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Disasters
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Antecedent Precipitation Index – A Dual Approach Between Soil Moisture and Normalized Difference Vegetation Index
(NDVT) as An Input to GIS Based Locust Control and Surveillance
Vegetation Index
The IRS LISS I data of path 34 and row 48 for three different dates of pass (11.7.92, 24.8.92 and 15.9.92) in the red (0.62 – 0.68 mm) and near infrared (0.77 - 0.86 mm) band were used to generate normalized difference vegetation index
(NDVI) by using the formula :
NDVI = (IR – R) / (IR + R)
Where IR and R are digital numbers in the near infra red bands respectively.
Soil sampling sites of the study area were identified on 1:250,000 toposheets and digitized for superimposition on IRS LISS I scenes to get
NDVI/DN values over three dates of pass for the purpose of developing growth profile of the vegetation with advancing monsoon and to correlate
NDVI/DN values with representative API values. All the IRS LISS I scenes were georeferenced and brought to 1:1 correspondence with each other before extracting
NDVI/DN values.
Results and Discussion
Calculated values of API ranged from 0.66 (site No 21) to a high as 10.17 (site No 18) (table 5)
Out of 25 soul sampling points, only 17 points were sorted out based on the existing landuse having exhibited moderate to good growth during rainy season. Correlation matrix was computed related API, volumetric moisture
(qv) and gravimetric moisture
(qg). API values calculated for each site on the date of soil sampling showed significant positive correlation with volumetric (r - 0.81*) and gravimetric r = 0.78*) soil moisture. (Table 6). Rao et. al !(993) in a ground based study also have shown significant positive correlation between root zone soil moisture and NDVI in a vegetated crop field. Volumetric moisture found to give higher significant correlation than qg as a matter of fact that q considers soil bulk density also.
Table 5: calculated API, Volumetric and Gravimetric moisture status as on 5.10.92.
|
Site No |
API |
VM (qv) |
GM (qg) |
|
1. |
1.52 |
0.020 |
1.28 |
|
2. |
1.27 |
0.046 |
3.55 |
|
3. |
5.00 |
0.009 |
0.62 |
|
4. |
1.74 |
0.034 |
2.44 |
|
5. |
1.45 |
0.192 |
13.76 |
|
6. |
0.33 |
0.062 |
6.05 |
|
7. |
0.71 |
0.013 |
0.96 |
|
8. |
0.94 |
0.220 |
14.69 |
|
9. |
0.48 |
0.015 |
1.00 |
|
10. |
2.78 |
0.073 |
6.72 |
|
11. |
1.04 |
0.009 |
0.59 |
|
12. |
0.95 |
0.033 |
2.03 |
|
14. |
0.95 |
0.024 |
1.83 |
|
15. |
1.00 |
0.012 |
0.83 |
|
16. |
2.46 |
0.057 |
4.49 |
|
17. |
1.39 |
0.014 |
0.97 |
|
18. |
10.17 |
0.057 |
3.63 |
|
19. |
3.2 |
0.032 |
2.31 |
|
20. |
0.72 |
0.025 |
1.83 |
|
21. |
0.66 |
0.042 |
2.98 |
|
22. |
0.68 |
0.022 |
1.63 |
|
23. |
0.72 |
0.019 |
1.36 |
|
24. |
1.75 |
0.009 |
0.59 |
|
25. |
1.54 |
0.037 |
2.72 |
Table 6: Correlation metric showing interrelation among API, GM (qg) and VM
(qv).
| API |
GM (qg) |
VM (qv) |
R |
| API |
1.0 |
0.78* |
0.81* |
| GM |
0.78* |
1.00 |
0.98* |
| VM |
0.81* |
0.98* |
1.00 |
*Significant at 5% level only.
As the calculated API takes care of sol moisture, potential evaporation and rainfall and giving higher significant correlation with qg and qv it can be accepted as a n alternative to qg and qv even in desertic terrain. In reality field soil sampling is tedious and time consuming, besides it may not represent the entire area due to large soil spatial variability.
In another effort it was thought worthwhile to take cumulative value of API over the growing period as an index of moisture towards development of desert vegetation. Cumulative values of corrected API
(CCAPI) for different sites are given in table 7.
Cumulative soil moisture over the growing period and locationwise mean NDVI/DN values of different dates of pass are given in Table 8.
Out of 3 different dates of pass CCAPI values found to be significantly and positively correlated (r = 0.74*) with 15.9.92 date of pass but CCAPI could not give significant correlation with other two DOP’s This may be attributed to the natural time lag between soil moisture and manifestation of canopy development. In dersert areas the poor vegetation status during onset of monsoon and short growing period of ephemerals could also be the reason for insignificant correlation during first two date of pass. But with advancing monsoon there is a good natural vegetation growth which gave significant correlation with CCAPI values.
Table 7: Calculated CCAPI of different sites on three different DOPs of IRS LISS I
| Site |
CCAPI on |
| 11.7.92 |
24.8.92 |
15.9.92 |
|
1. |
-9.99 |
755.27 |
852.71 |
|
2. |
-17.91 |
552.66 |
711.63 |
|
4. |
-6.24 |
106.77 |
922.10 |
|
5. |
-1.04 |
216.65 |
546.80 |
|
6. |
07.8 |
149.40 |
298.81. |
|
7. |
3.7 |
181.21 |
459.70 |
|
8. |
-9.12 |
180.48 |
469.70 |
|
10. |
-6.69 |
232.17 |
1233.82 |
|
11. |
0.00 |
384.50 |
707.56 |
|
12. |
-19.02 |
160.83 |
424.44 |
|
14. |
11.49 |
99.55 |
385.93 |
|
15. |
-17.88 |
164.90 |
458.53 |
|
16. |
-11.22 |
918.34 |
1261.16 |
|
17. |
0.00 |
315.92 |
684.05 |
|
18. |
0.00 |
358.71 |
820.63 |
|
19. |
0.00 |
297.21 |
608.57 |
|
20. |
0.00 |
297.21 |
608.57 |
|
21. |
125.68 |
895.35 |
1220.63 |
|
22. |
118.06 |
878,35 |
1220.63 |
|
23. |
-9.25 |
530.21 |
1221.19 |
|
24. |
4.41 |
217.18 |
738.27 |
|
25. |
119.44 |
896.09 |
541.13 |
Conclusion
Western districts of Rajasthan are prone to locust upsurge and breeding due to favourable bioclimatic conditions. The significant correlation of API integrates the three most important variables, viz. soil moisture, vegetation and rainfall, responsible for breeding and proliferation of locusts. In GIS for locust control and surveillance where variable attributes are combined with permanent attributes and other related informations to arrive at probable sites for locust upsurge,
API/CCAPI can alone used successfully as a variable attributes.
Table 8: NDVI/DN & CCAPI values on 3 dates of pass for selected sites
|
Site No. |
11.7.92 |
24.8.92 |
15.9.92 |
|
NDVI/DN |
CCAPI |
NDVI/DN |
CCAPT |
NDVI/ND |
CCAPI |
| 4 |
162 |
-6.24 |
179 |
106.77 |
161 |
922.10 |
| 5 |
131 |
-1.04 |
123 |
216.65 |
110 |
546.80 |
| 6 |
128 |
-7.80 |
117 |
149.41 |
121 |
298.81 |
| 7 |
129 |
3.70 |
101 |
181,21 |
84 |
459.70 |
| 9 |
119 |
-5.96 |
105 |
183.67 |
94 |
441.02 |
| 10 |
131 |
-6.69 |
103 |
232.17 |
86 |
590.91 |
| 12 |
164 |
-19.02 |
132 |
160.83 |
124 |
424.44 |
| 14 |
135 |
11.49 |
118 |
99.55 |
111 |
385.93 |
| 16 |
159 |
-11.22 |
161 |
918.34 |
162 |
1261.16 |
| 17 |
148 |
0 |
128 |
315.92 |
144 |
684.05 |
| 19 |
155 |
0 |
132 |
358.71 |
133 |
820.63 |
| 21 |
137 |
125.68 |
130 |
895.63 |
129 |
1233.82 |
| 25 |
136 |
4.41 |
117 |
217.18 |
114 |
541.19 |
Acknowledgement
Authors are grateful to Shri K Radhakrishnan, Director ?NNRMS/RRSSC for this constant encouragement. Sincere thanks are due to Dr D C Joshi and the staff of soil testing laboratory,
CAZRI, Jodhpur for their help during the analysis of soil sample. The suggestions received from Dr. Jagdeesh
Chandra, Jodhpur and the RRSSC staff are duly being acknowledged.
Refernces
- Applied Hydrology by Muttreja L N, 1990 Tat McGraw Hill, New Delhi.
- Black, C.A 1965, Methods of soil analysis, Part 1, Monograph No 9, Agronomy series, Madison USA.
- Blanchard, B.J, Schumugge, T.J and Rhoades, E, 1981, Estimation of Soil Moisture with API Algorithms and Microwave emission. Water resources Bulletin 17, 767-774.
- Choudhary, B.J and Venkatratnam L., Rao P. V.K., Ramana K.V, 1993, Relation between soil moisture and normalized difference vegetation Index of vegetated fields. Int Journal of Remote Sensing, 14 (4) : 444-449.
- Teng, W.L, Wang J.R and Doraiswamy P.C 1993 Relationship between satellite microwave radiometric data, API and regional soil moisture, Int. Journal of Remote Sensing, 14 (13) : 2483 – 2500.
- Wang, J.R 1985, Effect of Vegetation on soil moisture sensing observed from orbiting microwave radiometers, Remote Sensing of Environment, 17, 141-151.
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