Estimation of surface runoff potential of a watershed in semi-arid environment — A case study

An accurate assessment of run-off through aerial rainfall is a basic concept in most of the rainfall-runoff models, particularly conceptual models which emphasis a complete water balance. The run-off measurements by gauging can only be regarded as an index of rainfall and restrict our ability to successfully model the rainfall-runoff process. To overcome some of these problems, remote sensing satellite data are of immense use, particularly in mountainous and desert areas. Therefore in the present study, a typical watershed from a drought hit Banswara district of Rajasthan has been analysed using IRS-1B-LISS II satellite imagery for estimating the run-off potential under different geomorphic set-up. The run-off potential was estimated using SCS method based on the satellite data in conjunction with ground truth information collected during field visit. The results indicated that the soil and water conservation measures in the watershed would improve the existing water potential and storage capacity of the study area. Based on the study eight check dams and five lift irrigation schemes are proposed.     

地性线的山地区域的卫星影像几何精纠正

提出了一种山地区域基于DEM地性线的控制纠正新方法,该方法以数字地形模型DEM为无几何变形的控制基准纠正卫星影像。阐述了提取沟谷、山脊、山峰和凹地区域的地性线的原理和算法,给出了山地区域基于地性线进行卫星图像几何精纠正实施步骤,进一步讨论了地性线提取、控制点采集存在的问题,以及解决问题的途径。实验结果表明,对于山地区域,地性线的空间数量数倍于水系、道路等常规地图层;地性线来源于DEM,其空间稳定性和可靠性更高,可以用于山地区域的卫星影像的严格控制纠正。用该方法进行几何纠正处理,几何误差能控制在一个像元的水平上。     

利用稀少卫星影像控制点生产1:10 000 DOM

针对在野外像控点测量困难区域如何使用控制点快速纠正高分辨率卫星影像的问题,本文提出了基于稀少像控点的区域网平差方法,以实现在高精度、强现势性DEM的限制下,利用卫星影像快速制作1:10 000 DOM。并以阿拉善盟测区为例,进行了精度评价。结果表明,使用稀少像控点即可满足1:10 000 DOM的精度,可实现基于稀少控制点的遥感影像快速生产,有效地解决控制点难以获取时,造成DOM精度低且生产进度缓慢的问题。该方法能够为沙漠、林区等控制点缺乏地区提供一定的借鉴与参考。     

Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery

Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information and analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping depends on more than just analysis of reflectance values. In this research, it was found that incorporating climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.     

Analysis of different methods for monitoring the urbanization process

The monitoring of urban sprawl in agricultural and natural areas requires the frequent acquisition of information relative to land cover changes. The loss of high capability agricultural lands is a major problem. The sound management of resources requires the knowledge of the nature and orientation of the urban dynamics.

Remote sensing is a useful tool for highlighting areas where changes have occured,for determining the type of change and for quantifying these changes. A spatial‐temporal analysis of the urban processes is carried out for the urban area of Montreal, Canada. Different sources of information are used: three Landsat MSS satellite images acquired in 1972, 1979 and 1982, planimetric data from the Department of Municipal Affairs of Quebec and statistics compiled by Environment Canada.

The satellite data shows a sharp increase, in the order of 65%, in urban areas during the period under consideration. These results are compared with governmental data derived from classical photo‐interpretation techniques.

On one hand, we observe that the results obtained by automatic classification of the satellite data are superior in the order of between 5% to 30%, depending on the year and the different governmental sources. On the other hand, we discuss problems of homogeneity in the use of terms related to land cover between the various governmental organizations.     

Mapping of environs of Dharavi slums of Greater Bombay for site suitability using enhanced Landsat thematic mapper (TM) imagery

Slums are universal and a ubiquitous part of the urban landscape. Dharavi, the biggest slum in the whole of Greater Bombay, encompasses 4.0 sq.km. of reclaimed land with 3.50 lakh inhabitants and 75,000 hutments. Majority of the slums of Indian cities, being structurally small with high density of dwellings and uniform building material, seldom give subtle ’spectral signature’ on the satellite imagery. Here, an attempt has been made to map by visual techniques the land use of Dharavi and environs of 20 sq.km area, using optically enhanced Landsat (TM) FCC of January, 1986, on 1:25,000 sale, The study has clearly brought out the land use details, the areas undergoing reclamation, and those susceptible to hazards like floods and marine erosion. A few alternate sites, based on geomorphic attributes are suggested for resettlement of Dharavislum and their areas are also quantified. The results of the present work is a part of the project study completed for a larger area covering 150sq. kms.     

atmospheric corrections to thermal IR data for estimation of land surface temperature

Satellite data provides important inputs far estimating regional surface emisslviiy and surface temperature. The methodology for estimation of emissivity over heterogeneous areas is based on the calculation of fraction vegetation cover per pixel taking NDVI, reflectances of pure pixels as input. The surface temperature is calculated using a sptit-window equation, which depends on atmospheric water vapour, viewing angle and channel surface emissivities. In the present study model coefficients for atmospheric corrections to NOAA AVHRR thermal data Fqr tropical atmospheres have been derived with a view to operationally use the methodolpgy for generating land surface temperature information from satellite data. The results of the study show that the estimated temperature values are comparable with the ctimatological values over the region Suggesting the possible use of the methodology.     

Observations of urban and suburban environments with global satellite scatterometer data

A global and consistent characterization of land use and land change in urban and suburban environments is crucial for many fundamental social and natural science studies and applications. Presented here is a dense sampling method (DSM) that uses satellite scatterometer data to delineate urban and intraurban areas at a posting scale of about 1 km. DSM results are analyzed together with information on population and housing censuses, with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, and with Defense Meteorological Satellite Program (DMSP) night-light data. The analyses include Dallas-Fort Worth and Phoenix in the United States, Bogotá in Colombia, Dhaka in Bangladesh, Guangzhou in China, and Quito in Ecuador. Results show that scatterometer signatures correspond to buildings and infrastructures in urban and suburban environments. City extents detected by scatterometer data are significantly smaller than city light extents, but not all urban areas are detectable by the current SeaWinds scatterometer on the QuikSCAT satellite. Core commercial and industrial areas with high buildings and large factories are identified as high-backscatter centers. Data from DSM backscatter and DMSP nighttime lights have a good correlation with population density. However, the correlation relations from the two satellite datasets are different for different cities indicating that they contain complementary information. Together with night-light and census data, DSM and satellite scatterometer data provide new observations to study global urban and suburban environments and their changes. Furthermore, the capability of DSM to identify hydrological channels on the Greenland ice sheet and ecological biomes in central Africa demonstrates that DSM can be used to observe persistent structures in natural environments at a km scale, providing contemporaneous data to study human impacts beyond urban and suburban areas.     

联合星载光学与SAR图像的城市大面积建筑物高度快速提取

星载高分辨率光学图像与SAR图像广泛应用于城市建筑物高度提取,但光学图像存在缺少相关卫星参数的情况,而SAR图像则存在散射特征不完整以及提取效率低等缺陷。针对以上问题,本文提出一种联合高分辨率星载光学与SAR图像的城市大面积建筑物高度快速提取方法。首先,结合支持向量机(SVM)和形态学阴影指数(MSI)快速提取光学图像中的阴影并自动测量阴影长度;之后选择多个合适样本,基于模型匹配法从SAR图像中提取高度;最后将高度与阴影长度作线性回归分析,建立数学模型来提取其他建筑物的高度。该方法将不同卫星系统的数据和特征相结合,互相弥补各自缺陷,不仅提高了效率、降低了成本,同时满足精度要求。     

Detecting of Mining-Quarrying Activities in Turkey Using Satellite Imagery and its Correlation with Daytime to Nighttime Ratio Analysis

Satellite remote sensing images play an important role in environmental monitoring for mining industry. There are a number of environmental variables, soil and surface variables, associated with mineral activities, that are to some extent detectable easily with satellite earth observation data. The aim of this paper is to detect the quarrying-mining activities are located in seismically active regions of Turkey using satellite images. Because mining-quarrying blasts have been observed and listed as earthquakes in the seismicity catalogue by seismic networks. The presence of mines-quarries in an active seismic zone can cause errors during the analysis of the distribution of microseismicity and the editing of seismic catalogs. Therefore, this study is a meaningful analysis for seismic networks interested in tectonic researches, because it highlights areas where need to pay careful attention is advisable for identification of mining-quarrying blasts. The new digital database was created using the satellite images of mining and quarrying areas taken from the Google Earth program (http://maps.google.com). In the study, approximately 721 known and illegal mining-quarrying sources have been detected. That were organized in an informational atlas includes information on locations, geographic coordinates and satellite images of the mining-quarrying sources in Turkey which can be distributed as a CD or on the WEB. Kekovali et al. (International Journal of the Physical Sciences 6(15), 3784–3794, 2011) estimated potential mining and quarry areas of Turkey from the Kandilli Observatory Earthquake Research Institute & National Earthquake Monitoring Center (KOERI-NEMC) seismic catalogs using daytime to nighttime ratio analysis (Qm). In this study, the correlation between the estimated satellite locations of the mining and quarrying activities and the areas with Qm?≥?2.0 values taken from the previous study was examinated. The result of the study, the important potential mining-quarrying sources were estimated for Turkey includes: Kütahya, Mu?la, Manisa, Bal?kesir, ?stanbul, ?zmit, Edirne, Bursa, Bilecik, Tekirda?, Ankara, Konya, Eski?ehir, Malatya, Yozgat, K?r?kkale, Malatya, ?anl?urfa, Sinop, Trabzon. Monitoring and controlling mining-quarrying activities through traditional methods is quite difficult due to high costs and lengthy time in obtaining accurate and updated maps. The use of satellite images is an inexpensive and effective tool for mapping large mining-quarrying activity areas that can be also used to supplement data from environmental studies. In the future work, the satellite database can be processed and analyzed in order to produce a proper GIS database that includes important mining-quarrying sources of Turkey.     

FY-3B微波成像仪海洋数据无线电干扰识别

中国风云3号B星(FY-3B)上的微波成像仪MWRI通过5个频率(10.65 GHz,18.7 GHz,23.8 GHz,36.5 GHz和89.0 GHz)的双极化通道对地球表面进行监测。研究表明,MWRI资料的低频波段数据中存在着无线电频率干扰(RFI)现象,这些污染信号对遥感数据和反演产品质量产生极大的影响。本文尝试使用多通道回归方法和双主成分分析(DPCA)方法识别MWRI的10.65 GHz水平通道亮温海洋区域中的RFI信号。结果表明,双主成分分析法可以有效地识别出海洋上的RFI信号。微波成像仪10.65 GHz水平通道亮温数据中的RFI信号主要分布在地中海等欧洲附近海域,也存在于美国、日本、澳大利亚等近岸地区。     

Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City,Iran)

By using satellite imagery, the recognition and evaluation of various phenomena and extraction of information necessary for the planning of land resources or other purposes are easily accomplished. The purpose of this study is to compare the efficiency of seven commonly used methods of monitored classification of satellite data to evaluate land use changes using TM and OLI Landsat, IRS, Spot5 and Quick Bird bands as well as different color combinations of these images to detect agricultural land, residential areas and aquatic areas using object-oriented processing. Digital processing of satellite images was carried out in 1998 and 2016 using advanced methods. Training samples were extracted in five user classes by eCognition software using segmentation scale optimization, different color combinations and coefficients of shape and compression. The appropriate segmentation scale for arable land, human complications and the blue areas were, respectively, 50, 8 and 10. Then each image was classified separately using seven methods and extracted samples, and efficiency of each classification method was obtained by calculating two general health and Kappa coefficients. The results show that the accuracy of each classification method and the neural network with a total accuracy of 94.475 and Kappa coefficient of 92.095 were selected as the most accurate classification method. These results show that the sampling of educational samples with proper precision of the classes in the images and dependency probability of each satellite images pixel can be useful in classifying group available in helpful area.     

Refugee Camp Mapping Using Very High Spatial Resolution Satellite Sensor Images

Abstract

Short time‐intervals for complex response in unfamiliar areas cause refugee‐relief organisations to have a strong need for timely and up‐to‐date geographic information of the environment during humanitarian operations. The objective of this study is to provide an overview of relief organisations' need for detailed geographic information, and to assess the potential of the upcoming very high spatial resolution (VHSR) satellite sensors to provide this geographic information by mapping refugee camps and their environment on an operational basis. To demonstrate the use of VHSR satellite technology in relief operations, a pilot proof‐of‐concept study using a 1992 Russian KVR‐lOOO 2 m resolution panchromatic image of the six refugee camps in the Qala en Nahal settlement scheme in the Sudan was performed. The VHSR satellite sensor image was found to be useful for mapping refugee camp environmental parameters, such as land use, roads, rivers, and water sources, as well as camp infrastructure, including geographic positioning of camps, housing, and street network. The image also allowed for detailed camp area estimates. In addition, a statistically significant relationship between camp area and population was revealed for refugee camps included in this study. In operational use of VHSR satellite sensor data, relief agencies should be aware of the limitations of optical satellite images, in particular their reduced applicability during cloudy conditions.     

Built-up and vegetation extraction and density mapping using WorldView-II

This study demonstrates the use of high resolution WorldView-II satellite data in extraction of built-up land and vegetation using normalized index techniques. The PCA 1 and NIR 2 bands-based built-up index was proposed for extracting built-up land, which exhibit high accuracy. The normalized difference vegetation index based on Red Edge and NIR 2 bands of WorldView-II produced high accuracy inthe estimation of vegetation compared to the use of Red and NIR bands. The grid technique used in estimating built-up and vegetation density from precisely classified images provided better and accurate assessment of built-up and vegetation density in heterogeneous landscape of urban areas. This shows areas of very high to high built-up density are located in the central, western and southern parts, which are primarily devoid of vegetation. This study indicates possibilities of utilizing high resolution satellite data in urban landscape characterization using a grid-based technique.     

多角度NOAA卫星数据地面BRDF反射率的大气校正

本文利用连续数天的NOAA卫星数据，采用Rahman地表二向反射模型和基于地面BRDF反射率的大气校正方法反复迭代提取出多个角度大气校正后的图像，并对结果进行了分析。实验结果表明了在多角度遥感定量研究中BRDF大气校正的重要性，大气校正结果与地面实测结果相近，且迭代收敛迅速。     

Land Use/Cover Change Detection of Tiruchirapalli City, India, Using Integrated Remote Sensing and GIS Tools

The rapid growth of urban population in India is a cause of concern among country??s urban and town planners for efficient urban planning. The drastic growth of urban areas has resulted in sharp land use and land cover changes. In recent years, the significance of spatial data technologies, especially the application of remotely sensed data and geographical information systems (GIS) has been widely used. The present study investigates the urban growth of Tiruchirapalli city, Tamilnadu using IRS satellite data for the years 1989, 1992, 1995, 1998, 2001, 2004, 2007, and 2010. The eight satellite images are enhanced using convolution spatial enhancement method with Kernel (7?×?7) edge enhance function. Supervised classification method is used to classify the urban land use and land cover. The GIS is used to prepare the different layers belonging to various land uses identified from remotely sensed data. The analysis of the results show the drastic increase of built up area and reduced green cover within the city boundary limit.     

Improving satellite image classification by using fractional type convolution filtering

This letter shows how conventional methods for satellite image classification can be improved by applying some filtering algorithms as a pre-classifying step. We will introduce a filtering scheme based on convolution equations of fractional type. The use of this kind of filter as a pre-classification step will be illustrated by classifying MODerate-resolution Imaging Spectroradiometer (MODIS) data to map burned areas in Mediterranean countries. The methodology we propose improved the estimations obtained by merely classifying the post-fire images (i.e. without filtering) in the study areas considered.     

基于目标关联的多源卫星遥感图像兵营融合检测方法

目前的目标融合检测方法大都是基于多源遥感图像配准的,然而在实际的应用中,成像机理不同的多源遥感图像的精校正和图像间的配准是十分复杂的,难以确保其配准精度.为此,本文提出了一种基于目标关联的多源卫星遥感图像的兵营融合检测方法.该方法不对图像进行配准,而是根据单源图像的目标自动检测结果,利用图像的大地坐标信息,截取包含目标的同一地区的局部遥感图像,再分别提取多源遥感图像目标的特征,并根据其中冗余的特征,对提取的目标区域建立关联,再由关联检验确保特征关联的正确性,最后对目标特征进行融合决策,得到目标融合检测结果.实验结果表明,该方法能有效地利用多源遥感图像的信息,降低遥感图像目标检测的误判率,提高目标特征的准确度.     

Vegetation cover type mapping in mouling national park in Arunachal Pradesh, Eastern Himalayas- an integrated geospatial approach

Improving image classification and its techniques have been of interest while handling satellite data especially in hilly regions with evergreen forests particularly with indistinct ecotones. In the present study an attempt has been made to classify evergreen forests/vegetation in Moulirig National Park of Arunachal Pradesh in Eastern Himalayas using conventional unsupervised classification algorithms in conjunction with DEM. The study area represents climax vegetation and can be broadly classified into tropical, subtropical, temperate and sub-alpine forests. Vegetation pattern in the study area is influenced strongly by altitude, slope, aspect and other climatic factors. The forests are mature, undisturbed and intermixed with close canopy. Rugged terrain and elevation also affect the reflectance. Because of these discrimination among the various forest/vegetation types is restrained on satellite data. Therefore, satellite data in optical region have limitations in pattern recognition due to similarity in spectral response caused by several factors. Since vegetation is controlled by elevation among other factors, digital elevation model (DEM) was integrated with the LISS III multiband data. The overall accuracy improved from 40.81 to 83.67%. Maximum-forested area (252.80 km2) in national park is covered by sub-tropical evergreen forest followed by temperate broad-leaved forest (147.09 km²). This is probably first attempt where detailed survey of remote and inhospitable areas of Semang sub-watershed, in and around western part of Mouling Peak and adjacent areas above Bomdo-Egum and Ramsingh from eastern and southern side have been accessed for detailed ground truth collection for vegetation mapping (on 1:50,000 scale) and characterization. The occurrence of temperate conifer forests and Rhododendron Scrub in this region is reported here for the first time. The approach of DEM integrated with satellite data can be useful for vegetation and land cover mapping in rugged terrains like in Himalayas.     

Improving crop type determination using satellite imagery: A study for the Regione del Veneto,Italy

The Regione del Veneto (Italy) is cooperating with the University of California, Santa Barbara and other researchers in Italy and the U.S.A. to develop a system of econometric crop production modeling. Five crops are to be included in this project: small grains (wheat and barley), corn, sugar beets, soybeans, orchards and vineyards. A critical part of the crop yield modeling process is the identification of crops using multispectral satellite data. This paper explores two strategies to improve crop classification accuracies: (1) use of ancillary data stored in digital format and (2) use of multitemporal data. Ancillary information stored on digital files were used in this research to remove (mask) non‐agricultural areas from satellite image data. Comparison between the classification of masked and unmasked images showed that improvement ranged from 3% to 26% depending on crop type. The multidate classification was performed by compiling an image of transformed spectral bands and three TM‐5 bands. The transformed bands were TM band 4 over TM band 3. Based on the work conducted in this study it is clear that crop type determination from satellite imagery is possible for small field agricultural areas such as those found in Italy.