Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring-Reference-Cited by-同舟云学术

Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring

Published:2022-07-17 Issue: Volume: Page:
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Container-title:IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Antropov Oleg¹,Miettinen Jukka¹,Hame Tuomas¹,Yrjo Rauste¹,Seitsonen Lauri¹,McRoberts Ronald E²,Santoro Maurizio³,Cartus Oliver³,Duran Natalia Malaga⁴,Herold Martin⁵,Pardini Matteo⁶,Papathanassiou Kostas⁶,Hajnsek Irena⁶

Affiliation:

1. VTT Technical Research Centre of Finland, VTT,Finland,FI-02044

2. University of Minnesota,Department of Forest Resources,Saint Paul,MN,USA,55108

3. Gamma Remote Sensing,Gümligen,Switzerland,CH-3073

4. Wageningen University,Laboratory of Geo-Information Science and Remote Sensing,Wageningen,Netherlands,6708 PB

5. GFZ German Research Centre for Geoscience,Potsdam,Germany,14473

6. DLR German Aerospace Centre, Microwaves and Radar Institute,Wessling,Germany,82234

Funder

ESA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9883023/9883024/09884618.pdf?arnumber=9884618

Reference24 articles.

1. Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

2. Using hypertemporal Sentinel-1 data to predict forest growing stock volume;ge;BioRxiv,2021

3. Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements

4. TanDEM-X Pol-InSAR Performance for Forest Height Estimation

5. Stand-Level Stem Volume of Boreal Forests From Spaceborne SAR Imagery at L-Band

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1. Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images;Remote Sensing;2023-10-27

2. Accuracy of the evaluation of forest areas based on Landsat data using free software;Folia Forestalia Polonica;2023-06-01