Linear Imaging Self-scanning Sensor Research Articles

Temporal behaviour of surface waterlogged areas using spaceborne multispectral multitemporal measurements

Waterlogging and subsequent salinization and/or alkalization is the major land degradation problem in the irrigation commands of the semi-arid regions. Information on the nature, extent and spatial distribution of waterlogged areas is a pre-requisite for restoration of fertility, which has hitherto been generated conventionally. Realising the potential of spaceborne multispectral measurements in providing reliable information on spatial patterns of waterlogged areas in a timely and cost-effective manner, a study was taken up to delineate and monitor the spatial distribution pattern of waterlogged areas in Mahanadi command Stage-I covering parts of Orissa state, eastern India using Landsat-TM, Indian Remote Sensing satellite (IRS-1A) Linear Imaging Self-Scanning Sensor (LISS-II) and IRS-ID LISS-III data. A systematic on-the-screen visual interpretation approach after geo-referencing and radiometric normalization of digital multispectral data in a Silicon Graphics work station using ERDAS/ IMAGINE software was followed to realize the objectives. Results point to a significant increase in the spatial extent of waterlogged areas. Seasonally waterlogged areas increased from 29330 ha to 33421 ha and permanent waterlogged areas from 10870 ha to 12973 ha during the period 1988–89 to 1999–2000. Methodology and results are discussed in detail.

Sustainable development of land and water resources using geographic information system and remote sensing

Realizing the potential of spaceborne multispectral measurements in providing spatial information on natural resources, and of Geographic Information System (GIS) in integrating such information with the socio-economic data and other collateral information to arrive at derivative information, we report here the results of a study which was taken up in a watershed in Charkhari block of Mahoba district, northern India, to generate the information on natural resources from Indian Remote Sensing Satellite (IRS-1B) Linear Imaging Self-scanning Sensor (LISS-II) images through a systematic visual interpretation, and its subsequent integration with the collateral information in a GIS environment to develop optimal land use plan/action plan for sustainable development of its land resources. Since permanent vegetation cover in the watershed has been dwindling due to population pressure, the need for establishing more vegetation cover has been stressed.

An evaluation of fuzzy classifications from IRS 1C LISS III imagery: A case study

Remote sensing data are frequently used to produce crisp and fuzzy classifications for land cover applications. Fuzzy approaches are attractive for classification of images dominated by mixed pixels. Recently, fully-fuzzy classification that can incorporate mixed pixels in all the three stages of a supervised classification has been recommended. In this Letter, the results of a case study on fully-fuzzy classification of Indian Remote Sensing (IRS) 1C Linear Imaging Self Scanning Sensor (LISS) III imagery are reported. The results illustrate that fully-fuzzy classification produces more accurate land-cover mapping than the conventional crisp classification. For instance, the areal extents of three dominant classes (i.e. agriculture, forest and grass) obtained from fully-fuzzy classification differ by only 13% from the actual areal extents, compared to 34% difference in area observed from crisp classification.

The delineation of reclamative groups of ravines in the Indo-Gangetic alluvial plains using IRS-1D LISS-III data

Ravines, the intricate network of gullies and almost the penultimate stage of soil erosion by water, need to be reclaimed and their fertility restored, and normal fertile land needs to be protected from eroding away. Information on the nature, extent spatial distribution and temporal behaviour of ravines is a prerequisite in this endeavour. Spaceborne multispectral measurements by virtue of synoptic coverage at regular intervals hold immense potential for deriving the desired information in a timely and cost-effective manner. We report here the results of a study that was carried out to delineate the reclamative groups of ravines in the Bhind district of Madhya Pradesh, central India, using Linear Imaging Self-scanning Sensor (LISS-III) data from the Indian Remote Sensing Satellite (IRS-1D) mission. The approach essentially involves on-the-screen monoscopic visual interpretation of false colour images generated from pre- and post-monsoon LISS-III digital data after geo-referencing to Survey of India topographic maps at 1:50 000 scale, on a Silicon Graphics workstation using ERDAS/IMAGINE version 8.3, in conjunction with the ground truth and information on the terrain's relief derived from topographical maps. Information on land use/land cover derived from temporal LISS-III data enabled three reclamative categories of ravine to be delineated, namely shallow, medium and deep. The area under deep, medium and shallow ravines has been estimated to be 112 ha, 12 700 ha and 28 600 ha, respectively. The land use/land cover of these units further helped in identifying the areas requiring immediate attention.

Evaluation of IRS-1c LISS-3 satellite data for defoliation assessment on Norway spruce and Scots pine

This study evaluates the performance of IRS-1c Linear Imaging Self-Scanning Sensor 3 (LISS-3) data for assessment of slight and moderate defoliation, induced by the different stress factors affecting the boreal forests of Västernorrland, Sweden, in stands dominated by Norway spruce and Scots pine. It shows that accuracies as good as with Landsat TM can be produced with IRS-1c LISS-3 data, and that defoliation in pine could be assessed with only a slightly lower accuracy than in spruce. As in previous studies of spruce defoliation, the near-infrared (NIR) band was best correlated with spruce defoliation. Among the single bands, the NIR band was also best correlated with pine defoliation, although the relationship was even stronger with the ratios between NIR and red, and NDVI. Compared to spruce, assessment of pine defoliation appeared to be more sensitive to the presence of species other than pine. The strength of the relationship between LISS-3 data and defoliation increased significantly (from r=−.73 to r=−.91 for NIR/red, note however the reduced sample size), when only plots with more than 80% pine, rather than 70%, were included in the analysis. An attempt to normalize satellite data for species composition did not raise correlation coefficients for spruce data, and only slightly for pine data. Topographic normalization on the other hand, strongly increased the correlation coefficients between NIR radiance and spruce defoliation. A large part of the pine plots were located on near-horizontal ground; thus, topographical normalization only slightly increased correlation coefficients. This study verifies that high-resolution satellite data provide a means to assess the overall needle loss situation on a forest stand level provided that: the stands are fairly homogenous, not too open, dominated by spruce (>60%) or pine (>70%), located in level or moderately undulating terrain (<15° slope), and express a needle loss range of at least 20%. The utility of IRS LISS data in addition to earlier evaluated Landsat TM data strongly improves the possibility to perform operational satellite-based monitoring in areas with frequent cloud cover.

Bandpass solar exoatmospheric irradiance and Rayleigh optical thickness of sensors on board Indian Remote Sensing Satellites-1B, -1C, -1D, and P4

This paper reports the bandpass solar exoatmospheric irradiance and Rayleigh optical thickness for six categories of sensors, namely, the linear imaging self scanning sensor (LISS)-I, -II, -III, the wide field sensor (WiFS), the panchromatic (PAN), and the ocean color monitor (OCM) which have been flown on Indian Remote Sensing Satellites (IRS)-1B, -1C, -1D, and -P4 platforms, based on laboratory measurements of relative spectral response (RSR) of each band. These estimates have been compared with similar contemporary international sensors.

The vegetation and waterlogging dynamics as derived from spaceborne multispectral and multitemporal data

Poor water management coupled with inadequate drainage in the irrigated commands of the arid and semi-arid regions leads to waterlogging and subsequent salinization and alkalization. Space-borne multispectral data have been found to be extremely useful in deriving information on salt-affected soils and waterlogged areas. The study, reported here, aims at delineating and monitoring the extent of waterlogged areas and vegetation cover in part of the Indira Gandhi Canal command area, Rajasthan State, Western India, during the period 1975 to 1995 using Landsat Multi-Spectral Scanner (MSS) and Thematic Mapper (TM), and the Indian Remote Sensing Satellite (IRS-1A) Linear Imaging Self-scanning Sensor (LISS-I) data. Vegetation condition was assessed by generating the Normalized Difference Vegetation Index (NDVI ) from multispectral digital data whereas waterlogged areas were delineated through a systematic visual interpretation approach. An objective comparison of the temporal NDVI images has revealed two striking phenomena: (i) the areas which were non-vegetated during 1975 were found supporting more and more vegetation especially crops in successive years due to introduction of canal irrigation; and (ii) the palaeochannel (old river course) which supported very good crops during 1975 gradually became waterlogged during the 20 year period thereby resulting in the extinction of croplands therein. The detailed methodology and observations made are reported in the article.

A remote sensing-based integrated approach for sustainable development of land water resources

Sustainable development of natural resources calls for utilizing these resources based on their potential and limitations. A remote sensing and geographic information system-based approach for the sustainable development of land and water resources has been developed. The approach involves the extraction of information on soils, water resources (both surface and ground water) and land use/cover from the Indian Remote Sensing (IRS-1A/1B) satellite's Linear Imaging Self-scanning Sensor (LISS-II) data, together with terrain and other information from ancillary sources, and integrating them with the social, cultural and economic needs of the people, and using a GIS as a tool. The integration helps in the generation of an action plan identifying specific interventions in a watershed based on contemporary technology, in consultation not only with scientific specialists in agriculture, horticulture, water resources, land use, forestry and soil conservation, but also with social scientists, non-governmental organizations and beneficiary farmers in the watershed. The early results of the implementation of action plans in some selected watersheds have been rewarding in that positive trends in slowing down land degradation, raising groundwater levels and greening of wastelands have been noticed. The paper concludes that, for sustainable development, a holistic and systemic approach is essential that takes into account analysis of multiple objectives, impact and risk analysis, and decision-making at the grass-roots level. Remote-sensing data plays a crucial and integral role in this endeavour.

Detecting soil information in a predominantly black soil region using Indian Remote Sensing Satellite (IRS-1B) Linear Imaging Self-scanning Sensor (LISS-II) data

Spaceborne multispectral data have been operationally used for deriving information on soil resources since the early 1970s. In this study an attempt has been made to evaluate the potential of the Indian Remote Sensing Satellite (IRS-1B) Linear Imaging Self-scanning Sensor (LISS-II) data for mapping soil resources in part of northern India through a systematic monoscopic visual interpretation approach. Soils were classified up to series level. A strong correlation between the image elements and different categories of black soils has been observed, indicating thereby the potential of such data for providing reliable information on soils in the black soil region.

Inventory of salt-affected soils and waterlogged areas: A remote sensing approach

Waterlogging and subsequent salinization and/or alkalization are the major land degradation processes in irrigated agricultural lands of arid and semiarid regions. The use of spaceborne multispectral data has been shown to have potential for deriving information on the nature, extent, spatial distribution and magnitude of various degraded lands. Indian Remote Sensing Satellite (IRS-1B) Linear Imaging Self-scanning Sensor (LISS-I) and Landsat Thematic Mapper (TM) data in the form of standard false colour composite (FCC) prints at 1 100000 scale covering Nagarjunsagar Left Bank canal command area in Andhra Pradesh, southern India, have been used to delineate waterlogged areas and salt-affected soils through a systematic visual interpretation approach. Waterlogged areas are estimated at 1380ha whereas salt-affected soils of mostly saline-sodic nature cover 6830ha of the command area. The methodology used and results are described in detail.

An inventory of salt‐affected soils and waterlogged areas in the Nagarjunsagar Canal Command Area of southern India, using space‐borne multispectral data

Information is presented on the nature, extent and spatial distribution of waterlogged areas and salt-affected soils, derived through systematic visual interpretation of standard false colour composite (FCC) prints on a 1:100 000 scale generated from the Indian Remote Sensing Satellite (IRS-1B) Linear Imaging Self-scanning Sensor (LISS-I) and Landsat–Thematic Mapper (TM) data for the Nagarjunsagar Right Bank Canal Command Area, Andhra Pradesh. A total of 1710 ha of land in the coastal region has been found to be waterlogged. Salt-affected soils cover an area of 42 800 ha, with saline–sodic soils covering 28 480 ha emerging as the dominant category. To make optimal use of these lands and to prevent further degradation both preventive and ameliorative measures have been advocated. © 1998 John Wiley & Sons, Ltd.

Delineation of salt-affected soils and waterlogged areas in the Indo-Gangetic plains using IRS-1C LISS-III data

Waterlogging and subsequent salinization and/alkalinization is the major land degradation problem in irrigated commands in the arid and semi-arid regions. The Linear Imaging Self-scanning Sensor (LISS-III) digital data acquired by the Indian Remote Sensing Satellite (IRS-1C) over part of the Indo-gangetic plains are digitally analysed for delineating salt-affected and waterlogged areas on a IBM RS-6000 system. A comparison with the IRS-1B LISS-II data, with comparable spectral bands and acquired very close to LISS-III data, reveals a relatively poor overall classification accuracy achieved from this data as compared to LISS-II data. This may be attributed to higher intra-class spectral variations resulting from high spatial resolution LISS-III data. The classifiers capable of exploiting the spectral variations in spatial domain, like segmentation, textural analysis, etc., need to be used for exploiting the full potential of IRS-1C LISS-III data for mapping salt-affected soils and other degraded lands.

The inventory and monitoring of eroded lands using remote sensing data

In order to prevent soil loss and protect fertile lands from soil erosion, precise information on the nature, extent and magnitude of soil erosion is a prerequisite. In the study reported here, Landsat MSS, TM and Indian Remote Sensing Satellite (IRS-1A) Linear Imaging Self-scanning Sensor (LISS-II) data have been visually interpreted to derive qualitative information on both eroded lands and shifting cultivation areas. Three categories of eroded lands, namely (i) nil to slight, (ii) moderate, and (iii) severe to very severe were delineated. Opticalcum-digital enlargement of LISS-II data helped to pick up small pockets of eroded lands and shifting cultivation areas that were not discernible at a 1 :250 000 scale. Temporal variation observed in the extent and distribution of eroded lands is also discussed in detail.

Nature of suspended solids and IRS1A-LISSI data: A case study of Tawa reservoir (Narmada Basin)

IRS1A-LISSI spectral digital data were analyzed to determine the feasibility of quantifying the concentration of suspended solids in the water by this sensor. For this purpose, a small tributary (Tawa) of a major river basin (Narmada Basin) in central India was studied for ground truth evaluation of digital data obtained from the Indian Remote Sensing Satellite-1A Linear Imaging Self-Scanning (IRS1A-LISSI) sensor. Tawa reservoir water samples were collected on 20 October 1988 concurrent with IRS1A overpass. The samples were analyzed to determine the concentration of total suspended matter, grain size, and mineralogy. The results indicate that, in the concentration range between 10 mg/L and 50 mg/L, a positive functional relationship exists between the concentration of suspended solids and the visible wavelength Bands 1, 2, and 3 (0.45–0.68μ m). It has been observed that mineralogy and grain size are the main factors which influence the reflected radiance at lower concentration level (10–50mg/L) of suspended solids. It can be concluded that as the concentration of suspended solids in the 10–50mg/L range increases the spectral response increases. It can be stated that IRS1A-LISSI data provide a good foundation for further development of remote sensing as a practical tool in the estimation of suspended solids.