نوع مقاله : مقاله پژوهشی
موضوعات
عنوان مقاله English
نویسندگان English
Aim: This research aims to model land-use changes using the MDC, MD, and ML algorithms technique.
Materials & Methods: Landsat 5 and 8 satellite images were used to compare the MDC, MD, and ML algorithms in five classes between 1987 and 2021 and estimate the model's accuracy. This was done using the overall accuracy coefficient, kappa coefficient, manufacturer's accuracy, user's accuracy, and addition and omission error.
Finding: With the change of land-use to residential areas in 2021 compared to 1987, not only were agricultural lands damaged, but it also caused a change in the bed of the studied basin. The flood discharge at the outlet of the basin is reduced, and due to the change and encroachment of the riverbed, the width of the basin decreases. As a result, the capacity of the flood to pass decreases, and the flood intensifies.
Conclusion: The area of residential areas in 1987 compared to 2021 (from 0.08 to 3.12) ) has increased, which has led to instability among the land uses of the studied area. Such changes and developments can have negative effects on the environment and natural resources of the Midawood-Dalon basin. They will cause the spread of risks and damages caused by natural disasters such as river flooding. Also, by comparing the overall accuracy coefficient and the Kappa coefficient, for 1987, the maximum likelihood algorithm with the overall accuracy coefficient (33.34) and Kappa coefficient (0.13), and for 2021, the Mahalanoi distance algorithm with the overall accuracy coefficient (55.29) and Kappa coefficient (0.45) with higher accuracy than other methods.
Innovation: The land-use changes that have taken place have caused the inappropriate dispersion of land-use (rainy land, pastures, water resources) and human areas in such a way that a part of the basin area has changed use due to the city's physical growth.
کلیدواژهها English
Land-use in a general sense, is the type of land-use in the current state, which includes all land use in different sectors of agriculture, natural resources, and industry. In other words, it includes all the activities in the region, such as allocating land to rainfed and irrigated agricultural activities, residential areas, forests, pastures, mines, industrial facilities, and the like, which requires identifying these resources with strong techniques. With recent sensor and satellite technology advances, remote sensing has reduced the cost and time compared to other mapping methods. In the meantime, the classification of satellite images is widely used in processing remote sensing data. Due to their superior features, such as wide coverage, repeatability, and continuous updating, satellite images can be considered the first option in recognizing and preparing land-use. Also, remote sensing technology provides suitable facilities for preparing land-use maps. The value and functionality of these maps depend on their accuracy and accuracy, and one of the most essential information natural resource managers need is land-use maps.
In order to model the Mahalanoi Distance (MDC) algorithm in land-use changes and compare this technique with the minimum distance from the mean (MD) and maximum likelihood (ML) algorithms from satellite images provided by the United States Geological Survey (USGS). Landsat 5 and 8 satellites were prepared and used for 1987 and 2021, respectively. The images in pass number 166 and 167 and row number 038 include the scope of the research; the pre-processing stage is always considered one of the most important stages in processing satellite images. This stage includes the preparation of images for processing. Due to geometric distortions in remote sensing images, the pre-processing steps include geometric and atmospheric correction. By performing atmospheric corrections of two images and confirming the reference ground of the images using the image-vector method, the images were mosaiced with each other and then based on the border of the Midwood-Dallon basin, the images from the previous stage were cut and used as final images. Since one of the goals of the research is to investigate two time periods of land use in the Dalon Midavoud basin, the required images were selected among the available images that have the least cloud cover, the most amount of greenness in the plants and trees in the study area and the dates of the images are close to each other in a month. Also, ENVI 5.6 and GIS 10.8 software have been used to process and digitize these images. Five land-use classes were determined in the study area, including irrigated agriculture, pasture, rainfed lands, and residential and water areas. Supervised classification was done on them. In the continuation of the research, by preparing the output maps of the desired algorithms, the comparison of the MDC supervised classification model method, the minimum distance from the average, and the maximum probability have been discussed.
By comparing the extracted results, it can be concluded that the area of agricultural and water lands in 2021 compared to 1987 has decreased from 35.44 to 2.80 (declining state). The residential area in 2021, compared to 1987, from 0.08 to 3.12, has increased (rising state), which leads to instability among the land uses of the studied area. Unwise human actions and environmental interventions cause unprincipled environmental changes, problems, and instability. One of the obvious reasons for this is population expansion. Along with the expansion of the population, the conversion of agricultural lands into residential areas and road construction has increased. The land-use change to residential areas has not only damaged agricultural lands but also changed the studied basin's bed width and water depth. With the change of the riverbed, the flood discharge at the outlet of the basin is reduced, and due to the change and encroachment of the riverbed, the width of the basin decreases; as a result, the capacity of the flood to pass decreases, and when the flood occurs, it intensifies the flood. In addition, land use changes have caused the inappropriate dispersion of land use (rainfed lands, pastures, water resources) and human (residential) areas in such a way that a part of the surrounding area of the basin leads to the physical growth of the city and in other parts of the basin, the horizontal expansion of the city is evident.
The results show that land-use changes in the Midawood-Dalon basin primarily harm agricultural and garden use. With the changes in land-use, the potential and fertility of agricultural land have been lost. Using agricultural land for urban construction has caused the soil type, erosion, land slope, soil depth and texture, irrigation, drainage, and water retention capabilities to be less capable. Secondly, with the changes in the water area of the studied basin, its width has decreased, which results in the aggravation of flood risks. In the third degree, it leads to a qualitative and favorable change in the water resources of the Midawood-Dalon basin. These changes in 2021 are much more visible and tangible than the period of 1987. Also, the research findings show that the most obvious factors of land-use changes in the two time periods of the Midawood-Dalon basin are human activities that have caused many changes in land use, the increase of residential areas, and the expansion of rainfed lands. Due to the reduction of agricultural lands and pastures, they have devastating effects. Such changes and developments can have negative effects on the environment and natural resources of the Midawood-Dalon basin. They will cause the spread of risks and damages caused by natural disasters such as river flooding. Furthermore, the Mahalanoi Distance Model (MDC) is one of the suitable algorithms for evaluating and monitoring land use changes. This technique is highly accurate in extracting and producing the map of land-use changes, which can show the type and intensity of changes and the stability and instability of the region.
The manuscript did not receive a grant from any organization.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare no conflict of interest.
