GIS and Satellite Image Technology in Forest and Urban Forest Detection and Monitoring
A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".
Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 269
Special Issue Editors
Interests: earth observation; remote sensing (satellite, aerial and UAV); GIS; vegetation mapping; land use; forestry monitoring; crops and bare soil monitoring; dune and coastal vegetation monitoring; climate-changing; environmental SAR application
Interests: GNSS applications; landslide e-structures monitoring; UAV; laser scanning/Lidar; DEM/DTM
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forests are essential components of green infrastructure. Their health affects environmental quality in many ways, and the opposite is also true. The services they provide are recognized as key in tackling climate change effects at local and global scales. Furthermore, the several ecosystems that benefit from their presence are both natural and anthropogenic. Accordingly, recent international regulations and actions are committed to drawing and fostering good management practices, leading to more sustainable and effective approaches to natural resources’ conservation and use. However, forests are increasingly threatened by natural and anthropic hazards: fires, heat and drought, new pests and diseases, deforestation, dereliction and decay. Hence, it appears that forests and urban forest management require increasingly adaptive and responsive tools and strategies. Remote sensing/Earth observation (RS/EO) and GIS could be strategic to address this issue. RS/EO technologies provide a whole series of information that gives access to the mapping and multi-temporal analysis of the spectral behavior of tree species. Specific vegetation indexes can give insights into forest health status and complement future scenario elaborations. On the other hand, GIS-based technologies can add and implement the current knowledge with either spatial or multi-criteria analysis of the many variables influencing tree communities. Eventually, improvements in statistical correlations between remotely sensed and ground data have the potential to improve the quality and reliability of available information, especially for high-risk and difficult-to-reach areas.
This Special Issue aims to garner state-of-the-art RS/EO-based and GIS-based knowledge concerning forests’ and urban forests’ monitoring and management. Additionally, artificial intelligence and machine learning applications deriving useful empirical scenarios to reduce the effects of climate change are encouraged.
Dr. Michaela De Giglio
Prof. Dr. Maurizio Barbarella
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. Forests 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.
Keywords
- forest detection
- urban and periurban forest detection
- remote sensing
- earth observation
- GIS
- ecosystem services
- monitoring
- forest management
