Latest developments in tree species composition mapping ... ZSL 2019_Fabia… · Tree species...

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

IntroductionSupervised classification

2F. E. Fassnacht: Species Compositoin mappingWarsaw, 13.03.2019

Supervised classification

Remote sensing data

Training data

Classification algorithm

Categorical map

Overview

Introduction What makes the difference?

Tree species information from airborne data Hyperspectral

Multispectral / Aerial photographs

Tree species information from spaceborne data Sentinel-2

Discussion & Conclusions

Warsaw, 13.03.2019 F. E. Fassnacht: Species Compositoin mapping

IntroductionWhat makes the difference?

Leaves + branching

Habitus

Phenology

2525August

BroadleavedConiferous

Overview

Tree species information from airborne dataHyperspectral

Hyperspectral data

Examples

Combination of 3 SVM

classifications based on

hyperspectral data

Spring, Summer Autumn

Complex forest structure

8 species

Modzelewska et al. (in preparation)

Examples

Combination of 3 SVM

classifications based on

hyperspectral data

Spring, Summer Autumn

Complex forest structure

8 species

= overall acc. = kappa

Reference

(dominant

species)

Classification

(dominant

species)

Trends & Challenges

Larger areas

Multi-temporal data

Airborne surveys (lots of things can go wrong!!)

Comparably complex pre-processing

BRDF effects

Large datasets

Evaluation

Thematic Details

Operationality

Accuracy

Spatial details

120-180 € / km²

Overview

Reflecta

Tree species information from airborne dataLiDAR

LiDAR data

1064 n

1550 n

Intensity

3D-Structure

Two-approaches: area-based / individual tree based

∑∑∑∑∑

Area-based Individual tree based

Winter (leaf-off) + Summer (leaf-on) data

Area-based or individual tree-based classification of evergreen and deciduous species

Wasser et al. 2013

2525August

Individual tree-based

Kamińska et al. 2018

Crown Shape

Point distribution

Intensity information

∑ ∑ ∑ ∑ ∑ ∑

Single tree structure information

Holmgren & Persson 2004 Ørka et al. 2009

95% accuracy for

Separating Pine and Spruce

88% accuracy for

Separating Spruce and Birch

(large trees)

Single tree structure information

Axelsson et al. 2018

9 species

Overall accuracy structural metrics: 43,0%

Overall accuracy structure + intensity: 76.5%

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

Evaluation

Thematic Details

Operationality

Accuracy

Spatial details

62-240 € / km²

Overview

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?

Graphs from: Chollet & Allaire 2017

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

Thematic Details

Accuracy

Evaluation

Operationality Spatial details

35-62 € / km²

Overview

Tree species information from spaceborne dataSentinel-2

Sentinel-2

2 operating satellites

5-day repeat cycle

10-20 m pixel size

Free data

Examples

Tree species map for Baden-

Württemberg

Reference data from forestry

administration

Sentinel-2 mosaics for spring,

winter and summer from Google

Earth Engine

Forest area

Reference data avail.

2525August

Fabian Ewald Faßnacht 32

Reference data on

forest stand level with

species composition

(10%-steps)

Subset of all stands

with one species

holding at least 90%

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

Examples

Kappa = 0.75

Kappa = 0.64

2525August

Examples

Black Forest

(spruce dominated)Swabian Alp

(higher fractions of

broadleaved)

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

Thematic Details

Accuracy

Tree species information from airborne dataSpaceborne Sentinel-2 data

Evaluation

Operationality Spatial details

Overview

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)?

Discussion and Conclusions-

We are heading towards actually useful product that

could support forest management!

References-

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.

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).

Thank your for your attention-

Tree species information from spaceborne dataVHR data

Spaceborne VHR data

WorldView 2 and 3

0.3 – 2 m pixel size

8 – 16 bands

Examples

Object-based classification

Random Forest

Immitzer et al. 2012

Examples

Immitzer et al. 2012

Examples

Fassnacht et al. 2017

Challenges

Automated single tree delineations

Quality of individual WorldView-scenes differs a lot

Weather dependency

OperationalityThematic Details

Accuracy

Tree species information from airborne dataSpaceborne VHR data

Evaluation

Spatial details

19 $ / km²

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

Undetected (small) Red Oak standMixed Oak-Pine stand classified as

mixed Beech-Pine / BeechHS: Pine classified as Douglas Fir

Latest developments in tree species composition mapping ... ZSL 2019_Fabia… · Tree species...

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