latest developments in tree species composition mapping ... zsl 2019_fabia… · tree species...
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Latest developments in tree species composition
mapping from remote sensing
Dr. Fabian E. Fassnacht – Karlsruhe Institute of Technology
Warsaw, 13.03.2019 Fabian E. Fassnacht
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IntroductionSupervised classification
2F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Supervised classification
Remote sensing data
Training data
Classification algorithm
Categorical map
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3
Overview
Introduction What makes the difference?
Tree species information from airborne data Hyperspectral
LiDAR
Multispectral / Aerial photographs
Tree species information from spaceborne data Sentinel-2
Discussion & Conclusions
Warsaw, 13.03.2019 F. E. Fassnacht: Species Compositoin mapping
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IntroductionWhat makes the difference?
Leaves + branching
4F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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IntroductionWhat makes the difference?
Habitus
5F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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IntroductionWhat makes the difference?
Phenology
6F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
2525August
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IntroductionWhat makes the difference?
Color
7F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
BroadleavedConiferous
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8
Overview
Introduction What makes the difference?
Tree species information from airborne data Hyperspectral
LiDAR
Multispectral / Aerial photographs
Tree species information from spaceborne data Sentinel-2
Discussion & Conclusions
Warsaw, 13.03.2019 F. E. Fassnacht: Species Compositoin mapping
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Tree species information from airborne dataHyperspectral
Hyperspectral data
9F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Tree species information from airborne dataHyperspectral
Examples
Combination of 3 SVM
classifications based on
hyperspectral data
Spring, Summer Autumn
Complex forest structure
8 species
Modzelewska et al. (in preparation)
10F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Tree species information from airborne dataHyperspectral
Examples
Combination of 3 SVM
classifications based on
hyperspectral data
Spring, Summer Autumn
Complex forest structure
8 species
Modzelewska et al. (in preparation)
11F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
= overall acc. = kappa
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Tree species information from airborne dataHyperspectral
12F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Reference
map
(dominant
species)
Classification
map
(dominant
species)
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Tree species information from airborne dataHyperspectral
Trends & Challenges
Larger areas
Multi-temporal data
Airborne surveys (lots of things can go wrong!!)
Comparably complex pre-processing
BRDF effects
Large datasets
13F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Tree species information from airborne dataHyperspectral
Evaluation
14F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Thematic Details
Cost
Operationality
Accuracy
Spatial details
120-180 € / km²
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15
Overview
Introduction What makes the difference?
Tree species information from airborne data Hyperspectral
LiDAR
Multispectral / Aerial photographs
Tree species information from spaceborne data Sentinel-2
Discussion & Conclusions
Warsaw, 13.03.2019 F. E. Fassnacht: Species Compositoin mapping
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Reflecta
nce
Tree species information from airborne dataLiDAR
LiDAR data
16F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
1064 n
m
1550 n
m
90
5 n
m
432 n
m
Intensity
3D-Structure
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Tree species information from airborne dataLiDAR
Two-approaches: area-based / individual tree based
17F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Area-based Individual tree based
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Tree species information from airborne dataLiDAR
Winter (leaf-off) + Summer (leaf-on) data
Area-based or individual tree-based classification of evergreen and deciduous species
18F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Wasser et al. 2013
2525August
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Tree species information from airborne dataLiDAR
Individual tree-based
19F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Kamińska et al. 2018
Crown Shape
Point distribution
Intensity information
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Tree species information from airborne dataLiDAR
Single tree structure information
20F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Holmgren & Persson 2004 Ørka et al. 2009
95% accuracy for
Separating Pine and Spruce
88% accuracy for
Separating Spruce and Birch
(large trees)
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Tree species information from airborne dataLiDAR
Single tree structure information
21F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Axelsson et al. 2018
9 species
Overall accuracy structural metrics: 43,0%
Overall accuracy structure + intensity: 76.5%
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Tree species information from airborne dataLiDAR
Trends & Challenges
Multispectral LiDAR
Continued development of new ways to extract structural and intensity metrics
Most approaches require a single tree delineation with all its challenges (missed
trees, over-segmentation, etc.) WHERE ARE THE MAPS?
Structural features often similar within coniferous / broadleaved tree species
groups solutions for more than 3 species often unsatisfactory
22F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Tree species information from airborne dataLiDAR
Evaluation
23F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Thematic Details
Cost
Operationality
Accuracy
Spatial details
62-240 € / km²
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24
Overview
Introduction What makes the difference?
Tree species information from airborne data Hyperspectral
LiDAR
Multispectral / Aerial photographs
Tree species information from spaceborne data Sentinel-2
Discussion & Conclusions
Warsaw, 13.03.2019 F. E. Fassnacht: Species Compositoin mapping
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Tree species information from airborne dataMultispectral / aerial photographs
Multispectral / aerial photographs
25F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Tree species information from airborne dataMultispectral / aerial photographs
Multispectral / aerial photographs
Convolutional neural networks
Works on an image / not pixel!
Similar detection capabilities as a
trained human interpreter
Renessaince of RGB-information /
orthophotographs?
26F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Graphs from: Chollet & Allaire 2017
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Tree species information from airborne dataMultispectral / aerial photographs
Trends & Challenges
Steadily increasing number of examples
Adaption of the „image-based“ approach to deliver pixel-based results
Collection of sufficient training data
(BUT: there are large archives of orthophotos)
High-performance computer and GPU required
Algorithms become increasingly user-friendly but may still be challenging for the
applied sector
27F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Thematic Details
Accuracy
Tree species information from airborne dataMultispectral / aerial photographs
Evaluation
28F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Cost
Operationality Spatial details
35-62 € / km²
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29
Overview
Introduction What makes the difference?
Tree species information from airborne data Hyperspectral
LiDAR
Multispectral / Aerial photographs
Tree species information from spaceborne data Sentinel-2
Discussion & Conclusions
Warsaw, 13.03.2019 F. E. Fassnacht: Species Compositoin mapping
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Tree species information from spaceborne dataSentinel-2
Sentinel-2
2 operating satellites
5-day repeat cycle
10-20 m pixel size
Free data
30F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Tree species information from spaceborne dataSentinel-2
Examples
Tree species map for Baden-
Württemberg
Reference data from forestry
administration
Sentinel-2 mosaics for spring,
winter and summer from Google
Earth Engine
31F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Forest area
Reference data avail.
2525August
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Fabian Ewald Faßnacht 32
Tree species information from spaceborne dataSentinel-2
Reference data on
forest stand level with
species composition
(10%-steps)
Subset of all stands
with one species
holding at least 90%
cover
Extract reflectance
data of three Sentinel-
2 mosaics (10 random
pixels per polygon)
Spring
Summer
Winter
K-means clustering ot
the reflectance data
Select clusters that
are likely to represent
the target species
Size of the cluster (how many pixels)
Ecological knowledge
(coniferous have lower reflectance than broadleaved,
broadleaved have low reflectance in winter)
Use selected cluster
to train SVM
classification model
and map the species
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Tree species information from spaceborne dataSentinel-2
Examples
33F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Kappa = 0.75
Kappa = 0.64
2525August
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Tree species information from spaceborne dataSentinel-2
Examples
34F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Black Forest
(spruce dominated)Swabian Alp
(higher fractions of
broadleaved)
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Tree species information from spaceborne dataSentinel-2
Trends & Challenges
Developments of new approaches to create homogeneous Sentinel-2
mosaics over large areas (Level 3-data may even be provided by ESA)
E.g., artificial images from time-series might be a solution
Terrain shadows
Large datasets cloud computing approaches
Reliable reference data over large areas
35F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Cost
Thematic Details
Accuracy
Tree species information from airborne dataSpaceborne Sentinel-2 data
Evaluation
36F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Operationality Spatial details
free
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37
Overview
Introduction What makes the difference?
Tree species information from airborne data Hyperspectral
LiDAR
Multispectral / Aerial photographs
Tree species information from spaceborne data Sentinel-2
Discussion & Conclusions
Warsaw, 13.03.2019 F. E. Fassnacht: Species Compositoin mapping
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Discussion and Conclusions-
Lots of examples based on lots of different
dataset
No clear winner / Hyperspectral with highest
accuracies and number of species
Sentinel-2 has the tremendous advantage of
global cover and no cost
LiDAR (and probably VHR stereo-imagery):
Potential for „one-in-all solutions“
Deep-learning as a game-changer for VHR data
(incl. aerial photographs/orthophotos)?
38F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Nr
ofsp
ecie
s
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Discussion and Conclusions-
We are heading towards actually useful product that
could support forest management!
39F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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References-
40F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Pictures:
https://greatbarrierphotography.com/wp-content/uploads/2018/02/DJI_0051cg1920b.jpg
https://www.focus.it/site_stored/imgs/0004/008/foresta-conifere.630x360.jpg
https://mongabay-images.s3.amazonaws.com/14/0120Aerial_1026_3240_dark.jpg
Paper & books:
Chollet & Allaire 2017 - Deep learning with R, book, Manning publications.
Fassnacht et al. 2016 - Review of studies on tree species classification from remotely sensed data, RSE, 186, 64-87.
Holmgren & Persson 2014 - Identifying species of individual trees using airborne laser scanner, RSE, 90, 415-423.
Kamińska et al. 2018 - Species-related single dead tree detection using multi-temporal ALS data and CIR imagery, RSE,
219, 31-43.
Klaus 2018 - Sentinel 2-basierte Baumartenklassifikation für Baden-Württemberg und anschließender Vergleich der
erstellten Baumartenkarte mit Prognosen zur zukünftigen Standortseignung, Bachelor thesis, KIT, IfGG.
Modzelewska et al. (in preparation)
Ørka et al. 2009 - Classifying species of individual trees by intensity and structure features derived from airborne laser
scanner data, RSE, 113, 1163-1174.
Wasser et al. 2013 - Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested
Riparian Buffers During Leaf-Off and Leaf-On Conditions. Pols One, 8(1).
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Thank your for your attention-
41F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Fabian Ewald Faßnacht 42
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Fabian Ewald Faßnacht 43
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Fabian Ewald Faßnacht 44
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Tree species information from spaceborne dataVHR data
Spaceborne VHR data
WorldView 2 and 3
0.3 – 2 m pixel size
8 – 16 bands
45F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Tree species information from spaceborne dataVHR data
Examples
Object-based classification
Random Forest
46F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Immitzer et al. 2012
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Tree species information from spaceborne dataVHR data
Examples
47F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Immitzer et al. 2012
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Tree species information from spaceborne dataVHR data
Examples
48F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Fassnacht et al. 2017
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Tree species information from spaceborne dataVHR data
Challenges
Automated single tree delineations
Quality of individual WorldView-scenes differs a lot
Weather dependency
49F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
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Cost
OperationalityThematic Details
Accuracy
Tree species information from airborne dataSpaceborne VHR data
Evaluation
50F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Spatial details
19 $ / km²
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Tree species information from airborne dataHyperspectral
Examples
SVM classification based
on Sentinel-2 /
hyperspectral data
Up to 96% accuracy for
five tree species
Comparably simple
forest structure
Ghosh et al. 2014 / S2: unpublished
51F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019
Undetected (small) Red Oak standMixed Oak-Pine stand classified as
mixed Beech-Pine / BeechHS: Pine classified as Douglas Fir