Showing 22 results for Remote Sensing
Omid Ashkriz, Fatemeh Falahati, Amir Garakani,
Volume 11, Issue 3 (12-2024)
Abstract
The growth of settlements and the increase of human activities in the floodplains, especially the banks of rivers and flood-prone places, have increased the amount of capital caused by this risk. Therefore, it is very important to determine the extent of the watershed in order to increase risk reduction planning, preparedness and response and reopening of this risk. The present study uses the common pattern of the machine and the classification of Sentinel 2 images to produce land cover maps, in order to construct sandy areas and determine land issues affected by the flood of March 2018 in Aqqla city. Also, in order to check and increase the accuracy of the algorithms, three software indices of vegetation cover (NDVI), water areas (MNDWI) and built-up land (NDBI) were used using images. The different sets of setting of each algorithm were evaluated by cross-validation method in order to determine their effect on the accuracy of the results and prevent the optimistic acquisition of spatial correlation from the training and test samples. The results show that the combination of different indices in order to increase the overall accuracy of the algorithms and to produce land cover maps, the forest algorithm is used with an accuracy of 83.08% due to the use of the collection method of higher accuracy and generalizability than compared to. Other algorithms of support vector machine and neural network with accuracy of 79.11% and 75.44% of attention respectively. After determining the most accurate algorithm, the map of flood zones was produced using the forest algorithm in two classes of irrigated and non-irrigated lands, and the overall accuracy of the algorithm in the most optimal models and by combining vegetation indices (MNDWI) was 93.40%. Then, with overlapping maps of land cover and flood plains, the surface of built-up land, agricultural land and green space covered by flood was 4.2008 and 41.0772 square kilometers, respectively.
Dr Saleh Arekhi, Mr Habib Allah Kour, Somia Emadaddian,
Volume 12, Issue 46 (9-2025)
Abstract
Reducing the emissions caused by deforestation and forest degradation REDD is a strategy to moderate climate change, which is used to reduce the intensity of deforestation and greenhouse gas emissions in developing countries. In the last few decades, drastic changes in land use have caused a significant decrease in Hyrkan forests located in Mazandaran province. For this purpose, the aim of this study is to investigate the changes in land use and its prediction for the year 2050 using the Markov chain and the REDD project to reduce carbon dioxide emissions for the cities of Nowshahr and Chalus. Using the images of TM and ETM+ sensors of Landsat satellite, a land use map has been prepared in three time periods related to the years 1989, 2000 and 2021. Maximum likelihood method was used to classify images from supervised classification. From the error matrix, the Kappa coefficient in this evaluation was equal to 0.83 for 1989, 0.81 for 2000, and 0.92 for 2021. The results show that the forest cover decreases in 2050. In contrast, the area of range land, city, barren land, agriculture and wetland will increase. Based on the goals of the REDD project, the amount of carbon dioxide emissions was calculated until 2050. If the REDD project is not implemented, a large area of forest cover will be destroyed and a lot of carbon dioxide is released. The amount of carbon dioxide in the project area in 2021 is 49,681 tons and will reach 806,732 tons by 2051, and with the implementation of the REDD project in the region, this amount of gas can be increased to the equivalent of 402,321 tons. 404411 tons of carbon dioxide was prevented from entering the upper atmosphere of the earth. Examining changes using satellite images can help managers and planners to make more informed decisions.
