opportunities and challenges for monitoring tropical...
TRANSCRIPT
Dirk Pflugmacher1, Kenneth Grogan1,2, Sithong Thongmanivong3, and Patrick Hostert1!
!1Humboldt-University of Berlin, 2 University of Copenhagen, !
3 National University of Laos!!
Opportunities and challenges for monitoring tropical deforestation and
forest degradation in dynamic landscapes using Sentinel-2 !
Sentinel-2 For Science Workshop | 20-22 May, 2014 | ESA-ESRIN | Frascati!
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eastern to the southern portions of the Amazon basin.Large areas of deforestation are found on the Peruvianand Ecuadorian lower foothills of the Andes. Inside thebasin, pockets of deforestation are associated withsettlements and roads. Deforestation is reported to beon the increase in the coastal forests of Colombia andEcuador and in Guyana (Jimeno et al. 1995). InCentral America, the forest remnants are highlyfragmented. Fragments are being progressivelyreduced and only those areas which are inaccessibleor legally protected seem to be somewhat secure. Largeareas of forest are also becoming isolated at the regionallevel, highlighting the urgent need for establishingbiological corridors. Agricultural expansion and newsettlements are the main causes of deforestation on thiscontinent. The transformation from closed, open orfragmented forests to agriculture by clear-cutting is apredominant factor. Moreover, about 4 million ha ofmosaic or savannah woodland have been transformedinto agriculture. Two-thirds of this transformation ishappening in the Brazilian Amazon region (Achardet al. 2002).
(ii) AfricaDeforestation in the Congo basin is still limited torelatively few areas, and large-scale clear-cutting orsignificant agricultural expansion is not expected totake place very soon. Furthermore, the secondaryforest vegetation may act as a buffer if an acceleration ofslash-and-burn cultivation takes place locally. Thecauses of deforestation are manifold, ranging fromagricultural encroachment and illegal logging inCameroon to urban expansion and fuel wood supplyaround the major cities and refugee migrations inLiberia and eastern Democratic Republic of Congo(DRC). Shifting cultivation mainly occurs in secondaryforest mosaics and only partially affects the closedprimary forests. Agricultural colonization follows adiffuse spatial pattern, with population pressure beingparticularly high in eastern DRC. Selective loggingplays an indirect role, with logging roads facilitatinggreatly increased hunting pressure from poachers.
The upper Guinea forest in West Africa and easternforests of Madagascar contain exceptional biologicaldiversity. Both are under severe deforestation owing toslash-and-burn agriculture, logging and mining.
(iii) Southeast AsiaIn Southeast Asia, most of the forest remnants of bothcontinental Southeast Asia and the Indo-Malay Archi-pelago fall within current hot spots. The extensive forestresources of northeastern India are under intensiveexploitation for timber and conversion to agriculture.Selective logging and clear-cutting affectmany forests ofMyanmar, central and southern Laos and Cambodia.Shifting cultivation has led to further forest loss innortheastern India and the northern parts ofMyanmar,Laos and Vietnam. In Myanmar the impact of shiftingcultivation is believed to be on the increase. Plans forChina to open various access roads and railways fromYunnan to the Andaman Sea are likely to have a seriousimpact on the forest remnants of the ‘golden triangle’. InVietnam, conversion of the remaining natural forest isstill widespread in the central highlands, while the forestfragments in the north are rapidly being eroded. Theforests in Indonesia have in recent years suffered some ofthe most severe deforestation of anywhere. In Sumatra,forests have virtually disappeared under the pressure ofagriculture and plantations along a wide central south-north belt. A similar situation has developed inKalimantan, where plantations, but also extensiveexploitation and large-scale fires, have taken their toll.No reversal of such trends is likely to emerge in the nearfuture.
(g) Estimating deforestation in specific areasGlobal or regional statistics, although very usefulfor evaluating long-term extinction risks of speciesor families, can hide very dramatic local situations inareas of high biological interest or in regions whereforests represent the major source of revenue. In thoseareas, specific strategies must be developed for refiningthe estimates. We will now illustrate possible strategiesfor two specific domains: protected areas and
Figure 3. Main tropical deforestation fronts in the 1980s and 1990s from Lambin et al. (2003) and Lepers et al. (in press). Themap is based on the deforestation hotspots in the humid tropics of the TREES project (Achard et al. 1998), a time-series analysisof tree cover based on NOAAAVHRR 8 km resolution data (DeFries et al. 2000) and, for the Amazon basin, deforestation mapsderived from time-series of Landsat TM data (Skole & Tucker 1993). The map indicates the number of times each 0.18 grid wasidentified as being affected by rapid deforestation by the different datasets (pinkZ1, redZ2, dark redZ3).
380 P. Mayaux and others Tropical forest monitoring
Phil. Trans. R. Soc. B (2005)
on April 25, 2012rstb.royalsocietypublishing.orgDownloaded from
Mayaux et al. 2005!
Main tropical deforestation fronts in Southeast Asia in the 1980s and 1990s!
- 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)!
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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!
0
20
40
60
80
Intensive, cultivated
Intensive, fallow
Extensive, cultivated
Extensive, fallow
Old fallow
Ton
C /h
a
Trees
Dead trees
Shrubs and Bamboo
Litter and deadwood
Courtesy Bruun, Berry, Neergard
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E−2000−92 T−2000−100 E−2000−188 E−2000−236 T−2000−260 E−2000−268
T−2000−276 E−2000−284 E−2000−300 T−2000−308 E−2000−364 T−2001−22
T−2001−38 T−2001−54 E−2001−62 T−2001−86 E−2001−94 E−2001−110
E−2001−174 E−2001−222 E−2001−254 E−2001−270 E−2001−286 T−2001−310
Landsat Bands 5-4-7!
Spatial and temporal patterns of slash and burn agriculture!
1500 m
April!
April!
Oct!
Feb! Mar!
Nov! Dec! Jan!
Sep!Jul! Aug!
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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.!
Objectives!
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Study site!Houaphan province - Northern Laos!
§ Area: 16,500 km2, 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)!
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Atmospheric Correction!(LEDAPS)!
USGS Landsat Archive!All L1T data < 80% cloud
cover (1987-2012)!
Cloud Masking (Fmask)!
Vegetation Index (NBR)!
Change detection!
Yearly disturbance maps!
Annual composites!
Image stacks!
Change analysis
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Atmospheric Correction!(LEDAPS)!
USGS Landsat Archive!All L1T data < 80% cloud
cover (1987-2012)!
Cloud Masking (Fmask)!
Vegetation Index (NBR)!
Change detection!
Yearly disturbance maps!
Annual composites!
Image stacks!
Change analysis Normalized Burn Ratio (NBR) time series!
2000 1990 2010
Low seasonality – single clearing
Intermediate seasonality – multiple clearings
High seasonality - intensive cropping
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Atmospheric Correction!(LEDAPS)!
USGS Landsat Archive!All L1T data < 80% cloud
cover (1987-2012)!
Cloud Masking (Fmask)!
Vegetation Index (NBR)!
Change detection!
Change analysis
Yearly disturbance maps!
Annual composites!
Validation!
Validation
Image stacks!
E−2000−92 T−2000−100 E−2000−188 E−2000−236 T−2000−260 E−2000−268
T−2000−276 E−2000−284 E−2000−300 T−2000−308 E−2000−364 T−2001−22
T−2001−38 T−2001−54 E−2001−62 T−2001−86 E−2001−94 E−2001−110
E−2001−174 E−2001−222 E−2001−254 E−2001−270 E−2001−286 T−2001−310
Very high resolution imagery!
Field plots!
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Atmospheric Correction!(LEDAPS)!
USGS Landsat Archive!All L1T data < 80% cloud
cover (1987-2012)!
Cloud Masking (Fmask)!
Vegetation Index (NBR)!
Change detection!
Change analysis
Yearly disturbance maps!
Annual composites!
Validation!
Very high resolution imagery!
Validation
Image stacks!
Carbon model
Yearly forest
fallow age maps!
Yearly carbon stock maps!
Carbon model!!!!!!!!
Car
bon
dens
ity (M
g/ha
)
Initial forest
(age) map!
Ct=f(Age)
Field plots!
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Landsat data coverage (USGS Archive)!
• WRS-2: 128/46, 127/46!!• 168 images!
• 1988-1999: 1-2 images per year!
• 2000-2012: avg. 11 acquisitions per year!
2000-2012!
Wet season!
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1995 2000 2005 2010
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date
NBR
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1995 2000 2005 2010
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1995 2000 2005 2010
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1995 2000 2005 2010
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1995 2000 2005 2010
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113
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1995 2000 2005 2010
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1990 1995 2000 2005 2010
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1990 1995 2000 2005 2010
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1990 1995 2000 2005 2010
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Change analysis!!
• Seasonal minimum NBR (NBRmin) composites for dry season and wet season for each year followed by annual gap filling!
• Change thresholds: dNBR, dNBR%, NBRclear!
• Recovery: 90% of pre-disturbance value!
Annual NBRmin time series
Magnitude (dNBR)
Recovery duration
Post-disturbance value (NBRclear)
Pre-disturbance value (NBRpre)
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Results: Dry season time series!
NBRclear
dNBR
dNBR% Mean User’s and Producer’s Accuracy across all years: 75% - 80% !
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Wet season
Results!First clearing year
Dry season
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Forest signal recovery time!
Fast spectral signal recovers within 1-2 years!
Dry season time series
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1995 2000 2005 2010
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Results Forest clearing (1992-2012)!
First clearing year
14% stable grassland and permanent croplands
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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!
!
