Near Real-time Deforestation Detection using a Bayesian Approach to Combine Landsat, ALOS PALSAR and Sentinel-1 Time Series

Research output: Contribution to conferenceAbstract

Abstract

Detecting deforestation in near real-time (NRT) is essential for immediate law en-forcement in fighting illegal logging in tropical regions. Current remote sensing based NRT monitoring systems rely on MODIS time series imagery in order to provide fortnightly information on newly deforested areas at 500m resolution. Due to the low spatial resolution, however, small scale changes are missed, which precludes the rapid response of many human-induced deforestation activities that tend to be small scale. Missing data due to persistent cloud cover limtes optical-based NRT monitoring at scale medium resolution scale.
We present a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection at medium resolution scale. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. Results are presented using Landsat and ALOS PALSAR time series of an evergreen forest plantation in Fiji, where we emulated a near real-time scenario. Three-monthly reference data (plantation operations) covering the entire study areas was used to validate and assess spatial and temporal accuracy. We tested the impact of persistent cloud cover by increasing the per-pixel missing data percentage of the NDVI time series stepwise from ~50% (~6 observations/year) up to 95% (~0.5 observations/year) while combining with a consistent PALSAR time series of ~2 observations/year. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series. First results of using dense Sentinel-1 C-band time series in addition to Landsat and ALOS PALSAR-1 and -2 time series will be presented for a forest site in Ethiopia.
eywords: Multi-sensor fusion, Time-series, ALOS PALSAR, Landsat, Deforestation
Original languageEnglish
Publication statusPublished - 13 May 2016
EventLiving Planet Symposium 2016 - Prague, Czech Republic
Duration: 9 May 201613 May 2016

Conference

ConferenceLiving Planet Symposium 2016
CountryCzech Republic
CityPrague
Period9/05/1613/05/16

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