IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002 
  
valley, are encountered in the study area which were further sub- 
divide based on erosion intensity or cover percentage etc. On 
these physigraphic units , 11 soil types were identified under 
fourteen soil mapping units (table-2). The temperature regime of 
the soils is ‘hyperthermic’ and the moisture regime is ‘ustic’. The 
mineralogy of the soils mixedThe soils were classified at the level 
of soil families and their associations. The soils were classified in 
to four major soil orders viz., Alfisols, Inceptisols, Mollisols and 
Entisols. The soils of the test site are moderately deep to deep, 
reddish brown in colour, skeletal with medium to fine texture, 
medium to high in organic matter content, mostly well drained 
and are subject to moderate to severe soil erosion. The soil 
physical and chemical properties are discussed in detail else 
where (Ravisankar, 2001). 
6.2 Land use / land cover 
In the test site ten land use /land cover classes were identified 
viz., deciduous forest, degraded forest, kharif crop, shifting 
cultivation , forest plantations, gullied land, scrub land and waste 
lands. The dominant land cover class in the test site was forest 
class because the test site is a part of eastern ghats of India and 
occupied more than 7596 of the total study area. Three classes 
mapped under this category are deciduos forest, degraded forest 
and forest plantations. The agricultural land constitutes 16.58 90 
of the total area. Wasteland category occupied less than 1.5 90. 
The shifting cultivation, locally called as ‘podu’ was prevalent in 
2670 hectares. 
6.3 Land productivity assessment 
A mentioned above the land productivity of the test site was 
assessed with respect to crops, pasture and forest / tree species 
following a parametric approach of Riquier et al.,(1970). 
The LPI values were computed for all soil types by using the 
above mentioned formula with respect to crops, pasture and forest 
/ trees. The computation of LPI values were achieved by writing 
an ‘aml’ program ‘lpi’ in GIS environment. The individual 11 soil 
types are assessed for LPI parameters (table-3) and weights were 
assigned (table-4) following the procedure given in Sys et al., 
(1999). Table-4 also shows the actual LPI values along with LPI 
class for all 11 types of the soil in the test site. 
The coefficient of improvement was computed for all soil 
mapping units by taking the ratio of potential LPI and actual LPI. 
Table-5 and table-6 shows the coefficient of improvement for all 
soil mapping units with respect to crops and forest /tree species as 
these are the two foremost important land use /land cover classes 
in the study area. The study revealed that in the test site all soil 
mapping units have better coefficient of improvement with 
respect to crops (0.7 to 2.6 ) and minimum for forest / tree species 
(1.0 to 2.0). 
7. CONCLUSIONS 
Remotely sensed data enabled to prepare the soil and land use / 
land cover maps at 1:50,000 scale in time and cost effective 
manner. GIS utilities enabled to create data base and analyse the 
soil and site properties more effectively. The calculations 
involved in computipg LPI could be easily implemented through 
‘aml’ routines in GIS. The final outputs and area computations 
were also accomplished in GIS environment efficiently. The land 
758 
productivity assessment procedure should be automated in GIS 
which leads to the Decision Support System(DSS) for land use 
planning based on land productivity. 
  
  
LEGEND 
CROPS FOREST/ PASTURE 
OTHER TREES 
[7] Good + Good * Good 
[..] Average * Good * Good 
[ ] Poor * Average * Average 
BE Average * Average * Average 
Poor * Average * Poor 
pU Poor + Poor * Poor 
BEN To 
BEN Waterbody/Stream/River 
Figure-2 Land Productivity of map of part of Paderu mandal 
Visakapatnam district, Andhra Pradesh State, India. 
ACKNOWLEDGEMENTS 
The authors expresses their sincere gratitude to the Director, 
NRSA and Deputy Director (RS&GIS) for their encouragement in 
carrying out the work . 
REFERENCES 
Ahuja, R.L, Manchanda , M.L., Sangwan, B.S, Goyal, V.P and ‘ 
Agarwal, R.P, 1992. Utilisation of remotely sensed data for soil 
resources mapping and its interpretation for land use planning of 
Bhiwani district, Haryana. Photonirvachak, Journal of the Indian 
Society of Remote Sensing, Vol.20, no.2-3,pp.105-120. 
Prasad Jitendra, Suresh Kumar, Pande, L. M, Subrahmanyam, C, 
Patel, N.R and Saha, S.K, 1998. Soil resource mapping and 
evaluation using remote sensing and GIS for land use mapping — 
A case study of Kotdwar area. In remote sensing and GIS for 
natural resource management. Annual Convention and 
Symposium, November 26-28, 1997, Hyderabad. 
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