Relative performance of landsat MSS,TM and IRS LISS-I data for acreage estimation of Rabi sorghum

Acreage estimation of Rabi sorghum crop in Ahmadnagar, Pune and Solapur districts of central Maharashtra has been attempted using synchronously acquired Landsat MSS and TM data of 1987–88 season and IRS LISS-I data of 1988–89 season; in conjuction with near-synchronous ground truth data. The remote-sensing-based acreage estimations for the districts were compared with the respective estimates by Bureau of Economics and Statistics (BES). As the acreages were underestimated with the classification of standard four-band MSS data, the atmospheric correction of fourband MSS data and normalised differencing (ND) of the atmospheric-corrected MSS data were attempted. The main observations are: (1) the use of Landsat MSS data results in underestimation of sorghum acreage in comparison with BES estimation, (2) the atmospheric correction and ND transformation of MSS data are necessary for bringing acreage estimates in agreement with BES estimates, (3) Mid-IR data in band 1.55 to 1.75 μm are useful in improving the separability of land-use classes, and (4) remote sensing data with radiometric sensitivity comparable to LISS-I or Landsat TM and Signal-to-Noise ratios comparable to LISS-I data are suitable for accurate acreage estimation of sorghum.     

Multi temporal ERS-1 SAR data for identification of rice crop

Microwave sensors having all-weather capabilities provide an opportunity to monitor rice grown in monsoon season. An attempt has been made to identify rice crop using multitemporal ERS-1 SAR data in C-band (5.3 GHz). Data acquired on August 15 (D1), September 19 (D2), October 24 (D3) and November 28 (D4) 1993 were taken. Combinations of data acquired on different dates were used for identification of rice crop. Single-date IRS-1B LISS II data in visible and NIR bands acquired on October 23, 1993 was also used for comparison of estimated rice area. Analysis of the results has shown that a combination of SAR data acquired at the tillering (August), booting (September) and heading (October) stages of rice crop enabled identification and area estimation of rice crop grown under lowland conditions. Single-date SAR data acquired in the month of October was found to be better for identification of rice compared to other dates.     

ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation

Fusion of optical and synthetic aperture radar data has been attempted in the present study for mapping of various lithologic units over a part of the Singhbhum Shear Zone (SSZ) and its surroundings. ERS-2 SAR data over the study area has been enhanced using Fast Fourier Transformation (FFT) based filtering approach, and also using Frost filtering technique. Both the enhanced SAR imagery have been then separately fused with histogram equalized IRS-1C LISS III image using Principal Component Analysis (PCA) technique. Later, Feature-oriented Principal Components Selection (FPCS) technique has been applied to generate False Color Composite (FCC) images, from which corresponding geological maps have been prepared. Finally, GIS techniques have been successfully used for change detection analysis in the lithological interpretation between the published geological map and the fusion based geological maps. In general, there is good agreement between these maps over a large portion of the study area. Based on the change detection studies, few areas could be identified which need attention for further detailed ground-based geological studies.     

Study of dems derived from ERS-1/2 SAR and SRTM data

The DEM of the Bhuj earthquake affected area of 50 x 50 km was generated using the ERS-1/2 SAR tandem data (May 15—16,1996). Region growing algorithm coupled with path following approach was used for phase unwrapping. Phase to height conversion was done using D-GPS control points. Geocoding was done using GAMMA software. A sample data of DEM of Shuttle Radar Topography Mission (SRTM) of the Bhuj area is made available by DLR Germany. The intensity image, DEM and Error map are well registered. The spatial resolution of this DEM is about 25 m with height accuracy of a few meters. The DEM derived through ERS SAR data is prone to atmospheric affects as the required two images are acquired in different timings where as SRTM acquired the two images simultaneously. An RMS height error of 12.06 m is observed with reference to SRTM though some of the individual locations differ by as much as 35 m.     

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.     

Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data

In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research.     

Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

A vast portion of Newfoundland and Labrador (NL) is covered by wetland areas. Notably, it is the only province in Atlantic Canada that does not have a wetland inventory system. Wetlands are important areas of research because they play a pivotal role in ecological conservation and impact human activities in the province. Therefore, classifying wetland types and monitoring their changes are crucial tasks recommended for the province. In this study, wetlands in five pilot sites, distributed across NL, were classified using the integration of aerial imagery, Synthetic Aperture Radar, and optical satellite data. First, each study area was segmented using the object-based method, and then various spectral and polarimetric features were evaluated to select the best features for identifying wetland classes using the Random Forest algorithm. The accuracies of the classifications were assessed by the parameters obtained from confusion matrices, and the overall accuracies varied between 81% and 91%. Moreover, the average producer and user accuracies for wetland classes, considering all pilot sites, were 71% and 72%, respectively. Since the proposed methodology demonstrated high accuracies for wetland classification in different study areas with various ecological characteristics, the application of future classifications in other areas of interest is promising.     

A general framework of TOPSIS method for integration of airborne geophysics,satellite imagery,geochemical and geological data

This work presents the promising application of three variants of TOPSIS method (namely the conventional, adjusted and modified versions) as a straightforward knowledge-driven technique in multi criteria decision making processes for data fusion of a broad exploratory geo-dataset in mineral potential/prospectivity mapping. The method is implemented to airborne geophysical data (e.g. potassium radiometry, aeromagnetic and frequency domain electromagnetic data), surface geological layers (fault and host rock zones), extracted alteration layers from remote sensing satellite imagery data, and five evidential attributes from stream sediment geochemical data. The central Iranian volcanic-sedimentary belt in Kerman province at the SE of Iran that is embedded in the Urumieh–Dokhtar Magmatic Assemblage arc (UDMA) is chosen to integrate broad evidential layers in the region of prospect. The studied area has high potential of ore mineral occurrences especially porphyry copper/molybdenum and the generated mineral potential maps aim to outline new prospect zones for further investigation in future. Two evidential layers of the downward continued aeromagnetic data and its analytic signal filter are prepared to be incorporated in fusion process as geophysical plausible footprints of the porphyry type mineralization. The low values of the apparent resistivity layer calculated from the airborne frequency domain electromagnetic data are also used as an electrical criterion in this investigation. Four remote sensing evidential layers of argillic, phyllic, propylitic and hydroxyl alterations were extracted from ASTER images in order to map the altered areas associated with porphyry type deposits, whilst the ETM+ satellite imagery data were used as well to map iron oxide layer. Since potassium alteration is generally the mainstay of porphyry ore mineralization, the airborne potassium radiometry data was used. The geochemical layers of Cu/B/Pb/Zn elements and the first component of PCA analysis were considered as powerful traces to prepare final maps. The conventional, adjusted and modified variants of the TOPSIS method produced three mineral potential maps, in which the outputs indicate adequately matching of high potential zones with previous working and active mines in the region.     

HJ-1A CCD与TM数据及其估算草地LAI和鲜生物量效果比较分析

基于地面实测和PROSAIL模型模拟数据,研究了新型传感器HJ-1ACCD与TM数据一致性问题,分析了传感器天顶角和光谱相应函数差异的影响,对比两种传感器数据估算草地LAI和鲜生物量的效果,得出以下结论:(1)HJ-1ACCD和TM反射率数据的拟合系数R2在0.7322和0.9205左右,在反射率较小时,两种传感器数据一致性较好;随着反射率增大,HJ-1ACCD数值逐渐高于TM。总体而言,在可见光和近红外波段,两种传感器较为接近,其中红波段最接近。(2)两种传感器的NDVI数据一致性非常高,且受传感器天顶角和光谱响应函数影响作用较小(相对误差约为0.34%—0.53%),而反射率的相对差别在3.34%—9.54%。(3)传感器天顶角较光谱响应函数对反射率影响更大。(4)基于HJ-1ACCD反射率数据估算草地LAI和鲜生物量效果较好,其中以CCD2传感器估算效果最好。     

Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)     

A RESTful proxy and data model for linked sensor data

Abstract

The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.     

Mapping of wastelands of Kolar district using false colour composites of thematic mapper (TM) data

This study deals with the technique of remote sensing for identifying and deliniating wastelands in Kolar district of Karnataka. False colour composites of thematic mapper (TM) data supplemented by aerial photographs and toposheets wrere utiliesd for mapping wastelands. A map showing the geographic distribution of the wastelands in the districts was prepared on 1∶250,000 scales by compiling the individual wasteland sheets of 1∶50,000 scale. The seven different catagories of wastelands identified and mapped cover about 11.7% of the area in the district. A procedure for mapping wastelands has been worked out based on the experience gained in Kolar district which is a three phase system comprising image intrepretation of false colour composite of TM data, aerial photo interpretation and limited ground truth verification in the selected doubtful areas. This procedure was found to be adequate enough for mapping wastelands accurately in the shortest possible time with least expense and as such are recommended for mapping wastelands in other districts of the country.     

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.     

Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data

The analysis of rapid land cover/land use changes by means of remote sensing is often based on data acquired under varying and occasionally unfavorable conditions. In addition, such analyses frequently use data acquired by different sensor systems. These acquisitions often differ with respect to sun position and sensor viewing geometry which lead to characteristic effects in each image. These differences may have a negative impact on reliable change detection. Here, we propose an approach called Robust Change Vector Analysis (RCVA), aiming to mitigate these effects. RCVA is an improvement of the widely-used Change Vector Analysis (CVA), developed to account for pixel neighborhood effects. We used a RapidEye and Kompsat-2 cross-sensor change detection test to demonstrate the efficiency of RCVA. Our analysis showed that RCVA results in fewer false negatives as well as false positives when compared to CVA under similar test conditions. We conclude that RCVA is a powerful technique which can be utilized to reduce spurious changes in bi-temporal change detection analyses based on high- or very-high spatial resolution imagery.     

Evaluation of ICA and CEM algorithms with Landsat-8/ASTER data for geological mapping in inaccessible regions

Many regions remain poorly studied in terms of geological mapping and mineral exploration in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistic difficulties. Application of specialized image processing techniques is capable of revealing the hidden linear mixing spectra in multispectral and hyperspectral satellite images. In this study, the applications of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms were evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable implications for geological mapping and mineral exploration in inaccessible regions.     

利用MODIS数据和查找表的城区TM影像大气校正

影像大气校正精度的关键参数为气溶胶光学厚度,而城区大气条件复杂,对城区TM影像采用统一的大气参数进行大气校正,势必难以获得令人满意的校正精度.因此,本文提出了一种基于MODIS数据和查找表的大气校正算法,首先应用6S模型离线计算建立不同气溶胶光学厚度的大气校正系数查找表,然后基于MODIS数据反演气溶胶光学厚度,最后基于查找表和气溶胶光学厚度数据对长沙城区TM影像进行了逐像元大气校正.结果表明:基于查找表的校正算法与6S模型在线计算算法的校正精度接近,能够较好地进行大气校正;在水体区校正精度最高,而在城区校正精度相对较低.     

Burned area mapping and post-fire impact assessment in the Kassandra peninsula (Greece) using Landsat TM and Quickbird data

The purpose of this study was to assess the environmental impacts of forest fires on part of the Mediterranean basin. The study area is on the Kassandra peninsula, prefecture of Halkidiki, Greece. A maximum likelihood supervised classification was applied to a post-fire Landsat TM image for mapping the exact burned area. Land-cover types that had been affected by fire were identified with the aid of a CORINE land-cover type layer. Results showed an overall classification accuracy of 95%, and 83% of the total burned area was ‘forest areas’. A normalized difference vegetation index threshold technique was applied to a post-fire Quickbird image which had been recorded six years after the fire event to assess the vegetation recovery and to identify the vegetation species that were dominant in burned areas. Four classes were identified: ‘bare soil’, ‘sparse shrubs’, ‘dense shrubs’ and ‘tree and shrub communities’. Results showed that ‘shrublands’ is the main vegetation type which has prevailed (65%) and that vegetation recovery is homogeneous in burned areas.     

融合工程地质资料与GNSS高精度监测信息的黑方台党川黄土滑坡稳定性研究

针对当前滑坡稳定性评价方法难以准确获取评价结果的突出问题,本文提出了一种融合地下水、工程地质钻孔信息及灌溉资料等工程地质资料与GNSS观测的黄土滑坡稳定性评价方法。首先,基于高分辨率影像、高精度DEM、地层地貌等多源异构数据,建立滑坡精细三维地质模型体;然后,将滑坡外部高精度GNSS监测数据作为模型外部约束条件,进一步构建起融合工程地质资料与GNSS观测的黄土滑坡稳定性综合评价模型。本文方法能够将滑坡外部大地测量高精度监测数据与工程地质数值模拟手段有机融合,实现了滑坡外部形变信息与内部变形机制的有效耦合。通过我国典型黄土滑坡域甘肃黑方台党川实际发生的两起滑坡失稳事件验证表明,本文方法可有效地提高滑坡稳定性评价结果的精度及可靠性,获取了与试验区域滑坡实际失稳情况相一致的结果:HF06/07 GNSS监测点首先失稳,其次是HF09监测点失稳,最后是HF05监测点失稳。基于本文方法获取的滑坡失稳顺序与实际滑坡发生顺序高度一致,显著优于现有的滑坡失稳数值模拟法。     

北京地区地表反照率TM数据反演与分析

反照率是一个广泛应用于地表能量平衡、中长期天气预测和全球变化研究的重要参数。本文利用2010年的TM影像反演北京地区地表反照率,首先利用6S模型对影像进行大气校正,得到地表反射率,进而,利用TM的5个波段的反射率,根据经验公式,计算了北京地区的地表反照率;对北京市地表反照率的分布情况、不同下垫面的反照率、以及反照率与LST和NDVI的关系进行了分析,得出了结论。     

A Web-based system enabling the integration,analysis, and 3D sub-surface visualization of groundwater monitoring data and geological models

The 3D Water Chemistry Atlas is an intuitive, open source, Web-based system that enables the three-dimensional (3D) sub-surface visualization of ground water monitoring data, overlaid on the local geological model (formation and aquifer strata). This paper firstly describes the results of evaluating existing virtual globe technologies, which led to the decision to use the Cesium open source WebGL Virtual Globe and Map Engine as the underlying platform. Next it describes the backend database and search, filtering, browse and analysis tools that were developed to enable users to interactively explore the groundwater monitoring data and interpret it spatially and temporally relative to the local geological formations and aquifers via the Cesium interface. The result is an integrated 3D visualization system that enables environmental managers and regulators to assess groundwater conditions, identify inconsistencies in the data, manage impacts and risks and make more informed decisions about coal seam gas extraction, waste water extraction, and water reuse.