Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29001
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm
Other Titles: Reconstrução histórica de mudanças na cobertura florestal em várzeas do Baixo Amazonas utilizando o algoritmo LandTrendr
Author(s): Fragal, Everton Hafemann
Silva, Thiago Sanna Freire
Novo, Evlyn Márcia Leão Moraes
Keywords: flooded forest
land use change
landsat
monitoring
wetlands
Issue Date: Mar-2016
Citation: Fragal EH, Silva TSF & Novo EMLM (2016) Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm [Reconstrução histórica de mudanças na cobertura florestal em várzeas do Baixo Amazonas utilizando o algoritmo LandTrendr]. Acta Amazonica, 46 (1), pp. 13-24. https://doi.org/10.1590/1809-4392201500835
Abstract: The Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzea forest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.
DOI Link: 10.1590/1809-4392201500835
Rights: This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/)

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