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

Abstract

Abstract: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.

Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor

“…BFM has been applied to describe patterns of anthropogenic forest change [31], for reconstructing land use history [32] and observing regrowth dynamics after degradation [33]. It has also been used in combination with community-based monitoring [25], for long-term studies [34], and also to map vegetation decline in arid and semi-arid environments [35,36]. The most common data source used is Landsat satellites, where BFM results are often post-processed using the so-called magnitude of change.…”

Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series

“…Imagery from the Landsat archive was selected given its long record of regular observation and intra-annual observation frequency of nearly eight images per year over Picachos, which supports sub-annual forest disturbance [6]; and its 30 m (0.09 ha) spatial resolution, which is suitable for detecting small disturbance patches common in Picachos (mean =2 ha). We used a time-series of 149 Level-1 Terrain-Corrected (L1T) Landsat 5, 7, and 8 surface reflectance images (path/row 8/58) from 1996 to 2015 with cloud cover less than or equal to 50%; 225 additional images with more than 50% cloud cover were removed prior to analysis.…”

“…Despite the duration and geographic breadth of these changes, there remains limited understanding of the relative influence and timing of different drivers on forest cover change across much of Colombia. The Colombian Andes foothills were rather inaccessible three decades ago but have seen recent forest disturbance [4][5][6] due to favorable incentives for cattle ranching in the 1980s [7], the enlargement of coca cultivation in the 1990s through 2002 [2], and the more recent expansion of cattle ranching and other agricultural land uses [8,9]. Though National Protected Areas make up 12% of Colombia's land mass [10], protected areas are also threatened by migration and displacement, economic development, and illicit cultivation of coca (Erythroxylum coca Lam.)…”

Tropical deforestation monitoring using Landsat time series and breakpoint detection

“…BFAST Monitor relies on a high frequency of available observations during the historic period to avoid errors of commission and on high frequency of observations within the monitoring period to avoid errors of omission. Research combining multiple Remote Sensing data sources is increasingly prolific (Murillo-Sandoval et al, 2017) and broadly recognized as important avenue in remote sensing science (Reiche et al, 2016). Moderate Resolution Imaging Spectroradiometer (MODIS) (Hilker et al, 2009) and Advanced Land Observing Satellite (ALOS) fusion with Landsat were interlinked to populate past time series in order to increase observation density (Reiche et al, 2013).…”

“…Breaks For Additive Season and Trend (BFAST) (Verbesselt et al, 2010b) type models have been used to measure forest decline across the tropics (DeVries et al, 2015b;Pratihast et al, 2014;Schultz et al, , 2015, but in contrast to application remained local, similar to radio detection and ranging (RADAR) and optical time series fusion approaches (Reiche et al, 2018(Reiche et al, , 2015. The more popular BFAST Monitor (BFM) (Verbesselt et al, 2012a) applications have been found to be used robustly with different data streams (Dutrieux et al, 2015), to describe patterns of anthropogenic forest change (Jakovac et al, 2017), for reconstructing land use history (Dutrieux et al, 2016), track regrowth dynamics after degradations (DeVries et al, 2015a), in combination with community-based monitoring (Pratihast et al, 2014), for long-term studies (Murillo-Sandoval et al, 2017) and also to map vegetation decline in arid and semi-arid environments (Maynard et al, 2016;Watts and Laffan, 2014). Preferred sensors were most often on Landsat satellites, where BFM results were often post-processed using the so-called magnitude of change.…”