Google earth engine based three decadal landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand

Google earth engine based three decadal landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand

Monitoring and understanding the changes in mangrove ecosystems and their surroundings are required to determine how mangrove ecosystems are constantly changing while influenced by anthropogenic, and natural drivers. Consistency in high spatial resolution (30 m) satellite and high performance computing facilities are limiting factors to the process, with storage and analysis requirements. With this, we present the Google Earth Engine (GEE) based approach for long term mapping of mangrove forests and their surroundings. In this study, we used a GEE based approach: 1) to create atmospheric contamination free data from 1987-2017 from different Landsat satellite imagery; and 2) evaluating the random forest classifier and post classification change detection method. The obtained overall accuracy for the years 1987 and 2017 was determined to be 0.87 and 0.96, followed by a Kappa coefficient 0.80 and 0.94. The change detection results revealed a significant decrease in the agricultural area, while there was an increase in mangrove forest, shrimp/fish farm, and bareland area. The results suggest that interconversion of land use and land cover is affecting the landscape dynamics within the study area.

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Bibliographic Details
Main Authors: Pimple, Uday, Simonetti, Dario, Sitthi, Asamaporn, Pungkul, Sukan, Leadprathom, Kumron, Skupek, Henry, Som-ard, Jaturong, Gond, Valéry, Towprayoon, Sirintornthep
Format: article biblioteca
Language:eng
Subjects:K01 - Foresterie - Considérations générales, U30 - Méthodes de recherche, F40 - Écologie végétale, mangrove, télédétection, Landsat, cartographie de l'occupation du sol, couverture végétale, utilisation des terres, traitement des données, écosystème forestier, forêt, forêt tropicale humide, http://aims.fao.org/aos/agrovoc/c_4577, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_36766, http://aims.fao.org/aos/agrovoc/c_9000094, http://aims.fao.org/aos/agrovoc/c_25409, http://aims.fao.org/aos/agrovoc/c_4182, http://aims.fao.org/aos/agrovoc/c_10289, http://aims.fao.org/aos/agrovoc/c_1374842133961, http://aims.fao.org/aos/agrovoc/c_3062, http://aims.fao.org/aos/agrovoc/c_7976, http://aims.fao.org/aos/agrovoc/c_7701,
Online Access:http://agritrop.cirad.fr/586555/
http://agritrop.cirad.fr/586555/1/Pimple-JCC-2018.pdf
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Summary:Monitoring and understanding the changes in mangrove ecosystems and their surroundings are required to determine how mangrove ecosystems are constantly changing while influenced by anthropogenic, and natural drivers. Consistency in high spatial resolution (30 m) satellite and high performance computing facilities are limiting factors to the process, with storage and analysis requirements. With this, we present the Google Earth Engine (GEE) based approach for long term mapping of mangrove forests and their surroundings. In this study, we used a GEE based approach: 1) to create atmospheric contamination free data from 1987-2017 from different Landsat satellite imagery; and 2) evaluating the random forest classifier and post classification change detection method. The obtained overall accuracy for the years 1987 and 2017 was determined to be 0.87 and 0.96, followed by a Kappa coefficient 0.80 and 0.94. The change detection results revealed a significant decrease in the agricultural area, while there was an increase in mangrove forest, shrimp/fish farm, and bareland area. The results suggest that interconversion of land use and land cover is affecting the landscape dynamics within the study area.

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