IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring", Hyderabad, India, 2002 
  
  
  
  
  
  
  
  
  
  
  
  
    
  
  
   
  
  
   
   
  
  
   
   
  
  
     
     
    
  
   
   
    
   
  
    
  
   
   
   
   
   
  
  
  
  
  
Fig. 3 : Classified Image of Siyana block 
The separability analysis was carried out using confusion matrix 
technique. The result of this study was used to refine the training 
areas and finally selected the major classes, which show good 
homogeneity. The overall classification accuracy obtained from 
confusion matrix is 90.58%, whereas classification accuracy 
obtained for mango orchards is 88.58%. The confusion matrix 
(Table-2) results show that well developed mango orchards are 
clearly separable from other landuse classes but were mixing with 
aquatic weeds alongwith canal. The young mango orchards were 
less separable from field crops, because the intercropping in the 
new orchards. Therefore, the classification accuracy of young 
  
  
  
  
  
  
  
  
mango orchards is 86.06%. 
Name Old New Crop Fallow Settle- Water | Total Accuracy 
mango mango ment body % 
Old 426 26 0 0 0 16 468 91.1 
mango 
Young 9 142 14 0 0 0 165 86.06 
mango 
Crop 0 10 136 0 0 0 146 93.15 
Fallow 0 0 0 236 18 1 255 92.55 
Settle- 0 0 5 18 178 0 201 88.56 
ment 
Water 5 0 0 0 0 55 60 91.67 
body 
Total 440 178 155 254 196 72 1295 90.58 
  
  
  
  
  
  
  
  
  
  
  
Table 2: Confusion Matrix showing classification accuracy of 
mango and other classes. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
    
  
  
  
   
  
   
  
  
  
  
  
   
  
  
  
  
  
Karauthc 33.08 
Thal Inayatpur 91.01 
Ravani Katiri Khadar 69.08 
Ravani Katiri Bangar 285.46 
Shanpur 86.81 
Bugrasi, T.A. 400.76 
Dhaniawali 86.81 
Kirywali 118.79 
Ranapur 144.25 
Chandpur Poothi 40.15 
Mahav 25.18 
Hankri 338.86 
Chfingraothi Baruli 34.46 
Khand Mohan Nagar 18 
Bigraun 78.85 
Dehra 6.9 
Barahna 189.37 
Mahmuddinpur Buklana 197.81 
Bhagwanpur Alias Harwanpur 3:15 
Sega Jagatpur 13.7 
Badshapur Garhia 4.03 
Bankapur Pali Alias Bahrampur 0.06 
Barauli Basdeopur 64.28 
  
  
Table-3: Village- wise Mango Area 
5.2 Soil Suitability Assessment: 
The soil suitability of mango was assessed on the basis of land 
qualities of soil mapping unit. The land qualities of soils, which 
were used for mango suitability evaluation, are given in Table-4. 
The details of the composition and the taxonomy of various soil 
units are presented in Table-5 and shown in Fig. 4. The criteria 
for soil suitability of mango are given in Table-6. The criteria for 
rating land qualities into various suitability classes for mango 
orchard are presented in Table-7. 
  
  
  
  
  
  
  
  
  
  
  
  
    
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
      
  
  
  
Name of | Surfac Sub- Soil Drainage Slope | Erosion 
5.1 Village-wise Mango orchard estimation: soil series | € surface | Depth 
texture texture 
Village-wise mango orchard acreage estimation has been carried Kalkothi | Sand Sand S Well 1% Slight 
: . : io ; ; eep 
out using GIS technique.  Village-wise mango area is given Mohammad Sandv. A Sandy Les Well 13% | Slight 
Table-3. adpur loam loam deep 
Anupshah | Sand Sand Deep Excessive 3-5% | Very 
Village Name Area (in ha) f didi Severe 
Bihta * 123.98 Siyana Sandy | Sandy | Very | Moderately | 1% | Nil 
Werafirozpur 302.52 loam ey deep well 
oam 
Ghansurpur 147.28 Rampur Sandy | Sandy | Very Moderately | 196 Slight 
Bhainsora 97.31 loam clay deep well 
loam 
Longa 38.11 Mankari Loamy | Sandy Very Moderately 196 Slight 
Nancholi 38.27 sand loam deep well 
Badoha Bazirpur 87.59 Shikarpur | Sandy Clay Very Moderately 1% Slight 
: Ip - clay loam deep well 
| Siyana Rural 579.92 loam 
{1 Kisola 103.93 Uttarawati | Sandy Sandy Deep Imperfect 1-3% | Moderate 
| - loam clay to very 
Hazipur 173.35 
Jalalpur 250.45 
Sulaita 202.40 
  
MET d 
  
Table 
class