Abstract
Forests are critical ecosystems that provide multiple ecological services; therefore, accurate classification of their species is crucial for effective forest management and conservation. In this study, we compared and evaluated the performance of several Google Earth Engine (GEE) classifiers for extracting main forest species in Portugal. For this purpose, a time series of Sentinel-2 images was investigated for 2018 using a two-stage supervised classification workflow, which involved Random Forest, Naive Bayes, Classification and Regression Trees, Gradient Boosting Trees, and Support Vector Machine classifiers. In the first stage, Sentinel-2 images were analyzed to provide a binary land cover map from where the forest layer was extracted. The layer obtained was then used for masking the input features to the second stage. The latter stage discriminated six forest species by analyzing several input feature configurations. These included multiple spectral indices selected from the time series, as well as topographic factors. The performance of each classifier in species discrimination was assessed by comparing accuracy metrics obtained from the selected input configurations. The highest overall accuracy of the map was estimated at 68.75%, with a kappa of 0.62, for the Support Vector Machine classifier. The study's findings highlight the importance of algorithm selection using the GEE platform for accurate forest species classification in similar landscape characteristics, informing researchers, conservation practitioners, and forest managers for improved management and conservation strategies.
Original language | English |
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Title of host publication | Recent Developments in Geospatial Information Sciences |
Subtitle of host publication | Selected papers from IGISc 2023 |
Editors | Hugo Carlos-Martinez, Rodrigo Tapia-McClung, Daniela Alejandra Moctezuma-Ochoa, Ana Josselinne Alegre-Mondragón |
Place of Publication | Gewerbestrasse, Cham |
Publisher | Springer Nature Switzerland AG |
Pages | 159-171 |
Number of pages | 13 |
ISBN (Electronic) | 978-3-031-61440-8 |
ISBN (Print) | 978-3-031-61439-2, 978-3-031-61442-2 |
DOIs | |
Publication status | Published - 12 Aug 2024 |
Event | International Conference on Geospatial Information Sciences (iGISc 2023) - Universidad Iberoamericana in Mexico City, Ciudad de México, Mexico Duration: 14 Nov 2023 → 17 Nov 2023 Conference number: 2023 https://igisc.org/ |
Publication series
Name | Lecture Notes in Geoinformation and Cartography |
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Publisher | Springer Nature Switzerland AG |
ISSN (Print) | 1863-2246 |
ISSN (Electronic) | 1863-2351 |
Conference
Conference | International Conference on Geospatial Information Sciences (iGISc 2023) |
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Abbreviated title | iGISc 2023 |
Country/Territory | Mexico |
City | Ciudad de México |
Period | 14/11/23 → 17/11/23 |
Internet address |
Keywords
- Earth Observation
- Machine-Learning
- Forest Species
- Google Earth Engine
- Ecosystem Services