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.     

决策级融合的多分辨率遥感影像变化检测

针对多时相、多分辨率遥感影像数据的特点,充分考虑不同分辨率数据和不同变化检测应用的需求,将由粗到精数据集分层检测和决策级融合的思想引入到变化检测,以多时相多分辨率ALOS遥感影像为例,构建并试验了由粗到精变化检测的技术流程.该方法将ALOS多光谱数据视为粗数据集,将全色数据和融合数据视为精数据集,通过对3种数据集变化检...     

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.     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

基于频域滤波的高分辨率遥感图像城市河道信息提取

提出一种基于频域滤波的城市河道信息提取方法。首先对高分辨率遥感图像进行傅里叶变换得到频谱图, 并利用径向和角向分布图分析城市河道的频谱特征。其次, 基于城市河道的双线型特点, 将其分为边缘特征和低频信息两个部分, 并根据周期性纹理的频谱模型和地物频谱能量分布规律确定两个部分的频域识别标志。然后设计相应的扇环形带通log Butterworth滤波器和低通Butterworth滤波器分别对城市河道的边缘特征和低频信息进行提取, 并根据该两部分信息实现城市河道信息提取。最后对城市河道信息提取结果进行定量评价, 结果表明, 本文方法可以有效地实现城市河道的信息提取。     

利用遥感瞬时温度场研究云南山地气温直减率

基于MODIS影像反演地表瞬时温度场和气象站点30年平均气温实测数据,结合数字高程模型,研究了云南山地气温随海拔变化的规律。结果表明,降低或消除了坡向与坡度、纬度与经度变化的影响后,在云南全区域尺度上,海拔对气温作用而产生的气温直减率为0.53℃;在典型山地地貌类型区,山地气温直减率由高山区向低山区逐渐增大,滇西北高山区直减率为0.47℃,滇中、滇东中山区为0.51℃,滇南宽谷低山区为0.54℃,滇西北高山峡谷和滇南低山深切沟谷地带为0.54℃;由阳坡、半阴半阳坡至阴坡,山地气温直减率依次增大:阳坡直减率平均为0.52℃,半阴半阳坡平均为0.54℃,阴坡平均为0.55℃。除海拔之外,坡向、坡度等微地形因子亦是微格局气温场的主要地理响应因子。利用遥感数据反演的瞬时温度场研究山地气温直减率,是一种研究山地气温变化规律的新方法。     

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.     

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.     

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.     

基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究

本文以LAI作为结合点，讨论了利用复合型混合演化（SCE—UA）算法实现CERES—Wheat模型与遥感数据同化的可行性。CERES—Wheat模型同化后主要生育期和产量的模拟值分别与真实条件下模型相应模拟值以及实测值进行比较。结果表明，同化后CERES—Wheat模型的模拟精度对LAI外部同化数据的误差并不十分敏感。并且在LAI同化数据较少时，也可获得较好的同化结果。这一特点体现了SCE—UA算法应用于同化过程的优越性，为同化策略在区域冬小麦长势监测及估产中的应用提供了基础。     

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。