Fatemeh Firouzi; Taghi Tavosi; Peyman Mahmoudi
Abstract
Extended Abstract Introduction With recent advances in satellite remote sensing productions in past few decades, several indices have been provided for the study of vegetation dynamics, and especially for the assessment of drought impacts. Among these, two vegetation indices -Normalized Difference Vegetation ...
Read More
Extended Abstract Introduction With recent advances in satellite remote sensing productions in past few decades, several indices have been provided for the study of vegetation dynamics, and especially for the assessment of drought impacts. Among these, two vegetation indices -Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) - have gained the attention of various researchers. Therefore, the present study aims to investigate the reaction of these two vegetation indices (i.e. NDVI and EVI) to dry and wet years in a dry plain in Iran (i.e. Sistan plain in eastern Iran). Materials & Methods To assess the sensitivity of these indices to dry and wet years, two different databases were required. First, NDVI and EVI image base received from Terra satellite (MODIS sensor) for April, May and June 2000-2014, and downloaded from EOS website. Second, daily data base of Zabol synoptic meteorological station (for a statistical period of 30-years 1985-2014) received from Iran Meteorological Organization. After data acquisition, separate vegetation dynamics maps (for April, May and June) were produced for the study area based on the information derived through processing of MODIS sensor images (Terra satellite) using NDVI and EVI. Effective drought index (EDI) was used to determine the frequency of dry and wet years in Sistan plain. Results & Discussion Mapping of vegetation dynamics based on images received from MODIS sensor (Terra satellite) for a 15-year statistical period (2000 to 2014: April, May, and June) indicated that NDVI and EVI had significant differences in exhibiting the dynamics of vegetation in the study area. These differences were obvious in areas with average amount of vegetation (0.4-0.5 in both NDVI and EVI) and also in areas with sparse dispersed vegetation (0.3-0.4 in both NDVI and EVI). In average levels of vegetation, total area of vegetation calculated by EVI is much higher than what is calculated by NDVI, while in sparse and dispersed vegetation, total area of vegetation calculated by NDVI is almost higher than EVI. Subsequently by selection of a dry (2010-2011) and a wet year (2005-2006), we compared changes in total area of vegetation (average and sparse) calculated by NDVI and EVI. Regarding the response of these two indices to dry and wet years, it was concluded that NDVI shows a better and more logical response during droughts, while EVI provides better results in wet years. However, it should be noted that the mean annual precipitation of Sistan plain is so low (59 mm per year) and its evapotranspiration is so high (4800 mm per year) that precipitation does not play a significant role in vegetation dynamics of this plain. Therefore, water flow in Helmand River, which is the lifeblood of this desert, is much more important than this limited precipitation in Sistan plain; hence, we can conclude that meteorological drought monitoring indices cannot reflect the relationship between drought and vegetation dynamics in Sistan plain, and this makes it difficult to compare NDVI and EVI in the region. Conclusion In general, it can be concluded that NDVI is a more suitable index for dynamics of vegetation in plains such as Sistan, whose life depends not on precipitation but on water running in the river. Because of the computational nature of EVI, it responds better in areas with dense vegetation. According to the vegetation type obtained from MODIS sensor images and field visits, NDVI is a better index for these types of plains.
Saharnaz Shekoohizadegan; Hassan Khosravi; Hossein Azarnivand; Gholamreza Zehtabian; Behzad Raygani
Abstract
Abstract
Desertification means land degradation in arid, semi-arid and dry sub-humid regions in result of climate variability and human-activity. Desertification is the third major challenge for international community in twenty-first century after the two challenges of climate change and scarcity of ...
Read More
Abstract
Desertification means land degradation in arid, semi-arid and dry sub-humid regions in result of climate variability and human-activity. Desertification is the third major challenge for international community in twenty-first century after the two challenges of climate change and scarcity of fresh water.This phenomenon has been raised as one of the most striking aspect of environmental degradation and destruction of natural resources in the world.Desertification, byaffecting vegetation cover, water and soil, is a serious factor threatening national parks in arid and semi-arid regions including Iran.Executive actions related to desertification control must be based on the recognition of the current state of desertification and its intensity.The aim of this study was to evaluate and monitor desertification by usingvegetation indices (NDVI and EVI) extracted from MODIS satellite imagery and classification of desertification by using fuzzy logic.
Materials and Methods
The study area covers an area with about 47,244 hectares, which has been named as Bamou National Park.The height distribution of Bamou National Park shows that most of the area is locatedbetween 1700 and 1900 meters altitude and a maximum height of the study area is 2700 meters above the sea level.The average annual rainfall in the main station area representing the Shiraz station is 392.9 mm with a mean annual temperature of 17.9°C.Based on Domarten developed method, Bamou National Park has a semi-arid climate and is cold with winter rains.
In this research, to monitor and evaluate desertification in Shiraz Bamou national park, the annual changes in vegetation cover were studied during the period of 2000 - 2014. On the other hand, this paper tries to monitor desertification changes using long term-time series analysis of satellite data and vegetationcover indices (EVI & NDVI).Therefore, in this study, profile and map of annual changes were prepared on IDRISI Selva and then analyzed using the MOD13A1product, MODIS sensor, Terra satellite and Aqua system. Finally, using fuzzy logic, profile and desertification intensity map were prepared for 2000-2014. According to the climatic conditions of the region and based on expert opinion, the value of fuzzy classes index changes, the software IDRIDIselva and Arc GIS 10.2 severity of desertification on each indicator based on fuzzy logic was prepared.
Discussion and results
Based on the results of EVI & NDVI, vegetation destruction and desertification intensity have been more in the north west of the study area. The reason for this destruction and desertification is the construction of the new city of Sadra in part of the North West and the west of this park. It can be said that, this degradation is a new form of desertification entitled anthropogenic desertification.As a result of the construction of Sadra city in the western area of the park, it is practically impossible to protect this area.The results show that EVI is more sensitive than NDVI for monitoring parameters such as canopy cover, leaf area index, canopy structure, phenology, and stress plants. The EVI index due to greater sensitivity to changes in areas with high biomass (vegetation growth season) and mitigating the effects of atmospheric conditions on vegetation index values is more applicable to monitor vegetation changes than NDVI.This paper introduces fuzzy logicas one of the methods for classifying the severity of desertification. Fuzzy logic can be used to determine the boundaries of class and privilege of desertification indicators and explain the process. Fuzzy sets, or classes of fuzzy are no sharply defined boundaries and membership or non- membership of a place in particular.The severity of desertification in the form of fuzzy maps based on each available indicator provided the values between 0 and 1 as the classes of desertificationon the map.It can be concluded that for better management of desertification it is necessary to prioritize areas affected by desertification according to its severity.As a result, we can say that accurate desertification classification can be helped to manage this phenomenon. In fact, it is a set of unpleasant consequences that human environment brings. Hence, monitoring and evaluation of the severity of desertification and mapping always isone of the most important management andplanning tools to achieve sustainable development in the field of natural resources.
