State-of-the-Art Remote Sensing in South America
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 58483
Interests: remote sensing; land cover/land use mapping; forest degradation; change detection; data science; spatial modeling; ecosystem services
Interests: remote sensing; GIS; pasture mapping
Interests: spatial analysis; environmental monitoring; remote sensing image analysis; innovation
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; land cover/land use mapping; forest degradation; change detection; spatial modeling; ecosystem services; climate change; fire science
South America is a geo-diverse continent, home of several terrestrial biodiversity hot spots, and freshwater reservoirs with rich fishery biodiversity and vegetation varying from high-density tropical rainforest to grasslands. South American ecosystems are vulnerable to extreme climate change events, which can dramatically affect the forest, grasslands, surface water, glaciers, land productivity, and human wellbeing. Land use is another driver that modifies land cover on this continent. The interaction of land-use change with climate change on the land cover is not well understood in South America. The combined effect of climate change and land-use drivers can accelerate ecosystem tipping points, unlocking carbon from soil and vegetation to the atmosphere, altering the hydrological cycle, and lowering Net Primary Productive of aquatic and terrestrial ecosystems of this continent. Remote sensing has been vital to characterize land cover, land-use change, and climate change interactions in ecosystems of South America. In this Special Issue, we invite contributions that characterize the state-of-art of environmental remote sensing to further advance in the understanding of climate and land-use drivers on land cover. We invite submissions on the following topics:
- Time-series analysis of ecosystem change.
- Land cover/land use mapping.
- Early signs of ecosystem tipping points.
- Integrating long-term field and remotely sensed data.
- Climate change impact on ecosystem services.
- Interaction of climate and land use.
- Cloud computing, deep learning and machine learning applications.
- Multi-sensor integration for building long-term time-series.
- Incorporation of remote sensing information on planning and policy making.
- Future trends and gaps in remote sensing science in South America.
Dr. Carlos Souza Jr.
Dr. Laerte Ferreira
Dr. Washington Rocha
Dr. Ane Alencar
Mg. Santiago Banchero
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.
- Ecosystem service
- Climate change
- Change detection
- Tipping point
- Land cover/land use
- Fire modeling
- Urban mapping
- Cloud computing
- Machine learning
- Deep learning
- Data Science