Investigation of land use changes and its relationship with groundwater level (Case Study: Mallard County)

Document Type : Applied Article


1 Professor of Geomorphology, Faculty of Social Sciences, Mohaghegh Ardabili University, Ardabil, Iran

2 PhD Student in Geomorphology, Faculty of Social Sciences, Mohaghegh Ardabili University, Ardabil, Iran


Land use changing affected quantity and quality of the groundwater resources. As land use reflects human impact, concerns about global environmental change have increased today, and also warnings about the importance of land use issues and temporal change have increased. Accurate and timely recognition of these changes is very important to understand the relationship and interaction between humans and natural phenomena in order to make appropriate decisions [16]. Optimal management of natural resources of an area requires understanding the effects of land use change on the hydrological cycle of water in that area [8] It is also necessary to be aware of changes in water level in order to understand the status of groundwater aquifers and its optimal management. By assessing groundwater level fluctuations, it can be used in water resources management [6].
Materials and methods
In conducting this research, the Landsat satellite image series for 2020 from the Landsat satellite image of 8 OLI sensors was used in order to extract the land use map. Also for the year 2000, the LandsatTM 5 sensor image was used to prepare the land use map using visible and infrared bands.
Also, groundwater depth data of piezometer well in Mallard plain were used and the data period was from 2000 to 2020. The steps of the research were as follows: after statistical post-processing of piezometric wells, data elongation method was used to eliminate defects in the study data. The detection method used only to correct the data defects is the interpolation method performed by Neural Power software (based on artificial neural network). To normalize the data, logarithmic conversion was used in SPSS software and GS + software was used for statistical analysis. For atmospheric, radiometric and geometric corrections, ENVI5.3 software and radiance and flash methods were used and GIS10.5 software was used to prepare the desired maps. Object-oriented classification method was used in Developer64 eCogn software for land use classification. In the object-oriented classification method, spectral information is merged with spatial information and the pixels are segmented based on the shape, texture and gray tone of the image surface at a specific scale, and the image is classified based on these components [11].
Discussion and Results
The groundwater level map is shown in Tables (6 and 7). As can be seen from the map above, the highest average water level in 2000 for agricultural use was recorded at 64.50 meters and the lowest average water level for barren land use was recorded at 26.00 meters. Considering the land use map and groundwater level map of 2020, the highest average water level in this year belongs to agricultural use with 61.19 meters and the lowest average recorded water level is related to soil use with 28.00 meters. As can be seen from Tables (6 and 7), if we compare the water level of both years in the study area, it is inferred that the average level of all land uses has decreased in 20 years, except for grassland use, which indicates Groundwater is critical and overuse of these resources. Rangeland use has not only not decreased but has also increased significantly.
Knowing the ratio of uses and how it changes over time is one of the most important issues in planning and policy making. For this reason, in this study, in the first step, in order to classify and then examine the changes that occurred in a specific period of time in the Mallard plain. For this purpose, in this study, in the first stage, in order to classify and record changes over a period of 20 years, images were classified in an objective way in eCognition software and output maps were extracted in ArcGIS10.5 software. Classification accuracy per year Classification accuracy 2000 has an overall accuracy of 0.91 and a kappa coefficient of 0.89. While the classification in 2020 with overall accuracy of 94% and kappa coefficient of 0.92 has provided a relatively lower accuracy. Looking at the 2020 land use map, the rangeland class with an area of ​​151556 had the most and after that, the residential area with 69164 had the most area. Looking at the uses of two years, the results show a significant difference that the use of gardens has decreased significantly from 65981 in 2000 to 2256 in 2020, which indicates the lack of management and felling of trees and the destruction of forests and Gardens and its conversion into residential and agricultural areas, etc., also the use of residential area in 2000 has increased from 42187 to 69164.


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