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

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.

[1]  Mark G. Anderson,et al.  Selecting and conserving lands for biodiversity: The role of remote sensing , 2009 .

[2]  Rob J Hyndman,et al.  Phenological change detection while accounting for abrupt and gradual trends in satellite image time series , 2010 .

[3]  S. Ganguly,et al.  Amazon forests did not green‐up during the 2005 drought , 2009 .

[4]  A. Timmermann,et al.  Market timing and return prediction under model instability , 2002 .

[5]  Achim Zeileis,et al.  Shifts in Global Vegetation Activity Trends , 2013, Remote. Sens..

[6]  Lukas W. Lehnert,et al.  Land Cover Change in the Andes of Southern Ecuador - Patterns and Drivers , 2015, Remote. Sens..

[7]  Stephen V. Stehman,et al.  The Horvitz-Thompson Theorem as a Unifying Perspective for Probability Sampling: With Examples from Natural Resource Sampling , 1995 .

[8]  A. Dobson,et al.  Projected Impacts of Climate and Land-Use Change on the Global Diversity of Birds , 2007, PLoS biology.

[9]  Christopher E. Holden,et al.  An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations , 2016 .

[10]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[11]  W. Francesconi,et al.  Spatial modeling of deforestation processes in the Central Peruvian Amazon , 2016 .

[12]  Michael Schultz,et al.  Error Sources in Deforestation Detection Using BFAST Monitor on Landsat Time Series Across Three Tropical Sites , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  M. Herold,et al.  Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series , 2015 .

[14]  K. S. Willis,et al.  Remote sensing change detection for ecological monitoring in United States protected areas , 2015 .

[15]  Alexandre Bouvet,et al.  Estimating tropical deforestation from Earth observation data , 2010 .

[16]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[17]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[18]  Feng Gao,et al.  LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2 , 2013 .

[19]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[20]  David J. Harding,et al.  Amazon forests maintain consistent canopy structure and greenness during the dry season , 2014, Nature.

[21]  Marc Macias-Fauria,et al.  Sensitivity of global terrestrial ecosystems to climate variability , 2016, Nature.

[22]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[23]  S. Bruin,et al.  Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change , 2012 .

[24]  Shawn W. Laffan,et al.  Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region , 2014 .

[25]  Thomas Hilker,et al.  On the measurability of change in Amazon vegetation from MODIS , 2015 .

[26]  Haiyun Bi,et al.  Using an unmanned aerial vehicle for topography mapping of the fault zone based on structure from motion photogrammetry , 2017 .

[27]  M. Herold,et al.  Near real-time disturbance detection using satellite image time series , 2012 .

[28]  Teófilo Vásquez EL PAPEL DEL CONFLICTO ARMADO EN LA CONSTRUCCIÓN Y DIFERENCIACIÓN TERRITORIAL DE LA REGIÓN DE "EL CAGUÁN", AMAZONÍA OCCIDENTAL COLOMBIANA , 2014 .

[29]  Kenneth Grogan,et al.  Exploring Patterns and Effects of Aerosol Quantity Flag Anomalies in MODIS Surface Reflectance Products in the Tropics , 2013, Remote. Sens..

[30]  Michael Schultz,et al.  Performance of vegetation indices from Landsat time series in deforestation monitoring , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[31]  Joanne C. White,et al.  Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. , 2009 .

[32]  Stephen V. Stehman,et al.  International Journal of Applied Earth Observation and Geoinformation: Time-Series Analysis of Multi-Resolution Optical Imagery for Quantifying Forest Cover Loss in Sumatra and Kalimantan, Indonesia , 2011 .

[33]  T. Mitchell Aide,et al.  Consequences of the Armed Conflict, Forced Human Displacement, and Land Abandonment on Forest Cover Change in Colombia: A Multi-scaled Analysis , 2013, Ecosystems.

[34]  Nigel P. Fox,et al.  Progress in Field Spectroscopy , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[35]  M. Alonzo,et al.  Capturing coupled riparian and coastal disturbance from industrial mining using cloud-resilient satellite time series analysis , 2016, Scientific Reports.

[36]  A-Xing Zhu,et al.  Evaluating forest policy implementation effectiveness with a cross-scale remote sensing analysis in a priority conservation area of Southwest China , 2014 .

[37]  C. Tucker,et al.  Remote sensing of tropical ecosystems: Atmospheric correction and cloud masking matter , 2012 .

[38]  Jan Verbesselt,et al.  Using spatial context to improve early detection of deforestation from Landsat time series , 2016 .

[39]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[40]  M. Cochrane,et al.  Roads, deforestation, and the mitigating effect of protected areas in the Amazon , 2014 .

[41]  Eric Vermote,et al.  Atmospheric correction for the monitoring of land surfaces , 2008 .

[42]  Matthew F. McCabe,et al.  High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture , 2016, Remote. Sens..

[43]  E. Mansur,et al.  A global challenge needing local response , 2011 .

[44]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[45]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[46]  S. Ganguly,et al.  Why Is Remote Sensing of Amazon Forest Greenness So Challenging , 2012 .

[47]  Zhe Zhu,et al.  Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data , 2016 .

[48]  Christopher E. Holden,et al.  Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014) , 2016 .

[49]  M. Herold,et al.  Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia , 2015 .

[50]  Roy G. Grainger,et al.  Reconciling satellite‐derived atmospheric properties with fine‐resolution land imagery: Insights for atmospheric correction , 2011 .

[51]  René R. Colditz,et al.  Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series , 2015, Remote. Sens..

[52]  Dolors Armenteras,et al.  Understanding deforestation in montane and lowland forests of the Colombian Andes , 2011 .

[53]  El sector de ganadería bovina en Colombia: aplicación de modelos de series de tiempo al inventario ganadero , 2008 .

[54]  Ranga B. Myneni,et al.  Amazon Forests' Response to Droughts: A Perspective from the MAIAC Product , 2016, Remote. Sens..

[55]  Tania Stathaki,et al.  Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets , 2015, Biodiversity and Conservation.

[56]  Jan Verbesselt,et al.  Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology , 2013, Remote. Sens..

[57]  Hankui K. Zhang,et al.  Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.

[58]  A. C. Seijmonsbergen,et al.  Monitoring land use and land cover change in mountain regions: An example in the Jalca grasslands of the Peruvian Andes , 2013 .

[59]  Dolors Armenteras,et al.  Andean forest fragmentation and the representativeness of protected natural areas in the eastern Andes, Colombia , 2003 .

[60]  C. Tucker,et al.  Vegetation dynamics and rainfall sensitivity of the Amazon , 2014, Proceedings of the National Academy of Sciences.

[61]  W. Jetz,et al.  Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions , 2016, PLoS biology.

[62]  D. Armenteras,et al.  Effectiveness of protected areas in the Colombian Andes: deforestation, fire and land-use changes , 2012, Regional Environmental Change.

[63]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[64]  Warren B. Cohen,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation , 2010 .

[65]  C. Portillo-Quintero,et al.  Forest cover and deforestation patterns in the Northern Andes (Lake Maracaibo Basin): A synoptic assessment using MODIS and Landsat imagery , 2012 .

[66]  Hugh P. Possingham,et al.  Regional patterns of agricultural land use and deforestation in Colombia , 2006 .

[67]  M. Herold,et al.  Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series , 2015 .

[68]  Jan Verbesselt,et al.  Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series , 2016, PloS one.