Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change de...

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Bibliographic Details
Main Authors: Abdulqadeer Rash, Yaseen Mustafa, Rahel Hamad
Format: Article
Language:English
Published: Elsevier 2023-11-01
Series:Heliyon
Subjects:
Supervised classification
Change detection
Socioeconomics
Remote sensing
Landsat imagery
Online Access:http://www.sciencedirect.com/science/article/pii/S240584402308461X

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