Volume 4, Issue 3 (9-2017)
Journal of Spatial Analysis Environmental Hazards 2017, 4(3): 53-70 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hamzeh S, Farahani Z, Mahdavi S, Chatrobgoun O, Gholamnia M. Spatio-temporal monitoring of agricultural drought using remotely sensed data (Case study of Markazi province of Iran). Journal of Spatial Analysis Environmental Hazards 2017; 4 (3) :53-70
URL: http://jsaeh.khu.ac.ir/article-1-2749-en.html
URL: http://jsaeh.khu.ac.ir/article-1-2749-en.html
1- , saeid.hamzeh@ut.ac.ir
Abstract: (8137 Views)
As a result of climate change and reduction in rainfall during the last decade, drought has become big problem in the world, especially in arid and semi-arid areas such as Iran. Therefore drought monitoring and management is great of important. In contrast with the traditional methods which are based on the ground stations measurements and meteorological drought monitoring, using the remote sensing techniques and satellite imagery have become a useful tool for spatio-temporal monitoring of agricultural drought. But using of this technique and its results still need to be evaluated and calibrated for different areas.
The aim of this survey is to study the spatial and temporal patterns of drought using remote sensing and the regional meteorological data in the Markazi province. For this purpose, the MODIS satellite data between the years of 2000-2013 have been used to monitor and derived vegetation indices. Drought indices based on satellite data including the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Temperature Vegetation Dryness Index (TVDI), and Soil Water Index (SWI) were obtained from the MODIS satellite data for the period of study for different temporal scales (seasonal, biannual and annul).Then, correlation between obtained results from satellite data and standardized precipitation index (SPI) have been analyzed in all time periods.
Results show that study area has a low to medium vegetation cover. According to the results, the climate situation of the study area is more compatible with the seasonal results of the VCI, and VCI was selected as the best indicator for agricultural drought monitoring in the study are. The obtained results from the applying of VCI over the area show the drought condition in 2000 and 2008 and the wetness in 2009 and 2010 during the study period.
The aim of this survey is to study the spatial and temporal patterns of drought using remote sensing and the regional meteorological data in the Markazi province. For this purpose, the MODIS satellite data between the years of 2000-2013 have been used to monitor and derived vegetation indices. Drought indices based on satellite data including the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Temperature Vegetation Dryness Index (TVDI), and Soil Water Index (SWI) were obtained from the MODIS satellite data for the period of study for different temporal scales (seasonal, biannual and annul).Then, correlation between obtained results from satellite data and standardized precipitation index (SPI) have been analyzed in all time periods.
Results show that study area has a low to medium vegetation cover. According to the results, the climate situation of the study area is more compatible with the seasonal results of the VCI, and VCI was selected as the best indicator for agricultural drought monitoring in the study are. The obtained results from the applying of VCI over the area show the drought condition in 2000 and 2008 and the wetness in 2009 and 2010 during the study period.
Type of Study: Research |
Subject:
Special
Received: 2017/12/22 | Accepted: 2017/12/22 | Published: 2017/12/22
Received: 2017/12/22 | Accepted: 2017/12/22 | Published: 2017/12/22
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
