JAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002
logged areas in major command areas in different states like
Upper Tapi, Purna, Jayakwadi, Bhima, Krishna and Girna
(Maharastra) etc., were mapped and monitored at 1:50,000
scale using historical satellite data for Central Water
Commission. The salt affected soils were also mapped at
micro-level at 1:12,500 scale to a limited extent using PAN
merged LISS III data from IRS IC / ID satellite and
reclamation and management plans were suggested.
Remotely sensed datawere utilised in qualitative assessment
and monitoring of soil erosion in North Eastern states of
Manipur, Tripura and Arunachal Pradesh. Similarly, remotely
sensed data from TM and IRS-LISS-I/II, have also been used
in studying ravinous lands, waterlogged areas and impact of
mining on forest environment. The treated sub-watersheds in
Kundah, Lower Bhawani and Tungabhadra catchments were
monitored through digital techniques over a period of ten years
with the help of Landsat-TM data pertaining to kharif and rabi
seasons.
4. R&D ACTIVITIES
Research and Development (R&D) studies have been carried
out in soils and degraded lands using remote sensing
techniques especially whenever a new sensor was launched.
Under IRS-UP program, IRS-IA LISS-I and LISS-II sensors
data were evaluated for mapping soils at 1:250,000 and
1:50,000 scale, respectively (NRSA,1990a and b). The study
revealed that remotely sensed data from these two sensors were
on par with the Landsat-MSS and TM data with respect to
mapping soils. In one of the experiments at NRSA (1993b)
SPOT MLA and PLA data were evaluated for mapping soil
resources using monoscopic and stereocsopic techniques. The
stereo data was found to be more useful for delineation and
mapping of soils of high relief areas as compared to normal
and low relief areas due to poor resolution in third dimension.
The time lag between acquisition of stereo imagery plays an
important role in the interpretation of stereo satellite data.
5. DIGITAL TECHNIQUES FOR MAPPING SOIL
RESOURCES
Digital techniques allow correct radiometry, maximum of the
designed spatial resolution, utilization of all spectral channels
of the sensor and facilitate better discrimination of soil classes
and their phases of degradation. Digital analysis of satellite
data solely depends on spectral response of soil surface and the
spectral signature for same type of soils was found to vary with
the change of solar elevation angles, vegetation cover and
moisture conditions. Survey of literature reveals that attempts
for digital classification of soils (Kudrath et al, 1990; NRSA &
AISLUS, 1986) yielded poor results due to overlap of spectral
signatures and lack of established procedures for extracting
physiography information of the study area. In one of the
studies carried out at NRSA,(1994b) on soil mapping using
remotely sensed data from IRS-IA satellite revealed that more
number of mapping units can be delineated by visual
interpretation techniques as compared to digital techniques for
soil resource mapping. Digital analysis lead to generalization
of mapping units and the associated soil information.
To overcome difficulties in digital techniques efforts are going
on to develop context classifiers, decision tree classifiers,
708
neural network algorithms etc. In one of the recent studies at
NRSA (1997c), digital elevation model is developed for using
elevation information in generating colour coded soil map. The
inclusion of slope and elevation information in digital
classification of IRS-IC LISS-III data improved overall
classification accuracy substantially as compared to LISS-IIi
data alone. At NRSA (1998b), Artificial Neural Network
(ANNs) technique was attempted to classify the soils as the
maximum likelihood (MXL) classification algorithm was not
giving satisfying results. The lithology, slope, and elevation
information of the study area was incorporated along with
spectral response of soils from IRS-IA LISS-II data in ANN
classification technique. The comparison of ANN classification
results with MXL classification revealed that the classification
accuracy was improved by 7% from 88% to 95% due to
integration of multi source information in ANN technique.
6. EXPERT SYSTEM FOR CLASSIFICATION OF
SOILS
Expert systems (ES) have the capability of solving complex
problems, handle incomplete data, provide explanation for
conclusion reached and also decide on the next step to be taken
on a problem solving mission. Advances in artificial
intelligence and related fields have relevance to the problems
encountered in soil classification (Mc Cracken and Cate,
1986).At NRSA (1992) expert system for classification of
soils was developed using remote sensing techniques. In this
study efforts were made to establish the inter-relationships
among remote sensing data, ground features related to soil
morphology and classification.The parameters that are
considered in ES are image colour / tone, texture, pattern, soil
slope, physiography, drainage soil hazards and current land use
in addition to other parameters (ex. chemical/physical, soil
depths etc.) obtainable through ground truth. The ES
identifies soils at broad soil groups and classifies at higher
levels. This can be further improved to classify the soils at soil
series / family level.
7. GIS TECHNIQUES IN LAND EVALUATION
STUDIES
Land evaluation provides a rational basis to analyse various
soil, climate and land parameters to arrive at optimum solution
to various problems of natural resources. In the land evaluation
process GIS has become an important tool because it enables
to integrate the complex decisions to be taken under multi-
variant situations of the resource base and their dynamics.
Survey of literature reveals that GIS techniques are being
employed for a variety of studies like Land Evaluation Studies,
Soil suitability for Crops, Watershed Management, Integrated
Management of Natural Resources. Land evaluation principle
is based on matching the requirements of a land for specific
use with the characteristics of inherent soil, climatic,
topographic and other natural resources and is concerned with
the assessment of land performance when used for a specific
use. Major GIS applications in land evaluation include land
capability classification, land irrigability classification,
irrigation water management in command areas, crop
suitability, generation of optimal agricultural land use plans
etc. Studies were also carried out to identify sites for
