HYLIGHT: Integration of airborne hyperspectral imagery and laser scanning data to improve image processing and interpretation

Transcription

1 EUFAR2 - EUropean Facility for Airborne Research HYLIGHT: Integration of airborne hyperspectral imagery and laser scanning data to improve image processing and interpretation Ils Reusen VITO ils.reusen@vito.be and the HYLIGHT consortium JRA1 HYLIGHT EUFAR2 1st General Assembly meeting , BELSPO, Brussels

2 Outline Consortium Rationale Objectives Methodology Work description Common data sets Deliverables Implementation plan Budget Impact

3 Consortium VITO (BE) DLR (DE) UZH (CH) INTA (ES) PML (UK) CVGZ (CZ) ONERA (FR) TAU (IL) TU-Vienna (AT)

4 Rationale HSI state of the art sensors are available in EUFAR TA (APEX, AHS, Eagle-Hawk, ) More ALS and state of the art Full-Waveform (FW) ALS sensors will become operational the next years in Europe (Czech Globe, NERC ARSF, ) Mutual benefit of integrated use of HSI and (FW) ALS HSI improving ALS products ALS improving HSI products HSI-ALS fusion

5 Objectives To develop, test and validate improved HSI processing using ALS. To develop, test and validate improved ALS processing using HSI. To make HYLIGHT (Integration of airborne hyperspectral imagery and laser scanning data to improve image processing and interpretation) tools freely available worldwide.

6 Methodology HyperSpectral Imaging (HSI) Hundreds of spectral bands Airborne Laser Scanning (ALS) Discrete ALS vs Full-waveform ALS Integrated use of HSI and ALS

7 HSI measurement principle HSI=HyperSpectral Imaging HSI=passive remote sensing HSI=is the acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an image it is possible to derive a complete reflectance spectrum (A. Goetz)

8 Concept of passive remote sensing [ µm]

9 Hyperspectral Imaging

10 HSI advantages Large number of spectral bands Spatial resolution (order of 0,5-2 m depending of the flying height) Full reflectance spectrum for each pixel (VNIR- SWIR) for characterization of objects at the Earth surface Applications ranging from biodiversity mapping, forestry, urban, snow/ice, water quality, soil mapping,.. BELSPO HABISTAT project: habitat quality map (27 classes)

11 ALS measurement principle ALS=Airborne Laser Scanning ALS=active remote sensing ALS=along and across track LiDAR (Light Detection And Ranging) ALS=study of an object by emitting a certain amount of laser energy and by the analysis of the backscattered energy (range, amplitude, etc.)

12 Discrete Echo vs. Full-Wave-Form LIDAR Echo waveform Discrete echos Laser foot print: ~ 0.2 3m Acquisition of the all echos using a sampling interval of ~1 ns FWF ALS

13 Discrete Echo vs. Full-Wave-Form LIDAR

14 DTM derivation from ALS point clouds ALS point cloud (combined from all ALS flight strips) Selection of Last Echo points Filtering /classification (separation of terrain and off-terrain points; e.g. using robust filtering) Computation of the DTM using all points classified as terrain

15 ALS Data - DTM derivation: Example Filtering/Classification DTM

16 ALS advantages Vertical vegetation structure Penetration through small gaps in the canopy Backscatter within and below the crown Backscatter of the terrain Measurement process is based on geometric optics High sampling density (e.g. 5 points/m 2 ), therefore ALS is a good detector for single trees

17 ALS usage Multiple usage of ALS data Topographic models (DTM, DSM), buildings, power lines, parameters for vegetation, Parameters from ALS for forestry Forest area mapping Single tree detection if the ALS point density is high enough(e.g. > 5 points/m² depending on the forest types / structure) Tree height estimation Stand heights, crown cover, forest structure, Stem volume / biomass estimation for large areas

18 HSI-ALS integration Little Rissington (UK) calibration site: LiDAR DEM overlaid with Eagle flightlines PML

19 ALS to improve HSI processing (1/2) Positioning improvement by using more accurate DSM/DTM deduced from ALS FW ALS allows to better distinguish low vegetation from the terrain which leads to better DTM (Doneus and Briese, 2006; Wagner et al., 2008) Visibility estimation (input for the atmospheric correction) from the image using structure information deduced from ALS (Example 1) Filter unwanted pixels (shadow, low S/N, ) from ALS structure data (i.e. form) before studying the reflectance (i.e. functioning) of forests (Niemann et al., 2009) (Example 2) ALS structure information to determine structure class specific kernels to improve the BRDF correction (Example 3) Currently kernels for BRDF correction are defined for broad land/use land cover classes

20 ALS to improve HSI processing (2/2) Slope and vertical vegetation structure/height deduced from ALS can be used as additional layer to distinguish spectrally similar pixels and lead to improved vegetation species maps (Example 4) Analysis of forest areas by advanced remote sensing systems based on HS and LiDAR data (Dalponte, PhD 2010): The elevation channel of the first LiDAR return data plays most important role for increasing the discriminability of the forest classes DSM can improve the reflectance retrieval even in the shadow (Example 5)

21 Example 1. Visibility estimation from the image Account for vegetation structure when deriving visibility from the image: make DDV mask based on R and NIR thresholds; apply an atmospheric correction using several visibility values; interpolate VIS for R/NIR = 0.01 (Richter et al., 2006) Left: Dense Dark Vegetation (DDV) mask Right: Hyperspectral image Structure influences retrieval of correct visibility values!! Optimizing the visibility estimation by taking into account forest structure this allows for a more precise atmospheric correction.

22 Example 2: Filter Unwanted (shadow, low S/N) pixels ndsm (DSM-DTM) Orthophoto ndsm

23 Example 3. View and illumination angle effects seen by a HS image BRDF: reflectance of a target as a function of illumination and viewing geometry; Source: depends on wavelength and structural and optical properties of the target. Left: back scattering (sun behind observer) Right: forward scattering (sun opposite observer) Optimizing the kernel based BRDF correction by taking into account forest structure: needed for a more accurate classification (e.g. pine, deciduous, mixed forest); kernel correction coefficients depend on forest structure (e.g. via look-up table).

24 ALS IS ALS+IS Example 4: Vegetation classification based on hyperspectral and height information HSI and ALS can be used for joint classification: Increase the separability between different types of vegetation (based on height) Frischknecht, C.; Kneubühler, M. & Morsdorf, F. Brandgutdifferenzierung in einem Wildland- Urban-Interface mit Hilfe von Laser Scanning und Bildspektrometrie Dreiländertagung DGPF, SGPF und ÖVG, Wien, , 2010

25 PELICAN Images: 8 bands Example 5: The use of the DSM to significantly improve the classification Reference classification 32, 0 0,0 54% Classification with Flat surface assumptions Classification taking into account the DSM 78% ICARE: A physically-based model to correct atmospheric and geometric effects from high spatial and spectral remote sensing images over 3D urban areas", S. Lachérade, C. Miesch, D. Boldo (IGN), X. Briottet, C. Valorge (CNES), H. Le Men (IGN), Volume 102, Numbers 3-4 / December, 2008, Special Issue on CAPITOUL Experiment (Special Editors: L. Gimeno, V. Masson and A. J. Arnfield), Meteorology and Atmospheric Physics Publisher Springer Wien, pp

26 HSI to improve ALS Different cover types deduced from HSI can be used to improve ALS classification and thus DTM extraction Different cover types deduced from HSI can be used for improvement or validation of ALS derived products such as canopy cover, subcanopy topography and/or vertical distribution of canopy material

27 ALS-HSI fusion Fusion of HSI and ALS data can occur using several approaches (Jones, 2010): Decision-level: datasets are processed independently, with end results integrated in a GIS Feature-level: characteristics are identified and represented through combining information from multiple datasets Pixel-level: datasets are directly fused and processed simultaneously for end results Most fusion studies have occurred at the decision and/or feature-level, with few explicitly involving pixel-level fusion.

28 Work description Task 1: ATBD and DPM (ONERA) Task 2: Common data sets (CVGZ) Task 3: Prototyping, testing and development of HYLIGHT tools for the EUFAR toolbox (VITO) Task 4: HYLIGHT contribution to the EUFAR handbook (VITO) Task 5: Technical and scientific coordination and activity reporting (VITO)

29 Common data sets UK: New Forest AISA Eagle and Hawk-LiDAR data set (NERC ARSF-PML) AISA Eagle (hyperspectral VNIR) LiDAR (intensity) LiDAR DEM overlaid on SRTM DEM

30 Common data sets Czech Republic: Bily Kriz and Stitna research sites HSI data Aisa Eagle; spatial resolution 0,5m; spectral res. 10nm ALS data discrete Leica ALS60 LIDAR System; 5 points/m 2 In-situ tree parameters: Height, DBH (Diameter Brest Height), LAI (Leaf Area Index), Map of individual trees

31 Common data sets B E L G I U M F L A N D E R S W i j n e n d a l e b o s # A e l m o e s e n e i e b o s # K e r s s e l a e r s p l e y n # W A L L O N I A N W E S K i l o m e t e r s

32 Common data sets B E L G I U M F L A N D E R S W i j n e n d a l e b o s # A e l m o e s e n e i e b o s # K e r s s e l a e r s p l e y n # W A L L O N I A N W E S K i l o m e t e r s

33 Common data sets B E L G I U M F L A N D E R S W i j n e n d a l e b o s # A e l m o e s e n e i e b o s # K e r s s e l a e r s p l e y n # W A L L O N I A N W E S K i l o m e t e r s

34 Deliverables Del. N Deliverable name WP N Nature Diss level Del date lead D23.1 ATBD+DPM for improved HSI processing using ALS and improved ALS processing using HSI R CO 15 VITO WP23 JRA1 13,75 D23.2 HYLIGHT (HSI-ALS) tools for the EUFAR Toolbox O PU 30 VITO WP23 JRA1 32,00 D23.3 Chapter on "Integrating airborne hyperspectral imagery and (full-waveform) laser scanning data for improved image processing and interpretation" for integration in the EUFAR Handbook WP23 JRA1 11,25 R PU 35 VITO 57,00

35 Implementation plan RP1 = first 18 month period RP2 = second 18 month period RP3 = last 12 month period WP no. Month WP23 Task 1 JRA1 Task 2 HYLIGHT Task 3 Task 4 Task 5 Milestones * * * deliverable * milestone activity duration dependency Milestone Milestone name no. WPs involved Expected date Means of verification 28 1 st JRA1 meeting WP23 JRA1 6 Meeting report 29 2 nd JRA1 meeting WP23 JRA1 15 Meeting report, common datasets, ATBD and DPM available 30 3 rd JRA1 meeting WP23 JRA1 27 Meeting report, prototypes demonstration

36 Budget Participant number (short name) Method applied for Indirect Costs (Overheads) calculation Personnel costs ( ) Durable Equipment costs ( ) Consumables ( ) Travel & Subsistence ( ) Other costs ( ) Total Direct Costs (w ithout subcontracti ng) ( ) Indirect Costs (Overheads) ( ) Subcontracting costs ( ) Total costs ( ) Requested EU funding ( ) VITO Real ,00 0,00 500, ,00 0, , ,00 0, , ,00 DLR Real ,62 0,00 0, ,00 0, , ,74 0, , ,52 UZH Specific flat rate ,00 0,00 0, ,00 0, , ,00 0, , ,00 INTA Simplified ,00 0,00 0, , , , ,25 0, , ,68 PML Real ,00 0,00 0, ,00 0, , ,24 0, , ,18 CVGZ Specific flat rate ,00 0,00 0, ,00 0, , ,20 0, , ,40 ONERA Simplified ,04 0,00 0, ,00 0, , ,81 0, , ,13 TAU Specific flat rate ,00 0,00 0, ,00 0, , ,00 0, , ,00 TU Vienna Specific flat rate ,00 0,00 0, ,00 0, , ,00 0, , ,00 TOTAL ,66 0,00 500, , , , ,24 0, , ,91

37 VITO contribution ATBD+DPM DSM to calculate shadow fraction in hyperspectral image Geometric correction taking into account ALS-derived tree height Classification of tree species taking into account ALSderived tree height Other TBD Common datasets (Wijnendaele bos, Aelmoeseneiebos, Kerselaerspleyn) Tools for EUFAR toolbox Contribution to EUFAR Handbook

38 UZH contribution topic 1: Canopy Structures Pixels of canopies can contain both illuminated and shadowed components. This affects the retrieval of biophysical variables as the mixed pixel s reflectance is wrongly interpreted. Goals/Approach: Exact co-registering of APEX and LIDAR Subpixel retrieval of illuminated and shadowed fractions Spatial convolution with true spatial response function of APEX Correction of pixel value for corrected parameter retrieval

39 UZH contribution topic 2: Illumination and Shadows Atmospheric correction results are often still biased in shadowed areas where standard DTMs are not providing the required information. Goals/Approach: Use LIDAR for shadow correction in higher resolution airborne spectroscopy imagery