NASA Goddard Space Flight Center, May 12, 2016
Jan Verbesselt, Lammert Kooistra, Martin Herold, Arun Kumar Pratihast, Mathieu Decuyper, Achim Zeileis, Shijo Joseph, Valerio Avitabile, Loïc Dutrieux, Johannes Reiche, Niki De Sy, Brice Mora, Nandika Tsendbazar, Frehiwot Tessema, Dereje Beyene, Elias Gebremeskel, Kalkidan Mulatu, Svane Bender Kaphengst, Daniela Tunger, Bianca Schlegel, Mesfin Tekle, Muluken Mekuria, Kafa Zone Bureau of Agriculture
Born and raised in St. Thomas, ON, Canada
BSc: University of Toronto - 2005
MSc: Wageningen University - 2010
PhD (Remote Sensing): Wageningen University - 2015
Chengquan Huang (UMD), Megan Lang (FWS)
John Jones (USGS), Irena Creed (UWO)
Remarks on open data, tools and methods
Monitoring small-scale disturbances: Kafa, Ethiopia
Monitoring post-disturbance regrowth: Madre de Dios, Peru
Integrating community-based monitoring data and LTS to characterize forest changes
Landsat legacy represents longest continual space-borne observation record
Opening of the archive in 2008 - paradigm shift in change monitoring
Streamlining L1 data generation (LEDAPS, FMASK, ...)
Bi-temporal (e.g. MAD, post-classification), Annual (e.g. LandTrendR, VCT), "All-available" (e.g. CCDC, bfastmonitor)
..."openness" not just limited to Landsat...
How can these data and tools be leveraged to address current problems?
e.g. REDD+ MRV - transparency and reproducibility are key to maintaining credibility
Breaks For Additive Season and Trend
Decomposition of time series into harmonic, trend and noise (residual) components
Segment a time series based on "most likely" breakpoints
e.g. - Detecting phenological change due to fire regimes in forests
Define a monitoring period and a history period
Fit model to observations history period
Check if "new" acquisitions in the monitoring period follow expected model based on hypothesis testing
Breakpoints are determined by some measure of deviance from "expected" values
e.g. Cumulative Sums (CUSUM), or more localized Moving Sums (MOSUM) of residuals
Normalized by expected deviance (noise) in history data
Econometrics: measure of "structural change"
Chu et al. (1995), Biometrika. DOI:10.1093/biomet/82.3.603
bfastmonitor to dense Landsat time series
UNESCO Biosphere Reserve - inaugurated in 2011
Moist Afro-montane cloud forests
One of few remaining natural coffea arabica habitats
Forest conservation and restoration project by NABU, Germany
Participatory Forest Management (PFM) and Community-Based Monitoring (CBM)
Despite sparse time series, small-scale changes are mapped using breakpoints
Data gaps -- temporal uncertainty
Do all breakpoints signal disturbance? What about magnitude?
Change magnitude (independent of breakpoint) measured as median of residuals in each 1-year interval
Visualizing change dynamics using breakpoints and magnitude
Calibration and validation based on visual interpretation of LTS data
The best model for disturbances considers breakpoints and magnitude (median of residuals)
Gaps still exist, especially for degradation
Limitations include index, cal/val data (more on this later)
R package for pre-processing LTS and applying
What happens after a disturbance?
How well can we track post-disturbance regrowth with LTS?
Dense canopy forests
Recent infrastructure development leading to rapid changes to forests
Very dynamic: forest loss often followed by land abandonment, secondary regrowth
"Gold rush" within the last decade a major driver of forest change
Recall: MOSUM is a measure of deviation from a "stable" history model
At and after a disturbance, the MOSUM become very large
Forest regrowth following a disturbance should bring the MOSUM back to near-zero conditions
Different MOSUM bandwidths (h) affect its sensitivity to this process
w - minimum time between disturbance (tB) and regrowth (tR)
s - minimum time in which MOSUM < critical boundary
R package for monitoring regrowth with LTS
Community-Based Monitoring (CBM) is seen as a key component to REDD+ Measuring, Reporting and Verification (MRV) systems
Increased engagement, ownership, benefits-sharing
Some research has shown potential use of CBM data for monitoring purposes (e.g. AGB measurements), but research on utility of data is lacking
Simple smart-phone based tool for field data collection
Exploits GPS, camera, and interactive features of smart phones to make data collection easy and convenient
30 forest rangers recruited from 10 local districts by Kafa Zone Bureau of Agriculture
Reforestation, community plantations, community outreach, extension services, monitoring activities
After monitoring trainings (incl. ODK tools), >1500 geo-located forms returned
Prototype for local forest monitoring system - potential for nesting in national FMS
Various attributes describe landscape, changes (if any), drivers of change, etc.
Geo-location and photo evidence are essential for QA
Pratihast et al. (2014), Forests. DOI: 10.3390/f5102464
Single robust linear model fit to entire time series
bfast used determine presence/absence of breakpoint in time series
Temporal covariates derived for each spectral band and index:
- trend (+delta), amplitude (+delta), overall trend, overall intercept
Training (personnel) and data collection took place in phases
Do the predictions improve over time as monitoring continues?
Degradation often confused for no-change by model in the training phase
Improvement of degradation predictions in operational phase
Increase in number of training data as well as improved quality of data
Band/Index-specific importance scores
Normalized ranks (compared class accuracies) over many model fits
SWIR[-derived] bands are essential
Local experts acting as "in situ sensors"
Ground-based, a priori identification of subtle change can enhance forest monitoring using LTS
- e.g. understorey degradation precedes subtle changes to the canopy
Continual capacity building can pay dividends in local forest monitoring systems
Drawbacks: how representative and sustainable are CBM data?
Data and Code are available
Landsat time series is a very powerful tool
- especially when all available data are used (Vogelman et al., 2016)
- Data fusion key to improving monitoring from space (Reiche et al., 2016)
Change monitoring sometimes limited by arbitrary class definitions
- e.g. 'deforestation' versus 'degradation', 'regrowth' versus 'no regrowth'
- consider more 'objective ecological' measures of forest condition and dynamics (Sexton et al., 2016)
- integration of LTS with in situ data/sensors can be of great help here
Community-based monitoring is a potentially cost-saving measure in monitoring systems
bfast (/ bfastmonitor) - http://bfast.r-forge.r-project.org/
bfastSpatial - http://github.com/dutri001/bfastSpatial
timeSyncR - http://github.com/bendv/timeSyncR
rgrowth - http://github.com/bendv/rgrowth
MulTiFuse - http://github.com/jreiche/multifuse
DeVries, B., Pratihast, A.K., Verbesselt, J., Kooistra, L. & Herold, M. 2016. Characterizing forest change using community-based monitoring data and Landsat time series. PLoS ONE Forests, 11(3), e0147121.
DeVries, B., M. Decuyper, J. Verbesselt, A. Zeileis, M. Herold & S. Joseph. 2015. Tracking disturbance-regrowth dynamics in the tropics using structural change detection and Landsat time series. Remote Sensing of Environment, 169, 320-334.
DeVries, B., Verbesselt, J., Kooistra L. & Herold, M. 2015. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sensing of Environment, 161, 107-121.
Verbesselt, J., Hyndman, R., Zeileis, A., & Culvenor, D. (2010). Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114(12), 2970-2980.
Reiche, J., de Bruin, S., Hoekman, D.H., Verbesselt, J., & Herold, M. 2015. A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection. Remote Sensing, 7, 4973-4996.
Schultz, M., Verbesselt, J., Avitabile, V., & Herold, M. 2015. Error Sources in Deforestation Detection Using BFAST Monitor on Landsat Time Series Across Three Tropical Sites. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 1-14.
Dutrieux, L.P., Verbesselt, J., Kooistra, L., & Herold, M. 2015. Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 112-125.
Pratihast, A.K., Herold, M., Avitabile, V., de Bruin, S., Bartholomeus, H., Souza, C.M., & Ribbe, L. 2013. Mobile Devices for Community-Based REDD+ Monitoring: A Case Study for Central Vietnam. Sensors, 13(1), 21-38.
Dutrieux, L.P., Jakovac, C.C., Latifah, S.H., & Kooistra, L. 2016. Reconstructing land use history from Landsat time-series: Case study of a swidden agriculture system in Brazil. International Journal of Applied Earth Observation and Geoinformation, 47, 112-124.
Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. & Willcock, S. 2016. An integrated pan-tropical biomass map using multiple reference datasets. Global Change Biology, 22, 1406-1420.
De Sy, V., Herold, M., Achard, F., Beuchle, R., Clevers, J.G.P.W., Lindquist, E., & Verchot, L. 2015. Land use patterns and related carbon losses following deforestation in South America. Environment Research Letters, 10, 124004.
De Sy, V., Herold, M., Achard, F., Asner, G.P., Held, A., Kellndorfer, J., & Verbesselt, J. 2012. Synergies of multiple remote sensing data sources for REDD+ monitoring. Current Opinion in Environmental Sustainability, 4(6), 696-706.