The Influence of Land Use Dynamics on Natural Hazards Using Remote Sensing Techniques
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 229
Special Issue Editors
Interests: remote sensing; photogrammetry; vegetation mapping; geoinformation; satellite image processing; spatial analysis for heavy metal detection
Special Issues, Collections and Topics in MDPI journals
Interests: geomatics; optical remote sensing; pixel-based and geographic object-based image analysis (GEOBIA); UAV applications; digital photogrammetry and spatial analysis; methodologies for multi-temporal analysis (change detection) and classification of optical satellite sensor data aimed at environmental, agricultural and cultural heritage monitoring and documentation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Over the last few decades, Earth’s surface has suffered a large number of land use changes, which are triggered mainly by the human pressure put on landscapes, such as through urbanization, forest management practices, agricultural practices, etc. The unexpected phenomena of this magnitude caused by these human activities were not managed sustainably, endangering Earth's long-term survival. Land use change increases the frequency and intensity of natural disasters, such as landslides, floods, and earthquakes. These catastrophes can have severe effects on the environment and claim many lives if they are not monitored and prevented in time.
Since remote sensing is widely acknowledged as the most important technology for mapping, monitoring, and the assessment of natural hazards, continuous advancements in RS enable us to quickly collect data concerning land use changes that can be employed in the study of natural disasters. Councils and emergency services can employ remote sensing to deliver both detailed and summary information, assisting in decision-making in the event of a hazard or disaster.
This Special Issue focuses on examining the impact of various natural hazards caused by land use, utilizing various remote sensing techniques. We welcome you to submit unpublished, original essays that describe recent developments in these fields or in closely related ones.
Dr. Alessandra Capolupo
Prof. Dr. Eufemia Tarantino
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.
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.
Keywords
- land use/land cover (LULC)
- natural hazards
- remote and proximal sensors for geospatial data collection
- artificial intelligence (AI)
- machine learning
- deep learning
- big geospatial data
- geographic information systems (GIS)
- satellite image processing
- multisource and integrated approaches for assessing and monitoring anthropogenic and geogenic hazards
- geomatics for natural hazard risk management
