Dmitry Aksenov Transparent World

1. Global Forest Watch &
Monitoring Forests Using
Remote Sensing
Dmitry Aksenov
Transparent World

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. GFW 1.0 (2000) – Mapping intact forest landscapes (IFL) –
made a background for voluntary logging moratoriums
and new protected areas in different regions

4. Non-filtered data: the only (so far) post-soviet map of Russian forests
published by Russian NGOs is based on satellite data

5. Photo: Ollivier Girard/CIFOR

6. Data Users
free
easy-to-use
interactive
timely

7. Forest Change Detection
frequent updates
high resolution
global coverage

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……..

25. 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

26. Low and medium resolution
satellite data could be still very
useful for forest monitoring

27. Forest fires monitoring –
among the most developed methods

28. Forest fires monitoring –
among the most developed methods

29. Forest fires monitoring –
among the most developed methods

30. Forest fires monitoring –
among the most developed methods

31. Low and medium resolution data are good for areas with large-scale
forest cover changes: clearcutting in Karelia, northwestern Russia

32. 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

33. 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

34. 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

35. 35
Even for industrial selective
logging in the Russian Far East
medium resolution is not enough.

36. Even for industrial selective logging in the Russian Far East
medium resolution is not enough.
Example: logging outside of the permitted

37. Selective logging in question in European Russia, Moscow Region

38. Monitoring illegal logging in Laos

39. Selective logging
WorldView-2,
0.5 meter /pixel

40. 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

41. 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

42. Separating forests from plantations

43. Plantation rotation cycle
Sumatra, Indonesia

44. Separating forests from plantations in tropics,
identifying types of plantations
A – oil palm
B, C – non-palm
D – Secondary forest / abandoned plantation

45. Automatic algorithms for single tree mapping

46. Moscow region, GeoEye images, August 2012
Measuring forest damage of pest outbreak

47. 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

48. 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

49. 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

50. 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

51. 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

52. STEP7: Classification
Classifying forests by degradation level in Madagascar:
separating natural multilayer forests from secondary and degraded

53. However, high resolution imagery
is still pretty expensive. There is
always a balancing between price
and quality

54. 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

55. 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

56. 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

57. Open data web portal of the Russian Space Agency

58. Canopus-B coverage for Georgia

59. Resource-DK1 coverage for Georgia

60. Tbilisi suburbs in the portal (Resource-DK1)

61. Open Landscape Partnership Platform:
involve more people in using high resolution data for
public sector monitoring projects around the world

62. 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

63. 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)?..

64. 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

65. 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.

66. The generation of compact ground station
is already at place

67. 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

68. 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

69. 73
Scanex receiving station
installations in the universities

70. 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)

71. 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

72. Thank you!
Dmitry Aksenov
Transparent World
picea2k@gmail.com