An overview of Lidar remote sensing of forests
![An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-1.jpg)
![Outline q Principle and History q Systems and Platform q Data processing / Forestry Outline q Principle and History q Systems and Platform q Data processing / Forestry](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-2.jpg)
![What is Lidar ? q LIght Detection And Ranging or Laser Scanning q Active What is Lidar ? q LIght Detection And Ranging or Laser Scanning q Active](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-3.jpg)
![History q Sixties : Airborne laser for measuring flight altitude q Seventies – Eighties History q Sixties : Airborne laser for measuring flight altitude q Seventies – Eighties](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-4.jpg)
![Systems q Full-waveform systems Record the complete range of energy reflected by surfaces q Systems q Full-waveform systems Record the complete range of energy reflected by surfaces q](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-5.jpg)
![Platforms ALTITUDE 500 - 1000 km SATELLITES (GLAS- 600 km / CALIOP- 705 km) Platforms ALTITUDE 500 - 1000 km SATELLITES (GLAS- 600 km / CALIOP- 705 km)](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-6.jpg)
![Data acquisition q Small Footprint Airborne Lidar e Forests - National Workshop on Info Data acquisition q Small Footprint Airborne Lidar e Forests - National Workshop on Info](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-7.jpg)
![Data Visualisation q Small Footprint Airborne Lidar 833 m 890 m Draix, France) e Data Visualisation q Small Footprint Airborne Lidar 833 m 890 m Draix, France) e](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-8.jpg)
![Data Visualisation q Small Footprint Airborne Lidar e Forests - National Workshop on Info Data Visualisation q Small Footprint Airborne Lidar e Forests - National Workshop on Info](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-9.jpg)
![Data Visualisation q Terrestrial Lidar e Forests - National Workshop on Info Systems for Data Visualisation q Terrestrial Lidar e Forests - National Workshop on Info Systems for](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-10.jpg)
![Point cloud Processing q Discrete Airborne Laser Scanning (ALS) q Small Scale parameter estimation Point cloud Processing q Discrete Airborne Laser Scanning (ALS) q Small Scale parameter estimation](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-11.jpg)
![Preprocessing Raw point cloud DTM 833 m 890 m 21 m 0 m Normalized Preprocessing Raw point cloud DTM 833 m 890 m 21 m 0 m Normalized](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-12.jpg)
![Forest Parameters q Estimating Field parameters from Lidar parameters Calibration Field = Function (Lidar) Forest Parameters q Estimating Field parameters from Lidar parameters Calibration Field = Function (Lidar)](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-13.jpg)
![Small Scale Mapping q Volume estimation (Naesset, 2005) Volume estimated per grid cell Summed Small Scale Mapping q Volume estimation (Naesset, 2005) Volume estimated per grid cell Summed](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-14.jpg)
![Large Scale Mapping q Tree-based approaches - Segmentation methods q Local maxima extraction on Large Scale Mapping q Tree-based approaches - Segmentation methods q Local maxima extraction on](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-15.jpg)
![Large scale mapping q Tree-based approaches - Segmentation methods q Direct segmentation of the Large scale mapping q Tree-based approaches - Segmentation methods q Direct segmentation of the](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-16.jpg)
![Individual tree approaches q Direct estimation of tree density and tree parameters q Improving Individual tree approaches q Direct estimation of tree density and tree parameters q Improving](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-17.jpg)
![Terrestrial lidar q Limited to small surfaces (Plots) q Very high density (mm) q Terrestrial lidar q Limited to small surfaces (Plots) q Very high density (mm) q](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-18.jpg)
![Terrestrial Lidar q Stem Characterization § Automatic Stem Extraction (PCA- Hough) (Bac et al. Terrestrial Lidar q Stem Characterization § Automatic Stem Extraction (PCA- Hough) (Bac et al.](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-19.jpg)
![Terrestrial Lidar (Bac et al. 2013) e Forests - National Workshop on Info Systems Terrestrial Lidar (Bac et al. 2013) e Forests - National Workshop on Info Systems](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-20.jpg)
![Terrestrial Lidar q Tree architecture § L-Architect (Côté et al. 2011) e Forests - Terrestrial Lidar q Tree architecture § L-Architect (Côté et al. 2011) e Forests -](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-21.jpg)
![Potential for Indian Forestry q Measuring biomass -> issue in complex tropical forests q Potential for Indian Forestry q Measuring biomass -> issue in complex tropical forests q](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-22.jpg)
![Variety of applications… Geomorphology Archeology Habitat Mapping Bird Erosion / Flooding Angkor ruins under Variety of applications… Geomorphology Archeology Habitat Mapping Bird Erosion / Flooding Angkor ruins under](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-23.jpg)
![Thank you ! Thank you !](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-24.jpg)
![Forest Parameter Estimation q Plot-based Approach N Lidar Plots Statistical descriptors N Field Plots Forest Parameter Estimation q Plot-based Approach N Lidar Plots Statistical descriptors N Field Plots](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-25.jpg)
![Point Classification q Example for an ALS system recording 2 returns q Issue: Point Point Classification q Example for an ALS system recording 2 returns q Issue: Point](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-26.jpg)
![Point Classification Unique return = Ground (First= Last) e Forests - National Workshop on Point Classification Unique return = Ground (First= Last) e Forests - National Workshop on](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-27.jpg)
![Point Classification First Return Vegetation Last Return Vegetation e Forests - National Workshop on Point Classification First Return Vegetation Last Return Vegetation e Forests - National Workshop on](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-28.jpg)
![Point Classification q Classification algorithms : extracting ground points q Lot of approaches and Point Classification q Classification algorithms : extracting ground points q Lot of approaches and](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-29.jpg)
![The Big Picture Forest tpye Biomass Texture DART Images (AMAP – CESBIO) Architecture Allometry The Big Picture Forest tpye Biomass Texture DART Images (AMAP – CESBIO) Architecture Allometry](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-30.jpg)
- Slides: 30
![An overview of Lidar remote sensing of forests C Véga French Institute of Pondicherry An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-1.jpg)
An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry
![Outline q Principle and History q Systems and Platform q Data processing Forestry Outline q Principle and History q Systems and Platform q Data processing / Forestry](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-2.jpg)
Outline q Principle and History q Systems and Platform q Data processing / Forestry q Airborne discrete Lidar q Terrestrial Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![What is Lidar q LIght Detection And Ranging or Laser Scanning q Active What is Lidar ? q LIght Detection And Ranging or Laser Scanning q Active](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-3.jpg)
What is Lidar ? q LIght Detection And Ranging or Laser Scanning q Active remote sensing measuring distance to target based on « time of flight » R = range t = time C = light speed ©Calypso, CNES, 2006 e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![History q Sixties Airborne laser for measuring flight altitude q Seventies Eighties History q Sixties : Airborne laser for measuring flight altitude q Seventies – Eighties](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-4.jpg)
History q Sixties : Airborne laser for measuring flight altitude q Seventies – Eighties : Airborne profiling systems (topography and forestry) q Nineties: Scanning systems with GPS and INS -> Georeferencing q 2000 ongoing : Industrial development – costs reduction e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Systems q Fullwaveform systems Record the complete range of energy reflected by surfaces q Systems q Full-waveform systems Record the complete range of energy reflected by surfaces q](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-5.jpg)
Systems q Full-waveform systems Record the complete range of energy reflected by surfaces q Discrete systems Record 1 up to N returns by emitted pulse q Scanning > 300 k. Hz Precision : 1 m xy; 0. 1 m z e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Platforms ALTITUDE 500 1000 km SATELLITES GLAS 600 km CALIOP 705 km Platforms ALTITUDE 500 - 1000 km SATELLITES (GLAS- 600 km / CALIOP- 705 km)](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-6.jpg)
Platforms ALTITUDE 500 - 1000 km SATELLITES (GLAS- 600 km / CALIOP- 705 km) High Altitude Planes (SLICER) 8 - 12 km 1200 - 3500 m 100 - 1000 m Mean Altitude Planes HELICOPTERS Low Altitude (corridor mapping 50 -150 m) Ground or Terrestrial Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Data acquisition q Small Footprint Airborne Lidar e Forests National Workshop on Info Data acquisition q Small Footprint Airborne Lidar e Forests - National Workshop on Info](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-7.jpg)
Data acquisition q Small Footprint Airborne Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Data Visualisation q Small Footprint Airborne Lidar 833 m 890 m Draix France e Data Visualisation q Small Footprint Airborne Lidar 833 m 890 m Draix, France) e](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-8.jpg)
Data Visualisation q Small Footprint Airborne Lidar 833 m 890 m Draix, France) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Data Visualisation q Small Footprint Airborne Lidar e Forests National Workshop on Info Data Visualisation q Small Footprint Airborne Lidar e Forests - National Workshop on Info](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-9.jpg)
Data Visualisation q Small Footprint Airborne Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Data Visualisation q Terrestrial Lidar e Forests National Workshop on Info Systems for Data Visualisation q Terrestrial Lidar e Forests - National Workshop on Info Systems for](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-10.jpg)
Data Visualisation q Terrestrial Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Point cloud Processing q Discrete Airborne Laser Scanning ALS q Small Scale parameter estimation Point cloud Processing q Discrete Airborne Laser Scanning (ALS) q Small Scale parameter estimation](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-11.jpg)
Point cloud Processing q Discrete Airborne Laser Scanning (ALS) q Small Scale parameter estimation -> Plot Level q Large Scale parameter estimation -> Tree Level q Terrestrial Laser Scanning (TLS) q Stem characterization q Tree architecture e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Preprocessing Raw point cloud DTM 833 m 890 m 21 m 0 m Normalized Preprocessing Raw point cloud DTM 833 m 890 m 21 m 0 m Normalized](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-12.jpg)
Preprocessing Raw point cloud DTM 833 m 890 m 21 m 0 m Normalized point cloud = Raw - DTM e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Forest Parameters q Estimating Field parameters from Lidar parameters Calibration Field Function Lidar Forest Parameters q Estimating Field parameters from Lidar parameters Calibration Field = Function (Lidar)](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-13.jpg)
Forest Parameters q Estimating Field parameters from Lidar parameters Calibration Field = Function (Lidar) Inversion q Multiplicative models q Stepwise approach e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Small Scale Mapping q Volume estimation Naesset 2005 Volume estimated per grid cell Summed Small Scale Mapping q Volume estimation (Naesset, 2005) Volume estimated per grid cell Summed](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-14.jpg)
Small Scale Mapping q Volume estimation (Naesset, 2005) Volume estimated per grid cell Summed by stand -> mean/ha Photo interpretation Grid Lidar Field Plots Terrain + Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Large Scale Mapping q Treebased approaches Segmentation methods q Local maxima extraction on Large Scale Mapping q Tree-based approaches - Segmentation methods q Local maxima extraction on](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-15.jpg)
Large Scale Mapping q Tree-based approaches - Segmentation methods q Local maxima extraction on raster + polygon fitting (Popescu et al. , 2003, 2004) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Large scale mapping q Treebased approaches Segmentation methods q Direct segmentation of the Large scale mapping q Tree-based approaches - Segmentation methods q Direct segmentation of the](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-16.jpg)
Large scale mapping q Tree-based approaches - Segmentation methods q Direct segmentation of the point cloud Lateral view Top view e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Individual tree approaches q Direct estimation of tree density and tree parameters q Improving Individual tree approaches q Direct estimation of tree density and tree parameters q Improving](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-17.jpg)
Individual tree approaches q Direct estimation of tree density and tree parameters q Improving equations for volume and biomass (height and crown dimension) q Crown dimension explain better AGB (Popescu 2003) q Stem to stem management -> thinning e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Terrestrial lidar q Limited to small surfaces Plots q Very high density mm q Terrestrial lidar q Limited to small surfaces (Plots) q Very high density (mm) q](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-18.jpg)
Terrestrial lidar q Limited to small surfaces (Plots) q Very high density (mm) q Utility for allometry, tree architecture and forest modeling e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Terrestrial Lidar q Stem Characterization Automatic Stem Extraction PCA Hough Bac et al Terrestrial Lidar q Stem Characterization § Automatic Stem Extraction (PCA- Hough) (Bac et al.](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-19.jpg)
Terrestrial Lidar q Stem Characterization § Automatic Stem Extraction (PCA- Hough) (Bac et al. 2013) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Terrestrial Lidar Bac et al 2013 e Forests National Workshop on Info Systems Terrestrial Lidar (Bac et al. 2013) e Forests - National Workshop on Info Systems](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-20.jpg)
Terrestrial Lidar (Bac et al. 2013) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Terrestrial Lidar q Tree architecture LArchitect Côté et al 2011 e Forests Terrestrial Lidar q Tree architecture § L-Architect (Côté et al. 2011) e Forests -](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-21.jpg)
Terrestrial Lidar q Tree architecture § L-Architect (Côté et al. 2011) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Potential for Indian Forestry q Measuring biomass issue in complex tropical forests q Potential for Indian Forestry q Measuring biomass -> issue in complex tropical forests q](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-22.jpg)
Potential for Indian Forestry q Measuring biomass -> issue in complex tropical forests q Conventional remote sensing -> signal saturation at low AGB q Lidar q Directly related to forest structure q No saturation with AGB q Best current data for plot and landscape estimation of forest parameters q Utility for calibrating texture indices from satellites images for ABG estimations at regional level e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Variety of applications Geomorphology Archeology Habitat Mapping Bird Erosion Flooding Angkor ruins under Variety of applications… Geomorphology Archeology Habitat Mapping Bird Erosion / Flooding Angkor ruins under](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-23.jpg)
Variety of applications… Geomorphology Archeology Habitat Mapping Bird Erosion / Flooding Angkor ruins under the forest canopy (Chase and al. , 2010) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Thank you Thank you !](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-24.jpg)
Thank you !
![Forest Parameter Estimation q Plotbased Approach N Lidar Plots Statistical descriptors N Field Plots Forest Parameter Estimation q Plot-based Approach N Lidar Plots Statistical descriptors N Field Plots](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-25.jpg)
Forest Parameter Estimation q Plot-based Approach N Lidar Plots Statistical descriptors N Field Plots Regression analysis Validation Large scale mapping e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Point Classification q Example for an ALS system recording 2 returns q Issue Point Point Classification q Example for an ALS system recording 2 returns q Issue: Point](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-26.jpg)
Point Classification q Example for an ALS system recording 2 returns q Issue: Point penetration within canopy First Return Vegetation Last Return Ground e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Point Classification Unique return Ground First Last e Forests National Workshop on Point Classification Unique return = Ground (First= Last) e Forests - National Workshop on](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-27.jpg)
Point Classification Unique return = Ground (First= Last) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Point Classification First Return Vegetation Last Return Vegetation e Forests National Workshop on Point Classification First Return Vegetation Last Return Vegetation e Forests - National Workshop on](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-28.jpg)
Point Classification First Return Vegetation Last Return Vegetation e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![Point Classification q Classification algorithms extracting ground points q Lot of approaches and Point Classification q Classification algorithms : extracting ground points q Lot of approaches and](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-29.jpg)
Point Classification q Classification algorithms : extracting ground points q Lot of approaches and algorithms q Best one Iterative Tin – Delauney triangulation 3 D points Local minima Initial TIN Surface TIN Densification Angle & Distance ©F. Bretar, 2006 Axelsson (1999) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9, 10 & 11 th May 2013 Bangalore
![The Big Picture Forest tpye Biomass Texture DART Images AMAP CESBIO Architecture Allometry The Big Picture Forest tpye Biomass Texture DART Images (AMAP – CESBIO) Architecture Allometry](https://slidetodoc.com/presentation_image_h/8dcb374599690093bd34794cc53e652d/image-30.jpg)
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