Evaluation of EO-1 hyperion data for agricultural applications

The present study was carried out to evaluate the satellite-based hyperspectral data available from Hyperion onboard EO-1 of NASA for agricultural applications. The study was carried out for Daurala block of Meerut district, using data of March 2005. The preliminary data analysis showed that there are 196 usable bands out of a total of 242 bands. Principal component (PC) analysis showed that about 99% of the information explained in 10 PCs. The atmospherically corrected reflectance, derived from satellite data had good agreement with the ground reflectance, observed using handheld spectroradiometer, with r² ranging from 0.85 to 0.98. A set of twenty most usable bands was selected by the criteria of maximum contribution to first five PCs and the band combinations with least inter-band correlations.     

Estimation and comparison of leaf area index of agricultural crops using irs liss-III and EO-1 hyperion images

Motivated by the increasingly availability and importance of hyperspectral remote sensing data, this study aims to determine whether current generation narrowband hyperspectral remote sensing data could be used to estimate vegetation Leaf Area Index (LAI) accurately than the traditional broadband multispectral data. A comparative study has been carried out to evaluate the performance of the narrowband Normalized Difference Vegetation Index (NDV1) derived from Hyperion hyperspectral sensor with that of derived from IRS LISS-III for the estimation of LAI of some major agricultural crops (e.g. cotton, sugarcane and rice) in part of Guntur district, India. It has been found that the narrowband NDVI derived from Hyperion has shown better results over its counterpart derived from broadband LISS-III. Linear regression models have been used which with selected subsets of individual Hyperion bands performed better to predict LAI than those based on the broadband datasets, although the potential to overfit models using the large number of available Hyperion bands is a concern for further research.     

Multiscale quantification of urban composition from EO-1/Hyperion data using object-based spectral unmixing

Quantification of the urban composition is important in urban planning and management. Previous research has primarily focused on unmixing medium-spatial resolution multispectral imagery using spectral mixture analysis (SMA) in order to estimate the abundance of urban components. For this study an object-based multiple endmember spectral mixture analysis (MESMA) approach was applied to unmix the 30-m Earth Observing-1 (EO-1)/Hyperion hyperspectral imagery. The abundance of two physical urban components (vegetation and impervious surface) was estimated and mapped at multiple scales and two defined geographic zones. The estimation results were validated by a reference dataset generated from fine spatial resolution aerial photography. The object-based MESMA approach was compared with its corresponding pixel-based one, and EO-1/Hyperion hyperspectral data was compared with the simulated EO-1/Advanced Land Imager (ALI) multispectral data in the unmixing modeling. The pros and cons of the object-based MESMA were evaluated. The result illustrates that the object-based MESMA is promising for unmixing the medium-spatial resolution hyperspectral imagery to quantify the urban composition, and it is an attractive alternative to the traditional pixel-based mixture analysis for various applications.     

Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans

In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels.     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

基于对象关系数据库的空间数据模型研究

分析了面向对象关系模型的数据结构,对Oracle Spatial组件中空间数据存储结构及对象关系数据模型进行了分析,分别给出了Oracle数据库中使用对象关系数据模型存储空间数据的构建方法,以及地图索引、元数据表的构建方法.     

运动目标轨迹的时空数据模型研究

运动目标的轨迹数据是空间信息系统的重要信息源。只有高效地利用运动目标的轨迹数据,才能更好地掌握多维动态变化的空间环境。本文在介绍了运动目标的概念和国内外研究现状之后,重点分析了运动目标的建模、存储、查询、索引及可视化技术并提出相应的解决方案。最终,实现对运动目标全过程的管理和应用。     

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACT

We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.     

Crop acreage estimation using ERS-1 SAR data

Synthetic Aperture Radar (SAR) data from the European Remote Sensing Satellite (ERS-1) acquired in July, October and November, 1992 covering the kharif season of the region were used separately and in combination to identify the major crops and for estimation of their acreage before harvest Separability indices were calculated for major cover types and it was found that single-date SAR data cannot be used for accurate identification of various crops. Multi-temporal colour composite facilitated better identification of crop types. Comparison of area estimates made with ERS-1 SAR and IRS-1B LISS II data showed that the commonly used digital data analysis techniques (per pixel classifiers) are not adequate for accurate estimation of crop acreage using SAR data.     

D-InSAR技术用于西安地区地面沉降监测的研究

InSAR技术是20世纪后期发展起来的一种新的测量方法,由于其具有能够实现全天时、全天候的对地观测,现已成为空间对地观测技术的研究热点。D-InSAR技术是在InSAR技术基础上发展起来的一种可以用来检测地面微小运动的新技术,其可以监测厘米级,甚至毫米级形变的精度。文中分析了D-InSAR技术用于形变监测中存在的误差及其减弱措施,并对该技术在西安地区的应用作了相应的分析。     

基于三维激光扫描数据的三维实景构建

利用三维激光扫描仪获取的空间数据,进行三维实景构建研究.首先对扫描仪获取的数据按照基于格网的方法进行压缩,然后对压缩后的数据建立三角网数字地面模型,最后将原始空间数据按照一定的拓扑结构映射到二维球面(α,θ面)并在α,θ面等角划分形成二维矩阵,以落在矩阵各栅格内的点云强度均值作为该处像素值以图像形式显示,再同实际影像比较,选出同名点,根据摄影测量后方交会原理计算得到影像的内外方位元素,由得到的内外方位元素计算出相应的纹理坐标,完成纹理映射过程.文中以Trimble地面三维激光扫描仪为例以地物作为采集对象,用Visual Studi0 6.0作为开发平台进行三维实景构建.     

Studies on object reconstruction from space using three-line scanner imagery

The paper investigates the capability of object reconstruction from space for two specific projects. Both of them are based on the use of three-line scanner systems allowing for simultaneous digital image recording from three different angles. An efficient model for combined point determination is proposed, considering the digital image data as well as orbit information, GPS and INS observations. The resulting accuracy is estimated from project oriented simulations. Finally, the possibilities for DTM generation, orthophoto production and stereo compilation from three-line scanner imagery are discussed.     

基于GML3.2的对象化空间数据组织与关系表达研究

数据组织是GIS系统建设的基础和前提,随着对地理世界认知的不断深入,逐渐形成了基于特征的空间数据模型。以基于特征的空间数据组织基本理论为基础,文章主要讨论基于GML3.2的空间数据组织与关系表达的关键问题。首先,简要介绍GML的基本概念,并详细阐述GML3.2的主要模式。在此基础上,讨论在GML应用模式设计中需要注意的相关原则。最后,总结探讨了实现基于GML3.2的对象化空间数据组织与关系表达的途径。     

Detection of sub-surface waterlogging using Terra-1 MODIS data

Waterlogging due to rising ground watertable, being a sub-surface phenomenon, is not amenable to detection by optical remote sensing. Microwave and thermal sensor data have, however, shown some promise in the detection of sub-surface waterlogging. The present study was taken up to evaluate the potential of near-IR, short-wave IR (SWIR) and thermal-IR data from Moderate Resolution Imaging Spectrometer (MODIS) aboard Terra-1 acquired during day-and-night time postmonsoon data for detection of sub-surface waterlogging. The approach involves retrieval of day-and-night land surface temperature (LST), generation of normalized difference of channel-2 and 6 (ND₂₆); 2 and 7 (ND₂₇); ground truth collection involving concurrent ground water table observations to satellite date of pass, thresholding of normalized differences (NDs) and correlating the NDs with depth of ground water table. Amongst various spectral indices, day and night-time LST difference (DLST) and night-time LST have been found to correlate well with the incidence of waterlogging (water table depth < 2m), followed by normalized difference of band-2 (841–876 nm) and band-7 (2105–2155 nm). The sensitivity of threshold limits for these indices was maximum for DLST followed by ND₂₆ and ND₂₇. Poor accuracy of detecting sub-surface waterlogging with thermal bands during day time is attributed to the non-corresponding of the time of Terra MODIS data acquisitions with thermal maxima of the terrain. Though the ND₂₇ gave better accuracy to detect subsurface waterlogging, it is very sensitive to threshold limits.     

利用GPS快速标定发射阵地的应用研究

根据实际情况提出了更合理的导弹发射阵地标定方案，研究了基于伪距单点定位的精度提高方法，介绍了基线向量解算，给出了GPS定向的基本原理和定向精度的误差分析，最后推导出由定向精度指标反算基线长度的公式。     

基于HJ-1A/1B数据的高寒草地牧草营养动态监测模型

为了掌控高寒草地全年的营养状况与更好地利用草地和草畜平衡,该文基于HJ-1A/1B多光谱CCD数据,通过监测草地牧草全年4期营养动态和分析对应时期的遥感指数,建立了直接和间接的遥感动态对比模型,获得了草地牧草4期生物量和粗蛋白含量.结果表明:基于HJ-1A/1B多时相、多光谱数据,利用间接模型反演4期草地牧草生物量和粗蛋白是一种可行的方法,尤其在枯草期和返青期;草地牧草全年营养状况变化很大,最大相差约9倍.     

茂县滑坡形变的Sentinel-1数据分析

为了获取2017年6月24日四川省茂县特大滑坡滑前的形变信息并分析其诱发因素,该文利用覆盖滑坡区域滑前40景Sentinel-1数据,采用合成孔径雷达干涉点目标分析(IPTA)技术,解算出该滑坡滑前2014年10月9日至2017年6月19日期间形变的年速率及形变的时间序列,并与降雨数据进行了对比分析。对于滑坡区植被覆盖严重的问题,该文利用永久散射体目标(PSC)对影像配准窗口大小及过采样因子不敏感的特性来识别PS点。分析结果表明,茂县滑坡在滑动前两年多时间内具有明显的形变,最大形变速率达到4(cm·a-1),而且在滑坡发生前半个月内形变有突然增大的现象,推测该滑坡滑前持续的强降雨是导致滑坡失稳的主要诱因。     

Reclamative grouping of ravines using Cartosat-1 PAN stereo data

Information on the depth and bed width of ravines (network of gullies) at large scales is critical for their reclamation and management. Hitherto such information has been generated from aerial photographs and space borne stereo images with medium to coarse ‘z’ – axis resolution. The present study, aims at demonstrating the potential of Cartosat ?1 (an Indian Earth observations satellite) stereo images with 2.5 m spatial resolution in deriving morphometric information on ravines for their reclamative grouping. The study area is a part of Jhansi and Hamirpur districts of Uttar Pradesh, northern India. The approach involves acquiring precise ground control points using Differential GPS (DGPS), triangulation, DEM extraction and generation of ortho image as well as anaglyphs for stereo viewing. The depth and bed width of ravines were measured in the field for validation. A comparison with field observations reveal that the bed width of ravines and depth can be measured successfully with Carto-1 stereo data. The anaglyph data was used to delineate various categories of ravines based on their depth and bed width. Results indicate that the Cartosat-1 stereo images are quite suitable for delineation of three categories of ravines, namely shallow (<3 m deep and <18 m bed width), medium deep (3–9 m deep and >18 m bed width) and deep (>9 m deep) which are important for their reclamation.