K. V. Ramana

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.

Cover: Land‐use/land‐cover change analysis in part of Ethiopia using Landsat Thematic Mapper data

Land cover and human or natural alterations of land cover play a major role in global-scale patterns of climate and biogeochemistry of the Earth’s systems. Landuse and land-cover changes have a direct impact on land management practices, economic health and social processes of concern at the national and global level (Ojima et al. 1994). Global assessment of changes in physical characteristics of land cover is therefore a fundamental input for models of global climate and terrestrial hydrology (Lambin and Strahler 1994). Several researchers have attempted to use digital satellite sensor data to address the land-use and land-cover change detection problem (Fung 1990, Lambin and Strahler 1994, Heo and FitzHugh 2000, Rogan et al. 2003). Several procedures including comparison of land-cover classifications, multi-data classification, image differencing/ratioing, vegetation index differencing, principal component analysis and change vector analysis have been employed for land-use and land-cover change detection (Singh 1989). We report here a study that was carried out (i) to derive information on spatial distribution of land use/land cover in part of Ethiopia, and (ii) to study its temporal behaviour during the period 1994 and 1997 using Landsat Thematic Mapper (TM) data. With a geographical area of 1883 km, the test site is bound by geo-coordinates 6u089 to 6u489 N and 37u249 to 37u429 E and forms part of Chencha Wareda and adjoining areas of Gamo Awraja, Gamo Geofa Kifle Hafer, Ethiopia. Landsat TM digital data acquired on 2 January 1994 and 11 February 1997 were used. Ethiopian Mapping Agency topographical sheets at 1 : 50 000 scale and published relevant report and maps were also referred to. The methodology involves radiometric normalization, geo-referencing, digital analysis and accuracy estimation. Radiometric normalization of Landsat TM data of 1994 and 1997 was carried out by converting DN values into radiance values using calibration coefficients provided by Markham and Barker (1986). For georeferencing the Landsat TM digital data, available 1 : 50 000 scale topographic maps were scanned on a Contex FSS-800 system and were rectified for scanning error. The output, thus generated, was taken as a reference for geo-referencing the Landsat TM data of 1994 by using ground control points on a Silicon Graphics Octane Work station using ERDAS/IMAGINE version 8.3 software. The Landsat TM data of 1997 was subsequently co-registered to Landsat TM data acquired in 1994 using an image-to-image registration tool. For deriving information on land use and land cover, spectrally homogeneous classes were initially made from Landsat TM data of 1997 using an unsupervised classification algorithm. Based on the results of unsupervised classification and ground data, a cluster of pixels representing various land-use and land-cover categories were selected as training sets

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 terrains 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.

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.

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.

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.

Mapping Soil Resources in Part of Northern India Using Spaceborne Multispectral Data

Abstract Spaceborne multispectral measurements have been found very useful tool in delineating soilscape boundaries. The Indian Remote Sensing Satellite (IRS 1B) Linear Imaging Self‐scanning Sensor (LISS‐II) data in the form of false colour composite (FCC) prints at 1:50,000 scale covering part of a complex terrain ‐ hard rock intermixed with the alluvium, were interpreted visually for mapping soil resources. The physiography and lithology of the terrain have been found to have a direct bearing on the occurrence of soils. The image elements which are the reflection of surface drainage, land use/land cover, wetness, etc have been helpful in segregating the broad physiographic units into their components. These sub‐divisions were ultimately found to be associated with the characteristic soils. The methodology and results are discussed in detail.