基于遥感的区域蒸散发研究

蒸散发是极其重要的水循环要素过程,影响着地表降水和辐射能的重新分配。遥感作为一种高效的空间信息获取和处理技术,把蒸散发的研究带到了一个新的局面。综述了遥感监测蒸散发的现状,介绍了遥感研究蒸散发的理论基础及常用的几种模型,指出了存在的问题并对未来发展进行了预测。     

定量遥感蒸散研究进展及关键问题

遥感技术为区域蒸散的定量提供了有效手段,在综述了国内外定量遥感蒸散研究概况的基础上,比较分析了目前较常用的几种遥感蒸散模型,并指出同类研究在地表温度与空气动力学温度差异、阻抗、平流、尺度效应及结果验证等方面存在一些问题,提出了提高定量遥感蒸散计算精度的具体措施,包括:运用高光谱提高地表温度反演精度;加强对瞬时地表蒸散转换为日总蒸散的研究;运用微波遥感解决阴雨天的地表蒸散;提高净辐射通量反演精度;借助动力学过程模型进行地表蒸散反演的研究;加强地面定标和试验遥感的研究等。     

Estimation of Crop Coefficient and Evapotranspiration of Wheat (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS

Remote sensing and Geographical Information System (GIS) techniques were used to estimate actual crop evapotranspiration of wheat crop grown in Tarafeni South Main Canal (TSMC) irrigation command of West Bengal State in India. The area under wheat crop was clipped from landuse/land cover map generated from Indian Remote Sensing Satellite P6 (IRS P6) image of January, 2004 for winter season 2003–04. The IRS P6 image and four wide field sensor (WiFS) images for different months of winter season were used to determine the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for area under wheat crop. The relationship between vegetation indices and crop coefficients (K_c) of wheat for corresponding months were developed. Based on these developed regression equations crop coefficient maps were generated for each month of wheat crop season. Monthly reference crop evapotranspiration (ETo) was estimated based on FAO-56, Penman–Monteith method. ETo was combined with spatially distributed K_c maps of different months of wheat crop season to generate crop evapotranspiration (ET_c) maps of each month. The crop water demand of wheat estimated using spatially distributed ET_c maps for months of December 2003, January 2004, February 2004, March 2004 (1st Fortnight) and March 2004 (2nd Fortnight) were found to be 3.98, 8.14, 4.66, 2.49, and 1.21 million cubic meter (MCM) respectively. Based on crop evapotranspiration the total crop water demand of wheat crop in irrigation command of TSMC was estimated as 20.48 MCM.     

Application of the Sebs Water Balance Model in Estimating Daily Evapotranspiration and Evaporative Fraction from Remote Sensing Data Over the Nile Delta

Estimation of evapotranspiration is always a major component in water resources management. The reliable estimation of daily evapotranspiration supports decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are used in conjunction with Surface Energy Balance (SEB) models to overcome difficulties in obtaining daily evapotranspiration measurements on a regional scale. In this study the SEB System (SEBS) is used to estimate daily evapotranspiration and evaporative fraction over the Nile Delta along with data acquired by the Advance Along Track Scanning Radiometer (AATSR) and the Medium Spectral Resolution Imaging Spectrometer (MERIS), and six in situ meteorological stations. The simulated daily evapotranspiration values are compared against actual ground-truth data taken from 92 points uniformly distributed all over the study area. The derived maps and the following correlation analysis show strong agreement, demonstrating SEBS’ applicability and accuracy in the estimation of daily evapotranspiration over agricultural areas.     

基于数据挖掘技术的遥感ET数据分析

以北京市房山区为研究区域,利用SAS数据挖掘工具,以遥感监测系统获得的海量ET数据和区域土地利用数据为数据源,分析了不同土地利用类型、不同时期ET的变化趋势及影响因素,结果表明:气温对ET的影响最大,气温和风速与ET呈正相关,降雨和相对湿度与ET呈负相关。SAS数据挖掘工具不仅能快速实现对海量数据的多样化统计分析,而且可以给用户展现丰富的图形,有利于对分析结果的解释和理解。     

Delineation of Flood-Prone Areas Using Remote Sensing Techniques

Flood problems resulting due to heavy rainfall and drainage congestion are being regularly experienced in plain areas of Bihar, India. Due to this problem, power plant located in Koa catchment, Kahalgaon, Bihar, is faces huge loss at the time of flood. In this paper, the flood-affected areas in Koa catchment have been mapped using remote sensing satellite data (IRS LISS III, 1999 and Landsat TM, 1995). A range of image processing techniques has been used, including simple density slicing, Tasseled Cap Transformation and water-specific index. The results obtained using different approaches have been analysed. Result indicates that a Normalized Difference Water Index (NDWI) based approach produced best results.     

Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data

Urmia Lake as a most vital water bodies in Iran, has been shrinking since the late twentieth century and its area has dramatically decreased. To develop and apply any plans to survive the lake, qualitative and quantitative analysis and any modeling, deriving physical information such as volume, area and their changes are very crucial. The objectives of this study were therefore, intended firstly, to study the bathymetry of Urmia Lake with a more satisfactory approach using Landsat- LDCM satellite image and in situ measurement data. The polynomial model was developed to predict the water depth in Urmia Lake area. This model was developed with the input series of reflectance values from blue, green, red and NIR bands in the Landsat- LDCM satellite imagery for Urmia Lake taken on 12 April 2013 of the sampling sites from actual depth measured were taken on the same date. Also, using a large archive of Landsat imagery (TM, ETM+ and LDCM), a counter of equivalent elevation were established for deriving the bathymetry of desiccated areas by mapping the edges of the lake and finally assembled with bathymetry derived from polynomial model. In-situ depth measurements were used to evaluate resultant derived bathymetric map. This comparison shows reasonable agreement between the Landsat-derived depths and those measured in the field with RMSE of 0.27 cm and R² = 0.91. The maximum and mean depths measured were 4.9 and 11 m respectively. The maximum depth measured was located at the upper part of the lake. As well as, developed multi-regression equation used for deriving another bathymetry map using Landsat- LDCM satellite image taken on Sep. 2015 for salt deposition monitoring. Results indicates that about 64 cm salt deposition has occurred during the last two years. Secondly, to make stage curves of lake, multi-temporal changes of water body have been derived from Landsat, MODIS and AVHRR satellite images sets since 1972. In this regard, the area of Urmia Lake at different level was estimated base on object oriented and pixel base classification using 78 satellite images. Finally, stage curve (volume- area- level relations) was derived from bathymetry map.     

基于高光谱数据的汉江中下游典型河段水体悬浮物遥感反演

选择汉江中下游典型河段作为研究区域,利用2012—2013年实测水质数据及高光谱数据,基于有效信息变量筛选和神经网络算法构建研究区水体悬浮物浓度高光谱反演模型,分析评价了模型性能与估测效果,讨论了研究区水体悬浮物浓度分布特征。研究结果表明:基于变量投影重要性指数和神经网络优势构建的高光谱反演模型在反演精度、稳定性和适应性方面表现出优异的性能;而对于基于简单相关性分析构建的单波段模型和比值模型而言,建模样本选择对模型精度有较大影响,导致模型反演精度、稳定性和适用性较差;汉江中下游典型河段水体总悬浮物浓度整体上在18.8~187.0 mg/L之间变化,季节性差异明显,即春、夏两季悬浮物浓度低于秋季。     

多源卫星遥感数据和水色遥感技术在鹤地水库采砂监察中的应用

近年来,鹤地水库的采砂活动频繁,相关管理部分迫切希望了解采砂活动在水库库区的时空分布,以指导库区的管护和执法活动。该基于多源卫星遥感数据和水色遥感技术对鹤地水库悬浮泥沙浓度进行了分析,结合现场调研,发现悬浮物浓度高值区存在采砂行为。认为基于多源卫星遥感数据和水色遥感技术可对水库的采砂行为时空变化进行监察,辅助库区管理人员执法。     

多光谱遥感数据最佳波段选择研究

分析多光谱遥感数据最佳波段选择的最佳指数方法,并指出该方法的不足之处.在实践过程中,根据多光谱遥感数据各波段用途、研究区地物的光谱特征及最佳指数来考虑最佳波段的选择,取得了比较好的效果.     

Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques

The objective of this study is to develop soft computing and data reconstruction techniques for modeling monthly California Irrigation Management Information System (CIMIS) evapotranspiration (ET_o) at two stations, U.C. Riverside and Durham, in California. The nonlinear dynamics of monthly CIMIS ET_o is examined using autocorrelation function, phase space reconstruction, and close returns plot. The generalized regression neural networks and genetic algorithm (GRNN-GA) conjunction model is developed for modeling monthly CIMIS ET_o. Among different input variables considered, solar radiation (RAD) is found to be the most effective variable for modeling monthly CIMIS ET_o using GRNN-GA for both stations. Adding other input variables to the best 1-input combination improves the model performance. The generalized regression neural networks and backpropagation algorithm (GRNN-BP) conjunction model is compared with the results of GRNN-GA for modeling monthly CIMIS ET_o. Two bootstrap resampling methods are implemented to reconstruct the training data. Method 1 (1-BGRNN-GA) employs simple extensions of training data using the bootstrap resampling method. For each training data, method 2 (2-BGRNN-GA) uses individual bootstrap resampling of original training data. Results indicate that Method 2 (2-BGRNN-GA) improves modeling of monthly CIMIS ET_o and is more stable and reliable than are GRNN-GA, GRNN-BP, and Method 1 (1-BGRNN-GA).     

基于多源遥感影像的武汉都市发展区湖泊变迁分析

城市湖泊作为一种重要的生态和旅游资源,其变迁趋势对城市的发展影响深远。为研究武汉都市发展区湖泊变迁情况,引入2013年6月获取的Landsat 8卫星数据,结合都市发展区1975—2013年近40 a 30多期遥感影像作为数据源,利用NDWI和谱间关系法,结合目视解译的方法提取区域内68个湖泊的时空信息,详细研究湖泊面和湖泊边界线的变化情况,基于GIS的空间分析技术和RS的变化检测的方法建立湖泊变迁专题图,并利用景观学指标分析湖泊变化原因。结果表明研究与武汉市政府水资源统计信息相符,为武汉市湖泊资源的保护和可持续发展提供了即时依据。     

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。     

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.     

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。     

水利遥感数据共享平台的高性能运行机制研究

随着遥感技术在水利应用上的逐步推进,海量多源遥感数据的低处理效率和共享程度成为该技术应用瓶颈。借鉴国内外现有技术经验,结合水利信息聚合应用特点,分析了解决该问题的空间数据密集和并行计算、快速检索与传输等几个关键支撑技术。通过对现有系统集成和应用模式的剖析,在理论上提出该运行框架的初步设计,为水利遥感数据中心的建设提供参考和建议。     

Coastal Urban Flood Simulation Using FEM,GIS and Remote Sensing

In this paper, a rainfall runoff model for coastal urban watershed considering the effects of tidal variations using Finite Element Method (FEM) is presented. Overland flow is modeled using the mass balance equation considering the impervious character of the urban watershed. Storm water flow through the channel is modeled using the diffusion wave form of the Saint Venant’s equations and considering the tidal variations. Galerkin’s FEM is used in the approximation of the governing equations. One dimensional linear line elements are used in the channel discretization. Further the mass balance based overland flow model and diffusion wave based channel flow model have been integrated for prediction of flood. Slope values for the overland flow are determined using the Geographical Information System (GIS) from the Digital Elevation Model (DEM) of the area. The landuse is determined using the remote sensing data. Remote sensing data is analyzed using the ERDAS Imagine and ArcGIS and the Manning’s roughness is calculated for each subdivision of subcatchment. The developed models have been verified with the models available in literature and are found to be satisfactory. Further, the integrated model has been applied to the runoff simulation of a coastal urban watershed in Navi Mumbai, in Maharashtra state of India to analyze the flooding in monsoon season along with the tidal influences. The model could satisfactorily predict the runoff due to monsoon rains coupled with the tidal variations. The developed model will be useful in the urban coastal flood analysis due to heavy rainfall and tidal effects.     

基于遥感大数据的安阳河流域洪水预报技术设计

在分析河南省安阳市安阳河流域洪水预报预警实际需求的基础上,针对流域目前已经建成的覆盖全流域的山洪灾害实时监测预报预警系统范围主要局限于受山洪影响的区域,而无法对受江河洪水影响以及受沿江梯级电站调度影响区域洪水灾害进行实时监测、预报及预警的客观实际,综合卫星遥感大数据降水预报技术以及GIS产汇流模型、BTOPMC分布式水文物理模型等现有流域洪水预报技术,提出的基于遥感大数据的安阳河流域洪水预报技术模型,该模型在2021年汛期发挥了十分显著的洪水测报功能,为流域带来显著的经济效益和社会效益。     

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.