New Approaches to Land Use/Land Cover Change Modeling

Dear Colleagues

Land use/land cover change induced by human activities has exerted profound impacts on ecosystems and human societies. In recent years, geo-big data has emerged as an important approach to generate reliable and accurate maps of land use/land cover change. Moreover, machine learning algorithms, which are capable of handling complex relationships within data, are also increasingly applied in land use/land cover modeling. Both of these new developments have provided opportunities to gain new insights of the consequences of land use/land cover change, and can help policy makers to address the potential challenges raised by global climate change. Therefore, we organize a special issue that is open to researchers interested in the field of land use/cover change modeling using geo-big data and machine learning. In this special Issue, we invite papers that focus on, but are not limited to, the following topics:

• Land use/land cover mapping with geo-big data
• Predictions of land use/land cover change based on machine learning
• Social and environmental impacts of land use/land cover change
• Sustainable urban/rural land use management
• Urban environmental change and evaluation

Dr. Yimin Chen
Dr. Guohua Hu
Dr. Yujia Zhang
Dr. Xuecao Li
Dr. Brian Alan Johnson
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.