A Brief Description of Recovery Process of Coastal Vegetation after Tsunami: A Google Earth time-series Remote Sensing Data

Arief, Mochamad Candra Wirawan; Itaya, Akemi

doi:10.7226/jtfm.23.2.81

Cited by 2 publications

(1 citation statement)

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“…To characterise LULC classes, reference points were sampled evenly across the target areas. For each target area, a set of ground truth reference points was built based on the author's knowledge and visual interpretation of high-resolution composite images created with Google Earth Pro (version 7.3.2.5776) [42,43]. These composite images were generated from 2019 and 2020 imagery of satellite and aircraft data at resolutions between 15 cm and 30 m with the built-in timeline tool of the Google Earth Composite Editor.…”

Section: Classification Scheme and Reference Datamentioning

confidence: 99%

Evaluating the Applicability of Global LULC Products and an Author-Generated Phenology-Based Map for Regional Analysis: A Case Study in Ecuador’s Ecoregions

Rugel

Ochoa

Menéndez

et al. 2023

Land

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An accurate and detailed understanding of land-use change affected by anthropogenic actions is key to environmental policy decision-making and implementation. Although global land cover products have been widely used to monitor and analyse land use/land cover (LULC) change, the feasibility of using these products at the regional level needs to be assessed due to the limitation and biases of generalised models from around the world. The main objective of the present study was to generate regional LULC maps of three target areas located in the main ecoregions of Ecuador at a resolution of 10 m using Google Earth Engine (GEE) cloud-based computing. Our approach is based on (1) Single Date Classification (SDC) that processes Sentinel-2 data into fuzzy rule-driven thematic classes, (2) rule refinement using Visible Infrared Imaging Radiometer Suite (VIIRS) data, and (3) phenology-based synthesis (PBS) classification that combines SDC into LULC based on the occurrence rule. Our results show that the three target areas were classified with an overall accuracy of over 80%. In addition, cross-comparison between the global land cover products and our LULC product was performed and we found discrepancies and inaccuracies in the global products due to the characteristics of the target areas that included a dynamic landscape. Our LULC product supplements existing official statistics and showcases the effectiveness of phenology-based mapping in managing land use by providing precise and timely data to support agricultural policies and ensure food security.

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Section: Classification Scheme and Reference Datamentioning

confidence: 99%