جستجوی مقالات مرتبط با کلیدواژه « پایش تغییرات » در نشریات گروه « منابع طبیعی »

Wetlands, as one of the most sensitive ecosystems on Earth, are always facing various changes in their range, and changes in cover and use are among the most effective of these changes. The land has always been affected by human activities and uses. Those human activities that are limited to certain places and find a relatively stable position, create human uses. Therefore, analysis of wetland change has become a management priority. land use/land cover (LULC) plays a key role in the study of environmental developments at the local, regional and global levels. Human activity and change in the Earth's surface lead to changes in the structure and ecological processes of the Earth's natural systems. These changes mainly affect the main aspects of land functions (including energy balance, water, soil, and food network). In addition, pressure on natural resources, which is due to the human need for environmental resources and is often influenced by population growth drivers, leads to changes in the Earth's surface. Landscape changes due to human interventions lead to different developments and trends in land use/land cover. Therefore, time/coverage analysis is very important for understanding and routing spatial changes from the past to the present and planning for the future. Today, high-resolution multispectral and multi-temporal satellite data are used as an essential tool for estimating aspects such as vegetation, deforestation, and urban sprawl. Remote sensing and GIS technology provide a platform for studying landscape deformation across the earth's surface. Remote sensing data provide valuable information in a relatively short time and cost-effectively. High-resolution satellite imagery or aerial photographs can be used to study land use/land cover changes in different ecosystems and areas. The fact that Shadegan Wetland is one of the international wetlands in the country, which is currently on the Montreux list due to human interventions, can assess the developments around the wetland, especially in the process and type of land use/land cover changes, in identifying the drivers The main impact on this wetland is associated with its practical importance and helping to remove this wetland from the Montreux list. And waterfront can be used to adjust the exit bill of this wetland from the Montreux list. In this study, integrated remote sensing and GIS methods have been used to detect land use/land cover changes in the enclosed area and affect Shadegan wetland.

The study area is located in Shabangan Wetland, surrounded by the Ozon Plain. Due to the immediate man-made effects on Shadegan Wetland, especially the role of the surrounding roads and waterways, this area was closed on the latest Google Earth satellite images and then transferred to the layers used. In this area, the international distance is 48 degrees and 19 minutes and 16 seconds to 49 degrees and 3 minutes and 44 seconds and the northern latitude is 29 degrees and 55 minutes, 44 seconds to 38 degrees, 28 minutes and 42 seconds at a distance of about 60 kilometres. It is located south of Ahvaz, the capital of the province, and 5 km south of Shadegan. In this research, images of the 20 years of the Landsat satellite from the years 1999-to 2019 have been used. ENVI software is also used to classify images. After preprocessing and making the relevant corrections using the supervised classification method and the algorithm, the maximum likelihood of processing and highlighting the images was done, and also the kappa accuracy and coefficient of each layer were estimated for accuracy. Then, the preparation of cover and land use maps included different classes of natural land cover and human land uses. In the detection, the most important changes were made around the Shadegan wetland, so in this process, major changes in the existing classes were considered. To detect changes, the Change Detection method was used in ENVI software, which can provide complete information on changes in land use/land cover types. Land use changes were selected in 5 periods with a time interval of 20 (2019-1999).

Five-time periods of satellite data on the use and coverage of Shadegan Wetland in the years 2017, 2014, 2001, 1999, and 2019 were prepared after pre-processing and making relevant corrections using the supervised classification method and the maximum probability of processing and highlighting algorithm. Pictures were taken. The Kappa coefficient and the overall accuracy coefficient were used to evaluate the accuracy of the generated maps and according to the results, the 2019 data had the highest kappa coefficient and the highest overall accuracy. According to land cover and land use classes, the land use/land cover map of the study area was prepared for the mentioned five time periods. The findings of this study show that the land area of Shadegan wetland has changed from about 90,000 hectares in 2001 to about 150,000 hectares in 1999 during the 20 years ending 2019 the area of the wetland has decreased by about 40% in two years. After that, the wetland lands have increased and this increase continues gradually until today. However, despite this increase, the area of the wetland has not been provided in 1999, the area has decreased by about 16% compared to this year.

Considering the trend of bare lands without cover and saline lands, it can be concluded that these two diagrams have an inverse trend towards each other, which can be seen at this point or the intersection of the two desired covers. For this purpose, the desired cover must be obtained, which is created by runoff, so that in a period, the lands began to lose their coverage and became saline lands and salt ponds. Also, considering the increase in uncovering land in 2001 and the water trend, it can be concluded that this increase was due to the decrease in surface water. Due to the trend of saline lands in the relevant period and being in line with the water trend, if the water supply of the wetland is provided, thousands of saline’s will become natural lands. Also, the relative increase in water in recent years and the decrease in bare uncovered land, and the increase in saline land, indicate that the water that replaces bare uncovered land is saline. The two groups of land use and agricultural activity did not cause drastic changes in the study period and according to Table 4, the average percentage of changes in these two land uses was 4.5% and more than 1%, respectively, which is expected to have a significant impact on There is no process of destruction and destruction of lands around the wetland and therefore cannot be considered as a critical factor.

In this research, occurred changes in the forests around Khorramabad between 1986 and 2018 using TM and OLI Landsat images were investigated. For this purpose, after making the necessary atmospheric and geometric corrections, the images were classified by the maximum likelihood algorithm in five classes with a total accuracy of 95% and a kappa coefficient of 0.94. By overlaying the images, the amount of lost forest (34 km2) was determined and as a dependent variable was imported into the multilayer perceptron (MLP) model. In the GIS environment, were prepared the effective factors in the process of deforestation (independent variables); then by using MLP, the deforestation process in the years under review was determined. It was also land use changes was extracted that the results show the highest changes belonged to the forest to barren land changes and finally the deforestation forecast for 10, 20 and 30 years displays a decrease of 4.6% for the year 1407, 7.5% for the year 1417 and 9.3 for 1427. The results of the network training involving all variables with mean squared error (RMS) of 0.13 indicate that the MLP-based modeling is accurate and also, using Receiver Operating Characteristic (ROC) index, the real amount of deforestation was compared to the result of the MLP model; which showed the high accuracy of the MLP model with 0.88 of the ROC.

Predicting land use changes using satellite imagery is now a useful tool for helping planners in complex situations. The purpose of this study was to detect and predict land use changes during the 28-year period (1986-2014) by CA- Markov model in the Safarood-Ramsar watershed of Mazandaran province. In this research, land use and NDVI maps were prepared using Landsat TM (1986), ETM+ (2000) and OLI (2014) satellite images. The accuracy of the CA-Markov model was estimated using the Kappa index of 87%. In order to calibrate the CA-Markov model, the land use map was prepared in 2014, and the Kappa coefficient of the mapping from modeling and user base map (2014) was 82%. The results showed that during the period between 1986 and 2014, the area of forest lands decreased by 10.26% and the total area of residential areas increased by 3.27%. The land use map for the years 2021 and 2028 was predicted by the CA-Markov model. The results showed that during the period 2014-2028, forested lands and rangelands decreased by 4.92% and 1.7%, respectively. Residential areas will increase by 8.04% and the agricultural land will change slightly, indicating the changes in land use to residential land.

Deforestation affects watershed processes and biochemical cycles and lead to soil erosion and lack of water in the catchment areas. This study is aimed to investigate the changes in forest land in the Chaloorood watershed on the west of Mazandaran province using Geomod. In this study, maps of forest in the years of 1987 and 2015 were prepared using satellite images. Then the suitability forest map was produced by making a regression equation between suitability criteria maps and forest changes map in the period of 1987-2015. Finally, by using forest map in 1987, forest suitability map and the number of modified pixels in forest land between 1987 and 2015, Forecast of the forest map for 2043 was done using Geomod. Also, by using the Validate function and classified forest map 2015, as a reference map, and the forecasting forest map 2015, as a comparative map, the validity of the production map was evaluated. The results showed that the area of forest land in 1987, 2015, and 2043 was 38683.65, 2464.354 and 15227.25 hectares, respectively. The extent of forest changes in the last 28 years and the next 28 years is 35.72% and 38.76% respectively. Forest changes in the period between 1987 and 2015 under the influence of factors such as distance from the road, forest cover density, distance from the village, slope and elevation above sea level, respectively. The Pseudo R2 and ROC coefficients are 0.29 and 0.85 respectively, which indicates the proper ability of the model to estimate forest changes over the past 28 years and the relative agreement of the model with the real changes. In this study the accuracy of resulting land use maps was 96%, which represent the appropriate capability of Geomod in land use changes modeling in Chaloosrood watershed.

In order to protect the reasonable and sagely of wetlands and also recognize the changes in their characteristics which can be caused by natural factors or human activities used remote sensing techniques and satellite image analysis. This study aimed to evaluate land use changes of Hoor Al Azim Wetland using LandSat ETM (2003) and OLI (2014) satellite images. After geometric and atmospheric correction, maximum likelihood and post-classification techniques were used to detect land use/cover changes. The overall classification accuracy and the Kappa coefficient for the produced maps to 2003 and 2014 were 0.91 and 0.89, respectively. Map classification of 2014 showed that the area of Hoor Al Azim wetlands has been decreased from 84300 to 45500 hectares. The results of change detection showed during the study residential, and rangeland area had increased, but agricultural and wetland had reduced. The findings of this study denoted that remote sensing data can provide appropriate information for specifying land use changes due to their repeatability, and broad vision. This approach will support adaptive management of wetlands such as Hoor Al Azim wetland.

In recent decades Caspian forest has been attacked by human intervention. Easy access, abundance and diversity of valuable forest products led to an increase in population density, development of new residential areas and activities of deforestation. Change detection is essential in the assessment and management of natural resources. The aim of this study, was to monitor changes in forests of Siyahmezgi watershed in two time periods (2000 and 2015), using LandSat ETM (2000) and OLI (2015) images. Images were geometric corrected using 20 ground control points that are randomly taken from all over the watershed area, and topographic maps. After selection of the best indicators of using Bhattacharyya distance, image classification using an artificial neural network algorithm was performed. The results of classification of neural network method of Siyahmezgi watershed in two time periods (2000 and 2015) showed that overall accuracy is equal to 95.75% and 95.96%, respectively. The area of forest lands during 2000 and 2015 has been reduced in size 213.55 ha. In addition, in this area dense rangelands have declined, but during this period the extent of dry farming and semi-dense rangelands have 169.95 and 9.6 hectares were added, respectively.

The loss of spatial balance and inharmonious physics development across regions، especially in the metropolitans has made monitoring changes in land use and its direction in harmony with reducing the harmful effects in the long and short run became the main issues of policymakers and policy planners’ concerns. The aim of the study was to detect changing in Shahriar''s land use via three methods of support vector machine (SVM)، neural network classifier (NNC) and maximum likelihood classifier (MLC) during (1987-2009) using LandSat digital images. The results showed that during a 22 years period، the urban development increased and agricultural land declined. The decline in agricultural land has been 37، 26 and 25 percent based on SVM، NNC and MLC methods، respectively. Furthermore، the result indicated that urban land has been increased 57 and 41 percent via SVM and NNC، while agriculture land has been decreased four percent based on MLC، which demonstrates the weaknesses in accounting estimates of this method.