Time Series Fusion of Optical and Radar Imagery for Improved Monitoring of Activity Data, and Uncertainty Analysis of Emission Factors for Estimation of Forest Carbon Flux Josef Kellndorfer, Oliver Cartus, Neeti Neeti, Richard Houghton, WHRC Curtis Woodcock, Pontus Olofsson, Chris Holden, Boston University

Objective (1) – Develop a Novel Method for the Generation of Activity Data Develop an algorithm from optical and radar time series fusion to produce the most accurate assessment of deforestation, forest degradation, and forest regrowth area estimates at annual time steps or better (i.e., activity data). Implement good practices for assessing uncertainty and accuracy of activity data (Olofsson et al. 2014, 2013). Objective (2) – Uncertainty Analysis of Carbon Emission Estimates from Activity Data and various Sources for Emission Factors Compile a database of country specific emission factors stratified by land cover categories linked with carbon density estimates from forest inventory and existing biomass maps. Study impact of uncertainties in activity data and emission factors on estimated carbon flux from a bookkeeping model in order to provide guidance to national MRV implementation. Objectives

Study Area Mexico: High priority sites for CONAFOR/PMN datasets: RapidEye (manual interpretation), ortho-photo, Peru/Colombia: GEO-FCT sites datasets: UAVSAR, RapidEye, other high-res data Field campaigns

Landsat/PALSAR FBD Data Availability

Landsat Time Series Modeling Zhu et al. (2013, 2014, RSE) Change Criterion Time series modeled as series of cosine/sine functions:

Landsat Time Series in the Tropics

Landsat Change Maps

Landsat Data Availability

Change detection with radar Landsat like time series modeling for PALSAR is not feasible because: 1) limited number of acquisitions available (in one season) 2) radar backscatter change due to changes in imaging conditions tends to be of stochastic nature  The Irish weather stone: if it is wet, it is raining Bi-temporal change detection approaches have been used large-scale (e.g., Santoro et al. 2012) Trends can be identified

PALSAR Change Detection Algorithm: 1. PALSAR FBD pre-processing in WHIPS 2. Multi-temporal filtering 3. Log-Ratio calculation for all image pairs in a time series 4. Automated threshold determination for identifying areas with backscatter in- and decreases (Kittler-Illingworth Criteria) 5. Linear image normalization to account for changes in environmental imaging conditions 6. Markov-Random-Field post-classification considering spatial context to improve boundaries of areas identified as “changed” (Pantze et al. 2014) Algorithm: 1. PALSAR FBD pre-processing in WHIPS 2. Multi-temporal filtering 3. Log-Ratio calculation for all image pairs in a time series 4. Automated threshold determination for identifying areas with backscatter in- and decreases (Kittler-Illingworth Criteria) 5. Linear image normalization to account for changes in environmental imaging conditions 6. Markov-Random-Field post-classification considering spatial context to improve boundaries of areas identified as “changed” (Pantze et al. 2014) Multiple Iterations

HV log ratio

Threshold Selection and Normalization Compensate for differing imaging conditions

Change Map Backscatter change can be a consequence of: 1) actual land cover/use change 2) soil moisture dynamics 3) agriculture, … Multi-temporal stack of images can help to identify areas for which land cover hasn’t changed  Is the change persistent ? Initial results show that number of PALSAR images is not always sufficient L-HV decrease L-HV increase Red – change persistent

Landsat Time Series vs. L-HV Backscatter Change

Summary and Outlook In many areas, Landsat provides dense time series with detailed information on land cover change/degradation The addition of radar imagery in these areas may be of use to compliment and extend (e.g., improved timing of change) the information already present in the Landsat timeseries Initial results show limitations of both, optical and radar, when used alone Limitations of CCDC due to persistent cloud cover (Colombia) L-HV backscatter change not only related to land cover change acquisition strategy of PALSAR not ideal to identify persistent change in land cover/use (single season, few annual observations) In parts of Colombia, the addition of radar is crucial (but is it sufficient?) Outlook: Field trips in 2015 to Colombia/Peru – recent degradation/deforestation/conversion Validation in Mexico alongside national Landsat/RapidEye change products for Mexico’s MRV