A national fuel type mapping method improvement using sentinel-2 satellite data-Reference-Cited by-同舟云学术

A national fuel type mapping method improvement using sentinel-2 satellite data

Published:2020-04-28 Issue: Volume: Page:1-21
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Stefanidou Alexandra¹,Gitas Ioannis Z.¹^ORCID,Katagis Thomas¹

Affiliation:

1. Faculty of Agriculture, Forestry and Natural Environment, Department of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2020.1756460

Reference88 articles.

1. Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands

2. Pixel and object-based classification approaches for mapping forest fuel types in Tenerife Island from ASTER data

3. Alonso-Benito A, Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A, Moreno-Ruiz JA, Garcia-Lazaro JR. 2016. Satellite image based methods for fuels maps updating. [Internet]. In: Neale CMU, Maltese A, editors. Edinburgh, United Kingdom; [accessed 2019 Sep 3]; p. 999821. http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2241990.

4. Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

5. Arroyo L, Healey S, Cohen W, Cocero D, Manzanera JA. 2005. Regional fuel mapping using an object-oriented Classification of Quickbird imagery. Proceedings of Semana Geomática.

Cited by 19 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. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA;Remote Sensing of Environment;2024-10

2. A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning;Forests;2024-02-29

3. Combining Landsat time series and GEDI data for improved characterization of fuel types and canopy metrics in wildfire simulation;Journal of Environmental Management;2023-11

4. Forest fuel type classification: Review of remote sensing techniques, constraints and future trends;Journal of Environmental Management;2023-09

5. Comparing Forest Understory Fuel Classification in Portugal Using Discrete Airborne Laser Scanning Data and Satellite Multi-Source Remote Sensing Data;Fire;2023-08-22