The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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consequently lower depth for that point relative to the adjacent 
topography. He has reported an error order of 1 meter at an 
average depth of 22. Radar sensors have the advantages of data 
collection over night and also turbid water depth computation. 
Hyperspectral airborne images like: Compact airborne 
spectrographic imager (CASI), Airborne visible-infrared 
imaging spectrometer (AVIRS), Advanced airborne 
hyperspectral imaging systems (AAHIS) and Digital airborne 
scanner (DIAS) are mostly used in biological investigations 
(coral mapping, chlorophyll estimation, identification of other 
marine vegetation, water temperature) and there are some 
activities on bathymetry information extraction from CASI data. 
CASI sensor has the ability to acquire 288 bands and gives the 
ability to the user to select the bands which are suitable for the 
bathymetric information extraction for a particular area of 
interest. Choosing the right wavelength makes it possible to 
calculate regression between depth and reflectance for clear and 
turbid water. But due to the confusing effect of variable depth 
on bottom reflectance, the computed depth measurements have 
limited accuracy of order 1 meter for up to the depth of 22 
meter, which does not satisfy bathymetry depth measurement 
standards. For deeper water, lidar sensors not only have the 
ability to measure the deepness down to two or three times the 
Secchi depth at 532 nm (equals to approximately 50 meters in 
clear water) but also have higher spatial resolution, below 
surface object detection larger than lxl m2, high data 
acquisition rate per m2, acquires direct 3D position and need 
less data post processing and rapidly is available on the 
emergency situations. The planimetric and vertical accuracy of 
Lidar sensor is dependent on the flying height. Currently Lidar 
data accuracy satisfies the bathymetric standards. Detection of 
the zero depth which is river banks and its displacement are 
very time consuming and expensive task by traditional 
hydrographic methods and can be detected by Lidar sensors 
more rapidly with lower costs. For the second factor which was 
coast or river surface and banks topography, only 
photogrammetry stereo images and Lidar sensor are capable of 
the topography extraction and the remaining sensors produce 
2D data. A comparison between Lidar and photogrammetry is 
demonstrated by Lane (Lane et al., 2003). He has given more 
priority to the photogrammetric approach in the flood extent 
extraction comparing to the Lidar sensor. But if one could focus 
on his results, it would be apparent that: 1) For flood extents, 
there is negligible difference between accuracy of data derived 
by Lidar and photogrammetry, 2) Photogrammetric method has 
problems for the water surface topography but against to this 
Lidar is capable of supporting those kind of information and 
also for large flood extends, photogrammetric approach needs a 
lot time for the data processing but Lidar data can be delivered 
to the user very rapidly on emergency needs. Bates (Bates et al., 
2002) and Cobby (Cobby et al., 2001) have done investigations 
on vegetation height extraction near from Lidar data to improve 
flood modeling which can not be detected by other remote 
sensing sensors. Pereira (Pereira et al., 1999) recommends 
using Lidar sensor instead of photogrammetric image due to its 
rapid and cheaper product. Man-mage objects like, bridge and 
building are particularly favored because provide a tool for 
Lidar calibration providing a check for horizontal and vertical 
alignment. 
Ackermann (1999) gives an overview to present status and 
future expectations of airborne laser scanners. Baltsavias 
(1999a, b) discuss the basic formulas and existing systems and 
Wehr and Lohr (1999) presents an introduction and overview to 
airborne laser scanners. Mohammadzadeh et al. (2006) gives a 
brief review to the some of the exiting research works in 
different applications of lidar technology. Hydrographic Lidar 
calculates the water body depth in shallow rivers and coastal 
areas using the time difference of blue-green channel and 
infrared channel reflected from the sea bottom and the water 
surface respectively. Schmugge et al. (2002) has made a brief 
survey on the past remote sensing solutions used in 
hydrological problems. Cunningham et al. (1998) and Irish et al. 
(2000) give a good overview on the airborne Lidar hydrography 
program. Among all the operating hydrographic Lidar sensors 
SHOALS (Scanning Hydrographic Operational Airborne Lidar 
Survey) is an airborne Lidar system in the world that collects 
both hydrographical topographical measurement in a single 
survey (Guenther et al., 2000). Before flight, some calibration 
processes are carried out by the instrument designing company 
and therefore some researchers have focused their research on 
fundamental aspects such as: laser scanner calibration (Adams, 
2000) and (Wagner et al., 2006), accuracy improvement 
(Latypov, 2002), strip adjustment (Bretar et al., 2004), noise 
reduction of lidar signal (Fang and Huang, 2004), lidar 
backscatter modeling (Fochesatto et al., 2004), lidar beam 
alignment (Latypov, 2005), and stability of laser swath width 
(Luzum et al., 2005). Also there are other calibration activities 
needed to be performed before the flight starts in the field. The 
position shift among laser scanner, GPS and IMU should be 
measured accurately to apply the spatial shift among them. Also 
scan rate, GPS/IMU data acquisition rates are not the same and 
should be synchronized. The maximum detectable depth by 
laser scanner is varying according to the water turbidity and 
small particles in the water. The white calibration disk should 
be used to calibrate the laser backscatter from different depth of 
the water body. Scan rate, flight height, flight lines, designing 
control points to mount GPS instruments and project cost 
should be determined before performing flight over the region 
of interest. During the flight, the human expert should check the 
overall accuracy of the data to avoid large and unexpected 
systematic errors. The coverage between acquired data should 
be monitored to avoid data gaps. Afterwards all the acquired 
data should be processed simultaneously to convert raw data set 
to LAS or ASCII format readable by lidar processing software. 
The primary effort in all the hydrographic applications is 
transformation of the Lidar point cloud into a desired projection 
system. Outliers can be filtered out to have more realistic 
dataset. Then advanced image processing algorithms are 
applied according to the user needs and application nature. The 
intensity information is interpolated or in some cases is 
estimated using other source of data to produce raster image. 
The use of intensity derived image and optical images makes it 
possible to better recognition of the outlines of the coastline and 
nearby standing objects. Various approaches are developed in 
each specific hydrographic and oceanographic case to obtain 
required value added information such as: rapid high-density 
measurements of the coastal zone (Saye et al., 2005); track 
movements of sand placed for beach nourishment (Shrestha et 
al., 2005); reveal linkages between changes in offshore 
bathymetry and shape of the shoreline (Thoma et al., 2005); 
flood prediction (Webster et al., 2004); water surface (Hwang et 
al., 2000) and bottom reconstruction in bathymetric lidar (Pillai 
and Antoniou, 1997) ; coast or river surface and banks 
topography (Bates et al., 2003), (Charlton et al., 2003); 
Vegetation height (Bates et al., 2002), (Mason et al., 2003), 
(Cobby et al., 2003), and (Cobby et al., 2001); effect of man 
made objects on tidal rivers (Gilvear et al., 2004); tidal 
channels geomorphology (Lohani and Mason, 2001). In the 
following part a synthesized discussion around important 
exiting problems in lidar data processing and possible solutions 
will be discussed.