Study of spectral signatures for exploration of Bauxite ore deposits in Panchpatmali,India

Spectral analysis technique has been utilized to identify the Bauxite mineral occurrences in Panchpatmali, Orissa, India. Spectral processing of Landsat ETM+ data has been carried out by converting the digital data from quantized and calibrated values to reflectance values. Minimum noise fraction transformation is used to determine the inherent dimensionality of reflected Landsat ETM+ data, to segregate noise in the data, and to reduce the computational requirements for subsequent processing and interactively to locate pure pixels within the data-set, projecting n-dimensional scatterplots. Spectral processing results are displayed in the form of images corresponding to each group of pixels (endmembers). Mixed tune matched filtering method has been applied on Landsat ETM+ images which gave three score (abundance) images for three different classes (endmembers) such as Bauxite, vegetation and soil. Further, mineralized zones are identified using image fusion of ERS-2 SAR and Landsat ETM+ data using intensity-hue-saturation technique.     

Development of a modified bare soil and urban index for Landsat 8 satellite data

Mapping of urban area has always been a challenging task due to its similar spectral characteristics with bare soil. The spectral characteristics of urban and bare soil being similar, causes confusion and misclassification among themselves. A new modified normalized difference soil index (MNDSI) has been proposed using PAN and Band 7 of Landsat 8. PAN band of Landsat 8 provides increased contrast between vegetation and land areas without vegetation. Subsequently, MNDSI was used to develop a new normalized ratio urban index (NRUI) by enhancing the capability of biophysical composition index (BCI) in two stages. First, a ratio urban index (RUI) was developed which discriminates urban and soil better than BCI. Second, RUI was further enhanced, subsequently known as NRUI, which is able to discriminate urban area from soil even better than RUI. MNDSI and NRUI show a good discrimination between soil and urban and may be useful for such purposes.     

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.)     

Delineation of soil associations for a part of tamil nadu by digital processing of landsat multispectral data

The utility of Landsat multispectral data for small scale soil mapping has been demonstrated in this study. The scene used for the study has path-row number 153-054 of Landsat-1 dated 26th February, 1973 covering parts of Ramnad, Tirunelveli and Kanyakumari districts of Tamil Nadu. Associations of sub groups have been delineated on 1 : 250,000 scale using computer-aided multispectrcl data analysis system (M-DAS). Soil map prepared using the computer has been found to be camparable with the soil map prepared by conventional methods at the same scale. Apart from the soil associations, other land use/land cover classes like water bodies forest/scrub/hiliy areas, crcps etc. were also categorised in the colour coded soil map.     

Age discrimination among eruptives of Menengai Caldera,Kenya, using vegetation parameters from satellite imagery

This paper reports an investigation to determine the degree to which digitally processed Landsat TM imagery can be used to discriminate among vegetated lava flows of different ages in the Menengai Caldera, Kenya. Since Landsat data display vegetation parameters well, and plant communities vary with type and depth of soil development, selective digital processing techniques were applied to take advantage of these characteristics for discriminating relative age differences of the underlying volcanics. A selective series of five images, consisting of a color‐coded Landsat 5 classification and four color composites, were compared with geologic maps. These included a color coded, modified, unsupervised classification and contrast enhanced, color composite images using TM bands 3–2–1, 4–3–2 and 7–5–3, and the first 3 Karhunen‐Loeve transformation axes that had been generated using 7 Landsat TM bands.

The most recent of more than 70 post‐caldera flows within the caldera are trachytes, which are variably covered by shrubs and subsidiary grasses. Soil development evolves as a function of time, and as such, supports a changing plant community. Progressively older flows exhibit the increasing dominance of grasses over bushes. It was found that the Landsat images correlated well with geologic maps, but that the two mapped age classes could be further subdivided on the basis of different vegetation communities. It is concluded that field maps can be modified, and in some cases corrected by use of such imagery, and that digitally enhanced Landsat imagery can be a useful aid to field mapping in similar terrains.     

Landsat image enhancement and analysis approach for geological studies of central part of Goa,India

With the advent of multispectral scanners and the availability of digital data, information extraction through remote sensing has become one of the viable tools for studying natural resources. Normally thick vegetation and soil cover are common obstacles while geologically studying an area remotely. The study area, Goa, is largely covered by settlements, private mines, and dense vegetation. This makes it difficult to decipher lithology, structures and to find their extension by ground surveying. In this paper, an attempt has been made to study a variety of image enhancement and analysis techniques to delineate geological features, lineaments, and several landuse features. The information gathered from land use features and vegetation cover is also utilized in delineating lithology and lineaments. Landsat Multi-Spectral Scanner (MSS) data both in the visual and digital form have been used for the analysis. Various photographic techniques such as Bas-relief, combined printing of positive and negative for different bands, color composites, and digital image processing techniques like ratioing, principal component analysis and ratioing of the first two principal components have been applied for geological information extraction. This paper examines comparative utility of enhancement techniques in studying geological aspects. It is found that the ratio image of PCI and PC2 gives most significant and detailed information with maximum contrast and sharp boundaries. Bas-relief images are excellent for identifying geomorphic features and lineaments.     

Agriculture field characterization using aerial photograph and satellite imagery

Successfully delineating management zones that differ in crop productivity is an important component of site-specific management. We compared the effectiveness of the digitally scanned color aerial infrared photographs and digital Landsat Thematic Mapper (TM) data for delineating within-field zones. The zones delineated using normalized difference vegetation index (NDVI) from TM data explained 34% of field yield variance compared with 9% for that from digitally scanned color aerial infrared photograph data. The zones from NDVI using Landsat TM were better able to capture spatial differences in plant growth and relatively stable soil attribute of surface soil organic carbon.     

不同植被指数在基于TVDI方法反演土壤水分中的应用

研究增强型植被指数基于Landsat-8数据反演土壤水分的可行性及适用性,分析研究区土壤水分总体分布,提高该地区应对干旱灾害的能力。基于温度植被干旱指数方法,以淮河流域上游地区作为研究区,基于2017年2月的Landsat-8影像,分别计算了地表温度、归一化植被指数、增强型植被指数,基于TVDI构建了两种土壤水分反演模型。研究比较了:1) EVI在TM数据中的应用特点;2)研究区土壤含水率的空间分布特征;3)两种模型反演结果的差异。结果表明:1)基于TM数据计算的EVI总体明显低于NDVI,但不同时间段的结果并不总是低于NDVI;2)基于EVI的模型结果精度低于基于NDVI模型结果。3)两种模型结果与植被覆盖度、地表温度的关系均为负相关,其中,基于EVI的模型结果与地表温度的负相关程度极高,即基于EVI的模型结果受植被影响较小,受温度影响程度高。     

Relative efficiency of larsys classifiers in soil mapping

Launching of Landsat series and flow of data within and beyond the visual spectrum furnished a potent tool for data acquisition to the earth resources scientists for expanding the teritories of knowledge. Increased capability of computer technology made many advancements possible in the field of Remote Sensing. LARS, Purdue, USA has developed several methodologies for abstracting information from Landsat products in various fields of application. The methods employing algorithms of maximum likelihood and minimum distance have been compared applying the techniques of pooling and deleting of LARS to classify soils of Hapur area, Uttar Pradesh, India. It was found that the maximum likelihood yielded a map with better dispostion of soil-scape but the minimum distance method, by deleting, is seen to be very efficient in class combination and CPU time. The results are discussed in this paper with illustrations.     

A note on visual interpretation of landsat imagery to locate possible locust habitat in a part of Rajasthan

Visual interpretation of Landsat data from an area in Rajasthan desert has been used to delineate vegetation and other associated parameters helpful for locust breeding and development. The results indicate the usefulness of Landsat in date locust surveys by reducing time and cost involved.     

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.     

Vegetation recovery monitoring over the waste dump in Haizhou opencast coalmine area,China, during 1975–2000 using NDVI and VF index

Vegetation condition monitoring has been done from 1975 to 2000 in the waste dump of Haizhou opencast coalmine area, China, using remote sensing techniques with the objective of improving our understanding of the temporal and spatial variation of vegetation recovery in the mining dump. Four historical vegetation indexes (NDVI, VF, soil brightness and vegetation greenness) from two Landsat 2 MSS images and two Landsat 5 TM images are extracted and analyzed. For the purpose of comparison and analysis two improved techniques such as normalization grading of change slope and image segmentation were used in this study. Based on the results obtained through the above analysis two conclusions are derived: (1) vegetation recovery in the study area is in an improved condition, (2) two remote sensing based vegetation indexes such as VF and NDVI are the optimal parameters to monitor vegetation condition, which could be used as the indicators of land reclamation progress in the mining area.     

基于TM的城市温度异常区分析

利用实时探空数据和单窗算法对2004年7月6日北京市TM图像进行地表温度反演，根据反演结果，采用阈值法将北京城区地表温度空间分布分为植被正常温度区、水体正常温度区、水体高温区、裸地正常温度区、建筑物低温区、建筑物正常温度区、建筑物高温区、植被建筑物混合高温区和植被建筑物混合正常温度区等9种模式。在此基础上，对水体高温区、植被建筑物混合高温区、建筑物低温区和建筑物高温区等4种温度异常区进行了实地抽样调查，详细分析了这些温度异常区形成的原因。     

植被调节下的干旱区不透水面覆盖率遥感估算方法

基于DMSP/OLS夜间灯光数据的居住区指数模型（HSI）广泛应用于区域尺度城市不透水面扩张监测。但是,在干旱区由于受到裸岩、沙漠、戈壁等低植被覆盖区干扰,HSI算法的精度和适应性受到了一定的影响。为解决这一问题,本文利用植被覆盖度作为调节系数,对灯光数据与植被指数进行动态调整,构建了适用于干旱区的城市植被调节不透水指数（VAISI）;然后采用SVR模型,通过机器学习的方法构建了城市不透率参考数据与VAISI之间的非线性关系模型,实现对干旱区区域尺度不透水面覆盖率估算;最后,对模型估算结果进行了精度验证和比较分析。试验结果表明：在干旱区,VAISI解决了由于灯光溢出问题及城市周边裸土等低植被覆盖等因素导致的城市周边裸土像元不透率估算过高问题,一定程度上提高了城市内部不透水面空间分布信息的表达能力,有效克服了非灯光区估算结果高于背景值的现象。平均相关系数R由0.69提升到0.79,RMSE由0.17降至0.14。     

Recognition of arid zone soil features from Landsat imagery

Usefulness of Landsat imagery in discerning major arid zone soils has been studied. Results are based on analysis of Band 7 coverage and Band 5 and 7 for a limited area followed by a comparison of these with the known soil distribution as seen in Bikaner, Jodhpur and part of Jalore, Pali and Nagaur districts. Results show that at Band 7 the dominant course loamy Typic Camborthids in association with dunes could be recognised. Vegetation was found non-interfering though surface soil moisture variation of the period immediately following monsoon months (Sept.–Dec.) appeared to do so. Hardpan soils were identifiable largely by their associated features than by soil characteristics proper. Fine loamy typic Camborthids could not be recognised at series level and as a group also these could be identified only in post-monsoon period when the land is devoid of much of its vegetation cover. Saline areas could be recognised but those occurring in South-eastern tract were largely inseparable from adjoining shallow soils. For these, Band 5 image of monsoon months was quite satisfactory. For all other soils, Band 7 was better than Band 5. Though light brown sandy soils in association with dunes are the dominant formations, past evolutionary history and source rock variability have given considerable heterogeniety to the soil cover of the arid zone. Natural resource survey activity over the years has provided ground information for nearly 30 percent of Westren Rajasthan and this incidentally covers major soils of the area albeit with few exceptions. With the Landsat imagery now becoming accessible, it was thought befitting to see how far soil variations as recognised in the course of above surveys could be discerned from the Landsat. Some encouraging reports on the use of the Landsat or similar data in small cale soil mapping are available in literature (Kristof and Zachary, 1970; El-Baz, 1978; Everitt and Gerbermann 1977). In our own country also usefulness of this tool has been demonstrated by Krishnamurthy and Srinivanan (1973) and Hilwig (1975). Recently Bhandariet al; (1976) while working in northern part of arid zone have shown that soil salinity mapping could be attempted with the help of Landsat data.     

Soil resource survey of silent valley and its environ using landsat MSS data

This paper presents a case study of the utility of Landsat MSS imagery for soil resoruces mapping in Silent Valley and its environs covering about 33,000 sq. km. area. A collective approach involving monoscopic visual interpretation of Landsat imagery in conjunction with the lithological and topographical information supported by limited field check has been followed to prepare a soil map on 1:250,000 scale showing sub-groups/association of sub-groups. Future prospect of using spaceborne data for soil mapping has also been discussed.     

Application of Landsat data in determining the range biomass in Jodhpur district,Westren Rajasthan (India)

The range biomass in three different soil types of Jodhpur district has been estimated from the computer print out of Landsat imagery. The total biomass in the younger alluvial soil varies from 36.1 to 35910.0 kg; in pipar soils it ranges between 23.2 to 21541.2 kg, and in Chirai soils total biomass varies from 26.6 to 6852.7 kg.     

新疆焉耆盆地土壤湿度时空分布特征

本文以新疆焉耆盆地为研究区,首先利用实测数据和Landsat 8 OLI遥感数据获取土壤调查植被指数（MSAVI）和地表温度（Ts）,构建Ts-MSAVI特征空间,拟合特征空间的干湿方程;然后利用该特征空间计算温度植被干旱指数（TVDIm）,反演9-11月的土壤湿度,探讨土壤湿度时空分布特征。试验结果表明：①遥感影像反演的TVDI与实地考察的土壤湿度显著相关（a=0.05）;不同土层中,TVDIm与10~20 cm土层湿度相关性最高（R=0.588）;②焉耆盆地湿度总体以半干旱为主（0.60.8）;土壤湿度空间分布上,焉耆盆地南侧为干旱区,西部和北部地区偏干旱,中部为湿润区域,对于该地区滨湖湿地和博斯腾湖附近小湖土壤湿度最高,博斯腾湖南部的沙地区土壤湿度最低,Ts与土壤湿度呈负相关;③10月湿地的TVDIm值最低,9月沙地的TVDIm值最高。TVDI模型应用于焉耆盆地取得较好的结果,可用于正确地估算土壤湿度,研究结果可为焉耆盆地生态环境和水资源提供重要的参数。     

土地覆被变化对陆地碳循环的影响—以黄河三角洲河口地区为例

土地利用/土地覆被变化是全球变化研究的重点，是影响陆地碳循环的一个重要因子。该文对黄河三角洲河口地区1992年和1996年9月份的TM影像进行非监督分类，做出该地区土地覆被类型分布图，以及估算土地覆被类型的变化面积，计算结果显示1992年该研究地区植被碳库和土壤碳库分别为11.43×10     

Comparison of reconnaissance soil maps prepared by conventional method and landsat imagery interpretation

Landsat images have been used in conjunction with topographical and geological information to prepare soil map of Mudhol taluk in Bijapur district, Karnataka state. The map has been compared with the reconnaisance map prepared by conventional method using 1:63,360 scale Survey of India toposheets. The study reveals that more accurate soil maps in terms of boundary delineation and composition of soil mapping units could be prepared by interpretation of Landsat images with adequate ground data. The method can thus be used in revising and improving many of the existing reconnaissance soil maps prepared by conventional method.