mapping natura 2000 heathland in belgium – an evaluation of ensemble classifier for spaceborne...
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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.