S.B. Hosseini; A. Saremi; M.H. Noury Gheydari; Hossein Sedghi; A.R. FiroozFar
Abstract
Introduction: Land use is an aggressive process applying to human activities and different uses accomplished over land. It can be argued that human actions can lead to significant changes in current state of earth’s surface. Changes in surface cover (land cover change) may in turn lead to alternations ...
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Introduction: Land use is an aggressive process applying to human activities and different uses accomplished over land. It can be argued that human actions can lead to significant changes in current state of earth’s surface. Changes in surface cover (land cover change) may in turn lead to alternations in balance of energy, water, and geochemical fluctuations at local, regional or global levels. Thus, studies on different land uses changes seem necessary in general environmental evaluation. LULC change detection include implementing multi-temporal Remote Sensing (RS) knowledge to analyze the historical LULC data (maps) and therefore helps in determining the trend of changes associated with LULC properties.
Materials and Methods: Image processing and performing supervised image classification helps to extract information from imageries. In this study, ENVI 5.3 software was used for processing two selected imageries in this project (2014 and 2017). Five LULC classes were established as forest, bare land, vegetation, mountain and water body. For each LULC class, 500 samples were collected at least and used for the supervised classification of images in ENVI. About half of these samples, which were used as “training samples” were collected from the study area through Land Surveying Geographical Positioning System GPS (ground truth data) and Google Earth images. The first step in pre-processing of LANDSAT 8 OLI data in this study referred to the collection of training samples for each class and validating the geometric accuracy of Landsat images, while the next step belonged to the conversion of DNs into At-Satellite radiance using algorithms such as FLAASH. Two dated Landsat images were compared via the supervised classification technique. In this classification technique, two or more images with different dates are independently classified. Maximum Likelihood Classification (MLC) algorithm as a supervised classification method was carried out using training areas and test data for accuracy assessment in ENVI 5.3 and accuracy assessment was done for both images using ENVI v5.3.
Results and Discussion: In order to recognize the past land use pattern of Tarom, researchers first focused on imagery of Landsat 8 ETM+ for the year 2014. Summary of supervised classification accuracy for the 2 different time frames (2014 and 2017) found from accuracy assessment showed that the highest accuracy was found for 2014 supervised classification (92.16% accuracy). Kappa value is also used to check accuracy in classification and having a Kappa value (0.81–1.00) denotes almost perfect match between the classified and referenced data. Different LULC classes had been recognized and used as the base map. From the identified LULC classes, Mountain area by 3524 km2 (62.75% of total land area) was the highest category, after which, came bare land areas with 1295 km2 (24.0%) coverage and vegetation area with 194.6 Km2 (3.7%). Forest was the next class with (2.7%) coverage whereas, water body (1.4%) and unknown pixels 8 km2 (0.15%) specified the least amount of coverage, respectively. Based on the 2017 image classification results, the highest category belonged to mountain area (3532 Km2, sharing 67.7% of total area). The remaining land uses were bare land (23.21%), forest (2.73%), vegetation (4.3%), and water body (1.75%). The unknown and uncategorized pixels were identifiable in this stage that shared 0.31% of the total area. The relative changes in land use and land cover from 2014 and 2017 images showed some irregular patterns in the study area. Land-use change from this period showed positive changes in most of the categories. About 31.4 Km2 of vegetation area had increased in 2014–2017 period which showed a positive change of (+16.14%). While a negative decrease (83 Km2, -6.4%) in bare land category. The results showed that the extraction of adequate samples from different classes of land cover/land use would increase the possibility of correct distinction of image pixels received from the satellite and accurate extraction of LULC classes. Thus, obtaining accurate results from the classification of images via the maximum likelihood method is depending on adequate and appropriate training samples. The trend of land-use changes found in this study, especially percentage increase in forest land and a decrease in bare lands will be helpful for policymakers to make appropriate decisions.
Conclusion: Land cover is the physical material at the earth’s surface and an essential variable which links the physical environment by human activities, and land use is the description of how the land has been utilized for the socio-economic activities purposes. Population growth increases the demand for food, water, and energy, which causes a prompt change in land cover and pattern of land-use. The mentioned process depends on the social and economic development of the nation. In order to have appropriate and unrestrictive management of natural resources (water and soil), it is necessary to have complete information about the pattern of land use and its alteration pattern over time. Thus, it can be concluded that remote sensing is a proper technique to investigate the land-use changes using satellite imagery. Spatiotemporal analyses of LULC help us to manage the environmental changes, which are an appropriate tool for decision-makers on water resources’ to enhance their decisions. In the presented study, LULC map for Tarom basin, Iran, acquired from OLI sensor data sets (Landsat-8) by applying a pixel-based classification method (MLC) with the aid of remote sensing technology. The results that are presented in this study proved the usefulness, effectiveness and also convenience of the MLC technique for generating land-use maps by using a free archive of Landsat data and processing the digital images through the ENVI software. Accuracy assessment using overall accuracy and kappa coefficient for 2014 and 2017, shows the performance of the used algorithm. What matters most in this regard is the accuracy, speed, and quality of land-use maps. In the present study, it was shown that due to high speed and accuracy in generating land-use maps of Tarom, MLC method, would act as the best classification method in this area. However, it is suggested to classify the data by using other methods and compare the results with image outputs provided by Landsat 8 satellite.
behrouz hosseini; yaghoub dinpazhoh; J. Nikbakht
Abstract
Introduction: Drought is a creeping natural phenomenon, which can occur in any region. Such phenomenon not only affects the region subjected to drought, but its adverse effects can also be extended to other adjacent regions. This phenomenon mainly starts with water deficiency (say less than long- term ...
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Introduction: Drought is a creeping natural phenomenon, which can occur in any region. Such phenomenon not only affects the region subjected to drought, but its adverse effects can also be extended to other adjacent regions. This phenomenon mainly starts with water deficiency (say less than long- term mean of variable under study such as rainfall, streamflow, groundwater level or soil moisture) and progress in time. This period can be ended by increasing the rainfall and reaching the mean level. Even after the ending of a drought period, its adverse effects can be continued for several months. Although, it is not possible (at least at this time) to prevent the occurrence of drought in a given region, it is not impossible to alleviate the drought consequences by scientific water management. Such a management should be employed before drought initiation as well as during it and continue on even after the end of the drought period. The frequency of the main drought characteristics is a major concern of this study. The Northwest of Iran recently encountered severe and prolonged droughts, such that a major portion of the Urmia Lake surface disappeared during the last drought in recent years. In order to study drought characteristics, we used the Reconnaissance Drought Index (RDI). This index is based on annual rainfall and potential reference crop evapotranspiration (abbreviated by PET here). This study employed the Monte Carlo simulation technique for synthetic data generation for analysis.
Materials and Methods: The information from the 17 synoptic weather stations located in the North-west of Iran was used for drought analysis. Data was gathered from the Islamic Republic of Iran’s Meteorological Organization (IRIMO). In the first stage of research, the ratio of long term mean annual precipitation to evapotranspiration was calculated for each of the stations. For this purpose, the Penman-Montheis (FAO 56) method was selected for PET estimation. In the second stage, the 64 candidate statistical distributions were fitted for the mentioned RDI’s of each station. The best statistical distribution was selected among the 64 candidate distributions. The best fitted distribution was identified by the chi-square criterion. The parameters of the distribution were estimated by the Maximum Likelihood Estimation (MLE) scheme. Then 500 synthetic time series (each of them have the same number of observed data) were generated employing the parent population parameters. The three main drought characteristics (namely duration, severity and magnitude) were obtained for each of the mentioned artificial time series. The maximum values for each of the mentioned drought characteristic were selected for each year. Then, a new time series having the 500 elements were obtained by collecting the chosen values for each station. Once again the best distribution was selected for each series. Drought characteristics for different return periods (2, 10, 25, 50, 100 and 200 years) were estimated for each station.
Results and Discussion: Preliminary results indicated that a negative trend existed in annual rainfall time series for almost all of the stations. On the other hand, the pattern of monthly PET histograms were more or less similar for all of the selected stations. The peak of the PET was mainly observed in the hottest month of year, whereas the lowest value of the monthly PET belonged to the coldest month of year. The results showed that the amount of annual rainfall time series decreases sharply, after the year 1991. However, PET values significantly increase for all of the selected stations. After calculation of RDI values, the histogram of annual RDI’s was plotted against the year. This is repeated for all of the selected stations. Figure. 6 shows the mentioned diagram for Tabriz station as an example. In the mentioned Figure, negative values of RDI (shown by red bars) indicated the drought years. A critical prolonged drought with a sixteen years duration period (neglecting the 2001 in which RDI value was a small positive value) was experienced in Tabriz. The maximum drought severity in Tabriz was estimated to be about -7 in RDI units. Urmia station experienced the longest drought period, starting from 1995 and ending in 2005. It can be concluded that although few sparse wet years were observed in some of the selected stations in the studied period, they cannot compensate the water deficiency accumulated during several consecutive years. The results showed that the lowest value of the ratio of drought severity in a 100 year return period to the corresponding value for 2 year return period was about 2.13 (belonged to the Tabriz station), whereas the highest value was 3.17 (belonged to the Tekab station). On the other hand, the lowest value for the ratio of drought duration in 100 year return period to its corresponding value for 2 year return period was 1.95 (experienced in the Makoo station). The highest mentioned ratio was 9.18 (observed in the Sardasht station). The lowest and highest value of the ratio of drought magnitude in 100 year return period to its corresponding value for 2 year return period were 1.17 and 2.74, respectively. The mentioned drought magnitude ratios were observed in the Urmia and the Khalkhal stations, respectively. The isoplethes of the three main drought characteristics (severity, magnitude, duration) for a 10 year return period was illustrated for the study area (Northwest of Iran).
Conclusion: In the present study RDI values were used to analyze drought characteristics of Northwest of Iran. The Penman-Montheis method was used to estimate PET (needed for RDI) values of the stations. The main three drought characteristics were calculated for each of the 500 synthetic time series. The results showed that nearly all of the areas under study experienced severe and prolonged droughts. It can be concluded that a sharp decrease in annual precipitation as well as the increase in PET (due to greenhouse effects of consuming fossil fuels as the main source of energy in the region) from 1995 to 2005 was observed in the study area. Scientific management of available water in the study area is extremely vital to alleviate the adverse consequences of drought. Several economic and social problems were anticipated in these arid and semi-arid regions of Iran.
