Remote Sensing Applications for Urban Tree Inventory, Health, and Management
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 65
Special Issue Editors
Interests: remote sensing; urban forestry; socio-ecological systems; GIS
Interests: remote sensing; urban forestry; urban heat island; GIS
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Urban trees are critical components of socio-ecological systems, delivering benefits that include cooling, stormwater regulation, carbon storage, and public-health services. Yet cities often lack timely, spatially explicit information on tree location, structure, species composition, and condition—information needed to equitably plan and manage urban canopy amid rapid development and accelerating climate hazards (e.g., heat waves, drought, pests, and extreme winds). Recent advances in airborne and terrestrial LiDAR, imaging spectroscopy, high-resolution multispectral imagery, UAS platforms, and cloud-based geospatial analytics have made it increasingly feasible to map and monitor urban forests at scales relevant to municipal decision-making while improving reproducibility and transferability across cities.
This Special Issue in Remote Sensing (MDPI) seeks to highlight cutting-edge remote sensing methods and applications that advance urban tree inventory and actionable monitoring. The topic aligns with the journal’s scope by emphasizing innovative sensing modalities, algorithms, accuracy assessment, and scalable workflows that translate remote sensing observations into operational urban forestry products for planning, risk reduction, and long-term management.
We welcome methods papers, research articles, and case studies addressing topics such as: urban tree detection and delineation; species and functional trait mapping; canopy height/biomass and structural complexity; tree health, stress, and mortality early warning; integration of LiDAR–spectral–thermal–street-level imagery; multi-temporal change detection and disturbance attribution; uncertainty quantification and validation strategies; domain adaptation and transfer learning across cities; linking canopy metrics to heat, air quality, equity, and infrastructure planning; and decision-support tools for municipal inventory updates and management prioritization.
Dr. Jonathan Pando Ocón
Prof. Dr. John Rogan
Dr. G. Andrew Fricker
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 250 words) can be sent to the Editorial Office for assessment.
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
- applied remote sensing
- urban forestry
- tree segmentation
- machine learning
- deep learning
- species/genus classification
- vegetation health assessment
- urban planning
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