geo tagging, field survey and many other techniques. Our real world has vertical labels for ground observer to identify and use this information. These vertical labels are present in form of names, logos, icons, symbols and numbers. These vertical labels ease us to work in real world. Satellites are unable to read these labels due to their vertical orientation. Making satellite/aerial imagery rich of attribute information, we have the possibility to design our world accordingly. Just like vertical labels we can also place real physical horizontal label for space sensors, to make this information directly available in high resolution satellite/aerial imagery. This work is about possibilities of such techniques and methods.

An Integrated Framework for Road Detection in Dense Urban Area from High-Resolution Satellite Imagery and Lidar Data

Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.

Quantizing and analyzing the feature information of coastal zone based on high-resolution remote sensing image

On the basis of realization of beach information and its differentiating of high-resolution remote sensing image on coastal zone, extracting objects are carried through RS multi-scale diagnostic analysis, and fast information extraction methods and key technologies are put forward. Meanwhile image segmentation methods are set forth for objects of coastal zone. And through the application of Otsu2D to the segmentation of water area and dock and the applying of Gabor filter to the separation and extraction of construction, some typical applications of high-resolution RS image are presented in the field of coastal zone surface objects＇ recognition. Quantizing high-resolution RS information on the coastal zone proved to be of great scientific and practical significance for coastal development and management.

Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province,Southwestern China

Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.

大气湍流对高分辨率遥感卫星的成像影响研究

遥感卫星在国防和民用探测等领域发挥着重要作用,而大气湍流严重影响高分辨率遥感卫星的成像质量。本文重点研究了遥感卫星对地探测时,相机口径、卫星轨高和大气湍流强度对空间相机成像质量的影响。首先,基于球面波传输模型和Kolmogorov湍流理论,针对空对地探测湍流波前进行仿真。然后,分析畸变波前随相机口径、卫星轨高和大气相干长度的变化规律,并推导出普适公式。在此基础上,进一步推导出空间相机成像分辨率随相机口径、卫星轨高和大气相干长度变化的计算公式。最后,研究了大气湍流对空间相机调制传递函数(MTF)的影响,并以MTF=0.15为基准,仿真分析了MTF相对误差随相机口径、卫星轨高和大气相干长度的变化规律。本研究为高分辨率遥感卫星的设计、分析和评估提供理论依据。

Optimization of post-classification processing of high-resolution satellite image:A case study

The application of remote sensing monitoring techniques plays a crucial role in evaluating and governing the vast amount of ecological construction projects in China. However, extracting information of ecological engineering target through high-resolution satellite image is arduous due to the unique topography and complicated spatial pattern on the Loess Plateau of China. As a result, enhancing classification accuracy is a huge challenge to high-resolution image processing techniques. Image processing techniques have a definitive effect on image properties and the selection of different parameters may change the final classification accuracy during post-classification processing. The common method of eliminating noise and smoothing image is majority filtering. However, the filter function may modify the original classified image and the final accuracy. The aim of this study is to develop an efficient and accurate post-processing technique for acquiring information of soil and water conservation engineering, on the Loess Plateau of China, using SPOT image with 2.5 rn resolution. We argue that it is vital to optimize satellite image filtering parameters for special areas and purposes, which focus on monitoring ecological construction projects. We want to know how image filtering influences final classified results and which filtering kernel is optimum. The study design used a series of window sizes to filter the original classified image, and then assess the accuracy of each output map and image quality. We measured the relationship between filtering window size and classification accuracy, and optimized the post-processing techniques of SPOT5satellite images. We conclude that (1) smoothing with the majority filter is sensitive to the information accuracy of soil and water conservation engineering, and (2) for SPOT5 2.5 m image, the 5×5 pixel majority filter is most suitable kernel for extracting information of ecological construction sites in the Loess Plateau of China.

High resolution satellite imaging sensors for precision agriculture

The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.

Improving georeferencing accuracy of Very High Resolution satellite imagery using freely available ancillary data at global coverage

While impressive direct geolocation accuracies better than 5.0 m CE90(90%of circular error)can be achieved from the last DigitalGlobe’s Very High Resolution(VHR)satellites(i.e.GeoEye-1 and WorldView-1/2/3/4),it is insufficient for many precise geodetic applications.For these sensors,the best horizontal geopositioning accuracies(around 0.55 m CE90)can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points(GCPs).However,these high-quality GCPs are not always available.In this work,two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed.Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30 m digital elevation model.The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%.

基于高分辨率卫星和无人机的广西滨海盐沼面积变化监测

滨海盐沼作为重要的海岸带生态系统,在海岸保护、生物多样性维持、固碳减污等方面发挥了重要的生态服务功能。及时准确地监测滨海盐沼分布情况和动态变化,对于科学地管理和保护本地滨海盐沼生态系统意义重大。本研究基于2019年和2021年多源国产高空间分辨率卫星数据,结合无人机自主性强、灵活机动、不受云遮挡影响的优势,对广西壮族自治区滨海盐沼开展遥感跟踪监测。研究结果表明,广西2021年滨海盐沼总面积为1 341.40 hm2,其中,北海市、防城港市和钦州市3个海滨城市的滨海盐沼面积分别为1 247.82 hm2、49.73 hm2和43.85 hm2。与2019年相比,广西2021年滨海盐沼总面积减少108.96 hm2,其中,北海市互花米草(Spartina alterniflora)面积减少107.05 hm2,钦州市短叶茳芏(Cyperus malaccensis)和芦苇(Phragmites australis)面积减少1.91 hm2,防城港市滨海盐沼面积不变。广西当地对入侵种互花米草的治理卓有成效,互花米草大范围减少,但局部区域的互花米草分布仍呈不断增长的趋势,仍需重视对互花米草的监测与防控工作。

量子多尺度融合的高分卫星影像建筑物变化检测

为了提高传统基于像元的高分辨率卫星影像变化检测方法的精度,本文提出了一种基于量子多尺度融合的高分卫星影像建筑物变化检测算法。首先,对双时相高分辨率卫星影像进行多尺度分割,构成多尺度影像数据集;然后,对多尺度影像数据集进行迭代慢特征变换,得到不同尺度的变化强度图,再利用量子理论对多尺度变化强度图进行融合,以得到融合后的变化强度图;最后,通过最大类间方差法完成变化强度图的阈值分割,得到二值化变化检测结果。利用两组不同时相的实际高分卫星影像,对本文算法进行了试验验证。试验结果表明,与单一尺度面向对象变化检测方法和熵权法多尺度融合方法相比,本文算法可以取得更高的建筑物变化检测精度。

基于高分卫星影像的湖南某地土地利用分类研究

随着遥感技术的快速发展,越来越多高分辨率遥感影像的出现,为快速准确地获取地面信息创造了有利条件。面向对象的分类方法在进行地物信息提取时,考虑了更多的分类特征,且能与地学知识以及其他专题特征相结合,使分类过程与人类的认知过程更加接近,已成为土地利用信息提取研究的主流方向之一。研究以高分二号影像为基础,探索多尺度分割最优参数的选取方法,构建了影像分类特征空间并对其进行优化。基于多层次分类体系提取土地利用信息,并进行精度评价。通过空间大数据对建设用地信息进行细分,并对其空间分布特征进行分析。

基于人工智能的多源遥感数据融合在电网勘测应用研究

随着卫星遥感、空间科技等技术的不断发展,通过卫星采集多源遥感数据已被广泛应用到诸多行业。将多源数据进行融合,生成信息更丰富、质量更高的图像,能更清晰地分析地物地貌地形情况,已成为近年来图像智能处理领域的技术研究前沿方向。文章基于人工智能卷积网络和注意力机制,提出一种统一融合网络,将不同光谱和空间属性的遥感源数据进行有效融合,生成具有精确光谱信息和清晰空间细节的高分辨率图像,为电网勘测选址选线等业务提供了辅助支撑的有效手段。实验结果表明,文章研究结果比现有典型方法具备更好的融合效果。

遥感大数据赋能青藏高原陆表水体空间分布信息认知

遥感大数据对赋能水利新质生产力发展具有显著作用,尤其在提升水资源调查、监测与管理效率方面展现出强大潜力。青藏高原被称为“亚洲水塔”,湖泊河流众多,对气候变化也最为敏感,然而由于地理位置特殊、地形环境复杂等因素,青藏高原水资源分布的精细化认知和监测一直是水利工作面临的一项挑战。针对青藏高原地区缺乏高空间分辨率陆表水体信息问题,提出分级弱监督信息挖掘策略,生成了青藏高原地区2 m分辨率陆表水体空间分布信息产品。经评估,产品总体精度为91.36%,用户精度为85.39%,生产者精度为91.71%,显示出较高的准确性和可靠性。研究成果不仅有助于深化对青藏高原地区水资源分布状况的认识,也展示出遥感大数据在复杂地形区域的应用潜力和价值,为水资源管理、生态保护以及区域可持续发展提供有力的数据支撑和决策依据。