جستجوی مقالات مرتبط با کلیدواژه « پارامترهای هواشناسی » در نشریات گروه « منابع طبیعی »

Modeling and showing the coverage of the land changes, provides a comprehensive view to researchers in various fields, including environmental and natural resources experts. One of the main methods of environmental studies is to study the land cover/use and vegetation area change. In addition to showing spontaneous changes in nature, changes affected by human activities also fall into this category. Human construction has accelerated these changes in line with its development, especially in recent decades. Today, with the development of space-related sciences and remote sensing in general, and the production of more satellite products, it is possible to display the land use of different areas without the need for field visits and easily. The different behavior of the waves received by the satellite sensor from the various phenomena, known as a spectral signature is the basis for cognition and detection of the uses of the map. Such studies in Qom province have also been considered due to the very urban growth trend and the existence of several different types of climates in the not-so-wide area of this province. Qualitative and quantitative study and display of environmental and peripheral changes in Qom province over a period of about 30 years are one of the main objectives of the present study to help identify the trend of changes in different classes and complications and to model these changes in the future. Also, recognizing the changes in the outlook of Qom province, makes possible the ground for future planning.

In the present study, study times and time steps were selected based on changes in climatic/meteorological parameters. These steps were selected 5 years apart from 1989 to 2019. The study time point was considered to be the end of spring and the beginning of summer. The reason for this was the end of the rainy season in the area. Then the images of various Landsat satellite sensors were taken at specified time steps, and these images were pre-processed, processed, and classified into 11 classes. These 11 classes included; bare land, salty land, sandy land, tree, rock, urban areas, agricultural lands, and 3 different types of range. The results were also presented quantitatively and qualitatively. Based on the available real data, which was obtained visually and by sampling from different classes, the two maximum likelihood and minimum distance classification methods in Qom province were properly evaluated, which of the two, the maximum likelihood method yielded relatively better results considering the whole province with all classes and was used in the final classification. Finally, class changes between time steps were calculated and presented as a change matrix.

The results show that between 2014 and 2019, urban, water, agriculture, and ranges (types 1 and 3) have grown significantly. Also, between the two steps of 2009 to 2014, on average, about 30% of the total rangelands, ie three different types of classified rangelands, have become barren lands. Also, in this step, the main change observed was the largest change of sandy lands to bare lands, the reasons for which need further investigation. An examination of the changes between 2004 and 2009 shows that the negative growth in urban areas is mainly due to the poor quality of Landsat 7 satellite imagery and the similarity of the spectral behavior of salt lands and urban areas. The other negatively growing classes, including lakes, have become saltier lands and rocky areas have become barren, as well as salt lands have become barren and sandy. Examining the changes between 1999 and 2004, it is concluded that the negative changes in the tree class are due to the spectral behavior of vegetation, and this class has become mainly agricultural and rangeland classes. In the lake class, a 4 % change to the salt and rocky class has been detected. Major changes in the bare land class of about 12% have been detected in the rock and sand class. Also, more than 50% of the total area of range classes has been converted to bare land class, which is significant. The study of changes from 1994 to 1999 shows that only 3 classes had positive growth and the rest of the classes have negative growth, most of which was related to the urban class and the main changes were focused on bare lands. Vegetation classes all had negative growth and also due to the spectral similarity of these classes with each other, there was no proper separation between them. 12% of the bare land class has also been turned into a sandy land class. The classification of images and the display of changes from 1989 to 1994 show that sandy soils, range type 1, trees, salt lands, and lakes have grown negatively. In total, about 34% of different types of ranges have become bare lands, which seems reasonable due to the negative change in water areas (lake) and in a way indicates a faster drought. The extent to which other classes change to the bare land class, which includes relatively large numbers, also confirms this in some way.

Considering the geographical location of Qom province and a large area of this province, especially in the eastern half of it, which includes desert lands, including barren, saline, and sandy land classes, the selection of the classes mentioned in this research makes sense. Considering the major coverage of the province, one of the problems in the present study was that almost the majority of the pixels covering the province had a lot of similar spectral behavior and this issue made the classification process difficult.  In general, the classification results related to the images taken in 2019, which is related to the recent time, show positive growth in urban, agricultural, range, and water areas according to the rainfall in early spring 2019 it was logical. Another important point related to this year is the extensive change and conversion of the class of rocky lands into different types of ranges. According to the original image taken from 2019 and the classified images, the error related to the degrees of gray is evident in those images. The software considers the similarity of the degrees of gray and the same spectrum of urban and salt classes as part of a class. These errors are also evident in bare and sandy classes.