Geomorphology
Mousa Abedini; Aboozar sadeghi
Abstract
The aim of this study is to calculate the nocturnal urban heat island (UHI) and its relationship with urban land use in the metropolitan area of Tabriz. Landsat satellite data and Sentinel 3 data were used for this research. The Landsat satellite data was processed in ENVI 5.3.1, and the Sentinel 3 data ...
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The aim of this study is to calculate the nocturnal urban heat island (UHI) and its relationship with urban land use in the metropolitan area of Tabriz. Landsat satellite data and Sentinel 3 data were used for this research. The Landsat satellite data was processed in ENVI 5.3.1, and the Sentinel 3 data was processed in SNAP software, with further statistical calculations and outputs performed using ARCGIS 10.8. In the Landsat data, the minimum temperature was 5.14°C and the maximum temperature was 23.91°C, with the highest temperatures observed in bare land, transportation, agriculture, industrial, residential areas, as well as low-lying areas and areas lacking vegetation cover. The highest percentage of area with temperatures ranging from 15.16°C to 17.88°C was approximately 45.75% of the city. Areas with temperatures above 19°C covered an area of approximately 31.45 square kilometers, which accounts for 12.58% of the city. In the Sentinel data, the minimum temperature was 12.84°C and the maximum temperature was 21.62°C, with similar land use patterns to the Landsat data. Areas with temperatures ranging from 17.29°C to 18.45°C had the highest percentage of area, and areas with temperatures above 19°C covered approximately 17.06 square kilometers, accounting for 6.82% of the total area. Based on the results, which showed similar temperatures and land use patterns in both satellite data, it can be concluded that either satellite can be used for extracting the urban heat island (nighttime temperature).
GIS&RS
sayyad asghari; hamid Soleimani Youzband; Aboozar Sadeghi
Abstract
Cereals are considered one of the most important sources of dietary protein, and wheat is a significant cereal crop with high protein content. Currently, the rapid and excessive population growth and the perceived shortage of available resources to meet essential human needs are among the biggest challenges ...
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Cereals are considered one of the most important sources of dietary protein, and wheat is a significant cereal crop with high protein content. Currently, the rapid and excessive population growth and the perceived shortage of available resources to meet essential human needs are among the biggest challenges facing the world. Accurate and up-to-date statistics and information on agricultural capacities form the foundation of proper planning and management in agricultural affairs.
Methods: In this study, Sentinel2-L2A satellite images were initially downloaded, and the Normalized Difference Vegetation Index (NDVI) was extracted using the set of images containing ground reflectance data. Then, the Support Vector Machine (SVM) and Random Forest classification algorithms were applied to the images using the R programming language in the Jupyter Notebook environment.
Results: Finally, it was observed that the Random Forest algorithm performed better and more appropriately, with an overall accuracy of 93% and a kappa coefficient of 87%, compared to the Support Vector Machine algorithm, which had an overall accuracy of 90% and a kappa coefficient of 82%. This preference is due to its higher accuracy and kappa coefficient, indicating a greater agreement with reality and higher prediction accuracy.
Conclusions: The results of these algorithms showed that each algorithm has its own strengths and weaknesses. The Support Vector Machine algorithm is used in many classification problems due to its simple structure and adequate performance. However, in this study, it performed weaker compared to the other algorithm, the Random Forest. The Random Forest algorithm usually provides accurate results due to its ability to combine different models and reduce the effect of overfitting. Nevertheless, its high computational complexity can be problematic in larger applications.
