Search

In line with the study of land use and land cover, remote sensing technology has been welcomed by many researchers as a source of spatial information production and suitable tools, which shows the accuracy and validity of these maps. The purpose of this research is to evaluate and compare the accuracy of preparing land use maps using two methods of remote sensing and one method of visual interpretation of Google Earth images in the Kasilian representative watershed. In this research, after taking educational samples using Google Earth software and implementing them on the Landsat 9 image of 2021, classification of images was done in ENVI software, and the land use map was prepared based on training samples, Neural Network and SVM methods. In the method of visual interpretation, all land uses in Google Earth images were manually digitized and a land use map was obtained. Then, the accuracy of the map was checked for all three methods and the results showed that the map obtained from visual interpretation of Google Earth images with overall accuracy and Kappa coefficient of 100% was in agreement with the ground reality compared to Neural Network and SVM methods with overall accuracies of 87.6% and 88.2% and Kappa coefficients of 76% and 77.8%, respectively. However, due to the time-consuming visual interpretation method, especially for large watersheds, and the acceptable accuracy of Neural Network and SVM methods, it is suggested to use advanced methods to prepare land use maps, especially in large watershed [ABSTRACT FROM AUTHOR]

One of the most important methods of extracting information from satellite data is various image classification techniques. The current research was conducted to separate and classify plant ecological units by classification tree analysis algorithm on satellite images and also the visual interpretation of Google Earth images in one of the semi-steppe rangelands of Chaharmahal and Bakhtiari province. After applying the classification algorithm in Idrisi TerrSet software, the software generated the error matrix, and then based on the values inside this matrix, the extracted statistics were evaluated. The results of visual interpretation showed that finally, seven types of plant ecological units that were different in terms of structural features were identified and expressed as descriptive statistics, including Astragalus verus, Bromus tomentellus, Scariola orientalis, Astragalus verus-Bromus tomentellus, Astragalus verus -Stipa hohenikeriana, Bromus tomentellus-Stipa hohenikeriana, and Stipa hohenikeriana. The results also showed that the overall accuracy and kappa coefficients of 0.92% and 0.89 for Landsat 8 images and 0.94% and 0.92 for sentinel 2 images were achieved. Based on the obtained results, it was found that satellite images and aerial images have a suitable separation capability to prepare a map of plant ecological units. Since the visual interpretation of Google Earth images is a time-consuming and expensive method, it can be concluded that satellite images, especially Centile 2, can be used as a tool due to their high resolution and highresolution images, practical and usable, provide accurate information and details of the earth's surface phenomena and be used as a suitable source for preparing a map of plant ecological units. [ABSTRACT FROM AUTHOR]

Continual access to precise information about the land use/land cover (LULC) changes of the Earth’s surface is extremely important for any sustainable development program in which LULC serves as one of the major input criteria. In this study, a supervised classification was applied to three Landsat images collected in 1986, 1998and 2018, providing mangrove forests change data in the coastal area of the Bushehr province. The supervised classification results were further improved by employing image enhancement and visual interpretation. Visual interpretation was not only useful in increasing the classification accuracy of the Landsat images, but also in identifying the mangrove forest cover. Post-classification comparisons of the classified images of the mangrove forest area of the Assaluyeh habitat zone during a period from 1986 to 2018 indicated that the area of these lands had been increased by 68.94 hectares, with a percentage change of 43.85%, which was an increase of 1.41% per year. These changes in the Deir habitat zone showed that the area of the land covered by the mangrove forest was 5.25 hectares, with a percentage change of 0.87. In the Mela Ganza habitat zone, the area of the land covered by the mangrove forest was 26.82 hectares, with a percentage change of 6.33, which was an increase in the annual rate of 0.20.

Continual access to precise information about the land use/land cover (LULC) changes of the Earth's surface is extremely important for any sustainable development program in which LULC serves as one of the major input criteria. In this study, a supervised classification was applied to three Landsat images collected in 1986, 1998and 2018, providing mangrove forests change data in the coastal area of the Bushehr province. The supervised classification results were further improved by employing image enhancement and visual interpretation. Visual interpretation was not only useful in increasing the classification accuracy of the Landsat images, but also in identifying the mangrove forest cover. Post-classification comparisons of the classified images of the mangrove forest area of the Assaluyeh habitat zone during a period from 1986 to 2018 indicated that the area of these lands had been increased by 68.94 hectares, with a percentage change of 43.85%, which was an increase of 1.41% per year. These changes in the Deir habitat zone showed that the area of the land covered by the mangrove forest was 5.25 hectares, with a percentage change of 0.87. In the Mela Ganza habitat zone, the area of the land covered by the mangrove forest was 26.82 hectares, with a percentage change of 6.33, which was an increase in the annual rate of 0.20. [ABSTRACT FROM AUTHOR]