Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thailand

Abstract

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.

Shifts in Growing Season of Tropical Deciduous Forests as Driven by El Niño and La Niña during 2001–2016

“…Additionally, Landsat scenes contain jagged pixels along scene edges, which could result in incorrect reflectance values along the edge of imagery. These jagged pixels were removed using a 450 m buffer applied from the edge of the mask inward [36,37]. The Otsu threshold was then performed based on NDVI calculated from Red and NIR band of Landsat to separate the forest and non-forest areas [38].…”

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

“…In terms of the methods, prior studies use a range of classification techniques such as the iterative self-organizing data analysis (ISODATA) clustering, maximum likelihood classification (MLC), hybrid, random forest (RF), classification and regression trees (CART), support vector machine (SVM), and object oriented classification among others [16,17,34,[37][38][39][40][41]43,66]. With the advent of cloud computing platforms with free access to petabytes of geospatial data, such as Google Earth Engine (GEE), it has now become increasingly accessible and straight-forward to analyze enormous amounts of satellite imagery covering large regions [37,[67][68][69][70][71][72]. While GEE offers more than 15 classification techniques, most studies rely on machine-learning algorithms [37,[68][69][70][71][72], such as CART and RF, since these have proven to be some of the robust methods for land cover classifications.…”

“…With the advent of cloud computing platforms with free access to petabytes of geospatial data, such as Google Earth Engine (GEE), it has now become increasingly accessible and straight-forward to analyze enormous amounts of satellite imagery covering large regions [37,[67][68][69][70][71][72]. While GEE offers more than 15 classification techniques, most studies rely on machine-learning algorithms [37,[68][69][70][71][72], such as CART and RF, since these have proven to be some of the robust methods for land cover classifications. Such methods based on free data and robust algorithms can be particularly beneficial for regular monitoring, including tracking SDG indicators.…”

Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia

“…The GEE derived Landsat Annual TOA Reflectance Composite data was created by taking the median value from image bands from the target year, plus or minus one year (Midekisa et al, 2017). TOA corrected Landsat data have been extensively used for land cover mapping and classification in different tropical ecosystems, including tropical Asia (Ramdani, Moffiet & Hino, 2014; Johnson & Iizuka, 2016; Dong et al, 2015; Pimple et al, 2018).…”