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Airborne discrete-return LiDAR data in the assessment of boreal mire surface patterns, vegetation and habitats

Mapping mires from the air – What can we do with timestamped and free photons?

Use of Airborne Laser Scanner Data for Ecological Applications –seminar. UMB, Norway. June 23, 2011. 11:00-11:30 a.m.

ilkka.korpela@helsinki.fiwww.helsinki.fi/~korpela p. +358-400-218305

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Contents

• A talk on the potential of mapping timber resources,vegetation, sites and habitats, drainage – restorationprocess, growth & yield etc. in airborne optical data

• One experimental (Case) study with LiDAR (Forest Ecology and Management 258: 1549-1566)

• Qs & discussion

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Mires in Finland

• Total area 9 milj. ha (29 % of land area, more in north)

• > 50 % drained (mainly for forestry, forest ameliariotion)

• Active and passive restoration

• Drained productive forests resemblemineral soil forests

• Pristine mires exhibit great variation in vegetation (ecohydrology & nutrients).

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Pristine Mires in Finland

• Pristine mires: Open - Composite - Genuine Forested

• ± multilayered canopy structures

• 0-5 m3/ha/a natural yield

• Site types chracterized by tree species, density, nutrient status, hummock-intermediate-hollow –level vegetation

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Mire vegetation and habitats using airborne optical RS – feasible?

X

YZ

LiDAR

Cam3

Cam2

Cam1

Imaging vs. scanning free vs. timestamped photons

- Photon source (shadowing)- View/Scan geometry (shadowing, occlusions)BRDF vs. monostatic/hot-spot

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Aerial imaging ALS

• External light (direct, diffuse)

• VIS - NIR 400-1200 nm

• Perspective mapping

• Atmospheric effects f(l, f, q)

• Regular sampling

• Shutter 1/150…1/1000 sec300..2000-km photon range

• 4-9 km, 40-90 cm GSDs

• FOVs ±30-40°

• 50+ €/min, to fly

• GPS/INS helps/must

• Going 3D is challenging

• WX is an issue

• Coherent self-made laser light

• Monochromatic (e.g. l = 1064 nm)

• Monostatic views (spherical)

• Atmosphere transmits well NIR

• Pointwise irregular sampling

• Pulses 5-10 ns, 1 GHz sampling,0.3 m välein, e.g. 38 m in depth

• 1-2 km, 15-60 cm footprints (1/e, 1/e2)

• Scan zenith angle <15°

• 50+ €/min, to fly

• GPS/INS, must

• 3D at ease

• WX is less of an issue

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Leaves and needles are dark in VIS, but bright in NIR

Structural variation results in apparent reflectance variation (e.g. species).

Thus, in images geometry and radiometry are closely linked.

Background in signal – here timestamped photons s.b. favored

BACKGROUND, SCALE?

T

R

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Scale (GSD/iFOV, footprint)

Lase

rpuls

si

Leaves and needles are dark in VIS, but bright in NIR

TREFLECTANCE?(or structure)

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Mire Vegetation & Habitats by airborne optical RS• Invariant features are called for (target/class –specific)

• Radiometric/Spectral features: - Sensors are compromises (not for foresters)- At-sensor-radiance data now available- Target reflectance spectra + BRDF

• Geometry, is much simpler to get right

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State-of-the-art in optical airborne sensing?

• Development of aerial cameras and production processes

• LiDAR sensors and data processing

Cameras

Leica ALS-60

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Development of aerial cameras and production processes

• Film-based stuff (cameras, development, scanning) is history. Instead,…

• CCD-frame sensors, f ~ 30-100 mm, PAN, R, G, B and NIR images, or imaging line sensors.

• Pixel-DNs in linear relationship withat-sensor-radiance (flat fielding, SRF), orpixel-DNs translate into radiances andtarget reflectance data (relative vs.absolute calibration, atmospheric calib.).

• Unlike with film, the radiometric chain is“traceable” and repeatable!!

• Prespective projection remains, BRDF effects as well, line sensors reduce themto be 1D.

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New paradigms in aerial imaging• More images per target (redundance),

multiangular imaging

• Geometric resolution improved (thru dynamics, no film grain)

• Enhanced radiometry (shutters?), dynamics,repeatability

• Direct georeferencing enables accurate on-the-fly EO

• Laws of optics/camera design remain (lens, shutter).

• Forests still exhibit challenging illumination conditions and occlusion-rich content

• Weather for photography is the same (Ð > 25° ?), (June 1 – August 30)

• Absolute radiometric calibration made possible inLeica ADS40 (soon in Z/I DMC) – reflectance imagery– transferability of interretation models! This constitutes a potential cost-efficiencyimprovement.

• Flying is still 50-60€ per minute. Þ above 3-4 km.

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NIR band in ADS40, 2 km

Tall-sedge fen

E. vaginatum (cottongrass) pine bog

+ Open mires

- Trees = shadows

- Low contrast forsparse crowns

+ B-leaved

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Ombrotrophic-minerotrophic fen

http://www.helsinki.fi/~korpela/Hyytiala/Ojitus_animaatio.html

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Mires in aerial images

• Visual change detection, ortho- and stereoimagery. Digital stereo workstations3-6 k€. GSD 30-50 cm for details. Monitoring at the tree level s.b. feasible, inprinciple.

- Changes in growing stock, water surfaces/flarks in open mires,hummocks/hollows.

• The use of target-reflectance images in open mires for vegetation changes. (illumination conditions well-posed).

• Mixing of under and overstory in composite mires and forested mires.Challenging.

• Overstory dominates in the signals.

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LiDAR

Object (Xa, Ya, Za)

½-way distance

Scan Zenith angles <20°

(X0, Y0, Z0, w, f, k) | txF = 100-200 kHz

Laserpulssens direktion(i,j,k) = f(w, f, k, a)

Objekts position:(Xa,Ya,Za) = (X0,Y0,Z0)(laser) + distans ´ (i,j,k)

Trajectory / Gyro-Attitude

Belyst område(fotspåret, footprint)

Transmitted photons / received photons

Om laserskanninginformation

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• c = 300 000 km/s ® 300 kHz @ 500 m ® MPIA, pulse l ~10 ns, 3 m, peak P.

• Not built for foresters!

• Monochromatic, typically NIR. Monostatic (hot-spot)

• Waveform sampling & storage. On-the-fly computation intodiscrete-return data. Max. amplitude – intensity

• LiDAR data are [i, j, k, X, Y, Z, intensity/waveform] vector data

• Intensity, ~ (geometry, silhuette area, reflectance).

LiDAR sensors and data

First echoes, homogenous reflectance

Asphalt, amplitude variation

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TXRX

t

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

-0.5 0 0.5 1 1.5 2

LiDAR sensors and data - intensity

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

-0.5 0 0.5 1 1.5 2

• Volumetric reflections dominate in vegetation

• Intensity data subject to losses Þ 1st, 2nd, 3rd-return data not comparable

• The larger the footprint – the less impact reflectance has in signal

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Not built for foresters - Between-sensor differencesFirst-of-many vs. single-return data, near-ground

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LiDAR sensors and data – vectors vs. points

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• Revolutionary 3D (‘As easy as making hay’)

• One-eyed LiDAR sees more than images, and never sees shadows

• Yet, it is optical instrument (attenuation of signal)

• Sampling deficits in XY are compensated by depth

• Resolution of photons, some meters (cf. camera 1000 km)

• Radiometric calibration still not accomlished, challenging in volumetric targets® IN-SITU data will be needed for quite some time→ Synthetic training data will eventually come

LiDAR sensors and data - Summary

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Case Lakkasuo (ForEcoMan 2009)

Research Qs

1) Mire topography 2) Intensit signal in mire vegetation 3) Habitat classification

Confining to pristine sites.Experimental work.

Silmäpäänlammi paludified pond

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Optech LiDAR in 2006

Discrete-return data1,3 km semi-dense in Lakkasuo 800 m dense Silmäpäänlammi

Grey value = intensity

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Intensity analysis

• In the open mire, intensity ~ moisture/wetness

• Weak gradient in nutrient status

25Grey level ± 0.5 m in Z, ground hits

LiDAR and mire topography

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LiDAR and mire topography

Less trees ® better sampling of terrain ® details available

Ombro-minerotrophic low vegetation (< 60 cm) had no influence in Z.

Hummock-Hollow –modeling?Ditch networkmodeling?

Sampling density and pattern -A priori information (ditch)

True Model

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Trial:

Even in accurate DEM, how to separate hummocks, hollows and intermediate surfaces….

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• Area-based, 100 m ja 400 m2 grid cells

• DEM/Hummock model features

• Typical H- and intensitydistribution metrics

• Training and validation trials: RF, c-SVM and k-NN classifiers

Example

Cumulative h-distributions in genuine forested types. Stocking gradient =[RaR…RhK]

LiDAR and mire habitas - Site classification, 21 types

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Vertical stratification:

h < 15 cm (DEM)surface hits

h > 15 cm Ç h < 1mfield layer hits

h > 1 m tree canopy hits

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Quest for invariant features

HQ4/HQ8 sepated RaR – IR (pine bogs) &KgK/MK/LhK (spruce swamps).

“Absolute” features were avoided (m, m3) to improve transferability of models.

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Results

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Open and composite types: the nutrient status (trophic state) was out of reach using surface and field layer data. Classifiers learned the h-distribution metrics + intensity (species admixture)..

Results

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Within-habitat variation was influencal (as always). Proportion of tree canopy hits (%)

Results

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Results

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Results

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Old field classes or new RS classes?

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Summary – LiDAR in mire

Intensity distributions predict weakly species admixture

Ground hits show where it is wet.

Field layer hits – were they really such (DEM?). LiDAR behavior near gnd.

Dense data – DEM of good quality – modelling surface patterns seems feasible in open/composite sites (ditches even, did not try)

H-distribution metrics!! Invariant (between-sensor, between-campaign etc.) (relative vs. absolute)

in-situ calibration is vital.

Monitoring and inventory systems for pristine/restorated mires can partly be based in LiDAR