4.02.05 - Remote sensing



Call for Submissions: 3D Remote Sensing for Forests – Progress and Perspective

Guest edited by Yong Pang, Qinghua Guo, Huaguo Huang, Lin Cao, John Kershaw, Manuela Hirschmugl and Xinlian Liang

Submissions are invited for a Special issue of 'Forest Ecosystems'.

Forests exhibit distinct vertical stratification, geographical variation, biological diversity, and are dynamic systems. To disentangle and understand these complex features, managers and scientists rely on efficient and reliable assessments of forest resources across spatial scales. The rise of three-dimensional (3D) observation technology has changed the application potential of remote sensing of forests. 3D forest observations include those from terrestrial, mobile, UAV, and satellite platforms using both active and passive sensors. Initially, laser scanning (LS), or Lidar, was the main data source. Now, multispectral and panchromatic images from airborne or satellite platforms are comparable with LS in terms of forest attributes estimation over large areas, and are expected to be applied practically in the near future. Meanwhile, the information that can be extracted is still limited and not yet adequately accurate for many research and management needs. Further development is required to improve the accuracy and reliability of the mensuration and the attributes estimated from these new technologies.

This Thematic Series covers a range of topics related to 3D remote sensing in forest environments, from terrestrial to spaceborne platforms, from active to passive sensors, and their applications across diverse forested landscapes. Review papers summarizing past and ongoing progresses and original research papers reflecting recent developments are particularly welcome, including studies about thematic information extraction, new techniques for forest mensuration, new missions, as well as new algorithms and applications.

Submission deadline: 31 October 2022
Contact: Dr. Li Hui (bjfulihui(at)gmail.com)
Details: https://forestecosyst.springeropen.com/3d-remote-sensing-for-forests

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Manuela Hirschmugl, Austria


Yong Pang, China

Ruben Valbuena, United Kingdom

Piotr Wezyk, Poland

About Unit

Unit 4.02.05 is specifically concerned with research on remote sensing methods and applications for forest monitoring.  Although remote sensing is used in many other units and working groups, this unit has the specific focus on bringing forward improved methods, tools, algorithms and techniques, which can support the research on forests on all levels. Therefore the scale of applications is very wide ranging from single tree asssessments to continental and global applications. Activities are carried out through informal discussions, meetings, and conferences (preferably jointly with other IUFRO Units), and by drawing attention to research needs and gaps in existing knowledge.

State of Knowledge

On the scale of large area monitoring with satellite data, current Earth observation (EO) missions such as the Sentinels acquire vast volume of data, e.g. for Sentinel-1 and 2 a new image every 5 days of almost every place on earth. By taking orbit overlaps into account, the time between consecutive images of the same region is reduced even further. Through high-quality georeferencing, it is possible to create consistent time series of satellite data making near real time applications and time series based classifications at a high spatial detail possible. Challenges can be found in the seasonality of the forests, irregular missing data or artefacts. Also, big data applications and the use of artificial intelligence for dealing with these vast amounts of data are currently in the focus of the research community.

On the scale of detailed monitoring, LiDAR systems play a significant role. They are employed from different platforms such as terrestrial scanners, handheld devices, drones and aerial surveys. Their fusion and combination with other data sets are current research topics of high relevance to local applications and also for the generation of reference data for multiple other uses. Using deep learing technology not only on image data, but on 3D point clouds is another important research issue.

Hot topics:

  • time series analysis
  • meaningful fusion of different sensor type data
  • near real time applications
  • artificial intelligence for various applications
  • deep learning for 3D point cloud analysis
  • space-based LiDAR