In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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Photo 4 (Part of Semi-Arid Region of Piaui State) 
Geographic Information Required 
Data about various components required for this research have 
been gathered from various, Federal, State, and Municipality 
Agencies, such as, Land Use, Soil, Soil Conservation, Slope and 
Elevation, Drought and Flood, Climate (Precipitation, Temperature 
and Humidity), Geology and Hydrology, Vegetation and Forest, 
Irrigation and Drainage, Socio-Economic, Municipality and State 
boundaries etc. 
3. Individual land use and cover classifications should be 
customized to facilitate interpretations of digital images with 
different resolutions. 
Image Processing 
A 1000 by 1000 pixel sub-scene of LANDSAT-TM and SPOT 
multi-spectral data (band 3,4, 5 & 1,2,3) were used for image 
analysis. More than 40 sites were visited in the study area, and 
reference data, such as soil, vegetation, geology, topography, 
climate and others are made to assist in supervised classification 
(Maximum Likelihood Classification-MAXCLAS). Various 
field trips served as a basis for accuracy assessment to derive 
various earth resources information. The digital interpretation was 
checked by three field trips. The relevant statistics, such as mean, 
mode, medium, standard deviation, variance and co-variance 
matrices wwere applied for our study. After inspection of the 
digital classification combined with the field work, finally 
resulted into 15, 17 and 12 categories of land use/land cover 
classification in Paraiba, Piaui and Ceara states at the Level II. 
(Anderson et al., 1976). The accuracy assessments of the 
transformed and no-transformed LANDSAT-TM and SPOT image 
were concluded to compare the best areas of known reference 
data with the same areas on Level II land use and land cover 
classification on a pixel by pixel base produced by supervised 
classification. The over all accuracy was found more than 85% in 
all the three areas. By using RECODE program of ERDAS 
Software on land use/land cover information resulted into 11,11 
and 6 categories of soil associations in each area. Re-coding was 
possible because of the high degree of correlation of land use and 
land cover with the features of other maps. Field observations 
conducted at the sites confirmed this relationship. 
Programs of ERDAS used for study 
Following programs of ERDAS Software in systematic sequence 
were used for unsupervised, supervised classification & accuracy 
assessment. 
For Unsupervised Classification: 
READ-CLUSTR-DISPLAY-COLORMOD-CLASNAM- 
RECODE-COLORMOD-CLASNAM-ANNOTAT-CLASOVR- 
BSTATS-LISTIT 
For Supervised Classification: 
READ-SEED-SIGDIST-SIGMAN-ELLIPSE-CLASNAM- 
MAXCLAS-DISPLAY-COLORMOD-CLASNAM-ANNOTAT- 
CLASOVR-RECODE-INDEX-RECODE-INDEX-COLORMOD- 
CLASNAM-ANNOTAT-SCAN-BSTATS-LISTIT. 
For Accuracy Assessment: 
READ-DISPOL-DIGSCRN-GRDPOL-CLASOVR-CLASNAM- 
SUMMARY. 
Criteria used for land use classification 
For our study of semi-arid regions of NE Brazil, the land use 
and cover classification system (Anderson et ah, 1976) is 
modified in accordance with the local climate, local needs and 
existing conditions. During the conduct of our project, we used 
the following important criteria: 
1. The interpretation accuracies in the identification of land use 
and land cover categories from remote sensor data should be 85% 
or greater. 
2. The multiple use of land should be recognized where possible. 
Digital Image Interpretation Procedure 
Subset from SPOT Scene 
Ground Truth 
Unsupervised Classification 
n^n unsupervised <^1; 
Select Training Areas ^ Select Test Areas 
Supervised Classificatic 
Derive Level I & II with Modifications 
Accuracy Assl Usinent 
▼ 
Geometric Correction 
I 
Derive Land Use/Land cover Map 
Derive Soil Associations Map 
1 
Assess Utility of Product