Combination of Lidar Intensity and Texture Features Enable Accurate Prediction of Common Boreal Tree Species With Single Sensor UAS Data-Reference-Cited by-同舟云学术

Combination of Lidar Intensity and Texture Features Enable Accurate Prediction of Common Boreal Tree Species With Single Sensor UAS Data

Published:2024 Issue: Volume:62 Page:1-8
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Kukkonen Mikko¹^ORCID,Lähivaara Timo²^ORCID,Packalen Petteri³^ORCID

Affiliation:

1. Bioeconomy and Environment Unit, Natural Resources Institute Finland (Luke), Joensuu, Finland

2. Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland, Kuopio, Finland

3. Bioeconomy and Environment Unit, The Natural Resources Institute Finland, Helsinki, Finland

Funder

Academy of Finland through the Finnish Flagship Programme for the Forest-Human-Machine Interplay Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences

Centre of Excellence of Inverse Modelling and Imaging

Project “Unmanned Aerial Vehicles in Forest Remote Sensing”

Project “Is climate-smart forestry a utopia without considering preferences of landowners? (UTOPIA)”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10371221.pdf?arnumber=10371221

Reference39 articles.

1. A century of National Forest Inventory in Norway – informing past, present, and future decisions

2. Inventory of Small Forest Areas Using an Unmanned Aerial System

3. Forest inventories for small areas using drone imagery without in-situ field measurements

4. Potential of remote sensing-based forest attribute models for harmonising large-scale forest inventories on regional level: a case study in Southwest Germany

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