Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard

In this work we have developed a theoretical model that helps the interpretation of the remotely sensed thermal infrared measurements carried out over citrus orchards. A detailed analysis of the different factors which take part in the definition of the effective emissivity and temperature (observation height, viewing angle, type of soil, dimensions and separation between orange trees) is made. The model was validated under vertical observation in a citrus orchard during seven nights. In this situation we have determined that the model performs to an accuracy of about 1%.     

Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model

Cloud cover is generally present in remotely sensed images, which limits the potential of the images for ground information extraction. Therefore, removing the clouds and recovering the ground information for the cloud-contaminated images is often necessary in many applications. In this paper, an effective method based on similar pixel replacement is developed to solve this task. A missing pixel is filled using an appropriate similar pixel within the remaining region of the target image. A multitemporal image is used as the guidance to locate the similar pixels. A pixel-offset based spatio-temporal Markov random fields (MRF) global function is built to find the most suitable similar pixel. The proposed method was tested on MODIS and Landsat images and their land surface temperature products, and the experiments verify that the proposed method can achieve highly accurate results and is effective at dealing with the obvious atmospheric and seasonal differences between multitemporal images.     

Farm-Level Yield mapping for precision crop management by linking remote sensing inputs and a crop simulation model

A study aimed at generating wheat yield maps of farmer’s fields by using remote sensing (RS) inputs was undertaken during the rabi season of 1998-99 in six villages of Alipur Block of Delhi State. RS derived leaf area index (LAI) were linked to wheat simulation model WTGROWS by adopting a strategy christened “Modified Corrective Approach”. This essentially uses an empirical relation of grain yield and LAI, which was derived from WTGROWS simulation model by running model for a combination of input resources, management practices and soil types occurring in the area. This biometric relationship was applied to all the wheat fields of the study area for which the LAI was derived from single acquisition of IRS LISS-III data (Jan 27, 99). The LAI-NDVI relation adopted was logarithmic in nature (R²=0.83) and was based on ground measurements of LAI in farmer’s fields in the same area. A comparison of predicted grain yield by the modified corrective approach and actual observed yield for the 22 farmer’s fields showed high correlation coefficient of 0.8 and a root mean square error (RMSE) of 597 kg ha^-1 which was 17% of the observed mean yield. Thus linking of RS information and crop simulation model provides an alternative for mapping and forecasting crop yield under highly variable cropping environment of Indian farms, which is a pre-requisite for implementing Precision Crop Management (PCM).     

Impact of discretization methods on the rough set-based classification of remotely sensed images

Abstract

In recent years, the rough set (RS) method has been in common use for remote-sensing classification, which provides one of the techniques of information extraction for Digital Earth. The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification. Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification. To assess the performance of discretization methods this article adopts three indicators, which are the compression capability indicator (CCI), consistency indicator (CI), and number of the cut points (NCP). An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods. To investigate the effectiveness of our method, this article applies three discretization methods of the Entropy/MDL, Naive, and SemiNaive to a TM image and three indicators for these discretization methods are then calculated. After comparing the three indicators and the classification accuracies of the discretized remotely sensed images, it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.     

The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery

Because atmospheric effects can have a significant impact on the data obtained from multi-spectral satellite remote sensing, it is frequently necessary to make corrections before any other image processing can be started. This paper describes a robust and relatively simple atmospheric correction method that uses pseudo-invariant targets (PITs) in conjunction with the empirical line method. The method is based on the selection of a number of suitable generic PITs, on the basis that they are large, distinctive in shape, and occur in many geographical areas. Whereas the multi-temporal normalization method corrects all images to a selected reference image, in this method images are simultaneously corrected using targets with a range of estimated surface reflectance values. The paper describes some applications of the method for a range of environmental studies involving water quality and air pollution monitoring, and mapping land-cover changes.     

Characterizing hydrothermal alteration zones in Hamama area in the central Eastern Desert of Egypt by remotely sensed data

Abstract

Remote sensing techniques provide meaningful information to mineral exploration by identifying the hydrothermally altered minerals and the fracture/fault systems. In this article, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed to detect the hydrothermal alteration zones in Hamama area in the central part of the Eastern Desert of Egypt. Band ratios and principal component analyses successfully revealed the extent and the geometry of the hydrothermal alteration zones that trend in an NE–SW direction. Matching pixel spectrum derived from Minimum Noise Fraction, Pixel Purity Index, and n-dimensional visualization with reference spectra allowed characterizing key hydrothermal alteration minerals, including chlorite, kaolinite-smectite, muscovite, and haematite, in a successive alteration pattern. Field investigations and X-Ray Diffraction analysis validated the results revealed by ASTER data. In addition, the present prospects of significant gold and massive sulphide mineralizations are consistent with the detected hydrothermal alteration zone.     

Statistical analysis and tectonic interpretation of the remotely sensed lineament fabric data associated with the North Almora Thrust,Garhwal Himalaya,India

A geostatistical analysis of the remotely-sensed lineament fabric data associated with the North Almora Thrust of Garhwal Himalaya has been carried out The analysis of data provided 18 major orientations of the lineament density girdles. Further statistical treatment of these derived lineaments using the method of analysts of directional data indicates that the lineaments can be classified into genetic groups. The result indicates that the North Almora Thrust and Kaliasaur Fault are not genetically related and occurred in separate phases of tectonic activities. The results also show that the Alaknanda river is flowing through a tectonically-controlled valley. The study suggests the suitability of statistical methods in lineament-fabric-data analysis for tectonic interpretations.     

The geology and structure of the area south of Gani,Kurnool District,Andhra Pradesh — a study based on remotely sensed data

Geological investigations in the area south of Gani, Kurnool District, Andhra Pradesh, were carried out with the help of aerial Photographs and multiband satellite imageries. The rocks of the Cuddapah Group and the Kurnool Group are exposed in the area under study. The Gani-Kalava anticline and the ENE-WSW trending Kalava fault are the important structural features of the area. The anticlinal structure could be better studied using the aerial photographs, while the fault is clearer on the imagery. Small scale folds involving the Cuddapahs and the Kurnools along the northern limb of the fold could be mapped using the aerial photographs. The Gani-Kalava anticline is a post-Kurnool structure, formed by movement along the Kalava fault and in the first instance the minor folds appear to be drags on the northern limb. However, certain structural and stratigraphic relations suggest that the folding in the Cuddapahs may be a pre-Kurnool phenomenon.     

Simulating the effects of land use changes on soil erosion using RUSLE model

The aim of this paper is to evaluate the impacts of land use change on soil loss. Soil loss was quantified using the revised universal soil loss equation model in Darabkola catchment. Land use maps of 1992, 1998 and 2012 were derived from Landsat Thematic Mapper data. The mean annual soil loss was therefore determined for these years. The results showed open-canopy forest area decreased by 36% between 1992 and 1998. Likewise, the decreasing trend of forest lands which are near to residential areas has continued from 1795 ha in 1998 to 1765 ha in 2012. Also the results indicate that the maximum annual soil loss ranged from 5.06, 6.19 and 15.23 ton h^?1 y^?1 in 1992, 1998 and 2012, respectively. Also, by assuming that all watershed conditions and land uses be constant in the future, then the area of close- and open-canopy forest and dry agricultural lands will be 23.23, 2.88 and 29.89 ha in 2040, respectively.     

Comparing the Use of Remote Sensing with Traditional Techniques to Detect Nitrogen Stress in Wheat

Abstract

An experiment was designed to compare ground‐based methods of nitrogen (N) stress detection with N stress detection using remote sensing. The study area, located in Minidoka, Idaho, is a 64 ha center pivot with a crop of Penawawa spring white wheat. Nitrogen was varied on four transects approximately 40 m wide and 805 m long. The N application rates chosen for the research were 0%, 40%, 100%, and 130% of normal. Nitrogen deficiency was quantified from tissue sampling, which was used as the response variable. Nitrogen was then measured or estimated at key stages in the wheat growth cycle using visual observation, a chlorophyll meter, and remotely‐sensed data. Visual observation was the normally‐employed method for area farmers. These methods of N stress detection were compared for accuracy, timeliness, usefulness, and cost. Remote sensing was comparable to the chlorophyll meter in accuracy. The chlorophyll meter was the timeliest method for obtaining a quantitative measurement.     

基于CA模型的防城港海岸线预测模拟

将多时相卫星遥感图像叠加技术与GIS空间分析方法相结合,对研究防城港海岸线演变特征及预测海岸线变化趋势具有重要的现实意义.首先,对防城港2000年11月及2010年10月获取的2个时相TM图像进行预处理、海岸线特征信息提取及空间分析;然后,根据相关控制因素及相应的邻居规则建立元胞自动机(cellular automata,CA)模型,利用蒙特·卡罗方法(Monte Carlo method)结合控制因素进行判断,最终确定元胞的转化状态.通过2010年实际海岸线与预测海岸线的叠置分析得知,2010年预测海岸线的数量精度为83.65％,空间位置精度为93.45％,都在误差允许范围内,证明利用蒙特·卡罗CA模型预测海岸线的方法是可行的.最后,结合CA模型算法及Matlab仿真技术实现了对2020年防城港海岸线的预测.     

基于激光扫描技术的变电站三维模型的建立

通过利用地面三维激光扫描仪获取的空间数据,进行惠泉变电站三维空间地物建模研究,建立三维仿真模型。通过研究发现,利用三维激光扫描技术可以快速建立三维空间的可视化模型,解决传统的测量仪器与测量方法在变电站等特殊领域的应用不足,为实现变电站资源、景观、安全、环境管理等社会资源的数字化、网络化及动态可视化,提供一个变电站的三维模型平台构建的新方向。     

城市空间框架数据库建设研究与应用

随着信息化的蓬勃发展及数字城市建设的需要,城市地理空间框架建设势在必行,其数据库建设作为整个框架建设的先导,必须建设合理的数据库模式才能将框架建设成功实现。文中重点探讨了数据库的建设原则及面向实体空间数据模型,并通过该模型设计出了数字城市地理空间框架数据库的建设模式,并于文章最后给出了在地籍信息管理系统运用该数据库模型的应用实例。     

基于CLUE-S模型的南京市土地利用变化模拟

本文以南京市为研究区,以南京市1986年的土地利用现状图为基础,分析研究区概况并根据数据的可获取性,选取13类土地利用变化驱动因素,利用逻辑斯蒂回归分析求解土地利用变化驱动因素作用系数矩阵。在此基础上运行CLUE-S模型,对南京市1996年的土地利用空间格局进行模拟。将模拟结果与南京市1996年土地利用现状图与进行对比,结果较为理想,模拟正确率达88.57%,KAPPA指数0.86。这说明CLUE-S模型具有成功模拟区域土地利用时空动态变化的能力,对土地利用预测、规划具有重要的指导作用。     

人口密度模型与CA集成的城市化时空模拟实验

有机地集成城市经典模型与元胞自动机 (CA)是一种有意义的理论实验。本文分无约束和有约束两种条件构建了城市化CA模型 ,推导出了基于均质地理背景和孤立城市的假设的城市人口密度时空模型 ,并进行了二者集成的实验研究 ,得出了如下结论 :(1)CA是城市化时空模拟的有效方法 ;(2 )经典的地理、城市模型可以有效地集成到城市化CA模型中 ,起到控制城市化轨迹的基本作用 ,在某种程度上能够弥补CA建模过于简单的不足。     

基于混合模型的平面坐标转换方法研究

介绍平面坐标转换的仿射变换和二次多项式变换法,这两种方法存在一定的不足,为了提高转换精度,提出"仿射变换+神经网络"的混合模型法。通过工程实例,比较发现混合模型比前两种模型的计算精度高,证明该方法的有效性,并得到具有工程应用价值的结论。     

模拟多光谱的土壤重金属含量反演研究初探

根据多光谱传感器的光谱响应函数,采用实测ISI921VF反射光谱数据模拟Landsat卫星ETM+传感器多光谱数据,在模拟光谱的基础上,通过光谱特征提取、构建土壤指数对土壤重金属Cu,Pb,As进行预测分析。研究显示,Cu,Pb与模拟ETM+光谱的B2,B3波段显著相关,As与DSI,RSI,NDSI相关系数在0.6以上,基于模拟多光谱建立的Cu,As模型精度较高,平均相对误差分别为7.9%,2.7%,表明模拟的Landsat卫星ETM+传感器多光谱具有预测耕地土壤重金属的潜力,为实现大范围监测土壤重金属污染提供新思路。     

基于SLR的GRACE卫星定轨中重力场模型对轨道精度的影响

以GRACE卫星为例,分析比较利用SLR观测资料进行卫星定轨时,采用不同重力场模型对GRACE卫星定轨精度的影响;以及重力场截断阶引起的积分轨道差异;同时,将定轨结果与采用GPS确定的定轨结果进行比较,分析与GPS定轨结果的差异.实验证明.重力场模型选择GGM02C的定轨结果优于选择JGM-3的定轨结果,基于SLR的定...     

递进式预测模型在滑坡变形预测中的应用

为达到提高滑坡变形预测精度的目的,利用量子算法和粒子群算法对支持向量机进行优化,并利用马尔科夫链对滑坡变形预测误差进行修正,综合构建滑坡变形的递进式预测模型。结果表明:通过量子算法及粒子群算法对支持向量机优化,克服支持向量机参数选取困难,实现预测过程的全局优化,并经过MC误差修正模型对滑坡变形预测值误差修正,提高预测精度及预测值稳定性,验证预测模型可行性和有效性,为滑坡变形预测提供一种新的预测方法。     

基于非等间距模型的建筑物沉降预测方法研究

该文基于实测资料进行建筑物沉降预测。在灰色模型和泊松曲线模型理论的基础上,引入对非等间距数列进行变换处理的方法,从而建立了非等间距预测模型。结合建筑物沉降监测资料进行分析比较,结果表明,两种预测方法均能较好地反映建筑物的沉降趋势。