Presentation is loading. Please wait.

Presentation is loading. Please wait.

Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Evaluating the potential.

Published byHope Eaton Modified over 9 years ago

Similar presentations

Presentation on theme: "Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Evaluating the potential."— Presentation transcript:

1 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages Lars Waser Kai Jütte Theresia Stampfer Landesforst Mecklenburg-Vorpommern Anstalt des öffentlichen Rechts Betriebsteil Forstplanung, Versuchswesen, Forstliche Informationssysteme

2 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Tornado „Doris“ 1/13

3 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Situation / goals  Tornado „Doris“ – severe storm & hail; 11 June 2010  > 2000 ha of forest affected / destroyed => Focus on detecting multiple stages of tree damages (ash / Scots pine) => Which tree species are affected most? (tree species classification)  Which satellite system is most appropriate? (costs, Pros / Cons) 2/13

4 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Study areas German State Mecklenburg-Vorpommern 3/13

5 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Satellite data - Passive optical system, 2 m - 8 bands RGBI, costal, yellow, NIR2, Rededge WorldView-2 images - Active system, X-band (3 cm) - 1.25 m TerraSAR-X images 4/13

6 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Training / reference data  7 Tree species: Delineated tree crown polygons (756)  4 stages of tree damages (ash / Scots pine): 4 x 120 polygons => Ash, beech, Douglas fir, larch, Norway spruce, oak, poplar Terrestrial survey Crown view from a crane 1 (0-25%) 2 (25-50%) 3 (50-75%) 4 (75-100%) destroyed foliage 5/13

7 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 In-situ spectral measurements 6/13 e.g. damage stage 3 a mobile crane Aerial images Terrestrial survey Crown view from: WorldView-2 images

8 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Training / reference data - Delineated tree crown polygons (756) Different stages of damaged Scots pine (2011) 7/13

9 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Classification  Pre-processing - De-Hazing+Atmospheric Correction - Original bands & 22 Vegetation indices - 20 indices (filters, textural & structural)  Signatures (variables)  Validation of prediction: external, independent reference data (polygons) - Noise reduction  Multinomial log. regression - Cross-Validation (10-fold) - Variable selection - Image segmentations 8/13

10 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Results TerraSAR-X Waser et al. in prep. for Remote Sensing of Environment WorldView-2 7 tree species ash Scots pine Overall acc. Overall acc. kappa 0.900.830.31 0.56 => ash and poplar > 0.7 ash, beech, Douglas fir, larch, Norway spruce, oak, poplar Stage of damage 1 3 2 4 1 3 2 4 0.83 0.73 0.65 0.78 0.62 0.52 0.44 0.56 0.78 0.71 0.62 0.72 0.62 0.53 0.39 0.59 10-fold cross-validated 9/13

11 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Results TerraSAR-X Waser et al. in prep. for Remote Sensing of Environment WorldView-2 7 tree species ash Scots pine Overall acc. Overall acc. kappa 0.900.830.31 0.56 => ash and poplar > 0.7 ash, beech, Douglas fir, larch, Norway spruce, oak, poplar Stage of damage 1 3 2 4 1 3 2 4 0.83 0.73 0.65 0.78 0.62 0.52 0.44 0.56 0.78 0.71 0.62 0.72 0.62 0.53 0.39 0.59 10-fold cross-validated 9/13

12 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Results TerraSAR-X Waser et al. in prep. for Remote Sensing of Environment WorldView-2 7 tree species ash Scots pine Overall acc. Overall acc. kappa 0.900.830.31 0.56 => ash and poplar > 0.7 ash, beech, Douglas fir, larch, Norway spruce, oak, poplar Stage of damage 1 3 2 4 1 3 2 4 0.83 0.73 0.65 0.78 0.62 0.52 0.44 0.56 0.79 0.71 0.62 0.76 0.62 0.53 0.39 0.59 10-fold cross-validated 9/13

13 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Overall accuracies of prediction (independent reference data) 81% 50% WorldView-2 versus TerraSAR-X: tree species WorldView-2TerraSAR-X 10/13

14 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 WorldView-2 versus TerraSAR-X: damaged ash WorldView-2TerraSAR-X 11/13

15 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 WorldView-2 versus TerraSAR-X: damaged Scots pine WorldView-2TerraSAR-X Overall accuracies of prediction (independent reference data) 72% 35% 12/13

16 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Conclusions & outlook Multispectral WorldView-2 have a high potential for classifying: - tree species (7) - 4 different stages of damaged ash and Scots pine TerraSAR-X data less (not) appropriate (exceptions) Calculation of indices significantly (p = 0.01) improved classification accuracies Reasonable amount of effort regarding data acquisition and pre-processing Testing aerial images (lower costs, more flexible) 13/13

17 Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 13/13 Thank you!

Download ppt "Evaluating the potential of satellite data to classify tree species and multiple stages of tree damages RU-Science Day 4 June 2013 Evaluating the potential."

Similar presentations

Road-Sign Detection and Recognition Based on Support Vector Machines Saturnino, Sergio et al. Yunjia Man ECG 782 Dr. Brendan.

Road-Sign Detection and Recognition Based on Support Vector Machines Saturnino, Sergio et al. Yunjia Man ECG 782 Dr. Brendan.
U.S. Department of the Interior U.S. Geological Survey USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests GLC2000-JRC.

U.S. Department of the Interior U.S. Geological Survey USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests GLC2000-JRC.
Estimating Anthropogenic Influence in Tropical Forests Using Charcoal Introduction Jessica Del Greco Advisors: Crystal H. McMichael, Earth System Research.

Estimating Anthropogenic Influence in Tropical Forests Using Charcoal Introduction Jessica Del Greco Advisors: Crystal H. McMichael, Earth System Research.
Data preprocessing before classification In Kennedy et al.: “Solving data mining problems”

Data preprocessing before classification In Kennedy et al.: “Solving data mining problems”
Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.

Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.
Application Of Remote Sensing & GIS for Effective Agricultural Management By Dr Jibanananda Roy Consultant, SkyMap Global.

Application Of Remote Sensing & GIS for Effective Agricultural Management By Dr Jibanananda Roy Consultant, SkyMap Global.
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.

Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
Remote Sensing Forest Fires: Before and After Rob Gaboy & Aimee Treutlein.

Remote Sensing Forest Fires: Before and After Rob Gaboy & Aimee Treutlein.
Detecting Emerald Ash Borer Infestation with Hyperspectral data using Spectral Mixture Analysis Silvia Petrova Objective The objective of this project.

Detecting Emerald Ash Borer Infestation with Hyperspectral data using Spectral Mixture Analysis Silvia Petrova Objective The objective of this project.
SEReGAP Land Cover Mapping Summary and Results Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada:

SEReGAP Land Cover Mapping Summary and Results Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada:
Remote Sensing of Kelp Dynamics NASA IDS Meeting 6/4/2007.

Remote Sensing of Kelp Dynamics NASA IDS Meeting 6/4/2007.
The use of Remote Sensing in Land Cover Mapping and Change Detection in Somalia Simon Mumuli Oduori, Ronald Vargas Rojas, Ambrose Oroda and Christian Omuto.

The use of Remote Sensing in Land Cover Mapping and Change Detection in Somalia Simon Mumuli Oduori, Ronald Vargas Rojas, Ambrose Oroda and Christian Omuto.
DR. JOHANNES HEINZEL (Dipl.-Geogr.) University of Freiburg, Department of Remote Sensing and Landscape Information Systems, 79106 Freiburg, Germany Use.

DR. JOHANNES HEINZEL (Dipl.-Geogr.) University of Freiburg, Department of Remote Sensing and Landscape Information Systems, Freiburg, Germany Use.
3 ème Journée Doctorale G&E, Bordeaux, Mars 2015 Wei FENG Geo-Resources and Environment Lab, Bordeaux INP (Bordeaux Institute of Technology), France Supervisor:

3 ème Journée Doctorale G&E, Bordeaux, Mars 2015 Wei FENG Geo-Resources and Environment Lab, Bordeaux INP (Bordeaux Institute of Technology), France Supervisor:
The Effects of Beech Bark Disease on the Health of American Beech (Fagus grandifolia) Trees in the College Woods Natural Area, Durham, NH Kevin McDermott,

The Effects of Beech Bark Disease on the Health of American Beech (Fagus grandifolia) Trees in the College Woods Natural Area, Durham, NH Kevin McDermott,
Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.
IGARSS 2011 Classification of Typhoon-Destroyed Forests Based on Tree Height Change Detection Using InSAR Technology Haipeng Wang 1, Kazuo Ouchi 2, and.

IGARSS 2011 Classification of Typhoon-Destroyed Forests Based on Tree Height Change Detection Using InSAR Technology Haipeng Wang 1, Kazuo Ouchi 2, and.
Accuracy Assessment. 2 Because it is not practical to test every pixel in the classification image, a representative sample of reference points in the.

Accuracy Assessment. 2 Because it is not practical to test every pixel in the classification image, a representative sample of reference points in the.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.

Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
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.

Similar presentations

Ads by Google