Remote sensing to reduce uncertainty in environmental policy in SE Asia. Daniel Friess, Edward L. Webb

Transcription

1 Remote sensing to reduce uncertainty in environmental policy in SE Asia Daniel Friess, Edward L. Webb

2 Dramatic mangrove loss 50% of all mangroves lost since ha lost in Malaysia in 25 years4 All mangroves lost by km2 in Sabah wood chip export to Japan5 Mangrove loss > x2 rainforest loss7 50% of mangrove loss due to shrimp aquaculture8 1 3% loss in SE Asia per year million ha lost in SE Asia from 1980 to ha lost in Indonesia in 5 years4 24% of mangroves globally are degraded6 38% global mangrove loss by shrimp culture6 Vietnam loss from 4000 km2 (1945) to 2130 km2 (1998)9 SE Asia shows the highest mangrove loss globally6 Malaysia loss 17% % of Singapore s mangrove lost % global decline per year3 Thailand ha ha between 1975 and Philippines 70% loss 1920 to India loss 50% % of Asian mangroves lost in 20th century8 50% of global wetlands have been lost Mangrove cover in Singapore from 13% to 0.5% of land area by 2002

3 Conservation needs solid baselines Payment for Ecosystem Services (e.g. REDD+). How to prove quantitatively that deforestation has been reduced? 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 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

4 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

5 Indonesia 20.9% of global mangrove cover (1st) (Spalding et al. 2010) km2

6 Australia 6.5% of global mangrove cover (3rd) (Spalding et al. 2010) FAO (2007) trend 7763 FAO (2007) pred academic All 2227 Govt FAO (2010) pred worst case 2020

7 Vietnam 0.7% of global mangrove cover (Spalding et al. 2010) Mangrove area (sq km) FAO (2007a) trend FAO (2007a) prediction 1500 FAO (2010) prediction 1000 IGO Academic Year

8 Peninsular Malaysia 0.8% of global mangrove cover (Spalding et al. 2010) Friess & Webb 2011, Environmental Conservation 38: 1 5

9 Why so much uncertainty? Poor transparency in historical estimates 1. lack of robust methodology (grey/govt. literature) how derived? Remote sensing? Best guess? 2. traceability of secondary info poor referencing, poor citation grey literature (Corlett, 2011) 3. propagation of erroneous info Friess & Webb 2011, Environmental Conservation 38: 1 5

10 The way forward We increase robustness by increasing transparency

11 Remote sensing State inventories for P. Malaysia Unsupervised env. modelling method for classifying land use change in the Ayerwaddy delta, Myanmar

12 Remote sensing Govt. studies e.g. CONABIO + for other forest types: + work by Giri and others (global, SE Asia, Philippines, Pacific) NASA LCLUC programmes Global Forest Info System EU TREES project FAO GFRA 2010 (Potapov et al. 2011) i.e. transparent mapping, accepted interpretation (GOFC GOLD REDD sourcebook)

13 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+?

14 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

15 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 dan.friess@nus.edu.sg

16 Such a monitoring system does not exist Variation in 6 trend lines: Grey denotes annual mangrove loss

17 3. Error propagation a UK example Saltmarsh loss = 100 ha a 1 (UK Biodiversity Group 1999) 2001 IPCC (2001) WG2, 3rd Assessment s of local govt. reports and policies 2004 Pontee et al Pilcher et al Airoldi & Beck 2007 Badley & Alcorn 2006 Hannaford et al. 2006

18 The way forward Also: World Mangrove Atlas, Giri et al. (2011) Kenya Neukermans et al. (2008) Madagascar Giri et al. (2008)