Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review
Author:
Funder
Agricultural Research Service
Publisher
Springer Science and Business Media LLC
Subject
Agronomy and Crop Science,Forestry
Link
https://link.springer.com/content/pdf/10.1007/s10457-023-00850-2.pdf
Reference71 articles.
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2. Amani M, Ghorbanian A, Ahmadi SA et al (2020) Google earth engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE J Sel Top Appl Earth Observations Remote Sens 13:5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052
3. Bégué A, Arvor D, Bellon B et al (2018) Remote Sensing and cropping practices: a review. Remote Sens 10:99. https://doi.org/10.3390/rs10010099
4. Bolívar-Santamaría S, Reu B (2021) Detection and characterization of agroforestry systems in the Colombian Andes using sentinel-2 imagery. Agrofor Syst 95:499–514. https://doi.org/10.1007/s10457-021-00597-8
5. Bordoloi R, Das B, Tripathi OP et al (2022) Satellite-based integrated approaches to modeling spatial carbon stock and carbon sequestration potential of different land uses of Northeast India. Environ Sustain Indic 13:100166. https://doi.org/10.1016/j.indic.2021.100166
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