Remote Sensing and Environmental Security

Transcription

1 Remote Sensing and Environmental Security Space Agencies & Operational monitoring systems Dirk H. Hoekman Niels Wielaard Vincent Schut

2 Remote sensing: Lifting the veil Train explosion Ryongchon North Korea, April Pre-explosion 13 May 2003 Post-explosion 27 April 2004 Copyright 2004 Globalsecurity.org Source: DigitalGlobe

3 Overview Overview of this presentation: 1. Environmental security issues from space (special focus on tropical forest regions) 2. Data and products availability 3. Policy trends main space agencies 4. Towards permanent operational systems Low resolution (1 km) monitoring High resolution (25 m) monitoring

4 ES issues from space: examples Linking remote sensing and environmental security issues 1. Illicit crop (coca) production 2. Fire and carbon emission 3. Deforestation and biodiversity loss

5 Example 1. Coca production 1992 ESA ERS-1 radar satellite ~40 km x 75 km Colonisation frontier Colombian Amazon

6 Example 1. Coca production Grey : forest Green : pastures, roads, some houses 1992 Baseline forest frontier ESA ERS-1 Radar (Detail) Enhanced contrast

7 Example 1. Coca production Grey : forest Green : pastures, roads, some houses 1993 Land cover changes in rural areas ESA ERS-1 Radar (Detail) Enhanced contrast

8 Example 1. Coca production Price of coca rises; Grey : forest Many characteristic small clear cut areas completely surrounded by forest borders appear Green : pastures, roads, some houses 1994 Price of coca rises Small clear cut areas completely surrounded by forest ESA ERS-1 Radar (Detail) Enhanced contrast

9 Example 1. Coca production Pictures of a bizarre economy Coca still the only cash crop; Guerillas (FARC) force poor farmers to grow coca Already 10 years ago the techniques existed ERS-1 radar satellite could have made continuous observations for the whole area of interest; Yet, it was not done

10 Example 2. Fire and carbon emission A burning issue Estimated burned area in Indonesia: SMOKE HAZE ~ 10 million ha HOT SPOTS

11 Example 2. Fire and carbon emission Largest carbon emission Central Kalimantan Releasing 0.25 GigaTon Carbon = 8% total global annual increase in single fire event Largest burned area East Kalimantan: Releasing 0.05 GigaTon Carbon Total emission: Gt of carbon 13-40% of annual anthropogenic emissions Largest annual CO 2 increase ever recorded SPOT VEGETATION Borneo 1998 Page et al. 2002

12 Example 2. Fire and carbon emission Socio-economic impact Air pollution 500,000 people hospital treatment Tourism dropped 90% US $ 4.5 Billion in Regional Losses c 2000 BBC c NASA TOMS Background NOAA - AVHRR (Glover and Jessup, 1999)

13 Example 2. Fire and carbon emission Biodiversity Loss

14 Environmental disaster Early warning Collapsed dome Canal JERS-1 SAR Time series

15 An enormous destruction in terms of CO2 emission, loss of biodiversity, land degradation and health risk is going on for almost 10 years, and still gets little attention. Already 10 years ago the techniques existed JERS-1 radar satellite could have made continuous observations for the whole area of interest; Yet, it was not done

16 .. the same area in Kalimantan, only 9 years ago

17 Example 3. Deforestation Forest loss Kalimantan Threat to global biodiversity 10 % world s plants 12 % world s mammals 17 % of all birds 17 % of all reptile and amphibian species (BAPPENAS, 1993) Source: Fuller et al. 2004

18 Forest loss - Illegal logging in Gunung Palung National Park km Source: Curran et al. 2004

19 ES issues from space: examples Examples illustrate: We can observe it clearly We could do it for much larger areas, even for all Why isn t this done? It is easy, costs are not prohibitive. Is it ignorance, lack-of-interest, fatalism, conflict of (hidden) interests,..?

20 Data and products, space agencies Data and products availability Policy trends main space agencies

21 Policy of main space agencies An enormous wealth of data, hardly explored! Useful information lost in oblivion -> data-graveyards An example ESA acquired over 2,000,000 ERS SAR scenes.. only 60,000 of these have ever been looked at (only 3%)!!

22 Data and products availability Data: raw data and higher level data products Public domain Non-commercial fee Commercial fee Example: SPOT VEGETATION data (see system example) Thematic products: Public domain Commercial Examples: Monitoring services such as weather forecast or (potentially) deforestation status and land cover change (see system example)

23 Policy of main space agencies United States NASA: All data in public domain MODIS ( m); SRTM (elevation) data: Digital topographic map of the world 30x30 m spatial sampling; SRTM01 map

24 Shaded relief of VEGETATION image Borneo Comparison old USGS30 vs. new SRTM03 global elevation map

25 Policy of main space agencies Europe ESA: ERS-3 and / or L-band? Discussion with user community required; commercial development not longer considered as the preferred option, however, for public domain funds seem to be lacking ENVISAT Advanced SAR (ASAR) Launched 1 March 2002

26 Policy of main space agencies Japan JAXA (formerly NASDA) Historical JERS-1 data archives still largely unexplored Selected agencies / institutes will produce large scale products to support international conventions such as Kyoto protocol, Ramsar, CBD

27 BOS-SarVision SarVision signed agreement with JAXA JAXA K&C Initiative Exploitation phase Insular Southeast Asia; high resolution Many acquisitions / year; BOS-SarVision Indonesia processing centre in Balikpapan

28 Towards operational systems Towards permanent operational systems: 1. Low resolution (1 km 250 m) monitoring system Acreage assessment and development from year to year Forest condition Link to administrative units 2. High resolution (25 m) monitoring system Fast response against illegal logging Reforestation monitoring -- Timber certification Debt for nature swap -- Carbon offset trading

29 Monitoring system for forest cover change insular SE Asia SPOT VEGETATION Spectral maps quarterly updates

30 Land cover maps quarterly updates

31 Jambi Zoom in on the province Jambi, Sumatra

32 Boundary systems Indonesia Link to additional information Five themes (functional layers for web-gis analysis) I. Administrative II. State Forest Areas III. Forest Concession Holder (HPH) IV. Industrial Plantation Forest (HTI) V. Biodiversity Additional map information (non-functional layers for web-gis analysis) VI. Roads VII. Rivers VIII. Altitude 1000m contour

33 Kaltim-Kalteng III. Forest Concession Holder (HPH) PT. Kodeco Timber still exports large amounts of timber However, already many years ago all forest has gone!!

34 Example: oil palm plantation establishment Investment criterion: no forest conversion Rough analysis low resolution images: Land under plantation much larger than official boundaries 1999: large patch of forest clearcut 2000: converted to plantation. responsible business

35 Deforestation status quarterly updates

36 Zoom in on the province Jambi, Sumatra Start Start Enormous deforestation in Jambi results from decentralization and collapse of authorities

37 Yearly low resolution monitoring Towards operational systems Low resolution (1 km 250 m) monitoring systems Based on SPOT Vegetation MODIS MERIS Provide transparency; awareness; are a tool for policy makers + All data can be put on Internet for public access

38 Towards operational systems High resolution (25 m) monitoring systems Based on ENVISAT ASAR ALOS PALSAR Disseminate results through internet Objectives : to inform law enforcement teams to fulfill monitoring requirements for carbon offset trading, etc

39 Fast Illegal Logging Response Envisat SAR Most timber is taken by outsiders, not by local communities

40 Results of SarVision s change detection algorithm can be updated every 35 days ENVISAT SAR change detection

41 Aircraft BOS leaves immediately after making flight plan Flight planning Geocoded video shots captured during air survey Illegal logging operation by HPHKM Owner is Gunawan He is now in jail

42 Detected logging is outside legal logging areas.

43 Time Line (example March 2004) March 9: Download data from European Space Agency (Tuesday) March 10: Change Detection algorithm on SarVision servers in The Netherlands. Resulting map sent to BOS Mawas field office. Production Flight Plan same evening March 11: Microlight survey collected evidence March 12: Integrated Illegal Logging Response Team left for identified sites March 13: Evacuation of confiscated chainsaws and police reports filed (Saturday!!!) April 12: Again more arrests with same system!

44 more details of change detection Camp Wetlands Camp research bridge construction

45 Conclusion The techniques are available; Data availability and development of new missions are issues of ongoing concern; Yet, hardly anything that is already developed materialized into operational services; BOS-SarVision system crucial for establishment of 540,000 ha Mawas conservation area and carbon offset trading deal; alternative jobs and income for local population; protection of last large wild orangutan population

46 Epilogue: Some facts Global Distribution Tropical Peat Forest Most of the world s tropical peat is located in Indonesia: 30 out of 50 Million ha Example Mawas Area Peat layer 10 m deep: Forest biomass: 5700 ton C/ha ton C/ha Global Stock: Gt Increase in CO The CO 2 increase is the largest ever recorded

47 Some more facts Undisturbed peat swamps are active carbon sinks and sequester Gt C per year in Indonesia (Sorensen, 1993) Degraded, drained peat swamps are carbon sources up to 100 Gt C in Indonesia. These stocks cannot be replaced. 340 Gt C is stored in tropical forests and 139 Gt C in temperate forests, but these stocks can be replaced within decades, centuries (Roy et al., 2001) The annual increase of atmospheric C is 3.3 Gt (IPCC, 2001) Therefore? Indonesia is a key player in global climate change during the next decades This fact is still largely ignored - Political solution through U.N. required Carbon crediting system Where is the logic? Credits up to 20 US$ per ton (CO 2 ) for storing carbon for a limited period in tree plantations; no credits for storing carbon for ever in peat swamps? Indonesia could get more income through forest conservation (permanently) than now from timber export (declining)

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