Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 37846
Special Issue Editors
Interests: land cover and land use change; land-atmosphere interactions
Special Issues, Collections and Topics in MDPI journals
Interests: landscape mapping; object-based image analysis using LiDAR; machine learning algorithm
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Land Cover and Land Use (LCLU) is an important component of the Earth system. It affects ecosystem services, water conservation, infectious disease outbreaks, food security, the climate system, urban forms, and so on. In the last few decades, remote sensing technology has evolved dramatically for better radiometric, spatial, and spectral resolution image products. Such advances in remote sensing, together with a variety of machine- and deep learning algorithms and multiple high-performance cloud computing platforms, further provide the potential and opportunities for a fine-resolution, global-scale, and dynamic LCLU mapping. In addition, along with the development of drone technology, the (near) real-time monitoring of land surface in different sectors, such as forestry and agriculture, has attracted much attention in recent years. The improved documenting of LCLU leads the scientific community to further explore the driving factors of LCLU changes and reveal the underlying mechanisms for better land use planning.
This Special Issue aims to solicit studies that provide insight about the remote sensing of land cover and land use mapping and its impact and driving factors at local, regional, or global scales. Topics may include anything from land cover and land use mapping for a certain vegetation type (e.g., pasture) at small scale to a more comprehensive large-scale evaluation or method. Therefore, multisource data integration for LCLU mapping, classification algorithms development, accuracy assessment, LCLU change detection, socioeconomic drivers, and so on, are all welcome.
Studies may address, but are not limited, to the following topics:
(1) LCLU mapping using multi-sources data (e.g., multispectral, hyperspectral, LiDAR, and drone images);
(2) Forest species mapping;
(3) Crop types mapping;
(4) Coastal wetlands mapping;
(5) Classification and change detection algorithms or methods development (e.g., deep learning algorithm);
(6) Accuracy assessment method development for established LCLU products;
(7) Impacts of LCLU changes on ecosystems, disease, the hydrology cycle, the climate system, and so on;
(8) Physical and socioeconomic drivers of LCLU;
(9) LCLU policy;
(10) LCLU monitoring.
Dr. Yaqian He
Dr. Fang Fang
Dr. Christopher Ramezan
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Keywords
- land cover and land use
- land cover and land use change
- remote sensing
- classification
- machine learning
- accuracy assessment
- socioeconomic drivers
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