Special Issue "Near Real Time Forest Inventory with Remote Sensing: Novel Techniques and Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Oleg Antropov
Website
Guest Editor
VTT Technical Research Centre of Finland, Espoo, Finland
Interests: imaging radar; SAR polarimetry; SAR interferometry; vegetation mapping; boreal forest; land cover change; machine learning; semantic segmentation
Special Issues and Collections in MDPI journals
Dr. Erkki Tomppo
Website
Guest Editor
Aalto University, Department of Electronics and Nanoengineering, Finland
University of Helsinki, Department of Forest Sciences, Finland
Interests: forest inventory, forest remote sensing, statistical methods
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Dr. Ronald E. McRoberts
Website
Guest Editor
University of Minnesota, Department of Forest Resources, Raspberry Ridge Analytics, USA
Interests: forest inventory, forest remote sensing, uncertainty assessment, model-based inference
Dr. Jaan Praks
Website
Guest Editor
Aalto University, Department of Electronics and Nanoengineering, Finland
Interests: microwave Earth Observation, SAR remote sensing, PolSAR/InSAR/Pol-InSAR, EM modeling of forests, hyperspectral remote sensing, nanosatellites
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Forest inventory programs aim to produce timely and accurate information for a wide range of forest parameters for a large variety of users and applications. Users include forest owners and forest owner groups, from private to government, national and regional authorities, forest industry, forest, environmental and climate research communities, development banks, as well as non-governmental and conservation organizations. Critical constraints in forest inventories are timeliness, processing costs, and the accuracy and precision of estimated parameters.  Many of the recent innovations involve remotely sensed data and related statistical estimation methods. Field data-based inventories with statistical sampling have a long history in producing estimates and uncertainty estimates for large areas. While airborne laser scanning with field observations facilitates accurate small area estimation, space-borne optical and SAR data appear to be effective information sources for producing large area forest resources estimates and mapping with frequent updates.

Further progress in the framework of forest resources mensuration are expected in the areas of novel imaging sensor geometries (particularly advanced SAR techniques), multi-sensor fusion, improved modeling techniques, big data and AI methodologies, advanced time series analysis, development of operational mapping applications and services and implementing software-as-a-service platforms. 

This Special Issue will highlight both new methods and applications that represent fundamental advances in the use of remotely sensed data for forest inventory applications and new uses of forest inventory data and estimates. All manuscripts must address validation and uncertainty assessment methods.

Submissions to the Special Issue are welcome until 31 June 2021.

Dr. Oleg Antropov

Dr. Erkki Tomppo

Dr. Ronald E. McRoberts

Dr. Jaan Praks

Guest Editor

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 papers will be 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 2200 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 inventory
  • above ground biomass
  • model-based inference
  • SAR remote sensing
  • airborne laser scanning
  • image time series
  • data fusion
  • machine learning
  • uncertainty assessment

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: PolSAR based prediction of forest parameters in boreal zone: Advanced PolSAR feature selection using ALOS-2 PALSAR-2 data
Authors: Parisa Golshani; Erkki Tomppo; Oleg Antropov; Jaan Praks
Affiliation: 1 Tarbiat Modares University, Department of Forestry, Iran 2 Aalto University, Department of Electronics and Nanoengineering, P.O. Box 11000, FI-00076AALTO, 02150 Espoo, Finland 3 VTT Technical Research Centre of Finland, P. O. Box 1000, FI-00076 VTT, Espoo, Finland

Title: Mapping forest disturbance due to selective logging in the Congo Basin with RADARSAT-2 time series
Authors: Oleg Antropov; Yrjö Rauste; Tuomas Häme; Jaan Praks
Affiliation: 1 VTT Technical Research Centre of Finland, P. O. Box 1000, FI-00076 VTT, Espoo, Finland 2 Aalto University, Department of Electronics and Nanoengineering, P.O. Box 11000, FI-00076AALTO, 02150 Espoo, Finland

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