Volume 8, Issue 24 (9-2019)                   DEEJ 2019, 8(24): 49-64 | Back to browse issues page

DOI: 10.22052/deej.2018.7.24.35

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Introduction: Climate change and human activities have a direct impact on land vegetation. Decreased rainfall and increased temperature are among the climate change factors leading to significant changes in water resources and energy balance in affected areas. On the other hand, human activities such as growing population, overgrazing and land use changes that make change in land conditions, also cause land degradation and desertification. Land degradation is a negative environmental process defined by many authors using different methods. Some authors have defined land degradation as reduced potential for land production or long-term decrease in ecosystem performance, while others have determined land degradation as decreased resources over time due to negative effects of human activities. Despite these definitions, all definitions imply that land degradation causes decrease in potential of land resources to meet the ecosystem needs. There are several methods for assessing land degradation at different spatio-temporal scales. Land degradation is the result of a prolonged decrease in vegetation and initial production at spatio-temporal scales. Therefore, long-term study of vegetation can be used as a strong index for assessing land degradation. According to studies, one can conclude that arid and semi-arid regions are very sensitive to climate change, and on the other hand, vegetation well illustrates these changes. Therefore, due to the fact that Fars province occurs in arid and semi-arid regions of Iran, this study aims to investigate the process of land degradation using trend analysis of climate data and vegetation indices in Fars province.
Materials and methods: Fars province covers 122000 km2, which accounts for 7.4% of the countrychr('39')s total area. Monthly products of NDVI index from Terra satellite, MODIS sensor MOD13A2 were used in this study in order to investigate vegetation. Also, climate data of temperature and precipitation with the monthly lag from the synoptic stations in the region, with appropriate distribution at the province scale and a common time base during the period of 2006-2016 were used. The NDVI products were used to calculate vegetation index for the period of 2006-2016. Google Earth software was used to assess NDVI values in different regions of Fars province. In order to collect the field data, the available images from Google Earth for each year were taken as random sampling points, and then the consistency rate of vegetation and NDVI index were evaluated. Also, we use used MODIS land cover-type product (MCD12Q1, 500 m) for land cover information 2006–2016. After obtaining climatic (temperature and rainfall) data for the stations in the present study, temperature and rainfall maps were prepared using inverse weighted distance interpolation method. Inverse Weighted Distance (IDW) method interpolates the unknown quantity by weighing the data around the point. Mann-Kendall test and Theil–Sen estimator were applied to calculate the monthly changing trends of vegetation, temperature and rainfall in the study area. Theil–Sen estimator was used to confirm the accuracy of the trend changes. After analyzing the trend of climatic data and vegetation index, the correlations between these indices were investigated. The linear correlation model was used for monthly NDVI, temperature and rainfall time series. All statistical analyses were performed in IDRISI software on a monthly basis for the period of 2006-2016.
Results: The changing trend of vegetation, rainfall and temperature showed that these indices show different changes during the period of 2006-2016. Changes in NDVI index indicate that the value of this index varies from 0 to 0/55. The changing trend of NDVI index showed a decreasing trend for this index during the time period, but climatic indicators of temperature and rainfall showed increasing trends. The trend of changes in NDVI index showed that this trend is not the same throughout Fars province so that 22.4% of the area of Fars province showed a decreasing trend in terms of vegetation. On the other hand, the decreasing rend of vegetation was significant for 7.8% of the study area. 13.2% of the area of province indicated no correlation as well as no trend for this period. The increasing trend for vegetation accounted for 64.4% of the area of province. The analysis of rainfall trends showed that this index had a decreasing trend in 86.1% of the area of the province. Rainfall changes without any trend accounted for 9.7% of the provincechr('39')s area, while the increasing trend for rainfall was found for only 4.2% of the province. The study of the temperature trend showed that only 2.4% of the province showed a decreasing trend. About 0.02% of the province showed temperature variations without trend, and 97.5% of the province indicated the increasing trend of temperature. The correlation between NDVI and rainfall indicated that in 81.8% of the province area, there was a negative correlation between vegetation and rainfall, and about 1.8% of the province had no correlation with the trend of changes in vegetation with rainfall. Positive correlation between these two parameters also was found for 16.4% of the province. The relationship between temperature and vegetation indicates that the negative trend of this correlation is observed in 53.3% of the provincechr('39')s area. Also, about 11.8% of the province did not indicate any correlation between vegetation and temperature. The positive correlation between these two indices was found for 34.9% of the province, so that the temperature increased with increasing vegetation, of which in 13.3% of these areas the correlation was positive and significant. The correlation between temperature and rainfall also shows that there is a negative relationship between temperature and rainfall over 97.4% of the province, of which has a negative and significant correlation was observed for ​​24.5% of the provincechr('39')s area. Also, in 1% of the area of ​​the province, there was no correlation between these two parameters and in 1.6% of the total area of ​​the province, the relationship between rainfall and temperature was significant and positive.
Discussion and conclusions: Based on the results, increasing and decreasing trends in the vegetation and climatic parameters were variable in the different regions of study area. An increasing trend (about 64.1%) for vegetation was observed in the study area and mainly in agricultural land use. On the other hand, during this time period, the trend of climatic parameters such temperature and rainfall were increasing and decreasing until 2016, respectively. Therefore, more than half of Fars province is characterized by rainfall shortage and increased temperature, which ultimately would result in reduced water for agriculture. The analysis of the vegetation trend in Fars province showed that 22.3% of the study area had a negative trend during this 12-year period. These areas are mainly rangeland and forests. According to results, correlation analysis showed a negative correlation between NDVI and rainfall and a positive correlation between NDVI and temperature in these areas. Another reason for this negative correlation is the adaptation of plants to the region conditions which has made the plants more resistant to drought and water stress (Lamchin et al., 2018).
Type of Study: Research | Subject: Land degradation
Received: 2018/12/5 | Accepted: 2019/07/14 | Published: 2019/09/14