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.     

Effect of acquisition date and TM spectral bands on wheat,mustard and gram classification accuracies

Single date classification accuracies of wheat, mustard and gram were investigated using TM data of five acquisition dates (January 24, February 9, 16,25 and March 12, 1988) and four band-combinations (TM 234 TM 345, TM 1234 and TM 2345) over an irrigated, optimum fertility site in Hisar (Haryana). Accuracies for wheat and gram were lowest on January 24 for all band-combinations and improved with later acquisitions. An interaction between acquisition date and band combination was apparent as accuracies with most optimal combinations remained high over the period from February 9 to March 12 ,while those with sub optimal combinations fluctuated widely from one date to another. The band-combinations which included middle-infrared (TM 2345 and TM 345) showed highest accuracies irrespective of crop and acquisition date while band combination of TM 234 consistently had lowest accuracies.     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

Relative utility of Landsat-TM and MSS data small scale soil mapping

The Landsat MSS and TM data in the form of false colour composite (FCC) prints at 1∶250,000 scale over parts of Mirzapur (U.P.) and Rohtas district of (Bihar) were interpreted monoscopically in concert with the collateral data and limited field check for soilscape boundary delineation. The study has revealed that at the mapping scale, except for improved image contrast and capturing features of relatively smaller dimensions, no additional advantage has been noticed with TM data over MSS data with respect to exhibition of soilscape boundaries. However, the capability of TM data to withstand enlargement upto 1∶50,000 which is not feasible with MSS data is an additional feature from soil mapping viewpoint.     

Ground validation of an algorithm for estimating surface suspended sediment concentrations from multi-spectral reflectance data

Pollution of water resources by sediments eroded from degraded watersheds is a critical concern around the world. Current methods for locating these eroding areas and off-site damage to water resources through visual observations and field sampling with subsequent laboratory analysis are time consuming and expensive. There is thus, a justified interest in developing algorithms for quick estimation of suspended sediment concentrations in large water-bodies from remotely sensed data. This paper presents the results of a ground validation study on characterization and quantification of surface suspended sediment concentrations (SSC) in sediment laden water bodies through an n-waveband specific numerical index, total information content. A comparison of SSC-predictive potential of the proposed new index, derived from four broad (100–300 nm) Landsat MSS, five broad (40–300 nm) Landsat TM and eight narrow (20–40 nm) IRS-P4 OCM spectral bands, with that of the conventional (NIR-Red and NIR+Red) indices, computed from the same spectral band data, is also presented. The study reveaied that at SSCs 250 mg/1, the proposed index (derived from either broad / narrow landsat MSS/TM or IRS-P4 OCM spectral data) could lead to SSC predictions (with mean errors within 20%) comparable with those obtained with the conventional indices (derived from the same spectral band data). It could further be observed that, in general, lower sediment concentrations (i.e. SSCs 150 mg/1) were associated with higher prediction inaccuracies. A comparison of the mean errors of predictions associated with the proposed and the conventional (NIR-Red and NIR+Red) indices computed from broad and narrow band data for SSCs 150 mg/I, revealed that an increase in number of wavebands (from 4 MSS to 5 TM or 8 OCM bands) and a decrease in the bandwidth of these wavebands (from broad MSS/ TM bands to narrow OCM bands) led to a significant increase in the prediction accuracy of the proposed new index. These prediction accuracies were observed to be the highest with the proposed index calculated from narrow OCM-P4 spectral data. However this could not be observed with the conventional indices at any of the SSC ranges and with the proposed index at SSCs 250 mg/l. This shows that the lower SSC-predictive potential of proposed index was a significant function of both the number and the bandwidth of spectral bands used for its computation. In fact in one of the cases, lower SSC (150 mg/l) -predictive accuracy of the proposed index was found to be significantly higher than that of the conventional (NIR+R) index. The proposed algorithm could thus compress the information contained in the entire reflectance spectrum of the sediment laden water bodies to their sediment type and concentration specific characteristic values. This characteristic of the proposed index was not shared by any of the conventional indices, based on only two waveband data. In fact the proposed index appears to be the only mean of completely compressing and quantifying the information contained in all the information channels of a narrow band spectrometer (consisting of 200 wavebands) to be shortly launched by ISRO for satellite based inventory of natural resources.     

Discrimination of low-grade magnetite ores using remote sensing techniques

The remote sensing community in geology is widely using the Multispectral Landsat Thematic Mapper (TM) data which has a wider choice of spectral bands (six between 0.45 and 2.35 μm, plus a thermal infrared channel 10.4-12.5 urn). These were evaluated for low-grade magnetite ores mapping over the high-grade granulite region of Kanjamalai area of Tamil Nadu state, India. The Fourier Transform Infrared (FTIR) spectroscopy data (0.4-4.0 μm) for powders of the magnetite ores exposed with granulite rock and published spectral reflectance data were used as guides in selecting TM band reflectance ratios, which maximize discrimination of magnetite ores on the basis of their respective mineralogies. The study shows that the weathering mineralogy of magnetite ores causes absorption features in their reflectance spectra which are particularly characteristic of the near infrared. Comparison of TM data with field and petrographic observations shows the presence of magnetite and aluminosilicate minerals & show strong absorption at 0.7-1 μ.m wavelength spectral region & increase in the product of two TM band ratios: band 5 (1.55-1.75 μm) to band 4 (0.76-0.9 μm) and band 3 (0.63-0.69 μm) to band 4 (0.76-0.9 μm). Various computer image enhancement and data extraction techniques such as interactive digital image classification techniques using color compositing stretched ratio, maximum likelihood and thresholding statistical approaches using Landsat TM data are used to map the low-grade magnetite ores of the granulite region. The field traverses and local verification enhanced to map the other rock types namely granulites and gneisses of the study area.     

Comparative evaluation of Indian remote sensing satellite and landsat thematic mapper data for geological and geomorphological applications

In order to evaluate the potentials of IRS‐1A Linear Imaging Self‐scanning Sensor (LISS‐I) data for geological and geomorphological applications and also to compare the IRS‐1A LISS‐I data with Landsat Thematic Mapper (TM) data, a study has been attempted for parts of Uttar Pradesh and Madhya Pradesh in Northern India. The first four spectral bands of Landsat TM sensor data which are similar and close to IRS‐1A LISS‐I senor have been utilised for the comparative evaluation. Various techniques employed for both the data set to derive the required geology and geomorphology related information include (i) band combination (ii) spectral response analysis (iii) principal component analysis (iv) supervised classification techniques and (v) visual observation of various outputs generated by the above methods. The Optimum Index Factor (OIF) method adopted for selecting suitable band combinations showed similar OIF rankings for IRS‐1A LISS‐I data and Landsat TM data. It has been visually observed that the band combination 1, 3 & 4 offers relatively better feature display. The spectral responses derived for various major geologic rock units such as Deccan Trap, Vindhyan Formation, Bundelkhand Granite and for a few landcovers such as surface water bodies and black soil show striking similarity in pattern for both LISS‐I and TM. The Principal Component (PC) analysis of both data sets suggested that the total scene brightness tends to dominate in the first PC. The percentage information contributed by PCs 1&2 as also by PCs 1,2 & 3 in both the LISS‐I and TM are comparable. It was observed from the classified image generated by performing supervised classification with a maximum likelihood algorithm that major geomorphic landforms were clearly distinguishable. Thus the qualitative and quantitative evaluation of both IRS‐1A LISS‐I and Landsat TM data showed that significant similarities exist between them. The study also revealed that IRS‐1A LISS‐I data can be effectively used for deriving geology and geomorphology related details.     

Landsat TM versus MSS data for forest type identification

Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) data were digitally analyzed for forest type identification in the Kisatchie Ranger District, Kisatchie National Forest, Louisiana. Ground‐verification maps were produced from field surveys and interpretation of 1.12,000 and 1: 58,000 color‐infrared (CIR) aerial photography of nine compartments. Stand boundary and soils maps were input to a digital Geographic Information System (GIS) with the Landsat and ground‐verification data.

‐ Unsupervised classifications of the Landsat data did not identify the above cover types well. Supervised classifications were tested by stand agreement to the ground verification. The highest four‐class agreement was obtained for the TM classification (76 percent). Three‐class (open, pine, and hardwoods) stand agreements (81 (MSS) and 85 (TM) percent) were not significantly different as tested by analysis of variance (alpha level 0.1).     

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.)     

陆地卫星MSS图像地表水域信息的机助识别提取

本文介绍了一种利用陆地卫星MSS图像进行水域信息机助识别提取的快速、经济而有效的方法。该方法以对水与非水信息的光谱特征,比值图像的数据结构特点和大气校正的研究与分析为基础。 研究表明,水与非水的主要差异集中于MSS4与MSS7波段,这两个波段的比值图像可在压抑阴影的同时将两图像的信息集中于一。根据对比值图像数据特点的分析,发现比值图像在作等比例拉伸并取整时所不可避免的信息损失主要集中在低值区,高值区则相对得到扩展增强。由于在MSS4/MSS7图像中,水的信息位于高值区,因此其水域识别能力优于MSS7/MSS4。但是,即使MSS4/MSS7图像也无法完全排除山区深阴影对水域识别的干扰。在进行MSS4/MSS7比值运算之前,首先对MSS4图像作粗略的大气校正,则可圆满地解决这一难题。在经大气校正后的MSS4/MSS7图像上,水体像元的值大于1,而深阴影及其它所有非水信息像元的值则小于1,因此,只要以此为门限值将图像二值化,即可获得精度很高的水域识别图像。完全消除阴影和其它因素的干扰。 本方法运算简单,处理速度为最小距离分类的3倍以上,且精度比分类方法高。它不仅适用于MSS图像,也适用于TM图像。采用本方法进行湖北省地表水域机助识别和面积测量的实际应用表明,在各种地貌类型区内本方法均能以较高的精度完成     

Combined Radar and Landsat data analysis for land use/cover studies over parts of the Punjab plains

L-band (HH) synthetic aperture radar imagery from Shuttle Imaging Radar-B (SIR-B) and Landsat multispectral scanner (MSS) images over parts of the Punjab plains were combined in order to utilize the complementary information contained in multispectral data sets. Among the various combination of Landsat MSS with SIR-B, the combination of Landsat MSS band 5 (0.6–0.7 μm) and band 7 (0.8–1.1 μm) with SIR-B data was found to be optimum in delineating landcover units. The integrated data was found to be superior in providing landcover information in comparison to SIR-B alone or a combination of landsat MSS band 4,5 and 7.     

A comparative study of supervised classifiers on a subscene in Junagadh district,Gujarat

This paper describes the results of a comparative study of five classifiers viz., maximum likelihood, modified maximum likelihood, minimum distance to mean. Fisher and min-max, for classifying a subscene of Junagadh district using Landsat Thematic Mapper (TM) data. The kappa coefficient of agreement (k) and per cent correctly classified pixels for training data are used as measures of overall performance. It is observed that maximum likelihood and modified maximum likelihood classifiers perform better than the other three classifiers for this data set. Band combinations (3, 4, S) and (2, 3, 4, S) perform better than the usual combination (1,2,3,4), possibly because of presence of middle infrared band (band 5) on a scene dominated by vegetation cover. The band combination (1, 2, 3, 4, 5, 7) performed the best.     

Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices

The quality and yield of tea depends upon management of tea plantations, which takes into account the factors like type, age of plantation, growth stage, pruning status, light conditions, and disease incidence. Recognizing the importance of hyperspectral data in detecting minute spectral variations in vegetation, the present study was conducted to explore applicability of such data in evaluating these factors. Also stepwise discriminant analysis and principal component analysis were conducted to identify the appropriate bands for accessing the above mentioned factors. The Green region followed by NIR region was found as most appropriate best band for discriminating different types of tea plants, and the tea in sunlit and shade condition. For discriminating age of plantation, growth stage of tea, and diseased and healthy bush, Blue region was most appropriate. The Red and NIR regions were best bands to discriminate pruned and unpruned tea. The study concluded that field hyperspectral data can be efficiently used to know the plantation that need special care and may be an indicator of tea productivity. The spectral signature of these characteristics of tea plantations may also be used to classify the hyperspectral satellite data to derive these parameters at regional scale.     

Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats,Zambia

Abstract

The paper describes the use of Principal Component Analysis (PCA) of remote sensing images as a method of change detection for the Kafue Flats, an inland wetland system in southern Zambia. The wetland is under human and natural pressures but is also an important wildlife habitat. A combination of Landsat MSS and TM images were used. The images used were from 24 September 1984 (MSS), 3 September 1988 (MSS), 12 September 1991 (TM) and 20 September 1994 (TM). They were geometrically co‐registered and, in the process, the 80m resolution MSS images were resampled to 30m using nearest neighbour resampling. Preliminary PCA revealed that for the MSS images most of the data variance was in near infrared reflectance while for the TM images it was in mid and thermal infrared bands. Holding sensor type constant, separate inter‐band correlation analysis for each image could indicate whether the wetland was drier or wetter on one date versus another. The 1994 image was made the reference image and equivalent green, red and near infrared bands from the other images were radiometrically normalised with those on the reference image. All the bands, three from each date, were then merged into a twelve‐band image on which PCA for change detection was undertaken. A colour composite of eigen images from the resulting principal components was used in change detection. Hydrological data, indicating long‐term reduced inflow of water into the wetland due to human regulation, help explain some of the wetland change detected. Compared to a classification comparison approach to change detection for this area, PCA was found to be very useful in indicating where change had occurred, though interpretation of the changes was difficult without reference to the input images. The methodology appears to have potential use in habitat monitoring for this wetland area.     

Temporal change detection in river courses and flood plains in an arid environment through satellite remote sensing

Temporal changes in ephemeral river courses and associated flood plains, which could not be detected by Landsat MSS due to its poor spatial resolution of 80m, have been identified and mapped within 10% accuracy by Landsat TM False colour composite because of its higher spectral and spatial resolution of 30 m. Over a period of 28 years (1958–86) the river courses widened upto 1.8 times through bank erosion due to the recurring flash floods. The flash floods have also caused morphological, soil fertility and landuse changes in the associated flood plains, which could also be monitored by the Landsat TM.     

基于TM影像的城市建筑用地信息提取方法研究

本文选用金华市Landsat TM影像为研究的数据源,在归一化裸露指数基础上,利用归一化植被指数提取出非植被信息,通过图像二值化、叠加分析以及掩膜处理去除了低密度植被覆盖区域的噪音信息,自动提取了金华城市建筑用地信息。研究结果表明,归一化裸露指数和归一化植被指数相结合的方法弥补了单一利用归一化裸露指数来提取城市建筑用地信息的不足,提高了提取精度,而且结果客观可信,是一种不经人为干预的、快速有效的提取城市建筑用地方法。     

Saline degradation of Indian agricultural lands : A case study in Khambhat Taluka,Gujarat state (India), using satellite remote sensing

In certain agricultural fields of Khambhat Taluka in Gujarat State, the salinity has increased considerably rendering the land completely infertile. The occurrence of salinity in this area can be attributed partly to subsurface sea‐water ingress and partly to improper land and water management practices prior to implementation of irrigation. Landsat MSS or TM and IRS IA LISS II data was used to test the feasibility of delineating saline soils by both visual image interpretation and digital analysis. The study of saline soils using multi‐temporal Landsat images of the year 1977, 1983, and 1987, indicated an evident increase in saline areas in past few years. The Soil Brightness Index (SBI) generated from the IRS‐IA data by the application of MSS equivalent coefficients brought out different categories of soil degradation. The supervised classification scheme aided in generating various salinity levels. The analysis of the soil samples of the above area exhibited increasing values of Electrical Conductivity (ECe), and the soluble cations with increasing levels of salinity.     

SPOT与TM图像的数字复合试验

陆地卫星TM图像含有丰富的光谱信息,SPOT全色波段图像数据分辨率较高,因此,如何将这两种图像数据有效地结合起来,在遥感应用领域中显得越来越重要。本文研究了SPOT和TM图像数据的数字复合方法。结果表明,复合后的图像提高了分辨率,增加了光谱信息。     

Hydrologic investigations in the north Chilean Altiplano using landsat ‐ MSS and ‐ TM data

In order to understand Late Glacial high lake levels in the dry Andes of Northern Chile, recent short ‐ to medium‐term fluctuations in the water budget of present lakes and brines (salars) and their relationship with the atmospheric circulation were investigated. A time sequence of four Landsat‐MSS images between November 1983 and August 1984 was analysed in terms of changing water surface and water volume of several lakes and salars. The variations of the open water bodies were interpreted as a result of the spatial pattern of summer and winter precipitation. Furthermore a method to determine water depth and water salinity of the very shallow salars and lakes by correlating field measurements and digital Landsat‐TM data is described. The resulting model to compute water depth was also applied to the MSS‐sequence, showing good results.     

Rice-acreage estimation for Orrisa using remotely sensed data

Rice is one of the most important foodgrains grown in India. Attempts have been made to estimate kharif rice acreage of Orissa state since 1986 using digital remote sensing data from Landsat MSS/TM and/or IRS-1A. Accuracies of the estimates obtained have been evaluated against BES (Bureau of Economics and Statistics) estimate. This paper describes the methodology adopted for rice acreage estimation of Orissa state, the results obtained for three years, i.e. 1986–87, 1988–89 and 1989–90, and their accuracy.