GPS devices to monitor snowfall

A strange feeling is getting stronger and stronger in me: we are able to do anything, we already have the devices, it is only a question of creativity, manpower (engineers power of course), and time. We are just able to do anything.
Way back I read some news about earthquake detecting hard disks, and now this:
http://sciencenow.sciencemag.org/cgi/content/full/2009/929/2

Geography and LiDAR II.

Interesting paper from Australia....
mapping of rivers from LiDAR datasets is very popular too....flood risk management.....

OPERATIONAL MAPPING OF THE ENVIRONMENTAL CONDITION OF RIPARIAN
ZONES OVER LARGE REGIONS FROM AIRBORNE LIDAR DATA
K. Johansen, L. Arroyo and S. Phinn

"ABSTRACT:
Riparian zones maintain water quality, support multiple geomorphic processes, contain significant biodiversity and also maintain the aesthetics of the landscape. Australian state and national government agencies responsible for managing riparian zones are planning missions for acquiring remotely sensed data covering the main streams in Victoria, New South Wales, and parts of Queensland and South Australia. The objectives of this paper are to: (1) assess the ability of LiDAR data for mapping the environmental condition of riparian zones; and (2) provide specifications for capturing and analyzing the LiDAR data for riparian zone mapping at large spatial extents (> 1000 km of stream length). LiDAR derived digital elevation models, terrain slope, intensity, fractional cover counts and canopy height models were used for mapping riparian condition indicators using simple algorithms and more complex objectoriented image analysis. The results showed that LiDAR data can be used to accurately map: water bodies (producer’s accuracy = 93%); streambed width (Root Mean Square Error (RMSE) = 3.3 m); bank-full width (RMSE = 6.1 m); riparian zone width (RMSE = 7.0 m); width of vegetation (RMSE = 5.6 m); plant projective cover (RMSE = 12%); vegetation height classes (vertical accuracy < r2 =" 0.40)."> 100,000 km of stream length."

LiDAR intensity correction

The second LiDAR intensity paper ..... here we find new information about topographic and distance effects.

Next time I write about photogrammetric and LiDAR integration opportunities. Very interesting research area!


TOPOGRAPHIC AND DISTANCE EFFECTS IN LASER SCANNER INTENSITY
CORRECTION
S. Kaasalainen, A. Vain, A. Krooks, A. Kukko

"ABSTRACT:
The effect of incidence angle on the intensity of laser backscatter has been studied in photonics and optics, but the applications of these results to remote sensing of natural land targets are limited, as well as the availability of experimental validation data for airborne and terrestrial laser scanning, where the incidence angle correction using the Lambertian scattering law is common. We have investigated the role of topographic (incidence angle) and distance effects in the radiometric calibration of monostatic terrestrial and airborne laser scanner data. Our results show that the Lambertian (cosine) correction is practically valid at incidence angles up to 20º, whereas at greater angles of incidence, the accuracy of data is still very limited to estimate the performance of any correction method. We also discuss the mixed effects of distance and target reflectance on terrestrial laser scanner intensity calibration, for which the number of applications is constantly increasing. As there are differences in the intensity detectors of different instruments, it is important that the effects of distance and target reflectance are well defined before using any terrestrial laser scanner in intensity
measurement.


SUMMARY

At the current levels of accuracy of ALS intensity data, the cosine correction of the incidence angle effect works reasonably well for most surfaces, even in the cases that the surface could not be approximated to be Lambertian. Together with earlier results (Kukko et al., 2008), it can be concluded that for most targets the incidence angle effect is practically within the error limits of the data at incidence angles up to about 20°. The computational cosine effect is about 6% in this range, which is well in the error limits of the intensity data produced by current ALS instruments (this is clearly seen in Fig. 5 and Kaasalainen et al., 2009a). It must also be taken into account that some surfaces may present a specular reflection at 0° (normal incidence), which may cause a peak in the intensity at this angle. More data with better accuracy than that provided by the current airborne scanners would be needed in cases where the incidence angle is greater than 20°, to be able to distinguish any difference between surfaces for which the Lambertian approximation does or does not work adequately. The intensity and distance effects in TLS are mixed, at least for some scanners. A further study on distance effects in TLS is in progress, with a comparison with data from other instruments (Kaasalainen et al., 2009b). This is particularly important in the applications where TLS are used in mobile (vehicle based) mapping systems and other (stationary) applications, where the distance of the target varies in a large scale. It is important that the detector effects are well known (to provide a correction scheme) before using any terrestrial laser scanner in intensity measurement."

Normalization of LIDAR intensity

There are a lot of research today about laser intensity, because we find a huge potential at Lidar intensity datasets. These information could help by different processes, like segmentation, classification or visualization. All of lidar applicatnions based on x,y,z parameters, and I think, in the future, we will use this extra information (i=intensity), which could mean 4th dimension of lidar.

Normalization of intensity is a very useful direction, here is a new paper:

NORMALIZATION OF LIDAR INTENSITY DATA BASED ON
RANGE AND SURFACE INCIDENCE ANGLE
B. Jutzi, H. Gross

" ABSTRACT:
The analysis of airborne laser scanner data to extract surface features is of great interest in photogrammetric research. Especially for applications based on airborne measurements, where the intensity is crucial (e.g. for segmentation, classification or visualization purposes), a normalization considering the beam divergence, the incidence angle and the atmospheric attenuation is required. Our investigations show that the same material of a surface (e.g. gabled roof) yields to different measured values for the intensity. These values are strongly correlated to the incidence angle of the laser beam on the surface. Therefore the intensity value is improved with the incidence angle derived by the sensor and object position as well as its surface orientation. The surface orientation is estimated by the eigenvectors of the covariance matrix including all object points inside a close environment. Further the atmospheric attenuation is estimated. The adaptation of vegetation areas is disregarded in this study. After these improvements the intensity does no longer depend on the incidence angle but may be influenced by the material of the object surface only. For surface modelling the Phong model is introduced, considering diffuse and specular backscattering characteristics of the surface. A measurement campaign was carried out to investigate the influences of the incidence angle on the measured intensity. By considering the incidence angle and the distance between sensor and object the laser data captured from different flight paths (data stripes) can be successfully fused. In our experiments it could be shown that the radiometric normalization of the intensity for the investigated areas are improved.


DISCUSSION AND CONCLUSION
For assessing the normalized intensity values nearly
homogenous regions have been selected interactively. The
variation parameter is selected as measure for the comparison of
the values before and after normalization. Mean and standard
deviation of this measure over all regions decreases by the
normalization, especially if all flights are included. For pulsed
laser systems a strong intensity variation could be observed. The
intensity inside a region shows a high variance even for a
constant incidence angle. This may caused by material features
or local surface effects. For nearly all regions the results for the intensity have been improved, even with region disturbances on the roofs like chimneys. The Lambertian model fits the investigated surfaces well. For specular reflectance based on the Phong model no significant improvements could be derived. This might depend on the diffuse backscattering characteristic of the material. Further investigations for this study were not possible because only one data set with surfaces of a single material with different orientations was available. For terrestrial laser data enhanced results can be expected, with a lower variance of the intensity, due to a better signal-to-noise ratio for the measured data. This paper proposes a general approach for intensity normalization considering diffuse and specular scattering characteristics of the surface. This assumption should be proved in future by investigating reference targets where the
backscattering characteristic is known or could be measured by reference measurements."

NETWORK SNAKES and LiDAR

I think, there is a lot of potential in laser intensity information. Now we use at the most applications the X,Y,Z parameters. We must collect more experiences about LiDAR intensity, because this extra information is very dependent from different properties (material, topography, laser footprint, flight direction....more about these later).

Very unique paper about the feature detection with help of intensity information:


ADAPTATION OF ROADS TO ALS DATA BY MEANS OF NETWORK SNAKES
J. Goepfert, F. Rottensteiner

"ABSTRACT:

In the Authoritative Topographic Cartographic Information System (ATKIS®), which is the main public topographic data base inGermany, the heights and the 2D positions of objects such as roads are stored separately in the digital terrain model (DTM) anddigital landscape model (DLM). However, for many applications a combined visualization and processing of these two data sets is useful, leading to the demand for a 3D representation of the objects. For this purpose an integration of the height and position is essential. However, discrepancies exist between the DLM and the DTM due to different kinds of acquisition, processing, and modeling. This inconsistency has to be solved within the integration algorithm. Airborne Laserscanning (ALS) is used to acquire the height information for the DTM. Therefore, intensity values of the ALS echoes, which contain the reflectance properties of the lluminated objects, as well as object heights can be exploited in addition to the terrain height. However, the ALS data contain the information related to the DLM-objects only implicitly. Usually, features, for example edges or distinctive points, have to be extracted which can be connected to DLM-objects to realize the integration process. In this paper the matching of the data sets is realized by network snakes which are able to use the implicit ALS information about the position of the DLM-objects without the feature extraction step. The initialization of the contour with the vector data enables the use of ALS data as image energy for an iterative optimization process. Examples are given which apply the concept of network-snakes for the adaptation of a road network to ALS data taking advantage of the prior known topology.

CONCLUSION:

In this paper an active contour approach is applied to the
adaptation of a road network to ALS data. Even with a simple
definition of the image energy by combining ALS height and
intensity information promising results could be achieved.
Based on the exploitation of the topology of given initialisation
the snake converges to the desired position in comparison to the
ground truth. Contour parts connected by nodal points support
each other during the optimisation process.
The introduced algorithm offers many possibilities regarding
the integration of object knowledge for future research. For
example, constraints about the shape of the objects (parallelism
of road edges, slope constraints) or relations to other ATKIS
objects can be incorporated in the initialisation as well as in
additional energy terms. Furthermore, a more sophisticated
formulation of suitable image energies concerning different
vector objects can improve the applicability of the method.
Additionally, the algorithm can be extended to other DLM
objects having a relation to features in ALS data such as rivers.
Subsequently, all the adapted objects provide a dense network
of shift vectors which can be used in addition to prior accuracy
knowledge in order to improve the consistency of the DLM and
ALS data."

Image energy from a combination of intensity and height values (green: ground truth; blue: initialisation; red: final position)

ISPRS Workshop Laserscanning 2009 I.

Hallo everybody,

Wednesday finished the ISPRS Laserscanning 2009 in Paris.

Thank you, congress was very professional!

160 people were from 25 countries at the workshop. 31 presenters, extra 4 company presentations and 27 posters were in the program.

31 presenters were from 13 countries. Iran and Greece cancelled.

(Country, number of oral presentation)

Austria	3
Canada	3
Finland	4
France	4
Germany	6
Greece	1
Iran	1
Italy	2
Japan	1
Netherlands	3
Swiss	1
Spain	1
UK	1

Two areas (forestry, urban) are very popular in the LiDAR researches, where the newest papers were presented on the week.

I think, the most interesting topics were:

Automatic classification and feature extraction

TLS calibration

Photogrammetry and LiDAR combination

Analysis and visualisation with Voxel

Usage of LiDAR Intensity information

I want to write more details on the next days about these topics and the papers.