Testing the quality of forest variable estimation using dense image matching: a comparison with airborne laser scanning in a Mediterranean pine forest-Reference-Cited by-同舟云学术

Testing the quality of forest variable estimation using dense image matching: a comparison with airborne laser scanning in a Mediterranean pine forest

Published:2018-05-11 Issue:14 Volume:39 Page:4744-4760
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Navarro José Antonio¹²^ORCID,Fernández-Landa Alfredo¹^ORCID,Tomé José Luis¹,Guillén-Climent María Luz¹,Ojeda Juan Carlos³

Affiliation:

1. Agresta Soc. Coop., Madrid, Spain

2. MONTES (School of Forest Engineering and Natural Environment), Universidad Politecnica de Madrid, Madrid, Spain

3. National Mapping Agency of Spain (IGN), Madrid, Spain

Funder

Ministry of Economy, Industry and Competitiveness

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2018.1471551

Reference60 articles.

1. Agisoft, LLC. 2016. “Agisoft PhotoScan User Manual : Professional Edition, Version 1.3.” User Manuals. Accessed May 5 2018. http://www.agisoft.com/downloads/user-manuals/.

2. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

3. A comparison between photogrammetry and laser scanning

4. High‐quality image matching and automated generation of 3D tree models

5. Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comparative study on deep‐learning methods for dense image matching of multi‐angle and multi‐date remote sensing stereo‐images;The Photogrammetric Record;2022-11-09

2. Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System;Microbiology Spectrum;2022-02-23

3. Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data;International Journal of Applied Earth Observation and Geoinformation;2021-12

4. Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry;Remote Sensing;2021-07-23

5. Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds;Remote Sensing;2020-10-30