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

Oleg Antropov*, Erkki Tomppo, Ronald E. McRoberts, Jaan Praks

*Corresponding author for this work

Research output: Contribution to journalSpecial issueScientificpeer-review

Abstract

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.

Original languageEnglish
JournalRemote Sensing
Volume14
Issue number21
Publication statusPublished - Nov 2022
MoE publication typeC2 Edited books

Keywords

  • boreal forest
  • image time series
  • irregular sampling
  • LSTM
  • semi-supervised learning
  • Sentinel-1
  • synthetic aperture radar
  • tree height

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