Mapping Natura 2000 heathland in Belgium – an evaluation of ensemble classifier for spaceborne angular CHRIS/Proba imagery

Jonathan C-W Chan1, Pieter Beckers2, Frank Canters1, Toon Spanhove3, Jeroen Vanden Borre3, Desiré Paelinckx3

1 Cartography & GIS research group, Geography Dept. Vrije Universiteit Brussel2 Division of Geography, Katholieke Universiteit Leuven3 Research Institute for Nature and Forest (INBO)

For IGARSS 2011, 24-29 July, 2011, Vancouver, Canada

HABItat STATus reporting with remote sensing methods (HABISTAT) 2007-2011

WP2000Analysis

WP2100Literature Study

WP2200Requirement

Analysis

WP3000 Data Collection

WP3100Field Work

WP3200Data labeling

WP3300RS Data

Acquisition

WP4000Data Processing

WP4100Spatial

contextual description

WP4300SR Image

reconstruction

WP6000Dissemination

WP6100Reporting

WP6200Publications

WP4200Data Modeling

WP4400Ensemble

Classifications

WP5000Exploitation

WP5100Structural Analysis

WP5300Operational Integration

WP5200Validation

VITO VUBUA INBO ALTERRA ALL

OUTLINE• Background of NATURA 2000 habitats• Remote sensing methodology for monitoring• Results• Conclusions

The Habitats Directive (92/43/EEC)

• Adopted in 1992 together with the Bird’s Directive, is the cornerstone of Europe’s nature conservation policy

• Two pillars: the Natura 2000 network of protected sites and strict system of species protection

• It protects over 1,000 animals and plant species and over 200 so called “habitat types” (e.g. Special types of forests, meadows, wetlands, etc.), which are of European importance.

• Main obligations for European member states– survey the conservation status of targeted habitats/species and report

to EU every SIX years (actual area, range, quality, and future prospect)– take measures to bring/maintain targeted habitats and species in

‘favourable conservation status’ (i.e. long-term maintenance assured)

Source: Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora (1992)

Heathland: a Natura 2000 site at Kalmthoutse Heide, Belgium

Figure 1. “Dry sand heaths with Calluna and Genista” (2310) is a Natura 2000 habitat commonly found in the study area. In favourable conditions, it consists of a mixture of dwarf scrub, open sand and patches of pioneer grasses and mosses (a); but as a result of eutrophication, encroachment with purple moor grass (Molinia caerulea) leads to a monotonous vegetation (b), with a heavily reduced ecological value.

(a) Favourable condition (b) Unfavourable condition

Heathland in Kalmthout

METHODOLOGY

• Data - Investigate angular hyperspectral CHRIS• Classifier - Testing tree-based ensemble classifiers using

support vector machines (SVM) as a benchmark comparison

• Accuracy assessment - ten independent runs with random re-sampling

Interesting features of CHRIS/Proba & its link with future sensor

18 SEP 2007 – Acquisition mode 3, 18 bands, 17m pixel size0° +36° -36° +55° -55°

Spaceborne CHRIS/Proba imagery - 18-62 bands between 0.4-1 μm - 17-34m spatial resolution- multi-angle acquisition at nadir,

±36°, and ±55° Future Sensor – EnMAP 2015- Operated by German Space Agency

(DLR)- Specs: 30m resolution (0.4-2.5 μm)- Angular viewing: +/- 30° off-nadir

2310Dry sand heaths with Calluna and Genista (on inland dunes)

Indicator Good local conservation status

Bad local conservation status

Remarks and explanations

A – good quality B – moderate quality C – low quality

Habitat structure cover of dwarf shrubs

≥ co-dominant < co-dominant dwarf shrubs include: Calluna vulgaris, Genista pilosa, Genista anglica, Vaccinium myrtillus, Erica tetralix

age structure of Calluna vulgaris

all phases present 2 or 3 phases present only 1 phase present phases are: pioneer, building, mature and degenerate phase

bare sand > 10% 1 – 10% < 1%

cover of mosses and lichens

> 10% 1 – 10% < 1% includes all mosses and lichens except Campylopus introflexus

Vegetation presence of key species

Calluna and 3 or more other key species (at least occasionally) present

Calluna and 1 or 2 other key species (at least occasionally) present

only Calluna present or all key species less than occasionally present

key species include: Calluna vulgaris, Agrostis vinealis, Aira praecox, Carex arenaria, Corynephorus canescens, Cuscuta epithymum, Filago minima, Genista anglica, Genista pilosa, Spergula morisonii, Teesdalia nudicaulis

Disturbances cover of grasses/Bush-indicators

< 30% 30 – 50% > 50% grasses include: Molinia caerulea, Deschampsia flexuosa, Agrostis spp.; bushes include: Pteridium aquilinum, Rubus spp.

cover of trees and shrubs

< 5% 5 – 30% > 30%

cover of invasive alien species

0% < 10% ≥ 10% in particular: Campylopus introflexus

Natura 2000 habitats evaluation spreadsheet of dry sand heath (2310)

Level1 Level2 Level3 Level4

H Heathland

Hd Dry heathland Hdc Calluna-dominated heathland

Hdcy Calluna-stand of predominantly young age

Hdca Calluna-stand of predominantlyadult age

Hdco Calluna-stand of predominantly old age

Hdcm Calluna-stand of mixed age classes

Hw Wet heathland Hwe Erica-dominated heathland Hwe- Erica-dominated heathland

HgGrass-encroached heathland

Hgm Molinia-dominated heathlandHgmd Molinia-stand on dry soil

Hgmw Molinia-stand on moist soil

Hgd Deschampsia flexuosa-dominated heathland Hgd- Deschampsia flexuosa-dominated heathland

HsShrub/Tree-encroached heathland

Hst Tree-encroached heathland Hst- Tree-encroached heathland

Hsr Rubus-encroached heathland Hsr- Rubus-encroached heathland

G Grassland

Gt Temporary grassland Gt- Temporary grassland Gt-- Temporary grassland

Gp Permanent grassland

Gpa Permanent grassland in intensive agricultural useGpap Species-poor permanent agricultural grassland

Gpar Species-rich permanent agricultural grassland

Gpn Permanent grassland with semi-natural vegetation Gpnd Dry semi-natural permanent grassland

Gpj Juncus effusus-dominated grassland Gpj- Juncus effusus-dominated grassland

F Forest

Fc Coniferous forest Fcp Pine forest

Fcpc Corsican pine

Fcps Scots pine

Fd Deciduous forest

Fdb Birch forest Fdb- Birch forest

Fdq Oak forest Fdqz Pedunculate oak

S Sand dune

Sb Bare sand Sb- Bare sand Sb-- Bare sand

Sf Fixated sand dune

Sfg Sand dune with grasses as important fixators Sfgm Sand dune fixated by grasses and mosses

Sfm Sand dune with mosses as dominating fixatorsSfmc Fixated sand dune with predominantly Campylopus introflexus

Sfmp Fixated sand dune with predominantly Polytrichum piliferum

W Water body Wo Oligotrophic water body

Wov Shallow, vegetated oligotrophic water body Wov- Shallow, vegetated oligotrophic water body

Wou Unvegetated oligotrophic water Wou- Unvegetated oligotrophic water

A Arable fields Ac Arable field

with crop

Acm Arable field – maize Acm- Arable field – maize

Aco Arable field - other crops Aco- Arable field - other crops

Classification Scheme at 4 levels – Kalmthoutse heide

An adapted classification scheme for spaceborne images – 10 classes

Hdcy Calluna-stand of predominantly young age 1. Calluna (Calluna-dominated heath 2310/4030)Hdca Calluna-stand of predominantlyadult age

Hdco Calluna-stand of predominantly old age

Hdcm Calluna-stand of mixed age classes

Hwe- Erica-dominated heathland 2. Erica (Erica-dominated heath 4010)Hgmd Molinia-stand on dry soil 3. Molinia (Molinia-dominated heath 2310/2330/4010/4030)Hgmw Molinia-stand on moist soil

Hgd- Deschampsia flexuosa-dominated heathland

Hst- Tree-encroached heathland

Hsr- Rubus-encroached heathland

Gt-- Temporary grassland 4. Temporary and permanent grasslandGpap Species-poor permanent agricultural grassland

Gpar Species-rich permanent agricultural grassland

Gpnd Dry semi-natural permanent grassland

Gpj- Juncus effusus-dominated grassland

Fcpc Corsican pine 5. Coniferious forests (Pine forests)Fcps Scots pine

Fdb- Birch forest 6. Deciduous forests (Birch and Oak forests 9190)Fdqz Pedunculate oak

Sb-- Bare sand 7. Sand and mosses (Bare sand and sand dunes with grasses and mosses as dominating fixators)Sfgm Sand dune fixated by grasses and mosses

Sfmc Fixated sand dune with predominantly Campylopus introflexus

Sfmp Fixated sand dune with predominantly Polytrichum piliferum

Wov- Shallow, vegetated oligotrophic water body 8. Shallow, vegetated oligotrophic water (3110/3130/3160)

Wou- Unvegetated oligotrophic water 9. Unvegetated oligotrophic water (3110/3130/3160)Acm- Arable field – maize 10. Agriculture and cultivated landsAco- Arable field - other crops

Geostatistical sampling method

Initial field driven2007

Random stratified2009

A total of 586 sampling points were gathered

Why ensemble classifiers and why tree-based Random Forest and Adaboost?

• accurate• fast• easy to use (minimum parameter tuning)• high interpretability (not a black box)• easy to understand• machine learning algorithms with extremely high

repeatability• robust with high dimensional input (well tested with

hyperspectral data inputs)• no assumptions on data distribution• robust with noisy (absence of) data• It is free!

Classification algorithms

RANDOM FOREST

• Tuning parameters– Number of trees– Number of input features used for each tree, randomly drawn from all the input

features• Two sequences

– 24 different numbers of trees, ranging from 1 to 700– 10 different numbers for the amount of input features used for each tree, ranging

from 1 to 10• Look at testing data and compare overall accuracies

ADABOOST.M1

• Multiclass AdaBoost using classification trees• Two parameters

– Number of iterations– Maximum depth of any node of the final tree

• Sequence of iterations– Comparing different numbers of iterations, ranging from 5 to 100

• Looking at differences when changing the maximum depth

Parameter tuning: Random Forest and Adaboost

SUPPORT VECTOR MACHINES

• Most time-consuming for tuning• First comparing the different kernel functions• Using a radial basis function kernel and a ‘grid-search’

– Searching the optimal values for the two parameters (gamma and cost)• Coarse grid• Fine grid• Even finer grid

Parameter tuning: SVM (radial basis function)

RESULTS: Overall Accuracy

RESULTS: Kappa values

RESULTS: Mean class accuracy

SVM – 3 imagesSVM - nadirMAPPING RESULTS

Only Nadir imageRandom ForestAdaboost

3 angular imagesAdaboost Random Forest

RESULTS BY TRIAL – Only NadirRandom Forest

H

L

RESULTS BY TRIAL: Only NadirAdaboost.M1

H

L

RESULTS BY TRIAL: Only NadirSupport Vector Machines

HL

RESULTS BY TRIAL: 3 angular images Random Forest

H

L

RESULTS BY TRIAL : 3 angular images Adaboost.M1

H

L

RESULTS BY TRIAL: 3 angular imagesSupport Vector Machines

H

L

Performance by class – a summary

Random Forest Adaboost

SupportVector

Machines

Nadir 3 angles Nadir 3 angles Nadir 3 angles

1 Calluna 38.7% 43.5% 41.1% 44.9% 44.1% 45.1%

2 Erica 41.9% 48.1% 44.2% 47.3% 37.3% 37.7%

3 Molinia 69.7% 74.9% 70.4% 73.6% 80.7% 79.0%

4 Grassland 60.4% 65.0% 61.1% 63.2% 64.3% 60.7%

5 Coniferous forest 59.1% 60.9% 56.4% 58.2% 64.6% 64.6%

6 Deciduous forest 60.0% 58.6% 58.6% 55.7% 54.3% 61.4%

7 Bare sand & mosses 65.2% 66.7% 62.9% 64.8% 55.2% 53.8%

8Water surface with vegetation 16.7% 15.6% 17.8% 20.0% 7.8% 11.1%

9 Water surface 68.8% 70.0% 66.3% 68.8% 65.0% 70.0%

10 Cropland 33.8% 37.5% 33.8% 46.3% 72.5% 60.0%

CONCLUSIONS• Angular images increased overall accuracy and provide a classification with less salt and pepper

effects.• Support vector machines has the highest accuracies, but does not improve much with more features. • Random Forest has the highest mean class accuracies• Parameter tunings with RF and Adaboost quite fast, comparatively easier.• Big variations in accuracy between trials; more trials may provide a better characterization of

algorithm behaviour.• General classification rates for Calluna (38-45%) or Erica (37-58%) -dominated heathland are low. A

better accuracy (69-80%) is observed in Molinia-dominated heaths. Future sensors covering full 0.4-2.5 μm range could increase accuracy.