Using GEOBIA to assess crown diameter            classes of Acacia tortilis in Bou-Hedma, Tunisia                 Kevin DE...
Partners   a GhentUniversity, Belgium    Laboratory of Forest Management and Spatial Information Techniques    (kevin.del...
Context Acacia raddiana forest steppe in Bou-Hedma national park, Tunisia    A. raddiana persists on edge of desert  key...
Context    Important activity in Bou-Hedma: restoration of original woodland     combating desertification      afforest...
Context Restoration of A. raddiana  induce local climate change    Consequences likely to occur        Change of soil t...
Aim In order to evaluate future trends  assess current situation  Aim        RS-based monotemporal assessment of amount...
Study area    Location        34°24’ N to 34°32’ N        09°23’ E to 09°41’ EContext – Aim – Study area – Imagery- Metho...
Study area    Location        34°24’ N to 34°32’ N        09°23’ E to 09°41’ E    Altitude        90 m - 814 m above sea...
Study area    Location        34°24’ N to 34°32’ N        09°23’ E to 09°41’ E    Altitude        90 m - 814 m above sea...
Field survey    Field conditions A. raddiana        Two spatial configurations            Plantations            ‘Natu...
Imagery    GeoEye-1 scene      Spatial resolution: 0.5 m PAN, 2 m MS      Radiometric resolution: 11 bits per pixel    ...
Imagery    GeoEye-1 scene      Spatial resolution: 0.5 m PAN, 2 m MS      Radiometric resolution: 11 bits per pixel    ...
ImageryContext – Aim – Study area – Imagery- Method – Results - Conclusions
Methodology                                                                                 GeoEye-1                      ...
Methodology                                                Image Objects                         Multi-resolution     GeoE...
Methodology                                                 Image Objects                              Multi-resolution   ...
Methodology                                         GeoEye-1                                                     PAN & MS ...
Methodology                                      GeoEye-1                                                  PAN & MS       ...
Methodology                                     GeoEye-1                                                   object features...
Methodology                        Regression    Accuracy                         modelsCrown diameter estimation         ...
Methodology                        Regression    Accuracy                         modelsCrown diameter estimation         ...
Methodology                        Regression    Accuracy                         modelsCrown diameter estimation         ...
Methodology                              Regression    Accuracy                               modelsCrown diameter estimat...
MethodologyAcacia objects                            Estimated tree attributes per Acacia object                          ...
ResultsAcacia objects                           Estimated tree attributes per Acacia object                               ...
ResultsAcacia objects                                 Estimated tree attributes per Acacia object                         ...
Conclusions Answer to RQ         Object-based segmentation/classification approach  suitably addressed          estimati...
THANK YOU FOR YOUR ATTENTION                         (kevin.delaplace, frieke.vancoillie, robert.dewulf)@ugent.be         ...
Using GEOBIA to assess crown diameter classes of Acacia tortilis in Bou-Hedma, Tunisia
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Using GEOBIA to assess crown diameter classes of Acacia tortilis in Bou-Hedma, Tunisia

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  • The results of the research were quite satisfying indeed. In this particular case the field survey was of major importance as we wanted to know the capabilities of the GeoEye image in determining individual tree attributes. Without field data, this would not have been possible. If you would just like to classify the image into tree and soil features, the field data would become less important. However, distinction between Eucalyptus sp. and Acacia sp. would still require knowledge of the local situation.

    As mentioned on the slides, this research was conducted at the University of Ghent (FORSIT Team), as my MSc. Thesis. If you are interested, you can download a PDF of this research at

    http://users.telenet.be/kevin.delaplace/thesis/

    Best regards,

    Kevin Delaplace
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  • Nice research!! ;)

    Would you say that field survey is as important as imagery?? (if not, could you tell me which percent??)

    Thank you in advance for your comments.
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Transcript of "Using GEOBIA to assess crown diameter classes of Acacia tortilis in Bou-Hedma, Tunisia"

  1. 1. Using GEOBIA to assess crown diameter classes of Acacia tortilis in Bou-Hedma, Tunisia Kevin DELAPLACE a, Frieke VAN COILLIE a, Robert DE WULF a, Donald GABRIELS b, Koen DE SMET c, Mohammed OUESSAR d, Azaiez OULED BELGACEM d, Houcine TAAMALLAH dGhent, September 24, 2010EARSeL Joint SIG Workshop: Urban - 3D - Radar - Thermal Remote Sensing and Developing Countries
  2. 2. Partners a GhentUniversity, Belgium Laboratory of Forest Management and Spatial Information Techniques (kevin.delaplace, frieke.vancoillie, robert.dewulf)@ugent.be b GhentUniversity, Belgium Department of Soil Management and Soil Care donald.gabriels@ugent.be c Flemish Government, Belgium Environment, Nature and Energy Department koen.desmet@lne.vlaanderen.be d Institut des Régions Arides de Médenine, Tunisia med.ouessar@ira.agrinet.tn, (Azaiez.ouledbelgacem, taamallah.houcine)@ira.rnrt.tn
  3. 3. Context Acacia raddiana forest steppe in Bou-Hedma national park, Tunisia  A. raddiana persists on edge of desert  keystone species pre-Saharan Tunisia zone  Past: Bou-Hedma no attention concerning biodiversity, protection and use  Result: desertification caused by excessive livestock grazing, partial land clearance, ploughing  soil erosion  1955: only few old trees left  Sixties: first actions to combat desertification/erosion  700 ha fenced  Tree nursery  Gradual restoration of vegetation  Two Integral Protection Zones  1977: part of the network of Biosphere Reserves of UNESCO  1985: regeneration actionsContext – Aim – Study area – Imagery- Method – Results - Conclusions
  4. 4. Context  Important activity in Bou-Hedma: restoration of original woodland combating desertification  afforestation and reforestation  Cooperation between Flemish Government & Direction Générale des Forêts of Tunisia  reforestation 50,000 ha with A. raddiana in historical geographic range  Part of larger project Kyoto Clean Development Mechanism (CDM)  scientific follow up of plantations, ecological and socio-economical consequencesContext – Aim – Study area – Imagery- Method – Results - Conclusions
  5. 5. Context Restoration of A. raddiana  induce local climate change  Consequences likely to occur  Change of soil temperature  Formation of humus by partial leaf shedding in summer  Influence on the cloud formation process, inducing precipitation  Interception of water by tree leaves and trunks, influencing erosion processes  However, scientific data remain scarce  Some phenological and ecophysiological studies  Little knowledge of composition, structure and by extension dynamics  useful to test efficiency of different management scenariosContext – Aim – Study area – Imagery- Method – Results - Conclusions
  6. 6. Aim In order to evaluate future trends  assess current situation  Aim  RS-based monotemporal assessment of amount of A. raddiana  Estimation of diameter and height class distributions of A. raddiana  Research questions  What is the most appropriate (semi-)automated image processing procedure?  Are we able to establish models to estimate individual tree attributes?  Are we able to provide an estimate of the structure of the Acacia raddiana forest steppe at Bou-Hedma National Park?Context – Aim – Study area – Imagery- Method – Results - Conclusions
  7. 7. Study area  Location 34°24’ N to 34°32’ N 09°23’ E to 09°41’ EContext – Aim – Study area – Imagery- Method – Results - Conclusions
  8. 8. Study area  Location 34°24’ N to 34°32’ N 09°23’ E to 09°41’ E  Altitude 90 m - 814 m above sea levelContext – Aim – Study area – Imagery- Method – Results - Conclusions
  9. 9. Study area  Location 34°24’ N to 34°32’ N 09°23’ E to 09°41’ E  Altitude 90 m - 814 m above sea level  Climate: Mediterranean arid with temperate winters average annual rainfall 180 mm average temp 17.2°C mean max temp 38°C mean min temp 3.9°CContext – Aim – Study area – Imagery- Method – Results - Conclusions
  10. 10. Field survey  Field conditions A. raddiana  Two spatial configurations  Plantations  ‘Natural’  Individuals & tree groups  Presence of Eucalyptus sp.  > 400 A. raddiana trees randomly sampled  Tree positions (UTM WGS84)  Tree bole diameter (basal and breast height)  Total tree height  Crown diameter (two perpendicular directions)Context – Aim – Study area – Imagery- Method – Results - Conclusions
  11. 11. Imagery  GeoEye-1 scene  Spatial resolution: 0.5 m PAN, 2 m MS  Radiometric resolution: 11 bits per pixel  Spectral resolution: PAN, Blue, Green, Red, NIR  Acquisition date:  Optimum: discrimination trees, soil and vegetation largest  Dry season: no lush vegetation, low cloud cover, maximum leaf development  1st of August 2009 at 10:18 GMTContext – Aim – Study area – Imagery- Method – Results - Conclusions
  12. 12. Imagery  GeoEye-1 scene  Spatial resolution: 0.5 m PAN, 2 m MS  Radiometric resolution: 11 bits per pixel  Spectral resolution: PAN, Blue, Green, Red, NIR  Acquisition date: 1st of August 2009 at 10:18 GMT  Cloud cover: 0%  Preprocessing:  Geometric correction by GeoEye Inc.  Coordinate system: UTM (WGS 84)Context – Aim – Study area – Imagery- Method – Results - Conclusions
  13. 13. ImageryContext – Aim – Study area – Imagery- Method – Results - Conclusions
  14. 14. Methodology GeoEye-1 MS bands Object-based approach Image Objects Multi-resolution Object feature GeoEye-1 / contrast-split calculation / NN PAN band 0 segmentation classificationMeasured tree attributes Input and reference data per reference object per reference object Acacia objects Object Features Crown diameter Tree height Crown diameter Segment 1 f1, f2, ………f200 5.2 2.5 Tree height Segment 2 f1, f2, ………f200 6.3 3.2 … ….. Segment i f1, f2, ………f200 4.1 2.1 Estimated tree attributes per Acacia object Small CD/TH/… Regression Accuracy models Large CD/TH/… Context – Aim – Study area – Imagery- Method – Results - Conclusions
  15. 15. Methodology Image Objects Multi-resolution GeoEye-1 / contrast-split PAN band 0 segmentation  Multi-resolution segmentation (eCognition Developer 8): Parameter Value Scale 40 Shape 0.2 Compct 0.5Context – Aim – Study area – Imagery- Method – Results - Conclusions
  16. 16. Methodology Image Objects Multi-resolution GeoEye-1 / contrast-split PAN band 0 segmentation  Contrast-split segmentation (eCognition Developer 8): Parameter Value Contrast mode Edge difference Min rel area dark 0.1 Min rel area bright 0.1 Min contrast 0 Min object size 10Context – Aim – Study area – Imagery- Method – Results - Conclusions
  17. 17. Methodology GeoEye-1 PAN & MS bands Image Objects Acacia objects Object feature calculation / NN 0 classification Type Feature (200) Customized FDI (NIR-(R-B)), SAVI, NDVI Layer values Mean, Min, Max, Border contrast, Contrast to neighbour pixels, Edge contrast of neighbour pixels, StdDev to neighbour pixels, Circular mean Geometry Area, Assymetry, Density, Compactness Texture after Homogeneity, Mean, Correlation, Ang 2nd Haralick moment, EntropyContext – Aim – Study area – Imagery- Method – Results - Conclusions
  18. 18. Methodology GeoEye-1 PAN & MS bands Image Objects Acacia objects Object feature calculation / NN 0 classification KIA=0.96 A. raddiana sp. Eucalyptus sp. SoilContext – Aim – Study area – Imagery- Method – Results - Conclusions
  19. 19. Methodology GeoEye-1 object features Measured tree attributes Input and reference data per reference object per reference object Object Features Crown diameter Tree height Crown diameter Segment 1 f1, f2, ………f200 5.2 2.5 Tree height Segment 2 f1, f2, ………f200 6.3 3.2 … ….. Segment i f1, f2, ………f200 4.1 2.1Measured Acacia’s: train & test sets Regression Correlation analysis Accuracy models Tree attribute Object feature Crown diameter Area Tree height Area Bole diameter Area Object feature Object feature Area GLCM Entropy Layer 4 (90°) Context – Aim – Study area – Imagery- Method – Results - Conclusions
  20. 20. Methodology Regression Accuracy modelsCrown diameter estimation R² = 0.64 RMSE = 1.67 m MAPE = 21.6 %Context – Aim – Study area – Imagery- Method – Results - Conclusions
  21. 21. Methodology Regression Accuracy modelsCrown diameter estimation R² = 0.96 RMSE = 14.7 pixels MAPE = 13.0 %Context – Aim – Study area – Imagery- Method – Results - Conclusions
  22. 22. Methodology Regression Accuracy modelsCrown diameter estimation R² = 0.58 RMSE = 1.61 m MAPE = 22.0 %Context – Aim – Study area – Imagery- Method – Results - Conclusions
  23. 23. Methodology Regression Accuracy modelsCrown diameter estimation 60 Measured crown diameter (field data) 50 Crown diameter derived from area (number of pixels) 40 Frequency Crown diameter derived from GLCM Entropy Layer 4 (90°) 30 20 10 0 [0,2] ]2,4] ]4,6] ]6,8] ]8,10] ]10,12] ]12,14] ]14,16] Crown Diameter Classes (m)Context – Aim – Study area – Imagery- Method – Results - Conclusions
  24. 24. MethodologyAcacia objects Estimated tree attributes per Acacia object Small CD/TH/… Regression models Large CD/TH/…characterising forest structure (arrangement of diameters), height and densityContext – Aim – Study area – Imagery- Method – Results - Conclusions
  25. 25. ResultsAcacia objects Estimated tree attributes per Acacia object Small CD/TH/… Regression models Large CD/TH/…Structure (Crown diameter)Context – Aim – Study area – Imagery- Method – Results - Conclusions
  26. 26. ResultsAcacia objects Estimated tree attributes per Acacia object Small CD/TH/… Regression models Large CD/TH/…Density Characteristics original forest steppe, 1900 1925 (Zaafouri et al, 1996) Kernel of 1 ha (200×200 pixels, 0.5 m resolution) Tree attribute Value Total of 2596 non-overlapping kernels Total tree height 9 - 10 m (max 10 m) Excluding border effect Trunk height 3 – 4 m at most  mean density = 8.4 trees/ha Mean basal diameter 15 – 20 cm 2.3 – 3 m  max density = 95 trees/ha (plantations) BD of biggest trees Density 4 – 25 trees/haContext – Aim – Study area – Imagery- Method – Results - Conclusions
  27. 27. Conclusions Answer to RQ  Object-based segmentation/classification approach  suitably addressed estimation of amount of A. raddiana  Established models  adequately estimate of individual tree attributes  Estimation of structure, height and density Acacia raddiana forest steppe at Bou-Hedma National Park Future activities  Fine-tuning of segmentation / classification steps  Thorough validation  Multitemporal assessment  inclusion of phenological information  optimal image acquisition time to estimate amount of Acacia’s and their attributesContext – Aim – Study area – Imagery- Method – Results - Conclusions
  28. 28. THANK YOU FOR YOUR ATTENTION (kevin.delaplace, frieke.vancoillie, robert.dewulf)@ugent.be Laboratory of Forest Management and Spatial Information Techniques, Ghent University, Belgium http://dfwm.ugent.be/formanGhent, September 24, 2010EARSeL Joint SIG Workshop: Urban - 3D - Radar - Thermal Remote Sensing and Developing Countries

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