remote sensing of forest cover: new techniques and daniel ... · visb,visg,visr, nir & 2xswir 4...

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Remote sensing of forest cover: new techniques and

opportunities or potential that is always just out of reach?

Daniel DONOGHUE

with support from the ForestSAFE team

The art of dividing up the world into little multi-

coloured squares and then playing computer games

with them to release unbelievable potential that's

always just out of reach.

- Jon Huntington,

CSIRO Exploration

Remote sensing is …

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• 1362 – Grote Mandrenke – 25,000 deaths

• 1703 – The “Great Storm” (UK)

• 1879 – Tay rail bridge disaster (UK)

• 1881 – The Eyemouth disaster (UK)

• 1999 – “Lothar” - (France, Germany)

• 2005 – “Gudrun” (UK, Denmark, Sweden)

WINDSTORMS & DISASTERS

Source: http://www.smhi.se

HURRICANE GUDRUN 8/9 January 2005

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• 75 million cubic metres were wind thrown or damaged.

• This corresponds to nearly an entire year’s cutting for the whole of Sweden.

• Approximately 80% of the damaged trees are Norway spruce, 15% are Scots pine and the remaining are deciduous trees.

Source: http://www.svo.se

THE IMPACT

• Swedish National Forestry Board

(Skogstyrelsen)

• Strategic Overview: Required within days

– Statistics on how much damage?

– Rough indication of extent?

• Operational: Required within weeks/months

– Detailed maps indicating damaged areas.

– Used for planning of clean-up operations

THE RESPONSE

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• ”International Charter: Space and Major Disasters”(www.disasterscharter.org) activated by Swedish Rescue Services Agency (Räddningsverket) – 14th Jan 2005

• Commission Metria to acquire digital air photography

• Program satellites to acquire images & access data archives

THE IMAGERY: DATA ACQUISITION

• Landsat-5 (30m)

• regular acquisition scheduled

• 16 days revisit

• SPOT-5 (10m)

• must be programmed (2-3 day revisit), expensive

• SPOT-4 (20m)

• must be programmed (2-3 day revisit), lower cost

• DMC (Disaster Monitoring constellation) (32 m)

• must be programmed (2-5 day revisit), early morning passes

• never tested

• AWIFS (60m)

• ENVISAT – ASAR

• RADARSAT

• Look at quick looks

• Order test data

• Wait

• Order test coverage

• Get test data, price?

• Initiate data acquisition

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(© LMV 2005) (© NBF 2005).

MapAerial photo – white

areas=100% blowdown

STUDY AREA: DAMAGE ASSESSMENT

Map from airborne line inventory by National Forestry Board

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13th December 2004 20th January 2005

Resolution: 150m

ENVISAT ASAR: WIDE SWATH PRODUCT

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5th November 2004 14th January 2005

Resolution: 30m

ENVISAT ASAR: IMAGE MODE PRODUCT

No data before storm: images from 23rd January 2005

Resolution: 30m

HH-pol.

ascending orbit

HV-pol.

ascending orbit

VV-pol.

descending orbit

ENVISAT ASAR: ALTERNATING POLARISATION

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flight alt.: <10 000 m, wavelength: 3-15 m, resolution: 3 m

CARABAS-II VHF SAR is able to see through snow, rain,

cloud, abd forest canopy

Tönnersjöheden2002 2005

Trees lying almost parallel to

flight direction appear as bright

elongated structures.

~1 km

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CARABAS: mean/max image

Reference Wind-thrown %

Reference 56 65 46

Wind-thrown 7 65 90

% 89 50 63

DMC

Reference Wind-thrown %

Reference 63 119 35

Wind-thrown 0 11 100

% 100 8 38

True

True

Classified

Classified

INTERPRETATION: RESULTS FROM SLU STUDY

• Medium resolution optical data of little value due to resolution, cloud cover, low sun angle or availability

• Available satellite RADAR data of no use

• Airborne CARABAS RADAR able to detect a significant amount of storm damage including infrastructure – expensive, time consuming to process and not commercially available technology

• Digital Metric Camera aerial phototography data useful and can be deployed easily.

CONCLUSIONS: SWEDISH EXPERIENCE

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KIELDER FOREST, NE England

pre-windblow

1993

post-windblow

1997

post-windblow

2003

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WIND DAMAGE

Evaluate a variety of remote sensing systems as sources from which

windblow can be interpreted.

AIM OF KIELDER FOREST CASE STUDY

METHODOLOGY

24 Ground truthed sites of potential windblow studied over 8 images.

16 Interpreters’ abilities to correctly determine windblow investigated.

Involved interpreters with different levels of knowledge of forestry and

experience

with remote sensing.

• Remote Sensing Experts (University Of Durham Staff)

• Forestry Experts (FE Scotland & FE England staff)

• Inexperienced Users (University of Durham Undergraduates)

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THE IMAGERY: MEDIUM SPATIAL RESOLUTION SENSORS

LANDSAT

Multispectral

SPOT

Multispectral

LANDSAT

Panchromatic

ASTER

Multispectral

30m Spatial Resolution 20m Spatial Resolution 15m Spatial Resolution 15m Spatial Resolution

2nd September 2002 26th October 2002 2nd September 2002 22nd March 2003

6 Spectral BandsVISB,VISG,VISR, NIR & 2xSWIR

4 Spectral BandsVISG,VISR, NIR & SWIR

Panchromatic 3 Spectral BandsVISG, VISR, NIR

THE IMAGERY: FINE SPATIAL RESOLUTION SENSORS

LiDAR

(Gridded)

Ikonos

Multispectral

Ikonos

Panchromatic

Aerial

Photography

4m Spatial Resolution 4m Spatial Resolution 1m Spatial Resolution 25cm Spatial Resolution

28th March 2003 13th March 2002 13th March 2002 14th May 2003

Canopy Height 4 Spectral BandsVISG,VISR, NIR & SWIR

Panchromatic 3 Spectral BandsVISB,VISG, VISR

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RESULTS

LiDAR (Gridded)

Ikonos Multispectral

Ikonos Panchromatic

Aerial Photography

Landsat Multispectral

SPOT Multispectral

Landsat Panchromatic

ASTER Multispectral

30m

20m

15m

15m

4m

4m

1m

25cm

Image Resolution Overall (16)

% Correct

59%

54%

54%

59%

74%

69%

74%

77%

Experts (9)

% Correct

60%

58%

54%

62%

75%

79%

81%

81%

Non-Experts (7)

% Correct

58%

51%

55%

55%

74%

56%

67%

74%

Basal Area (m2/ha)

Frequency

0 10 30 50

05

10

15

Top Height (m)

Frequency

10 15 20 25

04

812

Stocking (trees/ha)

Frequency

0 1000 2500

05

15

Dothi Score (0-100)

Frequency

0 10 30 50

010

30

Age (years)

Frequency

6 8 12 16 20

05

10

20

NZ MfE Carbon credit study - Pinus radiata – 66 plots

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Presence of Dothistroma

Presence of Dothisoma in Pinus radiata

NZ Tree counting from digital APs – K. Olfsson

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0

100

200

300

400

500

600

700

800

900

1000

0 200 400 600 800 1000 1200 1400 1600 1800

Field stocking (trees/ha)

Detected trees (trees/ha)

NZ Stocking estimates from tree counting

Stocking estimates from LiDAR

200 400 600 800

0200

400

600

800

1000

Predicted Stocking (trees/ha)

Stocking (trees/ha)

NZ Stocking estimates from LiDAR

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5 10 15 20

10

15

20

25

Lidar 90th Height Percentile

Top Height (m

)Certainty 50Certainty 60Certainty 65Certainty 70Certainty 80Certainty 85

NZ Height prediction from LiDAR

R2= 0.886 RSME = 1.36

0 5 10 15 20 25 30

510

15

20

25

30

Predicted Basal Area (m2/ha)

Basal Area (m2/ha)

R2= 0.62 RSME = 4.98

NZ basal area prediction from LiDAR

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Sitka/pine

mixture

Sitka dominated

15 m19 m

Galloway Forest District, Scotland

60, 000 hectares

22% planted using species mixtures

Pine

dominated

Species mix

Sitka spruce

Species mixture = Pine and Sitka spruce planted together

Current methods

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Species mapping

1. Spectral: LiDAR Intensity & SPOT 5

2. Canopy density: LiDAR

3. Combined estimate

Outcome = Quantification of volume

by species

Species mapping from LiDAR

0

10

20

30

40

Reflectance (%)

400 600 800 1000 1200

Wavelength (nm)

Lodgepole pine Sitka spruce

SPOT NIR 10 m

LiDAR NIR 10 m

Spectral curves

1. Spectral: SPOT 5 & LIDAR

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40

60

80

100

120LiDAR Intensity (DN)

960 980 1000 1020

Laser path length (m)

1. LiDAR Path length correction

LiDAR path length correction

1. Spectral: SPOT 5 & LIDAR

Species are

separated

LiDAR NIR more

sensitive than SPOT

0

20

40

60

SPOT 5 NIR (DN value)

0 20 40 60 80 100 120 140 160

LiDAR intensity (DN value)

Lodgepole pine Sitka/pine mixture Sitka Spruce

Dark target (water) Bright target (molina grass)

Plot of NIR response

Spot vs LiDAR

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256.6137.348.1182.417.914.31.415.9

Lodgepole

pine

156.280.330.5125.119.014.31.816.9

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Sitka

spruce

Species

mixture

N/A

18.013.00.915.8

62 pixelsLodgepole

pine*Pure

936.2186.0160.0573.525.616.12.217.5

35Sitka

sprucePure

Max.Min.S.D.MeanMax.Min.S.D.MeanNo. field plots

Volume (m3/ha)Top height (m)ObsTree

species

Crop

type

Field plot data (0.02ha)

40

60

80

100

LiDAR NIR intensity (DN)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1

Percentage Sitka spruce volume (%)

Clatteringshaws Laurieston

R² = 0.76RMSE = 7.0 DN

Pure Sitka sprucePure Lodgepole pine

1. LiDAR Int75% vs ground data

LiDAR Int75% vs ground data

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• Coefficient of

variation

• Skewness

• % ground returns

• Mean height

LiDAR first & last pulse distribution

Calculated Measures

LiDAR last pulse distribution

Intimate mixturePure Sitka spruce Pure lodgepole pine

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Coefficient of variation % ground returns

Skewness Mean height

10

15

20

25

30

Coefficient of variation (%)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1

Sitka spruce volume (%)


R² = 0.88RMSE = 1.70 %

Pure sprucePure pine

-1.5

-1

-.5

0

.5

Skewness

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1



R² = 0.09RMSE = 0.25


0

10

20

30

40

Percentage ground returns (%)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1



R² = 0.66RMSE = 4.01


5

10

15

20

25

LiDAR-derived mean height (m

)

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1



R² = 0.79RMSE = 1.49 m


0.050.97CV, mean, %Zero, Int75%,

skewness

0.050.96CV, mean, %Zero, Int75%

0.050.95CV, mean, %Zero

0.070.91CV, mean

Multiple regression

4.010.66Percent last pulse ground returns%Zero

0.250.09Skewness of height of first pulse

returns

skewness

1.700.88Coefficient of variation: First

pulse returns

CV

1.490.79LiDAR-derived mean height:

First pulse returns

mean

7.00.7675th percentile LiDAR intensity:

First pulse returns

Int75%

RMSE R2DescriptionVariable(s)

n = 54

Linear regression

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LiDAR intensity & canopy

density measures

LiDAR canopy density

measures only

SPOT 5 all spectral bands

Species/volume prediction from LiDAR

SPOT 2005 Galloway

�Height Image.

Regional Height

estimates

from a single image +

ground

control (Field Survey,

LiDAR)

Forest Stand Maps Catchment Management

Know proportion

closed canopy in a

Catchment. If river

fails Critical Load test

is catchment % over

30%?

How are the trees

performing?

Have they reached

canopy

closure when

expected?

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1. LiDAR intensity data show potential for

species and volume estimation

2. LiDAR pulse distribution data useful for

species and volume estimation

3. LiDAR measures used together can be

used to map species, volume by species,

as well as wind and other damaged

areas to very high levels of accuracy

CONCLUSIONS

Mini UAV

www.smartplanes.se

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• Monitoring fellings

• Estimate tree height in young crops

• Management of carbon credits

• Use with growth models

• Scaling up of biophysical models

• Monitoring plantation establishment

• Damage assessment – fire, wind, disease

• Land cover mapping

• Habitat mapping

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www.svo.sewww.svo.se//forestsafeforestsafe

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Glen Mhor

Landsat 5 TM Landsat 5 TM Landsat 7 ETM+

11 September 1989 13 May 1994 12 May 2000

{R,G,B} = {3,2,1}

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1989 1994 2000

Felling

1989 1994 2000

Growth 1989-2000

1989 1994 2000

Canopy Closed

1989 1994 2000

Felling

remote sensing of forest cover: new techniques and daniel ... · visb,visg,visr, nir & 2xswir 4...

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