Advances in Close-Range Sensing and GeoAI for Forest Mapping and Monitoring

Topic Information

Dear Colleagues,

Forests play a vital role in regulating the global climate and are a central focus of environmental research. Forest products also contribute significantly to the sustainability of many industries, ranging from construction to packaging. Reliable forest geospatial information is therefore increasingly important for supporting sustainable forest resource management and decision making.

Accurate geospatial information on forest structure, composition, and dynamics is fundamental for sustainable forest management, ecosystem monitoring, biodiversity conservation, and climate change mitigation. Recent advances in close-range sensing, UAV-based sensing, geospatial technologies, and GeoAI have significantly enhanced the acquisition, integration, and analysis of forest spatial information. Despite substantial progress, challenges remain in integrating heterogeneous geospatial datasets acquired from different sensing platforms and scales. Efficient geospatial data fusion, spatial semantic understanding, spatiotemporal analysis, uncertainty quantification, and transferable GeoAI models remain open research problems for forest information extraction and mapping.

This Topic aims to bring together recent advances in close-range sensing, UAV-based sensing systems, GeoAI, and geospatial information technologies for precise and efficient forest mapping, monitoring, and spatial information extraction, covering sensor and platform development, geospatial data processing, multi-source data fusion, spatial analysis, and rigorous performance evaluation.

Contributions are encouraged in areas such the following:

(1) Geospatial data acquisition, integration, and management using UAVs, terrestrial and mobile platforms, and other close-range sensing technologies for forest mapping and monitoring;

(2) Machine learning- and deep learning-driven methods for extracting and modelling forest spatial information, including individual-tree attributes, forest structure, and canopy characteristics;

(3) GeoAI-driven methods for forest health monitoring, disturbance detection, and ecosystem dynamics;

(4) Multi-source data fusion and spatial knowledge integration for forest characterization;

(5) Benchmarking, uncertainty analysis, and accuracy assessment of close-range sensing methods across diverse forest conditions;

(6) Automated and transferable GeoAI workflows for forest geospatial information extraction and spatiotemporal analysis across different environments.

Ultimately, this Topic aims to showcase multidisciplinary approaches integrating close-range sensing, UAV-based observation, geospatial information science, spatial data fusion, GeoAI, and spatiotemporal analysis for advanced forest mapping, forest digital twins, and geospatial decision support toward sustainable forest management.

Dr. Zuoya Liu
Dr. Samuli Junttila
Dr. Jianxin Jia
Dr. Jiri Pyörälä
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Drones drones	5.2	10.0	2017	21.1 Days	CHF 2600	Submit
Forests forests	3.1	5.4	2010	17.3 Days	CHF 2600	Submit
Geomatics geomatics	3.7	4.6	2021	21.6 Days	CHF 1200	Submit
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit

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