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A study on Singapore's vegetation cover and land use change using remote sensing

Published: 14 November 2022 Publication History

Abstract

While the benefits of trees are well-known, there are few studies on the vegetation cover in Singapore as traditional data acquisition is inefficient. In this study, we put together an efficient land use classification pipeline for the highly urbanized country using Sentinel-2 (S2) images. We adopted an object-based (OB) approach which uses Simple Non-iterative Clustering (SNIC) for clustering and Grey Level Co-occurrence Matrix (GLCM) for textural indices. Random Forest (RF) classifier was used for classification. We produced maps with 85.8% accuracy for the years 2016 to 2021. We then analysed the vegetation cover changes using change detection methods, and identified areas with significant vegetation loss (24.4km2 or 3.14% of our study area) or gain (40.4km2 or 5.20% of our study area). We also determined the type of land use conversions in these areas. This study contributes to tree management, environmental impact assessments (EIA) and policy-making. It also lays the groundwork for future studies on city livability.

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Cited By

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  • (2024)Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AIEnvironmental Monitoring and Assessment10.1007/s10661-024-12992-6196:10Online publication date: 4-Sep-2024
  • (2023)Lessons from applying SRGAN on Sentinel-2 images for LULC classification2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS61268.2023.00025(107-114)Online publication date: 8-Nov-2023

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cover image ACM Conferences
GeoIndustry '22: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications
November 2022
30 pages
ISBN:9781450395359
DOI:10.1145/3557922
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 November 2022

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Author Tags

  1. land use classification
  2. remote sensing
  3. vegetation cover changes

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  • National Research Foundation, Singapore
  • Ministry of Business Innovation & Employment, New Zealand

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View all
  • (2024)Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AIEnvironmental Monitoring and Assessment10.1007/s10661-024-12992-6196:10Online publication date: 4-Sep-2024
  • (2023)Lessons from applying SRGAN on Sentinel-2 images for LULC classification2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS61268.2023.00025(107-114)Online publication date: 8-Nov-2023

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