Opportunities and challenges for monitoring tropical deforestation and forest degradation in dynamic landscapes using Sentinel-2!

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1 Sentinel-2 For Science Workshop May, 2014 ESA-ESRIN Frascati Opportunities and challenges for monitoring tropical deforestation and forest degradation in dynamic landscapes using Sentinel-2 Dirk Pflugmacher 1, Kenneth Grogan 1,2, Sithong Thongmanivong 3, and Patrick Hostert 1 1 Humboldt-University of Berlin, 2 University of Copenhagen, 3 National University of Laos dirk.pflugmacher@geo.hu-berlin.de

2 Main tropical deforestation fronts in Southeast Asia in the 1980s and 1990s Mayaux et al Carbon emissions from land-use change in Southeast Asia in 80s ~75% of the region s total emissions (Houghton et al. 1999) - Significant transformations in land use (intensification) due to changes in land use policies, opening of the economies to regional international markets, improved market accessibility during past two decades - Need for monitoring deforestation and forest degradation (e.g. REDD+ MRV) 2

3 Forest mosaic landscapes in Southeast Asia Majority of uplands not covered by primary forest anymore Complex, fine-spatial patch work of forests, regrowth, and crops (shifting cultivation) Monsoon systems High cloud cover/aerosols Ton C /ha 80 Trees Dead trees Shrubs and Bamboo Litter and deadwood 20 0 Intensive, Intensive, fallow cultivated Extensive, Extensive, cultivated fallow Courtesy Bruun, Berry, Neergard Old fallow 3

4 Spatial and temporal patterns of slash and burn agriculture April E T E E T E Jul Aug Sep Oct Nov Dec Jan T E E T E T Feb T T E T E E m Mar Landsat Bands April 4

5 Objectives 1. Test the utility of high-resolution optical time series to monitor forest changes in mosaic landscapes of Southeast Asia. 2. Develop forest change maps as input for prediction models of historic forest carbon stocks and changes. 5

6 Study site Houaphan province - Northern Laos Area: 16,500 km 2, Population: 246,000 Tropical mountain and dry evergreen forest with monsoonal climate Prolonged shifting cultivation and fire, selective logging fire wood Since 2000s, shifting cultivation is starting to be replaced by permanent or short fallow maize (Vongvisouk et al., 2014) 6

7 Change analysis USGS Landsat Archive All L1T data < 80% cloud cover ( ) Atmospheric Correction (LEDAPS) Cloud Masking (Fmask) Image stacks Vegetation Index (NBR) Annual composites Change detection Yearly disturbance maps 7

8 Change analysis USGS Landsat Archive All L1T data < 80% cloud cover ( ) Atmospheric Correction (LEDAPS) Cloud Masking (Fmask) Normalized Burn Ratio (NBR) time series Low seasonality single clearing Intermediate seasonality multiple clearings Image stacks Vegetation Index (NBR) Annual composites High seasonality - intensive cropping Change detection Yearly disturbance maps

9 Change analysis USGS Landsat Archive All L1T data < 80% cloud cover ( ) Validation Atmospheric Correction (LEDAPS) Cloud Masking (Fmask) T E T Image stacks Vegetation Index (NBR) Annual composites Change detection Very high resolution imagery Field plots T E E E E T Yearly disturbance maps Validation 9

10 Change analysis USGS Landsat Archive All L1T data < 80% cloud cover ( ) Atmospheric Correction (LEDAPS) Validation Carbon model Yearly forest fallow age maps Initial forest (age) map Cloud Masking (Fmask) Image stacks Vegetation Index (NBR) Annual composites Change detection Very high resolution imagery Field plots Carbon density (Mg/ha) Carbon model C t =f(age) Yearly disturbance maps Validation Yearly carbon stock maps 10

11 Landsat data coverage (USGS Archive) WRS-2: 128/46, 127/ images Wet season : 1-2 images per year : avg. 11 acquisitions per year

12 Change analysis Annual NBR min time series Pre-disturbance value (NBR pre ) NBR Magnitude (dnbr) Post-disturbance value (NBR clear ) Recovery duration Seasonal minimum NBR (NBR min ) composites for dry season and wet season for each year followed by annual gap filling Change thresholds: dnbr, dnbr%, NBR clear Recovery: 90% of pre-disturbance value 12

13 Results: Dry season time series dnbr NBR clear dnbr% Mean User s and Producer s Accuracy across all years: 75% - 80% 13

14 Results First clearing year Dry season Wet season 14

15 Forest signal recovery time Dry season time series NBR NBR Fast spectral signal recovers within 1-2 years 15

16 Results Forest clearing ( ) 14% stable grassland and permanent croplands First clearing year 16

17 Summary Remote sensing in tropical mosaic landscapes is challenging Clearings coincide with dry season - detection complicated by phenology, fast spectral recovery, and clouds/aerosols Dense intra-annual time series data are needed in dynamic landscapes - periodic change is not sufficient Improved data coverage from Sentinel-2 (and Landsat 8) will improve detection of change and characterization of change processes (e.g. recovery) Spatially heterogeneous landscapes - Sentinel-2 s higher spatial resolution likely beneficial Data continuity between sensors and archives is important for long-term (historic) analyses Automated pre-processing standards are crucial orthorectification, atmospheric correction and cloud-masking 17

18 Thank you 18