The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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As DTM extraction is the most popular and main fundamental 
process in most of the application, so it is always important to 
seek for new accurate and precise methods for DTM production 
from Lidar data. Still there are some errors in almost all of the 
DTM calculation approaches which need to be investigated that 
affect the calculated height the objects above the terrain surface 
in the normalized DSM. Appropriate interpolation method 
should be chosen for surface reconstruction: Gridding or 
Tinning. The surface reconstructed by grid method, passes from 
all the points and resulting surface is smooth. TIN interpolation 
method is unreliable in sparse and missing data cases. Most of 
the current methodologies outlook is applying an isolate 
treatment of the Lidar data but there are some tendencies for the 
utilization of the structure based methodologies sensitive to the 
shape and neighborhood orientation of the locally adjacent 
points around the point of interest like 3D fuzzy-morphology 
algorithms. In forest application only single coniferous trees 
have been extracted that have more regular and simple shape 
for modeling. Better regression models with dynamic 
parameters considering topographic relation of the trees should 
be proposed for tree canopy extraction of different type. As 
objects are vague and represented as cloud point in Lidar range 
data, fuzzy and neural network approaches are well suited for 
this kind of data. For example, for roads, a fuzzy reasoning 
system can be established in a way that if the points’ heights are 
near to the DTM and have gentle slope and same intensity in 
the optical image then pixels are attributed as road. As another 
example, a neural based system could be trained to identify 
different trees shape. Newly there are some efforts on finding a 
direct relation between acquired optical image and the 
corresponding Lidar data to avoid resampling which affects the 
shape of the trees and objects. Future works would concentrate 
on evolving available statistical information and multi-source 
information into the decision-supported classification 
algorithms. The effective use of Lidar intensity data has not 
been established yet and as mentioned before it is limited in 
classification or matching process of Lidar data and optical 
images. As the Lidar intensity data is more representing the 
surface characteristics, it can be used along with optical data for 
better object classification. In more technical sentence, the 
result of the classification of Lidar data and optical data is not 
the same and a well-defined reasoning system is required to do 
an intelligent overlapping process for object extraction. 
Absolute orientation of low cost photogrammetric data using 
the automatically extracted 3D linear features from the Lidar 
intensity data could be another interesting topic which opens a 
new horizon on robust true ortho-photo generation in flood 
mapping or determining coastline border accurately. In 3D 
man-made object’s surface reconstruction especially in building 
extraction, which are built close enough to the water bodies, 
still there are unsolved problems. In bathymetric Lidar, water 
surface and bottom reconstruction are the most important 
concerns and a lot of efforts have been done to achieve higher 
accuracy and better spatial resolution. Still there are some 
uncertainties related to penetration depth of the laser beam, 
GPS/INS systematic errors, atmospheric effect, ground control 
point errors, foot print size, mission planning, processing of 
data and etc. and those need more investigations. Still coastal 
shoreline monitoring is not completely solved. Water body 
dynamic movements are simulated according to the acquired 
Lidar data for the prediction purpose of the water flows. 
Reliable Lidar data and better prediction models are required 
for improved results. Finally some important recommendation 
and future works are expressed by the authors as follows: 
1. For better waterfront detection, it is better to use ground 
data and non-ground data together, as sometimes there are 
misclassified. 
2. Proposing a method for better differentiating of ground 
returns from building or vegetation and from atmospheric 
aerosol for better overall accuracy. 
3. Providing better visualization tool for better detection of 
outlier of Lidar data and the ability of using video images 
simultaneously. 
4. Calibrating of Lidar data during the flight by some ground 
calibration targets due to varying conditions of the 
environment and internal calibration drift of the laser 
scanner components. 
5. Providing advanced feature extraction methods for 
discrimination of abrupt elevation changes like: water 
front, sea wall, cliffs, and coastal dunes. 
6. Integration and fusion of Lidar sensor with other type of 
images especially CASI images. 
7. Designing an improved automatic methodology for GIS 
data-base updating using extracted features from the Lidar 
data to facilitate the time-consuming task of manual 
editing. 
8. Investigation on the accurate water surface wave 
extraction especially in shallow areas to achieve better 
estimation of the depth and laser beam traveling path in 
the water body. One should consider the tidal effects on 
surface waves. Also distance between laser shots must be 
less than half of the surface wave height. 
9. Using the slope information of the adjacent topography of 
the river to use it as estimation parameters for river 
bathymetry and water height in very shallow area. 
10. Comparing depth derived from the Lidar data with other 
accurate data like echo sounder data to study the possible 
systematic errors or fusion of the data. 
11. Studying the errors caused by the projection 
transformations applied on the Lidar data. 
3. CONCLUSIONS 
Airborne lidar systems are rapidly developing and expanding in 
new applications. In this report, we focused on different 
applications of Lidar sensor in hydrology and oceanography 
especially for the river and watershed management. A good 
comparison has been demonstrated among all the available 
sensors and Lidar sensor seems to be an efficient sensor for this 
purpose. This shows that Lidar sensor provides efficient, rapid, 
and low cost tool for hydrological application, especially for 
coastal and river water management. But still there are some 
weaknesses on the Lidar data segmentation, visualization, very 
shallow depth measurement, water wave estimation and etc. 
Integration of lidar with imaging sensors and better processing 
algorithms would make a great development in obtaining more 
realistic and accurate 3D models of the geospatial objects. 
Please note that the above discussed issues are more the authors 
personal point of view. 
REFERENCE 
Ackermann, F., 1999. Airborne laser scanning: present status 
and future expectations. ISPRS Journal of Phogrammetry & 
Remote Sensing, Vol. 54, pages: 64-67.