Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Drought Monitoring during Growth Rangeland Coverage, Ardabil Province

Authors
saideh eiyni 1
saeide eini 2
1 ph.d.student, Ardabili Mohaghegh university
2 associate profesor, Ardabili Mohaghegh university
10.52547/jgs.21.60.1
Abstract
The aim of this research is to investigate drought stress in rangeland rangelands in Ardabil province. According to the monthly rainfall data, 4 synoptic stations of Ardebil province (Ardebil, Khalkhal, Meshgin Shahr and Parsabad Moghan) during the statistical period of 2016-1996 were used to calculate drought index (SEPI) index for 4 periods of 1, 3, 6 and 9 months. Landsat TM and OLI satellite imagery was also used to prepare landslide classification maps based on the maximum probability model and calculation of vegetation indices NDVI, EVI, SAVI and LAI. In order to investigate the relationship between the studied indices, Pearson correlation coefficient (R) and root mean square error (RMSE) have been used. The results of the classification showed that the extent of the rangelands of Ardebil province in 1394 in the year 1377, both in the rangeland and in the rangelands, is a significant decrease. According to the results of SPI, the drought condition during 2011-2015 is more than the other periods studied. Vegetation dispersal maps were based on decision tree tree classification algorithm and according to NDVI index for the studied months. Also, according to the results of the evaluation, the highest correlation was observed between the NDVI index and the 6-month SEPI index, and the lowest mean squared error was found between the SAVI index and the 6-month SEPI index, but in general, the most suitable indicator for Drought monitoring in Ardebil province pastures is a 6-month NDVI and SEPI indicator.
Keywords
Monitoring Drought
Rangeland
Land Use Classification
Vegetation indices
SEPI indicator
17- Alvalia, R., Cunha, P., Brito, SH., Seluchi, M., Marengo, J., Moraes, O., Carvalho, M. 2017. Drought monitoring in the Brazilian Semiarid region, Centro Nacional de Monitoramento e Alertas de Desastres Naturais,21: 2-15.
18- Dutaa, D., Kundu, A., Patel, N.R., Saha, S.K., Siddiqui, A.R. 2015. Assessment of agricultural drought in Rajashan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitiation Index (SPI), The Egyption Journal of Remote Sensing and Space Sciences, 18: 53-63.
19- Dong, H., Li, J., Yuan, Y., You, L., Chen, CH. 2017. A Component-based system for agricultural drought monitoring by remote sensing, PLOS ONE, 12(12): 1-15.
20- Himanshu, S.K., Singh, G., Kharola, N. 2015. Monitoring of Drought using Satellite Data, International Research Journal of Earth Scinces, 3(1): 66-72.
21- Jagerbrand, A. K., Mohau, U., Alatalo, J. M and Chrimes, D. 2009. Plant community responses to 5 years of simulated climate change in meadow and heat ecosystems at a subretic- Alpine site, Oecologia, 161: 601- 610.
22- Jinyoung Rhee, Jungho Im, Gregory J. Carbone. 2010. Monitoring agricultural drought for arid and humid regions using Multi- Sensor Remote Sensing data, International Remote Sensing of Environment 114: 2875-2887.
23- Michel J. Hill, Grahm E. Donald, Michael W. Hyder, Richard C. G. 2004. Estimation of pasture growth rate in the South West of Western Australia from AVHRR NDVI and climate data, International Remote Sensing of Environment 93: 528-548.
24- Richard, Y. and Poccard, I . 1998. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variaitions in Southen Africa, International Journal of Remote Sensing, 19(15): 2957-2920.
25- Singh, A. 1989, Digital change detection techniques using remotely sensed data, International Journal of Remote Sensing, 10, 989-1003.
26- Sumida, A., Nakai, T., Yamada, M., One, K., Uemura, S. & Hera, T. 2009. Ground- Bused Estimation of Leaf Area Index and Vertical Distribution of Leaf Area Density in a Betala ermanii Forest, Silva fennica 43 (5); 799-816.