Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia-Reference-Cited by-同舟云学术

Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia

Published:2022-09-07 Issue:1 Volume:59 Page:1446-1472
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Masolele Robert N.¹^ORCID,De Sy Veronique¹^ORCID,Marcos Diego¹^ORCID,Verbesselt Jan¹,Gieseke Fabian²^ORCID,Mulatu Kalkidan Ayele³^ORCID,Moges Yitebitu⁴,Sebrala Heiru⁴^ORCID,Martius Christopher⁵^ORCID,Herold Martin¹^ORCID

Affiliation:

1. Laboratory of Geo-Information Science and Remote Sensing, Wageningen University Research, PB Wageningen, Netherlands

2. Department of Information Systems, University of Münster, Leonardo-Campus 3, Münster, Germany

3. International Center for Tropical Agriculture (CIAT), Addis Ababa, Ethiopia

4. National REDD+ Secretariat, Environment, Forest and Climate Change Commission, Addis Ababa, Ethiopia

5. Center for International Forestry Research (CIFOR)- Germany gGmbH, Bonn, Germany

Funder

International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety

Norwegian Agency for Development Cooperation

European Commission Horizon Europe

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2115619

Reference85 articles.

1. Willingness to Pay for Watershed Management

2. BBC. (2019), 12. Did Ethiopia Plant Four Billion Trees This Year? BBC News. Retrieved from https://www.bbc.com/news/world-africa-50813726

3. Diversity of the Ethiopian flora

4. Trends and drivers of land use/land cover change in Western Ethiopia

Cited by 12 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. Downstream lingering attention transformer network (DsLATNet) for land use land cover classification: A bicolor deep learning framework;Applied Soft Computing;2024-11

2. Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations;International Journal of Applied Earth Observation and Geoinformation;2024-08

3. Data and information in a political forest: The case of REDD+;Forest Policy and Economics;2024-08

4. A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon;Scientific Data;2024-05-31

5. Mapping the diversity of land uses following deforestation across Africa;Scientific Reports;2024-01-19