Land use classification; Landsat imagery; Remote sensing; Humans; Iran; Cities; Neural Networks, Computer; Environmental Monitoring/methods; Algorithms; Artificial neural network algorithm; High-accuracy; Land cover; Landuse classifications; Large-scales; Overall accuracies; Pixel based classifications; Remote-sensing; Western Iran; Environmental Monitoring; Environmental Science (all); Pollution; Management, Monitoring, Policy and Law; General Environmental Science; General Medicine
Abstract :
[en] Scenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy.
Disciplines :
Agriculture & agronomy
Author, co-author :
Yaghobi, Soraya; Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
Daneshi, Alireza ; Department of Watershed Management Sciences and Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
Khoshnood, Sajad; Department of Watershed Management Sciences and Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
Azadi, Hossein ; Université de Liège - ULiège > TERRA Research Centre > Modélisation et développement
Language :
English
Title :
Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran.
Publication date :
18 March 2023
Journal title :
Environmental Monitoring and Assessment
ISSN :
0167-6369
eISSN :
1573-2959
Publisher :
Springer Science and Business Media Deutschland GmbH, Netherlands
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