Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT

This study applied a time series evapotranspiration (ET) data derived from the remote sensing to evaluate Soil and Water Assessment Tool (SWAT) model calibration, which is a unique method. The SWAT hydrologic model utilized monthly stream flow data from two US Geological Survey (USGS) stations within the Big Sunflower River Watershed (BSRW) in Northwestern, Mississippi. Surface energy balance algorithm for land (SEBAL), which utilized MODerate Resolution Imaging Spectro-radiometer (MODIS) to generate monthly ET time series data images were evaluated with the SWAT model. The SWAT hydrological model was calibrated and validated using monthly stream flow data with the default, flow only, ET only, and flow-ET modeling scenarios. The flow only and ET only modeling scenarios showed equally good model performances with the coefficient of determination (R²) and Nash Sutcliffe Efficiency (NSE) from 0.71 to 0.86 followed by flow-ET only scenario with the R² and NSE from 0.66 to 0.83, and default scenario with R² and NSE from 0.39 to 0.78 during model calibration and validation at Merigold and Sunflower gage stations within the watershed. The SWAT model over-predicted ET when compared with the Modis-based ET. The ET-based ET had the closest ET prediction (~8% over-prediction) as followed by flow-ET-based ET (~16%), default-based ET (~27%) and flow-based ET (~47%). The ET-based modeling scenario demonstrated consistently good model performance on streamflow and ET simulation in this study. The results of this study demonstrated use of Modis-based remote sensing data to evaluate the SWAT model streamflow and ET calibration and validation, which can be applied in watersheds with the lack of meteorological data.     

Evaluating Flood Hazard for Land-Use Planning in Greater Dhaka of Bangladesh Using Remote Sensing and GIS Techniques

Floods are a common feature in rapidly urbanizing Dhaka and its adjoining areas. Though Greater Dhaka experiences flood almost in every year, flood management policies are mostly based on structural options including flood walls, dykes, embankments etc. Many shortcomings of the existing flood management systems are reported in numerous literatures. The objective of this paper is to assess flood hazard in Greater Dhaka for the historical flood event of 1998 using Synthetic Aperture Radar (SAR) data with GIS data. Flood-affected frequency and flood depth calculated from the multi-date SAR imageries were used as hydrologic parameters. Elevation heights, land cover classification, geomorphic division and drainage network data generated from optical remote sensing and analogue maps were used through GIS approach. Using a ranking matrix in three dimensional multiplication mode, flood hazard was assessed. All possible combination of flood hazard maps was prepared using land-cover, geomorphology and elevation heights for flood-affected frequency and floodwater depth. Using two hazard maps which produced the highest congruence for flood frequency and flood depth, a new flood hazard map was developed by considering the interactive effect of flood-affected frequency and floodwater depth, simultaneously. This new hazard map can provide more safety for flood countermeasures because pixels belonging to higher hazard degrees were increased due to the consideration of higher degrees of ranks. The estimation of flood hazard areas revealed that a major portion of Greater Dhaka comprised moderate to very high hazard zone. Only a little portion (8.04%) was found to be the least vulnerable to potential flood hazard. Conversely, 28.70% of Greater Dhaka was found within very high hazard zone. Based on this study, comprehensive flood hazard management strategies for land use planning decision were proposed for the efficient management of future flood disasters.     

基于GIS和遥感数据的洪水风险分析

随着城市化进程的不断深化,洪涝灾害已经成为威胁人类生存的一种主要自然灾害。人们逐步认识到,仅仅使用工程措施来防御洪水是远远不够的,非工程措施近年来已进入到防洪领域。管理洪水、分析洪水灾害是非工程措施的一个重要方面。应用GIS工具研究ASAR数据和DEM数据获取研究区域的水深信息。以东洞庭湖区域为研究对象,运用3种常用的插值方法（IDW、Kriging和Topo to Raster）进行水面插值．但这些方法的插值效果不是很好。一种新的插值方法（ADDM,基于平均距离插值方法）被用来进行水面插值。相互比较的结果表明,ADDM方法在水深精度和水面连续性方面都优于其他3种方法。最后,根据ADDM方法获取的水深信息和洪水频率图制作东洞庭湖区洪水风险图。     

多源水利遥感数据的一体化管理机制研究

随着水利遥感数据及产品在水利业务中的普遍应用,存储和管理问题日益成为限制数据应用的瓶颈.针对水利遥感数据及产品的存储管理问题进行研究,以空间数据库引擎技术为基础,提出面向数据库的多源水利遥感数据一体化管理机制.开发并实现基于 C/S 模式的多源水利遥感数据管理系统,利用空间数据库引擎屏蔽数据的复杂性和异构性,通过统一的接口实现水利遥感数据的数据库存取.经过系统测试,证明该系统具有良好的数据存取性能,能够为上层的水利应用提供可靠的数据保障     

Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

Remote Sensing for Wetland Mapping and Inventory

ABSTRACT

This paper describes the state-of-the-art of the management of river sedimentation in China. Attention will be focused on the following problems: (1) the sediment load carried by main rivers in China; (2) the related sediment problems encountered in developing water resources; and (3) the methods in dealing with those problems.     

Improvement to the Thornthwaite Method to Study the Runoff at a Basin Scale Using Temporal Remote Sensing Data

Most of the popular hydrological models are intensive data driven hence, it has become a constraint in computing runoff of river basins where the meteorological data availability is scant. Studying environmental impact assessment on runoff has also become complex in many basins due to non-availability of sufficient historic meteorological data. Directly or indirectly, major components of hydrological cycle such as evapotranspiration and soil moisture are dependent on land use pattern at basin scale. Keeping in view of this, in this paper, an attempt was made to propose modification to simple monthly water balance model by integrating potential evapotranspiration with land use coefficients that were derived from the temporal satellite remote sensing data to compute runoff at basin scale. Godavari Basin, India was selected as study basin to demonstrate the approach. Monthly land use coefficients of all land use classes were computed during the calibration process of the model by matching the computed runoff with field runoff. Runoff during the last 18 years (1990–91 to 2007–08) was computed using the developed methodology. Four years datasets were used for model calibration and the rest of the data for model validation. Spatial annual groundwater flux, reservoir flux and domestic water consumption grids were computed using the field data and integrated with the model in computing runoff. From the Nash-Sutcliffe efficiency coefficient, it is found that computed runoff is very well matching the field runoff. The demonstrated approach is found to be more accurate and simple in computing runoff at basin scale in absence of high intensity meteorological data.     

Implication of Remote Sensing Data under GIS Environment for Appraisal of Irrigation System Performance

Water Resources Management - Poor irrigation management is a common issue of irrigated agriculture. Assessment of irrigation system performance is essential to improve the irrigation system, which...     

Assessing the Accuracy of Soil and Water Quality Characterization Using Remote Sensing

Assessing the risks of agricultural management practices on agro-ecosystem sustainability has special relevance in Ohio, USA due to the states prominence in agricultural production. However, identifying detrimental management practices remains controversial, a situation that may explain the inability to halt the recurring harmful algal blooms in inland waters, or the build-up of nutrients in the agricultural soils. Thus, detailed and accurate information is required to identify soils and water susceptible to degradation, and to support counteractive remedial measures. In this study soil and water spectral reflectance data were acquired with an Analytical Spectral Device, and modeled with laboratory measured physical and chemical properties using the Analysis of Variance (ANOVA) and decision trees. Results reveal no site differences in pH for the water, but the differences in electrical conductivity (EC) were significant. Similarly, the pH for soils did not vary significantly with depth increments. However, the no till (NT) managed soils had significantly higher pH. EC varied with depth of the water, whereas the soil carbon: nitrogen (C/N) ratio varied with management in 4 out of 5 sites. Finally, this study shows that remotely sensed data can be utilized to effectively characterize agricultural management practices based on inherent soil and water properties, thus providing information critical for assessing the efficacy of Water Quality Trading initiatives.     

基于遥感和GIS的流域社会经济数据空间化方法研究

人口和社会经济数据及其空间分布,在资源、环境及自然灾害评估中的重要性已被广泛认知。把遥感和GIS技术相结合,是探讨解决社会经济统计数据空间化的重要思路。以东江流域为例,以多期人口、GDP、土地利用数据为基础,建立东江流域人口与土地利用、GDP和土地利用的多元线性回归模型;以土地利用数据和100 m×100 m网格数据为基础,构建东江流域人口和GDP空间分布约束力指标图层;然后结合统计模型和面积内插,实现了东江流域2009年人口、GDP统计数据的空间化。在县域空间尺度上对模拟结果进行了验证,与同尺度研究工作进行了对比,结果显示模拟得到的人口和GDP空间分布数据,与同尺度的研究工作处于同一精度或者略高的精度水平,表明该方法是一种进行流域社会经济数据空间化处理的有效方法。     

利用遥感技术监测江苏海岸线变迁与滩涂围垦

掌握江苏沿海滩涂的现状与变化情况对于江苏沿海大开发具有重要的意义。利用1975年、1984年、1992年、2002年和2009年5个时相的多源遥感影像对江苏盐城—南通段海岸线变迁与滩涂围垦情况进行动态监测,获取每个时期发生变化的岸段、滩涂围垦面积等信息,并结合GIS的空间分析工具进行了定量分析。结果表明:利用遥感影像能够满足准确、快速监测海岸线变迁的要求;1975—1984年,江苏省海岸线快速向海域推进,滩涂围垦面积为46 045 hm2;1984—1992年间岸线推进速度有所放缓,新增陆地面积为9 955 hm2;1992—2002年与2002—2009年两个时间段内,海岸线全面加速向海域推进,围垦面积分别为53 055 hm2和36 360 hm2。总体而言,1975—2009年间江苏海岸线变化呈现快—慢—全面加速推进的特点。     

Understanding Seasonal Water Clarity Dynamics of Lake Dahuchi from In Situ and Remote Sensing Data

Obtaining, analyzing and understanding the seasonal dynamics of water clarity is of importance for understanding and managing lakes and sustaining their ecosystem services. This study aimed to explore the seasonal dynamics of water clarity and to analyze how water level, wind velocity and total precipitation influence this dynamics in Lake Dahuchi, China. The Secchi disk depths recorded in the field and derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images together demonstrated a seasonal pattern of water clarity, which was lower in winter and spring, increased in April or May to reach the highest values in summer, upon which it gradually declined from September onward. Piecewise linear regression analysis between water clarity and water level showed that water level could explain 70% of the variation of the logarithm of Secchi disk depth. The water clarity of Lake Dahuchi was primarily controlled by suspended sediment, while the seasonal variation of water level induced different sediment resuspension, thus we concluded that the water clarity seasonal dynamics of Lake Dahuchi was mainly regulated by seasonal variation of water level.     

Flood Hazards Mitigation Analysis Using Remote Sensing and GIS: Correspondence with Town Planning Scheme

Flood is an overflow of water that submerges land and the inflow of tide onto land. Floods usually cause large-scale loss of human life and wide spread damage to properties. In this study, integration of the satellite and GIS datasets are carried out to prepare the flood zonation mapping of Surat district, Gujarat, India. High resolution remote sensing images from Google-earth, IRS-1D, 1:50000 topographical maps are combined with hydraulic analysis and digital elevation model (DEM) to identify the flood susceptible area of the various zones divided as North, South, East, West, Central, South-East and South-West validated with the field surveys. The work is extended up to the Town Planning Scheme (TPS), to detect the most vulnerable areas in terms of submergence. Overall analysis indicates that more than 90–95 % of the area would be submerged if the flood of the same frequency happened over this flood plain in the near future. To mitigate the floods hazards, various remedial measures are suggested to lower the degree of danger owing to future flood events.     

基于MODIS数据的水体提取算法研究与实现

MODIS遥感数据以其自身特点已被广泛应用于洪水灾害监测.根据MODIS数据的特点以及水体的波谱特性,分别采用了归一化植被指数法、谱间关系法和自组织神经网络算法进行了水体提取试验研究,并对比分析了三种方法的优缺点,以及用C语言对这三种算法进行实现.     

Methodology for Quantifying Groundwater Abstractions for Agriculture via Remote Sensing and GIS

Among the difficulties and uncertainties that arise when determining water balance is the calculation of groundwater abstraction. This factor is particularly important in aquifers whose extension and heavy agricultural use make direct quantification methods unfeasible (i.e. flow meters and power consumption data). This study presents a method of quantifying groundwater abstractions for irrigation based on the analysis of multitemporal and multispectral satellite images. The process begins with a highly detailed classification of irrigated crops; these data are entered in a Geographic Information System, overlain with a correct estimate of the irrigation requirements of the crop, and corrected in accordance with the agricultural practices of the area. The results reveal the spatial and temporal distribution of the groundwater volume abstracted and used for agriculture. This methodology has been applied in the Mancha Oriental Hydrogeological System (Spain, 7,260 km²), where abstractions for agriculture comprise more than 90% of the hydrological resources consumed. In this context, accuracies of over 95% have been obtained with a cost sixty times lower than that of traditional methods.     

Remote Sensing and Economic Indicators for Supporting Water Resources Management Decisions

This paper demonstrates that combining spatial land surface data with socio-economic analysis provides a number of indicators to strengthen decision making in integrated water and environmental management. It provides a basis to: track current water consumption in the Inkomati Basin in South-Africa; adjust irrigation water management; select crop types; facilitate planning; estimate crop yields before harvesting, and consequently to forecast market price development. Remote sensing data and economic analysis can also be used to study the spatial distribution of water consumption as an indicator of equity in access to water resources. It even enables identification of farms that consume more irrigation water than formally allocated. Finally, it provides a basis to assess the cost-effectiveness of various ways to reduce agricultural water consumption. So, this approach is potentially useful for determining water consumption, refining water allocation policies, and determining the potential for water transfers through mechanisms such as water trading.     

Assessing Flood Induced Land-Cover Changes Using Remote Sensing and Fuzzy Approach in Eastern Gujarat (India)

Natural hazards such as flooding can cause changes in land-cover. The present study deals with the changes in land-cover in three worst affected districts (Anand, Vadodara and Kheda) of Gujarat state in India due to severe flood during 2005. The Indian Remote Sensing (IRS) P6 Linear Imaging Self Scanning (LISS) III satellite imageries of pre- and post-flooding periods were used as sources of information for the study area. Three classification approaches (unsupervised ISODATA, supervised Maximum Likelihood Classifier, and fuzzy rule based) were used to extract flood induced land-cover information. Results obtained from the above classification approaches were compared. Soft computing technique such as fuzzy based image classification gave better separability amongst classes as compared to hard classification techniques. The accuracy assessment showed that the fuzzy approach can predict land-cover more accurately than traditional approach and also showed great potential for dealing with mapping of flood induced land-cover. Unsupervised classification results for the period October 2004 to October 2005 revealed decrease in inland water bodies (14.49%) and agricultural area (6.42%) while increase in remaining land-cover. During February 2005 to February 2006, all land-cover classes decreased except agricultural fallow and sparse vegetation. In case of supervised classification, decreasing trend was observed only in case of agricultural area (6.78%) during October 2004 to October 2005. Similarly, during February 2005 to February 2006, increase in coastal water bodies (0.73%) and sparse vegetation (1.7%) was observed where as decreasing trend was noticed in the remaining land-cover classes. In fuzzy based classification, only decrease in agricultural area (7.09%) was observed from October 2004 to October 2005, whereas during February 2005 to February 2006, decrease in area was exhibited in all land-cover classes except coastal water bodies and sparse vegetation. Change detection indicated interchange of areas between inland and coastal water bodies and decrease in agricultural area leading to increase in area of agricultural fallow and sparse vegetation.     

Environmental Monitoring of Spatio-temporal Changes Using Remote Sensing and GIS in a Mediterranean Wetland of Northern Greece

Loss and degradation of terrestrial and aquatic habitats and degraded water quality are major environmental concerns worldwide. Especially wetlands are sensitive ecosystems that are subject to stress from human activities. Remote sensing techniques have been primarily used to generate information on land cover/use changes. Geographical Information Systems (GIS) and remote sensing can be used to provide a rapid or a large-scale understanding of lake change and in developing lake management strategies. The principal objectives of this study are to monitor and assess the spatial and temporal changes in land cover/use by using GIS, and to determine the main environmental factors affecting these changes. This paper presents a case study for the application of integrated remote sensing and GIS data for the classification and monitoring of the spatial and temporal changes in land use types. The study was conducted in a small natural wetland of Lake Cheimaditida, located in the East Mediterranean region of Northern Greece. Data analysis was conducted using GIS software. During the past several decades Lake Cheimaditida wetland has been influenced by many anthropogenic activities. The variables chosen for the assessment included condition of wetland and lake areas, present extent of wetlands relative to historic area, cover of natural habitat, wetland disturbances, etc. These variables address catchments properties that are important for maintaining and improving wetland habitats and water quality and assessment of trends useful for environmental monitoring. Land cover/land use patterns were assessed and compared using aerial photographs taken in 1945, 1969, 1982, and 1996. Over this period, reed beds enormously increased by 1,655.19%, while open-water areas and peat lands decreased by 74.05 and 99.5%, respectively. The significance of the changes in land cover distribution within the Lake Cheimaditida wetland are discussed in relation to the historical pattern of reed beds colonization, the importance of Phragmites australis in the process and the implications for strategic management of freshwater wetland resources.     

Remote Sensing and GIS Based Approach for Identification of Artificial Recharge Sites

The main aim of this paper is to demonstrate the capabilities of Remote Sensing (RS) and Geographic Information System (GIS) techniques for the demarcation of suitable sites for artificial recharge of groundwater aquifers, in the Loni watershed, located in Unnao and Raebareli districts, Uttar Pradesh, India. In this study, the SCS-CN model, groundwater depth data and morphological parameters (bifurcation ratio, elongation ratio, drainage density, ruggedness number, relief ratio, and circulatory ratio) have been used to delineate the recharge sites for undertaking water conservation measures. Augmentation of water resource is proposed in the watershed by constructing runoff storage structures, like check dam, percolation tank and nala bund. The site suitability for these water harvesting structures is determined by considering spatially varying parameters, like runoff potential, slope, groundwater fluctuation data and morphometric information of the watershed. GIS has been used as an effective tool to store, analyse and integrate spatial and attribute information pertaining to runoff, slope, drainage, groundwater fluctuation and morphometric characteristics for such studies.     

Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing

In the present study, soil erosion assessment of Dikrong river basin of Arunachal Pradesh (India) was carried out. The river basin was divided into 200 × 200 m grid cells. The Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data and the USLE was used to predict the spatial distribution of the average annual soil loss on grid basis. The average rainfall erositivity factor (R) for Dikrong river basin was found to be 1,894.6 MJ mm ha⁻¹ h⁻¹ year⁻¹. The soil erodibility factor (K) with a magnitude of 0.055 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ is the highest, with 0.039 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ is the least for the watershed. The highest and lowest value of slope length factor (LS) is 53.5 and 5.39 respectively for the watershed. The highest and lowest values of crop management factor (C) were found out to be 0.004 and 1.0 respectively for the watershed. The highest and lowest value of conservation factor (P) were found to be 1 and 0.28 respectively for the watershed. The average annual soil loss of the Dikrong river basin is 51 t ha⁻¹ year⁻¹. About 25.61% of the watershed area is found out to be under slight erosion class. Areas covered by moderate, high, very high, severe and very severe erosion potential zones are 26.51%, 17.87%, 13.74%, 2.39% and 13.88% respectively. Therefore, these areas need immediate attention from soil conservation point of view.