Investigation of Land Use Changes in Kardeh Dam Watershed Using Intensity Analysis Method

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Unmanaged land use change is one of the major challenges of the 21st century. The hydrological effects of land use and vegetation management are evident in the changes in runoff depth, minimum discharge, maximum discharge, soil moisture, and evapotranspiration. Land use changes are recognized as one of the main drivers of the hydrological changes in the catchment area. By developing urbanization,land use changes increases the risk of floods with decrease in time of discharge reaches at the peak. Land use changes may bring some problems in the region's climate, water cycle, and its natural habitat. Every year, population growth is accompanied by the increased demand for lands due to agricultural and housing purposes, while the industrial development leads to the loss of fertile lands and changes in the water balance of the region. Land use changes along with the increased urbanization, agricultural activities, and forest degradation are among the major drivers of changes in the water balance. The usual strategy for analyzing the spatial distribution of land use changes is first to identify the pattern of quantitative and qualitative changes, and then changing processes are examined. An estimation of the future prospect of land use changes would be an effective step in the sustainable water resources management to deal with the crises caused by the overdevelopment of land uses. Land cover maps play an essential role in the analysis of land use changes.
This study was conducted with the aim of a quantitative detection of land use changes in the watershed of Kardeh Dam located in Mashhad, Iran. Mashhad, with a total area of 3,288 square kilometers and 3 million inhabitants, is the second largest and populated city in Iran. The increased and unplanned development of this metropolis and the establishment of industries in Mashhad Plain have led to the change in the land use of all plain watersheds, which is not consistent with the existing water resources and has created serious risks for the management of this city. Therefore, the present study aims to investigate the land use changes in this basin in different categories and time periods from two spatial and temporal dimensions and at three levels of time, category level, and intensity based on the method presented by Aldwaik and Pontius (2012).
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Kardeh Watershed, with an area of about 54,425 hectares in Northeastern Iran, is located in 42 kilometers of the north of Mashhad (the second largest metropolis of Iran). This watershed supplies a part of the drinking water of Mashhad and also irrigates the agricultural lands located in the lower areas of Kardeh Dam. Nowadays, many researchers use the assumptions and innovative method of Pontius and Malizia (2004) to analyze their research results. Huang et al. (2007) used the intensity analysis to study the pattern of temporal and spatial variations in the land cover and land use in the catchment basin of Jiulong River in China. Aldwaik and Pontius (2012) expanded this method for the analysis of the intensity of changes to investigate the changes at three levels (1) time level land use changes; (2) the level of gain and loss for each category level; and (3) the intensity of land use changes and their transition from one level to another. The land use change analysis over the time is the highest level of the application of this method. The intensity analysis is a quantitative form for calculating the differences between the classes, and it is summarized in a transition square matrix with rows and columns having the same levels. With the analysis of intensity in each level, the degree of deviation between the observed changes and the assumed level of intensity can also be obtained (Aldwaik & Pontius, 2012).
The spatial data obtained from the satellite images of landsat archives to extract the land use data. One of the most common methods to categorize land cover is the supervised MLC method (Dean et al., 2003; Richards et al., 2006). The algorithm of this method is according to the likelihood of assigning a pixel to the target class (Lillesand et al., 2004). To categorize land use, the two methods of fuzzy and maximum likelihood were combined in this method. With the field investigations and the existing land use maps, the study area was classified into 5 category: 1) Rangeland; 2) Irrigated farming and orchards; 3) Rainfed farming; 4) Rock outcrop and bare land; 5) Residential. After defining land uses with the help of points registered at the field investigation stage and choosing the educational samples, the pixels on color images representing the reflection of the intended use or coverage were selected. To provide the ground truth map, the random samples were used; the accuracy of maps was evaluated through the pixel to pixel comparison of the classified maps with the ground truth.
By the classification of the images, their changes were investigated over the time from 1987 to 1998, 1998 to 2008, and 2008 to 2016 at the three levels of time interval, category, and transition. At each level, the stationary patterns of land use were compared at different time intervals. The intensity analysis is done based on a mathematical method which compares the observed temporal intensities with the uniform intensity.
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The results of the Kappa coefficient which calculated for all periods, show that all the Classifications are in an appropriate range and do not have a significant difference. The evaluation of the classification accuracy indicates the low level of accuracy of the producer’s on the BARR category in 2008, which is 77%. Other criteria have acceptable values, and intensity analysis across three time intervals shows that the highest quick changes in land use occurred over the period of 2008-2016, which is mainly due to the rapid growth of urbanization and the increased migration to the central city of Mashhad during this period. The rapid growth of urbanization and the increased demand for food has caused rapid changes in the land use in adjacent basins, which may justify the increasing land use changes in the studied area. In all time slots, the intensity of categorical land use changes indicates that Rangeland and Rainfed Farming category have the most changes compared to the entire period which can be mainly due to the reduction of recent rainfalls and droughts.
In all time slots, Rangeland accounts for the highest stationary. By comparing the different time slots, the middle period 1998-2008 has the highest and the period 1987-1998 has the least stationary. During the time interval of 1987-1998, the highest gain occurred from the category of Rained Farming to the Rangeland, which might be mainly due to favorable climatic conditions and low population density. During the time interval of 1998-2008, the highest loss occurred from the Rangeland category to the rainfed farming category, which is perhaps related to the sudden growth of Mashhad and the increase in the population of this metropolis due to excessive migration and consequently increased demand for food from neighboring basins.
Transition intensities representing the gain of target categories show that during the period 1998-2008, changes in the Bareland was significant, and these changes were towards into Rangeland. During the time interval of 2008 and 2016, the two categories of Rainfed Farming and Bareland, which cut the uniform line of change, have transited to Rangeland. Transition intensities representing the loss of target categories indicates the significant changes in the Rainfed Farming category, which is targeted at the Rangeland category in the period of 1987-1998. During this time interval, changes took place from Rangeland to Rainfed Farming. Changes from the Irrigated Farming category in the time interval of 1987-1998 to the Rangeland was the target, and it transited to Rainfed Farming category during 2008-2016. During the time interval of 1987-1998, the change from the Rainfed Farming category was limited to the Rangeland category; however, in the time interval of 1998-2008 and 2008-2016, this change took place from the Rainfed Farming category up to the two categories of Irrigated Farming and Rangeland. At all the time intervals, the Bareland category has transited to Rangeland and inverse.
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In the three studied periods, the results illustrate that the most changes and fluctuations are among the rangeland, irrigated and rainfed farming categories without a regular pattern. Rangeland is the only dormant category for both gains and losses in spite of being involved in most of the changes. As the Rangeland category has the highest area in this watershed, it plays an important role as a potential resource for conversion to other uses. The most important reasons include weaknesses in the enforcement of laws, population growth and the increased demand of food and agricultural activities, the development of infrastructure in rural areas including electricity which has led to expand pressured irrigation systems for converting rangelands to the rainfed and irrigated farming in high slopes. In the years affected by the drought, these developed lands could not be exploited, under rainfed and irrigated farmming, and then converted to uncultivated and rangelands. These sudden and non-regular changes in the steep slopes have increased the erosion and intensity of the uncovered bare land changes, which then would lead to severe floods.