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Article

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

1
Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA
2
Departamento de Topografía, Facultad de Tecnologías, Universidad del Tolima, Ibagué 73000 6299, Colombia
3
College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
4
Deceased
*
Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno and Prasad S. Thenkabail
Remote Sens. 2017, 9(2), 179; https://doi.org/10.3390/rs9020179
Received: 24 December 2016 / Revised: 13 February 2017 / Accepted: 15 February 2017 / Published: 21 February 2017
The spatial distribution of disturbances in Andean tropical forests and protected areas has commonly been calculated using bi or tri-temporal analysis because of persistent cloud cover and complex topography. Long-term trends of vegetative decline (browning) or improvement (greening) have thus not been evaluated despite their importance for assessing conservation strategy implementation in regions where field-based monitoring by environmental authorities is limited. Using Colombia’s Cordillera de los Picachos National Natural Park as a case study, we provide a temporally rigorous assessment of regional vegetation change from 2001–2015 with two remote sensing-based approaches using the Breaks For Additive Season and Trend (BFAST) algorithm. First, we measured long-term vegetation trends using a Moderate Resolution Imaging Spectroradiometer (MODIS)-based Multi-Angle Implementation of Atmospheric Correction (MAIAC) time series, and, second, we mapped short-term disturbances using all available Landsat images. MAIAC-derived trends indicate a net greening in 6% of the park, but in the surrounding 10 km area outside of the park, a net browning trend prevails at 2.5%. We also identified a 12,500 ha area within Picachos (4% of the park’s total area) that has shown at least 13 years of consecutive browning, a result that was corroborated with our Landsat-based approach that recorded a 12,642 ha (±1440 ha) area of disturbed forest within the park. Landsat vegetation disturbance results had user’s and producer’s accuracies of 0.95 ± 0.02 and 0.83 ± 0.18, respectively, and 75% of Landsat-detected dates of disturbance events were accurate within ±6 months. This study provides new insights into the contribution of short-term disturbance to long-term trends of vegetation change, and offers an unprecedented perspective on the distribution of small-scale disturbances over a 15-year period in one of the most inaccessible national parks in the Andes. View Full-Text
Keywords: time series; Andes; breaks for additive season and trend (BFAST); disturbance; tropical forest; multi-angle implementation of atmospheric correction (MAIAC); Landsat time series; Andes; breaks for additive season and trend (BFAST); disturbance; tropical forest; multi-angle implementation of atmospheric correction (MAIAC); Landsat
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MDPI and ACS Style

Murillo-Sandoval, P.J.; Van Den Hoek, J.; Hilker, T. Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes. Remote Sens. 2017, 9, 179. https://doi.org/10.3390/rs9020179

AMA Style

Murillo-Sandoval PJ, Van Den Hoek J, Hilker T. Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes. Remote Sensing. 2017; 9(2):179. https://doi.org/10.3390/rs9020179

Chicago/Turabian Style

Murillo-Sandoval, Paulo J., Jamon Van Den Hoek, and Thomas Hilker. 2017. "Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes" Remote Sensing 9, no. 2: 179. https://doi.org/10.3390/rs9020179

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