Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

Author(s)

Fragal, Everton Hafemann ^[1] ; Freire Silva, Thiago Sanna ^[2] ; de Moraes Novo, Evlyn Marcia Leao ^[1]

Affiliation

^[1] Inst Nacl Pesquisas Espaciais, Div Sensoriamento Remoto, BR-12227010 Sao Jose Dos Campos, SP - Brazil

^[2] UNESP Univ Estadual Paulista, Inst Geociencias & Ciencias Exatas, BR-13506900 Rio Claro, SP - Brazil

• Document type: Journal article
• Source: Acta Amazonica; v. 46, n. 1, p. 13-24, JAN-MAR 2015.
• Web of Science Citations: 7

Abstract

The Amazon varzeas 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 varzea 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. (AU)

• FAPESP's process: 10/11269-2 - Modeling of the spatial dynamics of macrophyte communities in the Amazon floodplain
• Grantee: Thiago Sanna Freire Silva
• Support type: Scholarships in Brazil - Post-Doctorate
• FAPESP's process: 11/23594-8 - Remote sensing applications for modeling human impacts on the ecological properties of wetland and aquatic environments in the Solimões/Amazon floodplain
• Grantee: Evlyn Márcia Leão de Moraes Novo
• Support type: Regular Research Grants