A Novel GNSS Technique for Predicting Boreal Forest Attributes at Low Cost

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • Wuhan University
  • Finnish Geospatial Research Institute


One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.


Original languageEnglish
Pages (from-to)4855-4867
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number9
Publication statusPublished - Sep 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Crowdsourcing, forestry, global navigation satellite systems (GNSSs), laser scanning, mobile mapping, radio propagation losses, REMOTE-SENSING DATA, INVENTORY ATTRIBUTES, POINT CLOUDS, LASER, BIOMASS, TERRESTRIAL, RETRIEVAL, ACCURACY, SYSTEM, CYCLE

ID: 15111724