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7. Global Forest Watch & Monitoring Forests Using Remote Sensing

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Dmitry Aksenov
Transparent World

Published in: Environment
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7. Global Forest Watch & Monitoring Forests Using Remote Sensing

  1. 1. Global Forest Watch & Monitoring Forests Using Remote Sensing Dmitry Aksenov Transparent World
  2. 2. 1. Maps based on satellite data are a communication tool Visualizing a problem – a way for finding common language among stakeholders, helping them to understand each other. 2. Curtain lifting by independent satellite data – No restricted areas and no permission needed – No remote areas for satellites – Non-filtered information from direct physical measurement – nobody could manipulate your interpretation – No one government , corporation or institution has a monopoly, so attempts to classify satellite data fail – Satellite images – a “black box" of our planet: no way for hiding something once recorded 3. Up-to-date and continuous information – Recent, often near-real-time information – True real-time technologies are coming – Time series available (basically for last 40 years) Why satellite images important for forest monitoring?
  3. 3. GFW 1.0 (2000) – Mapping intact forest landscapes (IFL) – made a background for voluntary logging moratoriums and new protected areas in different regions
  4. 4. Non-filtered data: the only (so far) post-soviet map of Russian forests published by Russian NGOs is based on satellite data
  5. 5. Photo: Ollivier Girard/CIFOR
  6. 6. Data Users free easy-to-use interactive timely
  7. 7. Forest Change Detection frequent updates high resolution global coverage
  8. 8. Additional layers • Additional forest change layers • Concessions (oil palm, logging, mining, etc) • Forest extent • Primary forest • Protected areas • Biodiversity hotspots • Forest carbon density • Community lands • Geo-tagged stories & photos And more on the way……..
  9. 9. Global Forest Watch • Using maps and RS as a communication tool • Putting together data from different sources • Employing continuous monitoring tools • Allows user feedback Challenges: • Good for a global view, needs adaptation on national and local levels (WRI now working with UNEP to launch national projects for Georgia and Madagascar) • Good in detected forest loss but weak in detecting forest degradation, often problematic with forest gain • So far based on low and medium resolution RS data from open sources only
  10. 10. Low and medium resolution satellite data could be still very useful for forest monitoring
  11. 11. Forest fires monitoring – among the most developed methods
  12. 12. Forest fires monitoring – among the most developed methods
  13. 13. Forest fires monitoring – among the most developed methods
  14. 14. Forest fires monitoring – among the most developed methods
  15. 15. Low and medium resolution data are good for areas with large-scale forest cover changes: clearcutting in Karelia, northwestern Russia
  16. 16. Deforestation in Central Kalimantan driven by oil palm plantations Monitoring of deforestation (1) and palm oil plantations spreading (2) (Indonesia, Central Kalimantan) Landsat 7 2001 Landsat 5 2006 2 2 1 1
  17. 17. Change of borders and dismemberment of forest (Madagascar, Zahamena Ankeniheny reserve) Deforestation in Madagascar Landsat time series visualize changes Landsat 2 05 June 1976 0 2.5 5 10 km Zahamena Ankeniheny reserve fiery forest clearing new non-forested areas Landsat 5 05 June 1976 Landsat 5 29 Sept 2001 Landsat 5 21 Feb 2011
  18. 18. What could high- and very high-resolution satellite data add to the forest monitoring? • Selective / illegal logging monitoring • Revealing reasons behind forest clearing • Separating forests from plantations • Tree species identification • Pest outbreaks monitoring • Identifying the most intact forest areas • Assessing impact of forest fires
  19. 19. 35 Even for industrial selective logging in the Russian Far East medium resolution is not enough.
  20. 20. Even for industrial selective logging in the Russian Far East medium resolution is not enough. Example: logging outside of the permitted
  21. 21. Selective logging in question in European Russia, Moscow Region
  22. 22. Monitoring illegal logging in Laos
  23. 23. Selective logging WorldView-2, 0.5 meter /pixel
  24. 24. Revealing reasons behind forest clearing Clearing for gold mining in Don Amphan NPA, Laos Worldview-2, 21st December 2012 Resolution: 0.5 m ©DigitalGlobe Inc. 2012 distributed by R&DC ScanEX
  25. 25. Gold Mining in Don Amphan NPA, Laos ©DigitalGlobe Inc. 2012 Distributed by R&DC Scanex ©Transparent World WorldView-2 21st December, 2012 Resolution: 0.5 m
  26. 26. Separating forests from plantations
  27. 27. Plantation rotation cycle Sumatra, Indonesia
  28. 28. Separating forests from plantations in tropics, identifying types of plantations A – oil palm B, C – non-palm D – Secondary forest / abandoned plantation
  29. 29. Automatic algorithms for single tree mapping
  30. 30. Moscow region, GeoEye images, August 2012 Measuring forest damage of pest outbreak
  31. 31. STEP1: Segmentation of spectral channel (resolution-10m; min. area 50 pix.) Classifying forests by degradation level in Madagascar: separating natural multilayer forests from secondary and degraded
  32. 32. STEP 2: Calculate local reflections minimum points from panchromathic channel (resolution-2.5m;window 5*5 pix) Classifying forests by degradation level in Madagascar: separating natural multilayer forests from secondary and degraded
  33. 33. STEP 3: Select “gaps” between trees of different size local minimum points with reflection less 80 DN Classifying forests by degradation level in Madagascar: separating natural multilayer forests from secondary and degraded
  34. 34. STEP 4: Calculate density of “gaps” (count points inside polygons/area Of each polygons*100) on 100 sq.m. Classifying forests by degradation level in Madagascar: separating natural multilayer forests from secondary and degraded
  35. 35. STEP 5: Maps of forest structure “compexity” based on density of “gaps” Classifying forests by degradation level in Madagascar: separating natural multilayer forests from secondary and degraded
  36. 36. STEP7: Classification Classifying forests by degradation level in Madagascar: separating natural multilayer forests from secondary and degraded
  37. 37. However, high resolution imagery is still pretty expensive. There is always a balancing between price and quality
  38. 38. Solution 1: Weighting price against resolution & spectral channels • 1.5-2.0 m. resolution data vs. 0.5-1.0 m. (Airbus vs. DG ?) • Panchromatic (b&w) images vs. multi-spectral (color) images • Larger scene size
  39. 39. Solution 2: Supporting sharing the satellite data • International institutions and governments should buy licenses for multiple users (usually for little extra funding) • Influencing satellite operators for shared license policy (one acquired the image could be shared) • Contributing information into the public domain, at least for non-profit applications
  40. 40. Solution 3: Supporting open satellite data • Supporting continuation of Landsat missions, Sentinel mission • Supporting image donation programs of private operators • Supporting open data policies from the governments
  41. 41. Open data web portal of the Russian Space Agency
  42. 42. Canopus-B coverage for Georgia
  43. 43. Resource-DK1 coverage for Georgia
  44. 44. Tbilisi suburbs in the portal (Resource-DK1)
  45. 45. Open Landscape Partnership Platform: involve more people in using high resolution data for public sector monitoring projects around the world
  46. 46. Open Landscape Partnership Platform: involve more people in using high resolution data for public sector monitoring projects around the world  Donate free access to VHR satellite data, provide simple tools to access and process them online  Engage local government, land management agencies, project entities, and civil society organizations  Invite a number of crowd-mapping projects in various countries  Strengthen social and environmental accountability in and around significant conservation landscapes and hotspots
  47. 47. Possible sources of the high-resolution images for GFW for Georgia • Russian high-resolution satellites • Possible donation of Israeli EROS-B satellite (0.7 meters per pixel, panhromatic) • RapidEye data already acquired by GIZ • Old WB-paid air photos • Possible donation from Airbus (SPOT data) – tbd • After all, Georgia is a small country. Why not to buy some data (WRI, WB, FLEG)?..
  48. 48. Solution 4: Reducing prices for VHR data as a market for non- military application would grow-up More projects involving VHR data VHR: too expensive for public sector Demand expands as the benefits are demonstrated New mechanisms are developed for sustaining the supply to public sector Raising the interest of satellite operators for public sector applications Limited market for public sector applications Insufficient frequency and coverage for public sector applications Not a priority for satellite operators
  49. 49. Solution 4: Scaling up Data acquisition HIGHER POSSIBLE PRICESHIGH TOTAL EXPENSES, CHEAPER PRICES PER SCENE LOWEST PRICE PER SCENE Ground receiving stations Long-term contracts with satellite operators Single scenes purchasing Monitoring for a single province in Niger may be expensive comparing to the price of a tree planting project. The monitoring price for the whole Sahel area would be insufficient in the overall budget.
  50. 50. The generation of compact ground station is already at place
  51. 51. License agreements License agreements on operational data reception: 2001 November IRS-1C/1D 2004 October RADARSAT-1 2005 February Monitor-E 2005 April EROS A 2005 October IRS-P6 2006 March SPOT 4 2006 August EROS B 2006 September IRS-P5 2007 April ENVISAT-1 2009 June CARTOSAT-2 2009 July SPOT-5 2009 July Formosat-2 2011 July RADARSAT-2 2011 December UK-DMC2 2007 June IKONOS 2007 March TerraSAR-X 2007 December ALOS 2008 May Kompsat-2 2009 January GeoEye 2012 May QuickBird 2012 May WorldView-1 2012 May WorldView-2
  52. 52. 72 Experience available already Nizhny-Novgorod State University after Lobachevsky Nizhny-Novgorod State University of Architecture & Civil Engineering after Bauman Ufa State Aviation Technical University Tyumen State University Astrakhan State University Altai State University Tomsk State University of Control Systems and Radioelectronics St-Petersburg State University of Aerospace Instrumentation Moscow State University of Geodesy and Cartography Moscow State University St- Petersburg State University Southern Federal University Siberian Federal University Ural Federal University Northern (Arctic) Federal University North-Caucasus Federal University University of Valencia, Spain University of Valladolid, Spain Kazakh-British Technical University, Almaty Kazakhstan National Technical University after Satpaev 27 RS centers at universities in Russia, Kazakhstan and Spain Belgorod State University National Mineral Resources University, St-Petersburg Saratov State University after Chernychevsky Perm State University Siberian State Aerospace University after Reshetnikov Samara State Aerospace University after Korolev
  53. 53. 73 Scanex receiving station installations in the universities
  54. 54. University competence centers • Equipped with ground stations • Having access to multiple satellites • Using image processing software complementing ground stations • Opening access to satellite data and products through university web portals (or/and shared portal / library)
  55. 55. Coming soon: small-size satellites • SPUTNIX – a startup daughter company by Scanex • A platform for low-orbiting small-size satellites of 10.. 50 kg • 20-25 meters / pixel resolution in up to four spectral channels • Up to 15 meters / pixel resolution in panchromatic • About 20 days turnover • 45.. 500 km wide scenes • Successfully launched in June 2014
  56. 56. Thank you! Dmitry Aksenov Transparent World picea2k@gmail.com

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