remote sensing to reduce uncertainty in environmental...
TRANSCRIPT
Remote sensing to reduce uncertainty in environmental
policy in SE Asia
Daniel Friess, Edward L. Webb
50% of all mangroves lost since 19007Dramatic mangrove loss
50 000 ha lost in Indonesia in 5 years41.9 million ha lost in SE
Asia from 1980 to 20054
1225.5 km2 in Sabah wood chip export to Japan5
Thailand 312700 ha 168683 ha between 1975 and 19932
75% of Asian mangroves lost in 20th century8
Mangrove loss > x2 rainforest loss7
24% of mangroves globally are degraded6
2.1% global decline per year3
38% global mangrove loss by shrimp culture6
Malaysia loss 17% 1965‐851
Philippines 70% loss 1920 to 19901 India loss 50% 1963‐19771
110 000 ha lost in Malaysia in 25 years4
Vietnam loss from 4000 km2 (1945) to 2130 km2 (1998)9
12% of Singapore’s mangrove lost 1980‐904
Mangrove cover in Singapore from 13% to 0.5% of land area by 2002
SE Asia shows the highest mangrove loss globally6
50% of mangrove loss due to shrimp aquaculture8
50% of global wetlands have been lost
1‐3% loss in SE Asia per year10
All mangroves lost by 21003
Conservation needs solid baselines
‐ Payment for Ecosystem Services (e.g. REDD+). How to prove quantitatively that deforestation has been reduced?
Uncertainty shown for tropical forest, global forest, peat swamp, with poor national inventories in developing nations (Grainger, FAO 2007 and others)
Current mangrove loss statistics do not meet these requirements
UNFCCC resolution FCCC/CP/2009/11/Add.1 (Copenhagen):
a monitoring system must be established and historical data utilised by signatory countries in order to “provide estimates that are transparent, consistent, as far as possible accurate, and that reduce uncertainties”
5 UNFCCC principles – transparency, consistency, comparability, completeness, accuracy
Do we have solid baselines?‐ In‐depth literature review: >600 data points – 461 points used‐ 6 Trends: FAO (2007a,b, 2010), inter‐/non‐governmental, academic, govt.‐ Linear regression models, ANCOVA, prediction intervals to highlight uncertainty
Indonesia
23 661 km2
20.9% of global mangrove cover (1st) (Spalding et al. 2010)
Australia
FAO (2007) trend 7763FAO (2007) pred. 7424
All 2227
FAO (2010) pred. 2147
academic
worst case 2020
Govt. 2101
6.5% of global mangrove cover (3rd) (Spalding et al. 2010)
Vietnam0.7% of global mangrove cover (Spalding et al. 2010)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
1940 1950 1960 1970 1980 1990 2000 2010
Man
grove area
(sq km
)
Year
FAO (2007a) trend
FAO (2007a) prediction
FAO (2010) prediction
IGO
Academic
Peninsular Malaysia
Friess & Webb 2011, Environmental Conservation 38: 1‐5
0.8% of global mangrove cover (Spalding et al. 2010)
Why so much uncertainty?
1. lack of robust methodology (grey/govt. literature)‐ how derived? Remote sensing? Best guess?
Friess & Webb 2011, Environmental Conservation 38: 1‐5
Poor transparency in historical estimates
2. traceability of secondary info‐ poor referencing, poor citation‐ grey literature (Corlett, 2011)
3. propagation of erroneous info
The way forward
We increase robustness by increasing transparency
Remote sensing
State inventories for P. Malaysia
Unsupervised env. modelling method for classifying land use change in the Ayerwaddy delta, Myanmar
Remote sensing
+ work by Giri and others(global, SE Asia, Philippines, Pacific)
Govt. studies e.g. CONABIO
+ for other forest types:‐NASA LCLUC programmes‐ Global Forest Info System ‐ EU TREES project‐FAO GFRA 2010 (Potapov et al. 2011)
i.e. transparentmapping, accepted interpretation (GOFC‐GOLD REDD sourcebook)
CONABIO (Mexico)
How do we ensure that all countries present data like this?
How do we connect academics, IGOs, Govts to provide accurate information for REDD+?
Conclusions
• Mangroves have not been quantified to the extent of other ecosystems: uncertainty will hamper future conservation policies
• Increasing transparency and rigour will improve historical estimates, while explicit methodologies will improve future prediction
• How to ensure accurate national monitoring systems – ASEAN level? Are current institutions adequate? Who should lead?
• These actions will provide environmental policy with more solid scientific foundations
Acknowledgements
‐ Jacob Phelps, Alan Ziegler, Nick Jachowski (NUS)
‐ C Giri, K Krauss (US Geological Survey)
‐ N Saleh (Universiti Putra Malaysia)
‐ C Sudtongkong (Rajamangala University, Thailand)
‐MM Than (Mangrove and Environmental Rehabilitation
Network, Myanmar)
‐ Landsat data shown acquired from GLCF
http://staff.science.nus.edu.sg/~apelab/friess.htm
Such a monitoring system does not exist
‐ Variation in 6 trend lines:
‐ Grey denotes annual mangrove loss
3. Error propagation – a UK example
Saltmarsh loss = 100 ha a‐1(UK Biodiversity Group 1999)
IPCC (2001) WG2, 3rd Assessment
2001
2002
2004Pontee et al. 2004Pilcher et al. 2004
2005
2006Badley & Alcorn 2006Hannaford et al. 2006
2007
Airoldi & Beck 2007
2010
+ 10s of local govt. reports and policies
2000 ‐ 2010
The way forward
Also: World Mangrove Atlas, Giri et al. (2011)
Madagascar ‐ Giri et al. (2008)Kenya ‐ Neukermans et al. (2008)