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Remote Sens. 2017, 9(1), 68; doi:10.3390/rs9010068

Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms

1
Centre for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Walter-Flex Str. 3, 53113 Bonn, Germany
2
Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
3
Remote Sensing Research Group (RSRG), Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany
Deceased on 9 August 2016.
*
Author to whom correspondence should be addressed.
Academic Editors: Guangxing Wang, Erkki Tomppo, Dengsheng Lu, Huaiqing Zhang, Qi Chen and Prasad S. Thenkabail
Received: 31 August 2016 / Revised: 13 December 2016 / Accepted: 1 January 2017 / Published: 12 January 2017
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
View Full-Text   |   Download PDF [7958 KB, uploaded 12 January 2017]   |  

Abstract

The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging environment for any remote-sensing assessment. Breakpoint detection with Landsat time-series data is an established robust approach for monitoring forest dynamics around the globe but has not been properly evaluated for implementation in the Andean Amazon. We analyzed breakpoint detection-generated forest dynamics in order to determine its limitations when applied to three different study areas located along an altitude gradient in the Andean Amazon in Ecuador. Using all available Landsat imagery for the period 1997–2016, we evaluated different pre-processing approaches, noise reduction techniques, and breakpoint detection algorithms. These procedures were integrated into a complex function called the processing chain generator. Calibration was not straightforward since it required us to define values for 24 parameters. To solve this problem, we implemented a novel approach using genetic algorithms. We calibrated the processing chain generator by applying a stratified training sampling and a reference dataset based on high resolution imagery. After the best calibration solution was found and the processing chain generator executed, we assessed accuracy and found that data gaps, inaccurate co-registration, radiometric variability in sensor calibration, unmasked cloud, and shadows can drastically affect the results, compromising the application of breakpoint detection in mountainous areas of the Andean Amazon. Moreover, since breakpoint detection analysis of landscape variability in the Andean Amazon requires a unique calibration of algorithms, the time required to optimize analysis could complicate its proper implementation and undermine its application for large-scale projects. In exceptional cases when data quality and quantity were adequate, we recommend the pre-processing approaches, noise reduction algorithms and breakpoint detection algorithms procedures that can enhance results. Finally, we include recommendations for achieving a faster and more accurate calibration of complex functions applied to remote sensing using genetic algorithms. View Full-Text
Keywords: forest dynamics; Landsat time-series; genetic algorithms; breakpoint detection forest dynamics; Landsat time-series; genetic algorithms; breakpoint detection
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Santos, F.; Dubovyk, O.; Menz, G. Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms. Remote Sens. 2017, 9, 68.

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