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Review

Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review

1
Remote Sensing Centre, School of Science, RMIT University, Melbourne, VIC 3000, Australia
2
Cooperative Research Centre for Spatial Information (CRCSI), Carlton, VIC 3053, Australia
3
Faculty of Resource Management, Thai Nguyen University of Agriculture and Forestry, Thai Nguyen, Vietnam
4
Department of Environment, Land, Water and Planning, Melbourne, VIC 3000, Australia
5
European Forest Institute, 08025 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 98; https://doi.org/10.3390/rs12010098
Received: 30 November 2019 / Revised: 17 December 2019 / Accepted: 24 December 2019 / Published: 27 December 2019
(This article belongs to the Section Forest Remote Sensing)
The free open access data policy instituted for the Landsat archive since 2008 has revolutionised the use of Landsat data for forest monitoring, especially for estimating forest aboveground biomass (AGB). This paper provides a comprehensive review of recent approaches utilising Landsat time-series (LTS) for estimating AGB and its dynamics across space and time. In particular, we focus on reviewing: (1) how LTS has been utilised to improve the estimation of AGB (for both single-date and over time) and (2) recent LTS-based approaches used for estimating AGB and its dynamics across space and time. In contrast to using single-date images, the use of LTS can benefit forest AGB estimation in two broad areas. First, using LTS allows for the filling of spatial and temporal data gaps in AGB predictions, improving the quality of AGB products and enabling the estimation of AGB across large areas and long time-periods. Second, studies have demonstrated that spectral information extracted from LTS analysis, including forest disturbance and recovery metrics, can significantly improve the accuracy of AGB models. Throughout the last decade, many innovative LTS-based approaches for estimating forest AGB dynamics across space and time have been demonstrated. A general trend is that methods have evolved as demonstrated through recent studies, becoming more advanced and robust. However, most of these methods have been developed and tested in areas that are either supported by established forest inventory programs and/or can rely on Lidar data across large forest areas. Further investigations should focus on tropical forest areas where inventory data are often not systematically available and/or out-of-date. View Full-Text
Keywords: Landsat time-series; forest aboveground biomass; biomass dynamics; change detection Landsat time-series; forest aboveground biomass; biomass dynamics; change detection
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MDPI and ACS Style

H. Nguyen, T.; Jones, S.; Soto-Berelov, M.; Haywood, A.; Hislop, S. Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review. Remote Sens. 2020, 12, 98. https://doi.org/10.3390/rs12010098

AMA Style

H. Nguyen T, Jones S, Soto-Berelov M, Haywood A, Hislop S. Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review. Remote Sensing. 2020; 12(1):98. https://doi.org/10.3390/rs12010098

Chicago/Turabian Style

H. Nguyen, Trung, Simon Jones, Mariela Soto-Berelov, Andrew Haywood, and Samuel Hislop. 2020. "Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review" Remote Sensing 12, no. 1: 98. https://doi.org/10.3390/rs12010098

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