Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications

Great new tutorial! If you work with ArcGIS and LiDAR datasets, you must download this document!

http://www.esri.com/library/whitepapers/pdfs/lidar-analysis-forestry.pdf

Mapping Ancient Civilization - New York Times

"For a quarter of a century, two archaeologists and their team slogged through wild tropical vegetation to investigate and map the remains of one of the largest Maya cities, in Central America. Slow, sweaty hacking with machetes seemed to be the only way to discover the breadth of an ancient urban landscape now hidden beneath a dense forest canopy...."



Source: http://www.nytimes.com/2010/05/11/science/11maya.html

Interesting article in NYTimes!

Spatial analysis of the Bronze Age sites of the region of Paphos in southwest Cyprus with the use of Geographical Information Systems - CAA

Surface analysis is also a very popular research area in GIS/Archaeology. I want to write more about Least-Cost Analysis in GIS later.

This work was mede by:
University of Cyprus, Archaeological Research Unit and Laboratory of Geophysical – Satellite Remote Sensing and Archaeo-environment, Institute for Mediterranean Studies, Foundation for Research and Technology




Part of Introcuction:

"The paper aims to study the Bronze Age site
distribution in the region of Paphos in SW Cyprus in
order to interpret site patterns. In the context of the
Paphos pilot project, entitled, “A long-term response
to the need to make modern development and the
preservation of the archaeo-cultural record mutually
compatible operations” (IACOVOU et al. 2009),
spatial analysis was performed, with the use of
Geographic Information Systems (GIS), on sites of
the Early, Middle and Late Cypriot period (3rd and
2nd millennium BC) that have been reported from
the region to this date...."

Dynamic Models to Reconstruct Ancient Landscapes - CAA

"In this paper a method of landscape analysis is
demonstrated through raster-based digital elevation
models (DEM) using the case-study of the Helike
Delta, Gulf of Corinth, Greece. In the Classical
Period, Helike was the seat of the Achaean League
and the worship centre of the god Helikonian
Poseidon. With the focus on the earthquake and
tsunami of 373BC, DEMs are generated using
dynamic models of sea level rise, tectonic and pulse
tectonic uplift, subsidence, and sediment deposition.
Starting with a DEM from the present day
landscape, simulated DEM models are generated for
the Early Helladic (2500-2100BC), Classical (480-
323BC), Hellenistic (323-1st Century BC), and
Roman (1st Century BC – 4th Century AD) periods...."

This presentation was the first which I saw. Landscape reconstruction is a very popular research direction. We try to create new digital terrain models with surface analysis. After this step, it is possible to visualize the environment changes.

That was a great presentation from Mariza Kormann and Gary Lock, University of Oxford.

Point Clouds from Imagery

"Get ready to generate high-resolution terrain information from your stereo imagery like never before. LPS eATE is a new technology for automatic terrain extraction, providing an unparalleled environment for processing terrain data. " -source: ERDAS


link: http://www.erdas.com/Resources/Webinars/UpcomingWebinars/tabid/91/currentid/3381/objectid/3381/default.aspx

Mapping Riparian Vegetation with Lidar Data

Combining GIS and lidar data enabled predictive mapping for riparian areas for a portion of the Sierra Nevada mountain range. Riparian areas pose many problems for vegetation modeling because of their narrow width, dendritic pattern, and the sensitivity of plant species to subtle changes in topography that cannot be easily recorded by coarse-scale digital elevation models (DEMs). Vegetation mapping and monitoring in riparian areas have relied heavily on field-based surveys that record the distribution of plant communities along transects perpendicular to the river. Typically, these studies also collect ancillary variables, such as stage elevation or height above the river (HAR), soil texture, and soil moisture, that are used to predict the distribution of vegetation types. However, these methods are extremely time consuming and do not allow for the development of predictive maps because the data collected cannot easily be extrapolated to a larger region. GIS and lidar data provide an opportunity to derive variables, such as HAR, for large areas, making wall-to-wall predictive mapping a possibility.“

Interesting paper, authors: By Thomas E. Dilts, Jian Yang, and Peter J. Weisberg, University of Nevada, Reno

ArcGIS Spatial Analyst extension was used, good example to create a model with ArcGIS from LiDAR dataset.

Link: http://www.esri.com/news/arcuser/0110/files/mapping-with-lidar.pdf

Airborne and Terrestrial Laser Scanning and geomorphology: possibilities, problems, and solutions

New Congress in Vienna:

European Geosciences Union
General Assembly 2010
Vienna, Austria, 02 – 07 May 2010

On this general assembly: "Airborne and Terrestrial Laser Scanning and Geomorphology" The session number is GM 2.2

"....Terrestrial Laser Scanning (TLS) is also increasingly applied for fast data capture of the surface, e.g., in detection and monitoring of mass movements and in other geomorphic studies requiring high accuracy and frequent repetition.
The application of both laser scanning technique results in data sets characterised by enormous data sizes, extremely high accuracy (up to cm-scale) and very high resolution. These properties compensate for the efforts invested in the data processing, however it means new challenges for the geomorphic evaluation. The wealth of laser scanning-derived DTMs can be used for geomorphic analyses in various forms (point cloud, TIN, grid) for analysis in flood-endangered regions, for natural hazard analyses like mass movements and are almost unbeatable in surface modelling of mountainous and karstic areas. They are also highly applicable in environmental change studies concerning the change in snow and ice coverage, soil creep, etc....."

Link:

http://meetingorganizer.copernicus.org/EGU2010/session/3080

AN INTEGRATED WORKFLOW FOR LIDAR / OPTICAL DATA MAPPING FOR SECURITY APPLICATIONS

SECURITY APPLICATIONS:

"ABSTRACT:

This paper elucidates the potential of LiDAR data for information generation for security applications. The study is embedded in the EU Network of Excellence GMOSS. General, security applications cover a large area from infrastructure monitoring (e.g. power stations, pipelines) or border monitoring to less tangible threats like terrorism and civil security / homeland security. It is demonstrated that for those security applications where the birds eye view can generally provide useful information LiDAR data are increasingly a valuable source of information, either stand alone or – preferable – in combination with optical data. The empirical work focuses on the extraction of some buildings and power lines. It is demonstrated that aggregated grid data in form of a DTM and DSM are only partially suitable to extract linear and point-type features such as power lines or small power transformation stations. Detectability clearly depends on the spatial resolution but generally 3D point clouds from first and last pulse information allow more sophisticated object extraction methods."

Full paper:

http://earth.definiens.com/sites/default/files/319_139_full.pdf

Global Mapper and Basic Visualization of GEON LiDAR Workflow Products

I like Global Mapper, cheap GIS program. You can create orthophotos in this program. The fine visualization of LIDAR datasets is possible, as well. Here is a link, a tutorial, how does it work…..

http://cws.unavco.org:8080/cws/learn/uscs/2008/2008Lidar/handouts/Global_Mapper_and_GLW.pdf

Building of robust multi-scale representations of LiDAR-based digital terrain model based on scale-space theory

DTMs are the most important products of airborne laser scanning systems. I search frequently the new methods….here is an example (title...) from Tarig A. Ali, very interesting research! More in "Optics and Lasers in Engineering" - 03. 2010

Until march....

And what is scale-space theory?

http://www.cs.jhu.edu/~misha/Fall07/Papers/intro-to-scalespace.pdf

Full-waveform ALS workshop - Exercise C - Classification and filtering of full-waveform ALS data

Third exercise. Last step of this workflow: Classification and filtering. Here we learned some interesting things about OPALS and SCOP++ software's. First time, when I used SCOP++, but I think, it is an useful software to create DTM's....

Classification and filtering of full-waveform ALS data from Gottfried Mandlburger.

"Program:

Perform the following tasks with one of the prepared datasets:

1) Digital Surface Model (DSM) using OPALS
a) Import first echoes of FWF dataset (project_first_echo.xyz ) into the OPALS Data Manager (opalsImport)

b) Calculate a Digital Surface Model (opalsGrid) and quality models (sigma, excentricity)

c) Visualize the resulting DSM as hill shading (opalsShade) and color coded raster map (opalsZColor)

d) Visualize the sigma and excentricity model (opalsZColor) and compare and interpret the results visually with respect to the hill shading

2) Analysis of Full Waveform (FWF) Attributes:
a) Import last echoes of FWF dataset (project_last_echo.xyz ) into the OPALS Data Manager (opalsImport)

b) Perform raster analysis of FWF attributes (opalsCell):
• Amplitude
• Echo width

c) Derive color coded visualizations of the attribute/echo width raster (opalsZColor)

d) Compare and interpret the results visually with respect to the hill shading

3) Perform a standard DTM filtering / classification of the point cloud
a) Derive a Digital Terrain Model based on the last echoes (project_last_echo.xyz) using SCOP++ (Robust Interpolation, strategy :Lidar DTM Default)

b) Visualize the DTM (hill shading, Z-Coding, Isolines …)

4) Filtering of the point cloud with pre-classification on echo width basis
a) Repeat the steps 3a and 3b for the last echoes with small echo widths (ew<1.9ns (project_last_echo_small_ew.xyz)

b) Compare and interpret the results visually with respect to the results of Step 3 by means of the hill shading

5) Extra task: Difference model
a) Derive a normalized Surface Model: nDSM = DSM-DTM (opalsDiff)

b) Visualize the nDSM (opalsZColor)"

Full-waveform ALS workshop - Experience with operational FWF ALS - Projects in Archaeology

Ninth Presentation

Michael Doneus: Experience with operational FWF ALS - Projects in Archaeology

CONTENTS:

- Archaeological background –Aerial Archaeology
- ALS andVegetation
- Technical Issues
o Types of Scanning Systems
o Filtering
o Georeferencing
o Acquisition -Time Frame
- Archaeological Issues
o Interpretation
o ComparisonwithTerrestrial Survey
o ALS andArchaeological Prospection
- ArchaeologicalApplications

CONCLUSION:

Only if we understand the technology, issues and limitations coming with data collection, filtering and interpretation we will be able to successfully apply ALS within various disciplines.

Full-waveform ALS workshop - Breaklines and DTM filtering with FWF ALS

Eighth Presentation

Christian Briese: Breaklines and DTM filtering with FWF ALS


Overview:

• Motivation
• Structure line extraction
- Automated structure line modelling
- Automatic start segments
• DTM generation
- Standard methods
- Improvement by full-waveform (FWF) ALS
• FWF attributes
• FWF data management
• Improved DTM generation based on an echo width threshold
• Extended robust interpolation by individual a priori weights determined from FWF
attributes





Summary:
Improved DTM determination with FWF ALS

• Additional Information available by FWF ALS data
Per echo: distance, amplitude, echo width, cross-section
• Interesting results in order to detect last echoes reflected by low vegetation
• Further studies are still necessary:
- Analysis of the influence of the footprint size, the incidence angle, …
- Analysis of the accuracy and reliability of the FWF attributes (distance, echo
width, amplitude, …)
- Comparison of different sensors
• Extension of the Filtering and Classification Methods
- additionally to the typically purely geometric criteria – additional FWF echo
attributes are available and should be integrated into the classification process
- however, a large area based detailed analysis of the advantages of the FWF
attributes is necessary in the future

Full-waveform ALS workshop - OPALS

Third Presentation

Gottfried Mandlburger, Johannes Otepka, Wilfried Karel: OPALS Software – Orientation and Processing of Airborne Laser Scanning data

new software concept, very great research!!!


Software Concept:

• Modular structure composed of small, well defined units (modules)
• Availability of modules as:
- Command line programs
- Python modules
- C++ classes via DLL linkage
• Individual process control via scripts
- Shell scripts (Unix/Linux), Batch (MS Win2000/XP/Vista)
- Python
• Management of point cloud data based on the OPALS Data Manager (ODM)
• Interfaces for efficient data interchange with DTM-, CAD-,GIS- and
Visualisation/Modelling-Software
- SCOP++, …
- AutoCAD, MicroStation, …
- ArcGIS, Grass-GIS, Quantum-GIS, …
- 3D-Studio, GeoMagic, Deep Exploration, SMS (Surface Modelling System)
• Use of standard and open-source libraries (boost, GDAL/OGR, CGAL, ...)
• Abdication of interactivity

Full-waveform ALS workshop - Principle of Full- and Online Waveform Analysis in Airborne, Mobile and Terrestrial Laser Scanning

Second presentation was from Riegl Company. I already wrote about Riegl VZ-400 scanner, here we heard some Online Waveform Analysis applications....

Dr. Andreas Ullrich, DI Peter Rieger: Principle of Full- and Online Waveform Analysis in Airborne, Mobile and Terrestrial Laser Scanning

Overview:

- Introduction
- Laser Scanner RIEGL LMS-Q680 for Full Waveform Analysis
- Full Waveform Analysis and Online Waveform Processing
- Full Waveform Sample Data
- Multiple Time Around Technique
- RIEGL V-Line Laser Scanners with Online Wavefrom Processing
- Calibrated Reflectance
- Multi Target Capability
- Online Waveform Sample Data

Full-waveform ALS workshop - Measurement Principle and Physical Fundamentals

First presentation

Wolfgang Wagner: Measurement Principle and Physical Fundamentals

Overview:

• How does a full-waveform laser scanner work?
• Important properties of laser light
• Range determination and range resolution
• Beam pattern and spatial resolution
• Radar equation
• Cross section of different targets
• Waveform generation
• Waveform analysis → Presentation of Andreas Roncat
• Radiometric calibration → Presentation of Christian Briese



Conclusions:

• Full-waveform laser scanners depict the measurement process in its
entire complexity
- Full-waveform system are compatible with ranging systems, but not vice-versa
• Advantages
- Algorithms can be adjusted to tasks
- More echoes as in first/last pulse systems
- Intermediate results are respected
- Neighbourhood relations can be taken into account
• Calibration of the data, i.e. conversion to cross section, is essential for
physical modelling efforts
• The additional data, i.e. amplitude, width, cross section, is valuable for
segmentation and classification purposes
- Classifying terrain and non-terrain points for DTM filtering

Full-waveform ALS workshop successfully completed

Hallo everybody,

Thank you, workshop was great, good organized. DTM generation is a big topic!!! I learned some new information about full-waveform....we see here a picture from Vienna...

Digital Terrain Models from Full Waveform Laser Scanning Workshop

TerraFormatics will post some information about this workshop!


organizers:

Univ.Prof. Wolfgang Wagner
Univ.Prof. Norbert Pfeifer

Institute of Photogrammetry and Remote Sensing
Christian Doppler Laboratory for Spatial Data from Laser Scanning and Remote Sensing
Vienna University of Technology
___________________________________________________________________________
Date: November 12 - 13, 2009
Venue: Vienna University of Technology-Gusshausstrasse 27-29-1040 Wien-Austria
___________________________________________________________________________



Motivation
Airborne laser scanning is a technology which has developed rapidly in the last few years. It has set new standards, especially for the generation of digital terrain models (DTM). A great advantage of laser scanning is the ability to “see” through gaps in the vegetation foliage. The DTM quality strongly depends on the correct classification of the 3D point cloud into terrain and off-terrain echoes. Especially dense and low vegetation is considered as problematic. With full waveform laser scanners, commercially available since 2004, a considerable amount of these problems can be overcome. Additionally, new quality indicators can be derived.

Goals
This workshop will teach the basics of full waveform laser scanning in lectures, exercises, and discussions. It will reach from the sensor specifications to terrain modeling. The following list of topics will be treated.

* Measurement principle and physical foundations
* Sensor properties and measurement process
* Waveform analysis and calibration
* Geo-referencing
* Signature analysis of full waveform parameters
* Classification and segmentation
* Filtering (classification) based on full-waveform information
* DTM derivation
* Quality control

Three practical exercises will be carried out, next to a demonstration of a full waveform sensor. Each participant will follow each exercise in groups – with a maximum of ten persons per group.

Full-Waveform filtering

Next generation scanners are integrated with full-waveform data acquisitions properties. Every single return pulse is documented. We can research the vertical conditions of the forest areas. Tree, bush, grass...different data class.


Interesting paper about full-waveform datasets....

INTEGRATION OF FULL-WAVEFORM INFORMATION INTO
THE AIRBORNE LASER SCANNING DATA FILTERING PROCESS
Y. -C. Lin and J. P. Mills

"ABSTRACT
Terrain classification of current discrete airborne laser scanning data requires filtering algorithms based on the spatial relationship between neighbouring three-dimensional points. However, difficulties commonly occur with low vegetation on steep slopes and when abrupt changes take place in the terrain. This paper reports on the integration of additional information from latest generation full-waveform data into a filtering algorithm in order to achieve improved digital terrain model (DTM) creation. Prior to a filtering procedure, each point was given an attribute based on pulse width information. A novel routine was then used to integrate pulse width information into the progressive densification filter developed by Axelsson. The performance was investigated in two areas that were found to be problematic when applying typical filtering algorithms. The derived DTM was found to be up to 0.7 m more accurate than the conventional filtering approach. Moreover, compared to typical filtering algorithms, dense low vegetation points could be removed more effectively. Overall, it is recommended that integrating waveform information can provide a solution for areas where typical filtering algorithms cannot perform well. Full-waveform systems are relatively cost-effective in terms of providing additional information without the need to fuse data from other sensors.

CONCLUSIONS
This study set out to investigate whether information derived from the latest generation full-waveform, small-footprint airborne laser scanning data could improve digital terrain modelling. A novel routine was designed to integrate waveform information into a commonly used filtering algorithm. The preliminary results have demonstrated that integrating pulse width information can provide a solution for areas where conventional filtering algorithms cannot perform well. On the top of an artificial mound, points rejected (Type I error) by a typical filtering algorithm can be correctly included in the developed routine. More low vegetation can also be correctly removed. However, rough or steep terrains with low vegetation cover, as well as forest terrain, still require further investigation and detailed validation. In addition, although identifying vegetation points becomes easier with the help of waveform information, it may be the case that in some densely vegetated areas insufficient “true” ground points exist for high-resolution DTM generation. In such cases, it might still be better to assume that the lowest point within a specified or adaptive window size is a ground point. The performance of existing filtering algorithms depends on the type of landscape (Sithole and Vosselman, 2004). Such approaches may require that users determine which type of landscape is being processed and then specify optimal parameters. As demonstrated in this paper, by using waveform information it is possible to automatically determine the landscape characteristics and then use the optimal parameter set for that specific landscape type. This will improve the automation of filtering procedures since less effort is required by users. Moreover, compared to ALS intensity values, pulse width information is easier to apply to different surveys since neither prior calibration or normalization procedures are required. Using full-waveform ALS data provides valuable physical and geometric information simultaneously. Such systems are relatively cost-effective in terms of providing multiple-information without the need to fuse data from other sensors."