This PDF file contains the front matter associated with SPIE Proceedings Volume 8286, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

We report a monolithic specialized high stimulated Brillouin scattering (SBS) threshold fiber laser/amplifier in the C and L band based on highly co-doped phosphate glass fibers. This represents an important new development for coherent LIDAR and remote sensing applications. By using single mode polarization-maintaining large core highly Er/Yb codoped phosphate fibers in the power amplifier stages, we have achieved the highest peak power of 2.02 kW at 1530 nm for 105 ns pulses with transform-limited linewidth, and with a corresponding pulse energy of about 0.212 mJ. The achieved high-energy pulses were frequency doubled by using a commercial periodically poled lithium niobate (PPLN) crystal, and the highest SHG peak power of 271 W has been achieved for the SHG pulses at 765 nm that can be used for oxygen coherent remote sensing. In the L band, more than 80 μJ fiber laser pulses at 1572 nm with 1-2 μs pulse width and transform-limited linewidth have been achieved by using a monolithic fiber laser system in MOPA configuration, which can be used for CO2 coherent remote sensing.

In 2008 we began a three-year NASA Earth Science Technology Office (ESTO) funded Instrument Incubator Program (IIP) focused on technology development for the Lidar Surface Topography (LIST) mission. The LIST mission is one of the Earth Science Decadal Survey missions recommended to NASA by the National Research Council (NRC). Our IIP objective is to demonstrate the measurement approach and key technologies needed for a highly efficient swath mapping lidar to meet the goals of the LIST mission. To demonstrate the concept we are developing the Airborne LIST Simulator (A-LISTS) instrument. In this paper we summarize the A-LISTS instrument characteristics and the approaches we are using to advance lidar capabilities and reduce risks for LIST.

As a new data source of remote sensing, airborne LiDAR data quality evaluation is of great importance. This paper focused on the comprehensive quality evaluation of airborne LiDAR data in the following aspects: data integrity, data accuracy , data density, interpretation ability of intensity and the quality of spatial data products from LiDAR data, which aimed to provide a complete reference to airborne LiDAR data quality for practical applications. For data integrity, a data void extraction method based on region growing was proposed to check the completeness of spatial distribution. Data accuracy is a key quality index for LiDAR data quality. Height offset statistics among overlapping strips were calculated to evaluate the relative accuracy. Data density is an important factor influencing the products quality from LiDAR data. The average point spacing can only demonstrate the whole density of the data, whereas the local density is much more important for specific applications. In this paper, the density distribution map is used to reflect the density variations for the whole data. Besides, interpretation ability of intensity is also used to evaluate the quality of the airborne LiDAR data. Quality of DTM was used to evaluate the LiDAR data at last.

Terrestrial Laser Scanners (TLS) measure distances and record angles to objects in the surrounding environment. In addition, a TLS also measures the amount of reflected light (intensity). The intensity value provided by the TLS is a function of the distance to the object, angle of incidence, the object's reflectance properties and internal parameters of the laser scanner. In some cases it is possible to obtain the reflectance properties of an object from the intensity value, distance and angle of incidence. The object reflectance provides information that can be used to improve the speed and accuracy in segmentation or registration algorithms. In order to model the relationship between intensity and reflectance it is necessary to calibrate the model for the specific laser scanner using reflectance targets. These targets have surfaces calibrated to a specific reflectance value. The targets are expensive and it is a time consuming task to calibrate the model in this manner. Alternatively, this paper presents a method to determine the 'normalized intensity' using a similar model but without using the calibrated reflectance targets. Instead, overlapping registered scans are used to determine the model parameters. The normalized intensity can be used in the same way that object reflectance is used in segmentation and registration algorithms. The only difference is that it is not a true reflectance value. The scans used to test the method were taken with a 3DLS-K2 TLS. It is shown that using this method produces normalized intensity values that are independent of distance, instead providing a value related to the actual object's reflectance.

Laser diode adopted in LiDAR (Light Detection And Ranging) onboard a small low-cost UAV (Unmanned Aerial Vehicle - UAV) platform usually requires its carried components to be light in weight, small in volume, and specially efficient in energy supply. A DC-DC converter and a fast switch are often applied for the traditional power supply to drive the LiDAR system. This traditional method is not proper for application in a UAV platform. This paper provides a novel power supply topology, which consists of two coupled coils, pulse generator circuit, and a fast switch. The power supply topology has been designed, assembled, tested, and confirmed to generate pulse voltage of 100-300 V, up to 120 A pulse current, 50-200 μs pulse width, and 50 Hz maximum pulse frequency. The driver circuit is very simple, but could restrain current surge efficiently. This ensures that the laser diode does not be disturbed. It is demonstrated that it is sufficient to drive a laser diode used in LiDAR onboard UAV, and meets the requirement of weight and volume. Now the driver has been manufactured for application in UAV-based airborne lidar sample system.

As the cost of LiDAR equipment is high and different projects prefer different goals, the flying scheme needs to be strict planned to save cost and energy. In this study, LiDAR's ability at different flying height was tested. Two trials with different flying height are compared. Analysis was conducted by considering the relative accuracy, intensity and penetration ability to find out the difference of two trials. The result shows the relative accuracy between these two trials is 0.37 m. True relative accuracy is achieved when removing canopy as large interpolation errors often exist at places with great slope changes like the canopy. Intensity values decrease greatly with the increase of flying height due to the longer travel distance and more energy loss in atmosphere. Statistical results showed that in each trial high intensity is apt to be from low canopy and only return. LiDAR vegetation penetration ability decreased greatly at low vegetation due to the enlarged footprint and weakened energy, but it doesn't change on large vegetation.

In addition to the well-developed bathymetric LiDAR (Light Detecting and Ranging) remote sensing technique, Airborne Hydrography AB (AHAB) has presented a new bathymetric LiDAR reflectance processing technique which provides new applications of producing seafloor reflectance image, seafloor identification and classification. In the past decade, HawkEye II bathymetric LiDAR systems produced by AHAB collected and processed over 100,000 square kilometer LiDAR reflectance data in more than ten countries in Europe, America, Oceania, Indian Ocean and Asia. In this paper, we introduce the background of bathymetry LiDAR, the algorithm and methods used in the bathymetric LiDAR reflectance processing, the reflectance image and seafloor classification applications.

In this paper, the ranging procedure and working principle of LiDAR were introduced. By analyzing the principle, it is found that related methods can cause time delay error and ranging error for different ground targets. Then, elements affecting LiDAR ranging precision were analyzed, and experimental results proved that un-calibrated ranging error can reach one meter for the targets one hundred meters away, which cannot satisfy the requirements of practical applications. Thus, ground calibration techniques of airborne LiDAR were analyzed, and the elements affecting LiDAR ranging precision are classified into three classes. Correspondingly, an equation was proposed to express the influences of ranging errors, a ground calibration scheme was designed for single LiDAR, and a solution method to the parameters was given. Experiments were done on a China-made LiDAR system, and experimental parameters proved that gray level had some influence on ranging values, with the range difference of different gray boards reaching centimeter level. Meanwhile, LiDARs with different parameter settings can also affect ranging values. However, due to the imprecise experiment process, the additive constant and scale constant were not obvious in the experiments, and the experiment needs improving in the future.

The matching of terrestrial laser intensity image and optical image is one of the active research areas in terrestrial laser scanning technique. In this paper, we introduce an algorithm based on gray scale to match intensity image and optical image. The similarity measure and the size of matching template are two crucial issues of this algorithm. Firstly, the correlation coefficient based on gray values and the mutual information based on statistical distribution are combined. Thus an integrated criterion is produced to extract corresponding points in an intensity image and an optical image. Secondly, an experiment was done to perceive the relations between the correlation coefficient and the mutual information of corresponding points and the size of a matching template. This image matching algorithm has two steps, coarse image matching and fine image matching. The coarse image matching step is carried out to search three precise pairs of corresponding points to calculate a mapping polynomial, by which a primary search region can be obtained for reducing the possibility of false correspondence and increasing the speed of image matching. While the fine image matching step aims at obtaining more precise corresponding points in the region determined by the mapping polynomial.

CALIPSO satellite has been making global lidar measurements since June 2006 and its lidar, CALIOP, will likely be the only lidar in space during the next several years. Laser altimetry data from space and aircraft-based atmospheric profiling lidars, such as CALIOP, have not been widely used in the community due to their limited vertical sampling resolution (30 meter) and broad laser pulse width (20 ns). This study intends to improve the CALIPSO laser altimetry data quality and provide a highly accurate altimetry data product to the laser altimetry community. In this study, a super-resolution laser altimetry technique has been proposed to provide improved lidar altimetry from a profiling lidar with relatively broad pulse width and slow sampling rate. Application of the technique to CALIPSO data leads to highly accurate CALIPSO land surface elevation measurements. The surface elevations will be derived from near 5-year CALIPSO global observations. The CALIPSO surface elevation results in Northern America derived by the new technique agree with the National Elevation Database (NED) high resolution elevation maps and a comparison suggests that the accuracy of the new CALIPSO land surface elevation measurements is better than 1 meter.

Due to the advantage of all-weather, multi-angle data acquisition, Synthetic Aperture Radar has been widely applied in many areas. However, the speckle noise affects its application seriously. Therefore, suppressing speckle noise effectively is significant for its analysis and application. Against the shortcoming that the Kuan filter can't both suppress speckle effectively and maintain the edge details suitably, we propose a new algorithm, which fuses the Kuan filter and the edge extraction technology. In this algorithm, first we use the Kuan filter with 5× 5 window to process the image, we can get the filtered image which suppress the speckle effectively, but the edge details and texture information loss seriously. Then we use the edge extraction technology to get the image's edge and texture information. At last the pixel values of the edge and texture area of the filtered image are replaced by the result of the edge extraction. Experimental result shows that the improved filtering method not only suppresses the speckle effectively, but also improves the capability of edge details and texture information maintaining. Compared with the traditional filters, the proposed improved filter is effective.

Bi-dimensional period-diagram methodology proposed by Ferretti in radar interferometry based on permanent scatterers (PS-InSAR) for estimating the deformation from neighboring differential phases (NDP) are affected by the distribution uniformity of normal baseline and temporal baselines and the quantity of interferograms. To overcome the shortage of Bi-dimensional period-diagram methodology, a new method, called solution-space search (SSS) method is proposed in this paper to estimate the parameters of PSs' NDP model. The experiment to detect ground subsidence over Lujiazui in Shanghai during 1992-2002 and the comparison with bi-dimensional period-diagram are performed. The result shows that the SSS method can be used to effectively and reliably estimate the deformations without considering the distribution uniformity of normal baseline and temporal baseline and the quantity of interferograms. Furthermore, more accurate estimations are achieved with SSS method.

The interferogram contains much noise which reduce the precision when phase unwrapping. In this paper, we filter the interferogram in the contourlet domain. The contourlet transform (CT) has flexible aspect ratios and can effectively capture geometry information of interferogram edges. However, the CT is lack of the feature of translation invariance. Hereby we study the cycle-spinning CT (CSCT) to convert the commonly CT to translation invariance. Firstly, we translate the original interferogram before being decomposed. Secondly, we decompose the translated interferogram using the CT, and modify the coefficients. Finally, we reconstructed the interferogram with the modified coefficients and translate back. In the experimentation, the data is selected both from the plain and mountain area. The results show that the CSCT outperform the discrete wavelet transform (DWT), the CT in terms of the residues number and the mean value of the correlation coefficient. In texture retrieval, the CSCT shows improvements in performance for various oriented texture and the results indicate a better compromise between noise removal and the detail preservation. Besides, in the mountain area, the CSCT performed well than in the plain area because there is more texture in the mountain area.

Synthetic Aperture Radar (SAR) is an active remote sensing system which can be installed on aircraft, satellite and other carriers with the advantages of all day and night and all-weather ability. It is the important problem that how to deal with SAR and extract information reasonably and efficiently. Particularly SAR image geometric correction is the bottleneck to impede the application of SAR. In this paper we introduces image registration and the Susan algorithm knowledge firstly, then introduces the process of SAR image registration based on Susan algorithm and finally presents experimental results of SAR image registration. The Experiment shows that this method is effective and applicable, no matter from calculating the time or from the calculation accuracy.

Advanced synthetic aperture radar (ASAR) is an all-weather, day-and-night and high-resolution imaging instrument carried out by EnviSat. Planetary missions related to Earth surface observation benefit from ASAR. Conventionally, EnviView is used to decode ASAR data and display ASAR data information and images. However, EnviView has very limited functions and is unable to export meaningful images when the data was acquired in some bad weather condition such as after heavy rain as the data is distorted by various factors such as the soil moisture. Furthermore, EnviView is not user friendly and not flexible to use. This paper presents a user-friendly ASAR data visualization and image enhancement toolbox based on the very popular MATLAB software environment. The image enhancement is important for meaningful image analysis and accurate target identification of poor data due to weather or other environment factors. A case study is presented with image enhancement for a poor ASAR data.

The dominating measurement error of Terrestrial Laser Scanner comes from laser ranging, which is also the major cause of "tailing points" appearing at the edge of targets scanned by coaxial laser scanner. Since the existence of such abnormal points, general least squares sphere fitting method cannot correctly position spherical targets or determine the dimension of geometries. Base on the working mechanism of coaxial laser scanner, a surface fitting model is designed in this paper for the spherical targets, and utilize robust least squares method to get model parameters from scanning data with obvious edge noise, then conclude the sphere radius and centre point coordinate. In the end, this paper accomplishes two experiments separately for really scanning data and synthesized data subjecting to scanner's error distribution. The result demonstrates that proposed fitting method can weaken the impact of abnormal points on the edge of spherical targets effectively, get geometric parameters of spherical targets accurately, and exactly locate the spherical targets.

Aiming at the problems of difficult unwrapping and phase noise in InSAR DEM reconstruction, especially for the high-resolution TerraSAR-X data, this paper improved the height reconstruction algorithm in view of "remove-restore" based on external coarse DEM and multi-interferogram processing, proposed a height calibration method based on CR+GPS data. Several measures have been taken for urban high resolution DEM reconstruction with TerraSAR data. The SAR interferometric pairs with long spatial and short temporal baselines are served for the DEM. The external low resolution and low accuracy DEM is applied for the "remove-restore" concept to ease the phase unwrapping. The stochastic errors including atmospheric effects and phase noise are suppressed by weighted averaging of DEM phases. Six TerraSAR-X data are applied to create the twelve-meter's resolution DEM over Xian, China with the newly-proposed method. The heights in discrete GPS benchmarks are used to calibrate the result, and the RMS of 3.29 meter is achieved by comparing with 1:50000 DEM.

Information extracted from remote sensing data plays an important role in the environment, hydrology and geology study. Optic remote sensing image has plenty of spectrum information and microwave can reflect land surface texture and penetrate ground to some extent. Fusion of microwave and optic remote sensing image will take advantage of mutual complementary information, and extract subsurface information more available. A comprehensive fusion approach between different remote sensing data was proposed, and the ASAR and ETM+ data were chosen as data source. Firstly, panchromatic and multi-spectral images of ETM+ were fused with principal component analysis (PCA) method. Spectral information and spatial detail information of the merged image has been enhanced compared to the original images. Secondly, ASAR and merged ETM+ data were fused using three methods, including multiplicative, Gram-Schmidt and discrete wavelet transformation (DWT). DWT fusion was the primary research content. The image quality after fusion was evaluated by means of visual effects, entropy, average gradient, correlation coefficient and standard deviation. The results show that the image fused with DWT has the highest accuracy, in which more surface and subsurface information can be expressed better. This research will build a foundation for making full use of ASAR and ETM+ data.

Filter window selection is one of the key issues in SAR image despeckling. This paper proposes an adaptive windowing method for robust estimation of the local statistics for despeckling filters, which is based on the combination of confidence interval and morphological reconstruction. A preliminary homogeneous window of each pixel is firstly shaped from an initial window according to the confidence interval inferred by a given confidence probability. The confidence probability is chosen adaptively according to the homogeneity facts of the initial window. Subsequently, this preliminary window is refined by morphological reconstruction under the region adjacency constraints. As a result, a homogeneous window for filtering with arbitrary shape is obtained, which is continuous in both radiometric and spatial domain. The experimental results show that the proposed adaptive windowing method performs better in the term of the window accuracy and gets better balance between speckle reduction and structure preservation with two other commonly used windowing method.

Over the last two decades, Synthetic Aperture Radar Interferometric (InSAR) has proven a remarkable potential tool for observing the Earth surface, especially for mapping the Earth's topography and deformation. It can resolve surface displacement with centimetric accuracy, tens meters of spatial resolution and monthly temporal resolution. The permanent scatterer (PS) technique has been developed later in the 1990s first by A. Ferretti to overcome the major limitations of repeat-pass SAR interferometry: temporal and geometrical decorrelation as well as atmospheric delays. The Ferretti's method, referenced as the Permanent Scatterers TechniqueTM in their patented procedure, works best in urban areas containing large numbers of man-made structures, which tend to be angular and often produce very efficient reflectors that dominate background scattering. In this paper, a series of fifteen ENVISAT ASAR acquisitions of the city of Nantong, located at the mouth of Yangtze River adjacent to Shanghai, covering the period from 2006 to 2007, was analyzed. The PS-InSAR technique and Geographic Information System (GIS) spatial analysis were used to detect ground deformation in the urban area. Results show there was no large, continuous subsidence occurs in the city, however seven subsidence bowls were found. This can be used to define the risk zones for future ground subsidence.

LIDAR (LIght Detection and Ranging) [1] is an optical remote sensing technology that has gained increasing acceptance for topographic mapping. LIDAR technology has higher accuracy than RADAR and has wide applications. The relevant commercial market for LIDAR has developed greatly in the last few years. LAS format is approved to be the standard data format for interchanging LIDAR data among different software developers, manufacturers and end users. LAS data format reduces the data size compared to ASCII data format. However, LAS data file can only be visualized by some expensive commercial software. There are some free tools available, but they are not user-friendly and have less or poor visualization functionality. This makes it difficult for researchers to investigate and use LIDAR data. Therefore, there is a need to develop an efficient and low cost LIDAR data toolbox. For this purpose we have developed a free and efficient Matlab tool for LIDAR data conversion, visualization and processing.

LiDAR (Light Detection and Ranging) is an active remote sensing technique for acquiring spatial information. It can quickly acquire three-dimensional (3D) geographic coordinate information of ground surface and ground targets, and has typical advantage in such applications as urban planning, 3D modeling, disaster assessment, etc. This paper presents an extraction method for interested buildings using three-dimensional laser point cloud data which are filtered and organized by the kd tree. First, the algorithm determines candidate points of a building from non-ground points and clusters them on the constraints of distance so that single building target can be segmented. Second, for each segmented building target, the algorithm extracts its edge points and regularizes its edge. The extracted building feature information is provided for quickly searching target of interest. At last, the method is proved to be effective based on the analysis of measured data. The method is no point cloud interpolation error, and is not affected by the size or shape of a building.

Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) is an emerging technique that combines interferometric SAR and polarimetric SAR techniques and has shown its effectiveness in the detection of buried weak targets. The detection performance is affected by the SAR parameters as well as the covering characteristics. In this paper, the effects of covering characteristics on the detection performance were emphasized and experimentally investigated by a ground-based Pol-InSAR system. Firstly, the detection principle for buried weak target by Pol-InSAR technique was presented, which is based on the use of interferometric coherence variation with polarization. Then the ground-based two dimensional rail (TDR) SAR used for investigation was introduced. Furthermore, the experiment target scene was designed and the effects of different covering type, different covering moisture, and different covering depth on the detection performance of weak targets were shown and analyzed. Preliminary results confirmed the effectiveness of Pol-InSAR technique used for weak target detection and it would be helpful for the further investigation of this technique.

The applicable choice of filters for InSAR is one of the key procedures, which is associated with the quality of interferogram. The data of InSAR interferogram was decomposed by empirical mode decomposition (EMD). A given signal was decomposed into different Intrinsic Mode Functions (IMFs) filled with the condition. Further investigation of the algorithm is demonstrated below with regard to the multi-resolution standpoint. Empirical mode decomposition includes two operators. The IMF calculation operator and residual calculation operator define the process of similar to high frequency and low frequency filters. Then, the multi-solution structure is realized by decomposing the low frequency step by step. Therefore, the filtered noise-related IMFs together with the other IMFs can be used to restructure the denoised signal. The processing result has confirmed this method feasibility. Comparing the empirical mode decomposition with the general methods, such as median filter, Lee filter, Goldstein filter, using the quantitative evaluation index, i.e., standard deviation (STD) and equivalent number of looks (ENL), the result shows that empirical mode decomposition is powerful to interferogram speckle noise suppression and residues reduction, as well as it can be preserved details information. The method proposed can improve the accuracy of interferometric products.

As a new image multiscale geometric analysis tool, the nonsubsampled contourlet transform (NSCT) has many advantages such as multiscale, localization and multidirection, and can efficiently capture the geometric information of images. Therefore, when the NSCT is introduced to image fusion, the characteristics of original images can be taken better and more information for fusion can be obtained. In this paper, a novel fusion algorithm for fusion of the synthetic aperture radar (SAR) image and multispectral images using conjointly the intensity-hue-saturation (IHS) transform and NSCT is proposed. In the proposed method, atrous wavelet is adopted to extract the detail information in low frequency parts fusion, and a new salience measure named as local inner product is introduced to select the high frequency coefficients. A PALSAR HH image of ALOS satellite despeckled by the Lee-sigma filter and HJ-1 multispectral images are used to evaluate the performance and efficiency of the proposed method. The fused images of each method are evaluated by qualitative and quantitative comparison and analysis compared with some traditional fusion rules. The experimental results indicate that the proposed method has the merits of better preservation of image definition and less loss of spectral information.

This paper firstly suggested that multi-scale map data updating is based on the updating of larger scale map data, and the other smaller scale map data are updated step by step. Nowadays, there have been quite a few technical means to update the larger scale data, including field surveying, digital photogrammetry, remote sensing, LiDAR and digitization from the existing topographic maps, etc. Secondly, an updating method for incremental generalization cascade updating was provided according to the demands for map data at intermediate and small scale in china. The matching association table was established based on the determination of the matching relation and similar values through the method of the matching of multi-scale spatial features. The incremental information was then acquired through variation detection considering cartographic generalization impact. It was used to realize the incremental updating of map data. Furthermore, the cascade updating of multi-scale data were realized by the next matching association table, which making sure of uniformity of multi-scale data.

In this paper, a new method for automatic detection of roads from Light Detection and Ranging (LIDAR) data is presented. A morphological filter and an elevation difference threshold are first combined to classify the original data. In the following step, a height fitting difference algorithm is introduced and performed to calculate height fitting difference with a multi-direction template for each pixel. The algorithm acquires two pieces of information about roads: the least squares fitting difference and the corresponding orientation. Then, the Otsu's method is applied to obtain a road map with the fitting difference feature. After performing the Euclidean distance transform on the segmented road map, road centerlines are searched in the distance map. Next, the centerlines are connected and optimized so that long and smooth road centerlines are obtained. Finally, road boundaries are found by setting a proper width value for each road centerline. The proposed method has been tested on various complicated urban images. Experimental results demonstrate that our new method works efficiently and correctly.

In this paper, a direct 3D visualization environment with DEM, DSM and DOM from LiDAR point cloud data is presented to replace the stereo observation of photogrammetry image pairs for vector map extraction. A unified DEM, DSM and DOM data model is designed to manage huge data volume and then to construct the 3D visualization environment. With the 3D visualization of LiDAR data, the landscape can be recovered, and thus the vector map can be directly digitized. Also the contour lines can be overlaid in the 3D visualization environment for the further quality check and control. The presented vector map extraction approach can reduce the professional equipment and software investment.

Due to the strengths and weaknesses of the existing filtering methods, an improved plane fitting algorithm is presented for filtering of airborne LIDAR data in the paper. Firstly, resampled LIDAR data were segmented using region growing method, and the largest connected region was selected as the initial ground area. Secondly, non-ground points in the initial ground area were removed by a slope threshold, which is suitable for the study area. Finally, the ground points were interpolated using Kriging method and the filtering results were gained. The dataset provided by International Society for Photogrammetry and Remote Sensing (ISPRS) was selected as the test data. The influences of different slope thresholds on the filtering results were given after filtering experiments. The result shows that the optimal slope threshold in this area was 0.5 and the overall error rate was only 4.29%. Contrast to the traditional filtering method based on plane fitting, the proposed method is more simple and practical.

Full-waveform LIDAR systems have already been proved to have large potentialities in characterizing the landscape. Especially in the forestry area, more detail information is provided by waveform data processing and new opportunities are inspired for point cloud classification from waveform characteristics. Generally, different objects response to the emitted pulse diversely, which is incarnated in the waveform data. But acquired data is influenced by several factors, so it cannot be directly used in wide area before calibration. Within one flight, some factors such as laser scanner systems, atmosphere conditions, etc. can be considered as constant. Therefore, range between sensor and object could be regarded as one of the most important factor and was introduced to calibrate Gaussian decomposition results of waveform data. Meanwhile, the number of return echoes was also considered in calibration process. After these improvements, the parameters including Gaussian amplitude, standard deviation and energy extracted from waveform data by Gaussian decomposition method were applied for test area classification. A supervised classifier was implemented to distinguish building, grass, conifer and broadleaf. Then the accuracy of the classification results of calibrated and non-calibrated was analyzed, which indicates that the calibrated full-waveform data possibly increase the potential application in landscape identification.

The paper describes three kinds of popular matching algorithms of overlapping strips of LiDAR, and introduces the matching algorithm of TerraMatch. The processing of matching overlapping strips can be outlined as the following steps. Firstly, using TerraScan module to preprocess raw LiDAR data of the single flight strip. It can classify low points and ground feature separately for each flight line. Then, read these ground feature points to TerraMatch module. TerraMatch uses these ground feature points to match overlapping laser data. After many times iteration, the iteration continues until roll correction value, pitch correction value and heading correction value and other parameter correction values all are smaller than a preset threshold. The parameters of the last iteration act as optimal parameters. In order to verify the TerraMatch's functions, we used actual data set to assess the experimental result. Lastly, a conclusion is drawn that the algorithm of TerraMatch can effectively reduce or remove the discrepancies of LiDAR data set between overlapping strips and is worthly promoting.

Aligning two point clouds is the iterated closest point algorithm which starts with two point clouds to estimate three translates and rotations. Traditional registration are searching the optimal solutions at the cost function of the minimum residual squares without consideration of points covariance. Closed-form or iterative least squares methods are performed to search the solutions, and total least squares (TLS) methods are introduced in recent years. The ordinary least squares (OLS) and OTLS methods can not work on the heteroscedastic cases. So element-wise weighted TLS (EWTLS) and row-wise weighted TLS (RWTLS) methods are introduced to solve the rigid body transformation problem after the initial values obtained by Procrustes analysis method. Comparative studies are made with the weighted and unweighted estimators of OLS, TLS, mixed OLS and TLS, EWTLS and RWTLS. The results indicate that the RWTLS method is the highest accuracy estimator, and be much more accurate than the unweighted OLS and TLS methods.

The images acquired by polarimetric SAR radar systems are characterized by the presence of a noise named speckle. This noise, have a multiplicative nature, corrompt at the same time the amplitude and the phase which complicates the data interpretation, degrades the performance of segmentation and reduces the targets detectability. From where need to pretreate images by adapted filtering methods, before carrying out their analysis. In this article, we study the polarimetric wightening filter PWF of Novak and Burl which treats the polarimetric covariance matrix to produce a filtered intensity image. We propose two methods to improve the PWF filter: the first integrates the technique of Lee edge detection to improve the filter performance and detect fine details of the image. This method is called LSDPWF (Lee Structure Detection PWF). After detecting the edges, we filter the detected regions in the polarimetric channels by the PWF filter. The second combines the method of filtering by wavelet thresholding with PWF filter using the stationary wavelet transform SWT. This method is called EPWF (Enhanced PWF). In the wavelet thresholding, we use the soft thresholding which sets to zero the amplitudes of coefficients that are below a certain threshold. So we propose to extend the wavelet thresholding, to apply it in polarimetric SAR images and use the polarimetric information to calculate the threshold on the wavelet coefficients. We implemented these filters and applied them to RADARSAT-2 polarimetric images taken on the areas of Algiers (Algeria). A visual and statistical evaluation and a comparative study are performed. The performance evaluation of each filter is based on smoothing homogeneous areas and preserving edges.

A novel methodology base on object-oriented MRF is proposed in order to obtain precise segmentation of high resolution satellite image. Conventional pixel-by-pixel MRF model methods only consider spatial correlation and texture of each pixel fixed square neighborhood. The segmentation method based on pixel-by-pixel MRF model usually suffers from salt and pepper noise. Based on the analysis of problems existing in pixel-by pixel MRF model methods of highresolution remote sensed images, an object-oriented MRF-based segmentation algorithm is proposed. The proposed method is made up of two blocks: (1) Mean-Shift algorithm is employed to obtain the over-segmentation results and the primary processing units are generated based on which the object adjacent graph (OAG) can be constructed. (2) MRF model is easily defined on the OAG, in which special features of pixels are modeled in the feature field model and the neighbor system, potential cliques and energy functions of OAG are exploited in the labeling model. The proposed segmentation method is evaluated on high resolution remote sensed image data-IKONOS. The experimental results show the proposed method can improve the segmentation accuracy while simultaneously obviating "salt and pepper noise" phenomenon and reducing the computational complexity greatly.

The traditional LiDAR Point Cloud filtering and classification algorithms are mostly based on the elevation information, which are difficult to distinguish bare-ground and non-ground point with this single data source. In this article, fused image information such is used in the filtering process. Through the image segmentation, each segmentation object are extracted with the shape and spectral information, then these vector space objects and point cloud are matched and the decision tree is set up based on topology relationship for filtering. In order to test the performance of the proposed algorithm, test area with thick buildings and roads were applied, which could achieve a better filtering effect.

By fusing with other sensory data, especially high resolution imagery, LiDAR can be a good source of information for DEM extraction and feature extraction because it provides integrated information of geometric (surface), spectral and spatial property. Nowadays airborne LiDAR system vendors such as Leica and Toposys and others are providing systems with integrated camera capturing 3D point cloud and high resolution images simultaneously, for example, Leica's ALS50II, ALS60, and Toposys' FALCON II. The full potential of an integrated system in surveying and mapping has to be explored yet. In this paper, taking example of Toposys' FALCON data, we discuss some issues of data fusion: (1) cross sensor data registration, including geometric error budget; (2) two methods of fused data generation - imagery fused with range image re-sampled from point cloud and point cloud with assigned image pixel attributes. (3) Occlusion problem and how to solve it. We also show the segmentation results by a combined segmentation algorithm carried out on the fused multiple layer data. The results demonstrate the advantages of data fusion due to rich information and cues of objects in the fused data.

With a great progress in the research on digital city and related commercial applications, the construction of 3D city models has been matured gradually as a research topic in recent years. When constructing 3D city models on large scale, it is inevitable to have available reconstruction tools that offer a high level of automation and produce valid models reliably within the required accuracy. In this paper, a new method for the automatic generation of 3D building models from directly observed point cloud captured by airborne LiDAR system is presented. At first, Delaunay TIN model is constructed based on the building points. Then an edge length ratio based trace algorithm is used for refining the building's boundary. And the extracted outlines are regularized applying the regression analysis to obtain the better geometric quality. Then the same plane points set are clustered via those points in a triangle facet having the similar normal vector value, which determines the roof structures. Finally, with the regular outlines and roof structures information, the building models are constructed. The data in the study area provided by www.terrasolid.fi are used to test the approach. The experimental results show that this proposed approach is able to construct building models effectively.

LIDAR (Light Detection And Ranging) has become a reliable technique for the data collection of the earth surface. Building extraction based on LIDAR data is one of the important applications. The image edge is one important feature of a building, which can reflect the most valuable information of the image. And it can roughly describe the characteristics of the building. So building edge extraction from LIDAR data is of great significance. This paper presents an automatic extraction method of building edges from LIDAR data, using image segmentation technology. In the first stage, the LIDAR point cloud data are converted into DSM images. In the second stage, a threshold selection method based on the transition region determination is used to segment the DSM images. Finally, the building edges are extracted on the segmented image. The results obtained on the data sets with different ground point density, gathered over the town of Xuzhou (China) with TopScan airborne laser scanning system, are shown to illustrate the effectiveness of the proposed approach.

With the development of LIDAR, InSAR and other new technologies used in the data acquisition, the three dimensional point cloud data has become a very important data source in the Geomatics applications. LIDAR provide a convenient way to acquire a massive three dimensional point data. A high efficiency algorithm for the management and searching of LIDAR data is an important foundation of the procedure of LIDAR processing such as filtering, display and threedimensional reconstruction. How to deal with a large amount of discrete data is not only one of the focuses and challenges in the area of LIDAR point cloud data processing, but also an important content in some other areas such as the DEM generation. While in some ways the storage, organization and management solutions of these massive points will affect the efficiency and accuracy in the following processing. In this paper a dynamical regular grid nets method will be introduced. This method will then be used in some airborne LIDAR filtering experiments of different areas. We will see the procedure and result of the point cloud data processing using this dynamic regular grid nets method. Accordingly, the effectiveness of this method will be proved.

This paper presents a framework for segmentation and topological relationship reconstruction of building planar surfaces by using airborne LiDAR point clouds. The analysis of a planar surface structure is fundamental to almost any applications involving LiDAR data, especially building reconstruction. The proposed framework consists of two steps. Firstly, point clouds is segmented using an improved RANSAC (RANdom SAmple Consensus) algorithm with variant consensus set threshold. It is designed to solve under- or no- segmentation problem. It reduces consensus set threshold when the original RANSAC could not find valid planes, hence small planar surfaces would be extracted. Then, the topological relationship planar surface is constructed based on estimating connectedness of connecting point pairs between each pair of adjacent planar surfaces. The types of connectedness of planar surface are divided into three categories and a statistical method is used to estimate the connectedness type. The reconstructed topological relationship is described by an adjacent graph and can be utilized in the building modeling. Experiments show the effectiveness and efficiency of the proposed framework.

This paper presents an approach for the registration of terrestrial laser scanning point clouds based on geometric models while there are no targets in the scanning process. We obtained the axis vectors of the cylinders that are fitted from the segmented point clouds in the overlapping areas. Then we solved the parameters of the rotation and translation by using the axis vectors and the centers of the cylinders. Further, we analyzed the registration accuracy and its influencing factors. The test result shows that the geometric model based approach actually offers high degree of flexibility and meets high accuracy for registering terrestrial laser scanning data.

This paper presents a new method of automatic edge extraction by LIDAR-optical fusion adaptive for complex building shapes. Different building features are extracted respectively from the two data sources and fused to form the ultimate complete building edges. Firstly, the points of each roof patch are detected from LIDAR point cloud, which consists of four steps, namely filtering, building detection, wall point removing and roof patch detection. Secondly, the initial edges are extracted from images using the improved Canny detector which is conducted by the edge location information from LIDAR point cloud in the form of edge buffer areas. Finally, the roof patch and initial edges are integrated to form the ultimate complete edge by mathematical morphology. This method presents an innovative strategy to fuse the two data sources to get building edges of high accuracy, which don't impose any constraints or rules on building shape. So this method is fully data-driven. The experimental results demonstrate that our method can automatically extract the accurate edges of various buildings of complex shapes, which have high robustness for complex scene.

In the reverse engineering, the dense and disordered point cloud data contain a huge number of redundancy, which inevitably leads to the significant challenges for the tasks of the subsequent data processing. This paper presents a single axis searching arithmetic to obtain the neighborhood information of a point cloud, and then based on all boundary points extracted and reserved, a non-uniform data reduction scheme, according to a specified curvature threshold and the proportion of reserved points in the k-nearest neighbors, is proposed. The experimental result shows that this approach has a strong ability for identifying boundary points, and can directly and effectively reduce the point cloud data, meanwhile keep the original geometric feature.

Terrestrial Laser Scanning (TLS) is a high-precision measurement technique, which has been widely used in many fields. Since large objects cannot be measured from one station, the data from multiple stations need to be connected together. The common target approach is the widely used. In TLS, targets are used in the registration, geo-referencing, and also as check points, therefore their positioning accuracy directly affects the precision of data processing results. As a spherical target can be scanned from any direction, it is more widely used than a plane target. This paper model the surface of a target by using the Levenberg-Marquardt method of nonlinear least squares to acquire the coordinates of the target centres. Comparing the obtained radius to the given one, we can evaluate the proposed method. The precise determination of target centres can provide accurate data for three-dimensional modelling.

The establishment of high quality triangular mesh is one of the key steps in 3D laser scanning data processing. Traditional triangulation algorithms have been proposed directly on the basis of adjacency relation between points in 3D space. However, when the point density is non-uniform or the noise exists, the problems such as surface hole, dough sheet overlapping and inconsistent normal appear easily. In this paper, a triangular mesh establishing algorithm based on ellipsoidal projection is proposed. After comparing the theory of ellipsoidal projection and cylindrical projection, the proposed triangular mesh establishing algorithm is analyzed in detail including basic idea and implementation method. To evaluate the performance and efficiency of the proposed algorithm, two experiments are then carried out on the 3D point cloud data of a foundation pit. The results indicate that though the computational efficiency of proposed algorithm is a little inferior to the algorithm based on cylindrical projection, the proposed algorithm is more effective for establishing point cloud of both top and bottom of the object and the original topological relation of 3D scanning points can be maintained better.

Classic Kalman Filter is a dynamic and efficient data processing method, but there are some limitations. Robust estimation theory will be introduced to the Classical Kalman Filter (CKF) method, that is: Robust Adaptive Kalman Filter (RAKF). There is a clear advantage in reducing the observational errors and the state prediction errors context. In this paper, it uses a dam deformation monitoring example to illustrate that the RAKF is more reliable than the CKF in the deformation monitoring data processing effectively, and it is obviously in inhibiting the aspect of the state prediction errors and the observational errors. It is a viable and effective method of estimation method.

In the surface deformation monitoring, synthetic aperture radar differential interferometry (D-InSAR) has the advantages of all-weather, large-scale and high accuracy, it is hard to form interferogram for limited factors such as spatial decorrelation, temporal decorrelation and atmospheric effect. For the reason, the method of PS-DInSAR was proposed. However, the method needs so many SAR images, more than twenty scenes. Therefore, the method based on the phase stacking of PS for surface deformation monitoring was proposed and verified. The PS-DInSAR model and D-InSAR model are combined and simplified under certain conditions that assume the phase error of atmospheric disturbances are random and equal in an interferogram and the deformation is linear. The optimal master image for interferometric combinations is selected by comprehensive correlation function model. Then the PS points are detected and the Delaunay triangle is established according to the PS. The Minimum Cost Flow is used based on the Delaunay triangle of PS to unwrap the phase. Then the deformation and deformation rate are obtained by the linear analysis for temporal series of interferograms. At last, nine ENVISAT images captured during 2003.6-2006.3 in Tianjin area were processed, and the mean subsidence rate of this area was obtained.

In the recent years, the estimation of forest volume using radar data has developed greatly. However, as the radar data was large scale, the efficiency of processing based on KNN decreased seriously. Moreover, because the different K and distance measured method could result in the different accuracy, the treatment could have a low degree of automation under the condition of keeping the relatively better precision. Therefore, the study implemented a tool which could have the feature of fast and automatic processing radar data based on KNN. For enhancing the efficiency of processing, the tool was implemented in the way of parallelization by using the message passing interface (MPI) technology and run on the high performance cluster environment. To certain the suitable parameter automatically such as K and the appropriate distance measured method during the processing; the study used leave-one-out cross-validation method to check the precision and selected the optimum model based on the accuracy. The result shows that the tool accelerated the computation speed as eight time as before while ensuring the treatment precision and improved the automatic degree of the treatment. To some extend, it solved the bottleneck of processing large scale SAR data.

Synthetic aperture radar signal can propagate through hazardous weather and atmospheric conditions with heavy capabilities of SARs have attracted significant interest in remote sensing communities. The high-resolutions radar satellite missions like TerraSAR-X has launched in June 2007 and the images reach 1 m in azimuth and range in spotlight mode. Due to the high resolution, the detailed information of the observed scene we can distinguish from the TerraSAR-X HS mode image according to the SAR geometry of single-look slant-range complex data. This paper is concerned with the technique of how to use spaceborne SAR technology in a wide range of natural disaster conditions to monitor the power grid's security in particular. The power transmission tower in HS mode product is very distinguishable and the reflected forms of them are no longer point targets, so the extraction of tower height can be achieved from the single image of SSC product. At last the status of power transmission tower can be assessed based on the result of height extraction in the emergency response for the sudden natural public Disaster.

The high resolution images of TerraSAR has made it able to reflect the detail characters of large-scale manmade structures, so monitoring local deformation of large-sized buildings comes to be available. Former research had shown that InSAR phase of the dam surface was stable and consecutive. This paper aimed to make a further proof of the viability of monitoring dam's deformation using 3-meter-resolution strip mode images of TerraSAR. So we made a time series analysis of dam surface's coherence for the next step. Our dataset had eleven images covering three medium size basins of Shenzhen. Coherence of different features in the basin area including dams was extracted to make a comparative analysis. Two different combination methods were designed to create interferometric pairs to find the influence of time baseline and perpendicular baseline to coherence of different cultures. In our research, it was find out that coherence of dam surface was mainly influenced by time baseline. In short time baseline pairs (eleven days), coherence of dam surface was about 0.2 higher than vegetation slope in average. DInSAR process was suitable for short time baseline interferometric pairs, other methods such as PS will be needed for long time baseline interferometric pairs.

The ecological restoration in mining area is one of the study hot spots in the field of resources and environment at present. The vegetation biomass is used as the ecological restoration evaluation index in mining area in the paper. The synthetic aperture radar image after ecological restoration in mining area is used to classify different kinds of vegetation covers. Integrating the field data and the data of L band, the average total backward scattering coefficient which corresponds to the synthetic aperture radar image is calculated and the relation model between the average total backward scattering coefficient and vegetation biomass is established. At last the vegetation biomass is assessed in Jiuli mining area. The results show that the vegetation biomass characteristics which are assessed by using synthetic aperture radar image data and the field data of vegetation biomass characteristics have better consistency in Jiuli mining area. The effects of ecological restoration can be evaluated by using this relation model effectively and accurately.

Taking the land subsidence and ground fissure in Xian as the research object and nine Envisat ASAR images spanning the whole year of 2009 as the data source, time series surface deformation of Xian city from January to December in 2009 are obtained by SBAS-DInSAR approach, where the baselines and topographic phase were refined using ground control points (GCP). The results show that Xian is the state of continuous subsidence in 2009 and subsidence funnels are shaped nearly in oval whose long axis direction was approximately parallel to ground fissure direction. Meanwhile, the regional and seasonal characteristics were displayed for these subsidences regional. In addition, spatial distribution of strong activities and active regulation within the year between ground fissures and land subsidence are uniform based on this investigation.

Vegetation cover is an important parameter used in monitoring ecological changes of the source region of Yangtze, Yellow and Lantsang Rivers and understanding human activities. Thus, how to extract the large area's vegetation fraction quickly effectively is an open question. The traditional linear spectral mixture analysis (LSMA) assumes that the spectral reflectance is a mixture of several fixed endmember spectral values, which ignores considerable within-class variability. However, multiple endmember spectral mixture analysis (MESMA) overcomes the disadvantage by allowing the number and types to vary on a per-pixel basis. This paper proposes a stepwise spectral mixture analysis (SSMA) containing two steps of MESMA and adding the endmember fraction rationality rule in each step. The aim of the first step is to detect the pixels that didn't contain vegetation information at all and these pixels would be masked out. In the second step, MESMA is used to unmix the pixels only reserved in previous process. The results show that SSMA is more accurate than LSMA in extracting the vegetation fraction for the Three-Rivers. This means that SSMA is a good substitute for LSMA in studies on ecological changes. The concept of SSMA also can be applied for other large study areas.

Because of groundwater over-exploitation, the land subsidence of Tianjin city has become more and more serious. Some researchers tried to use D-InSAR technique to monitor the subsidence of this area. However, D-InSAR has some problems such as space and time baseline decorrelation and atmospheric influence. In order to obtain long time series land deformation with high accuracy, SAR image set to monitor land subsidence has become a research focus. PSDInSAR (Persistent Scatterers-DInSAR) and SBAS (Small Baseline Sets) are two classical methods to monitor land subsidence by SAR images. The former chooses one image as the master and the latter chooses several images as the masters according to the threshold of baselines. Using eighteen ERS images and SRTM DEM data the land subsidence of Tianjin city from 1992 to 1998 was obtained by two methods mentioned. 18906 and 42041 PS points were identified respectively by PS-DInSAR and SBAS. While, 16.4 mm/a and 24.2 mm/a of the mean maximal subsidence velocities were obtained respectively. Although they are different in values, the distributions and subsidence laws of points are nearly same.

The Wulanmulun town, located in Inner Mongolia, is one of the main mining areas of Shendong Company such as Shangwan coal mine and Bulianta coal mine, which has been suffering serious mine collapse with the underground mine withdrawal. We use ALOS/PALSAR data to extract land deformation under these regions, in which Small Baseline Subsets (SBAS) method was applied. Then we compared InSAR results with the underground mining activities, and found high correlations between them. Lastly we applied Distributed Dislocation (Okada) model to invert the mine collapse mechanism.

Detailed data acquisition under goldmine is very difficult with traditional technology due to its dangerous, dark and damp environment. 3D Laser scanner is useful to capture the 3D data underground a goldmine because it can capture density points quickly and safely. Based on the experiment of applying 3D laser scanning technology to the underground survey in a goldmine, the method of 3D data capture and data processing are introduced in detail. 7 scans were performed to capture almost 29 million points within 2 hours using Trimble FX Laser Scanner. Accurate spatial information such as section, surface area and volume and 3D model of roadway are obtained from the point clouds at last. Results of the experiment indicate that 3D laser scanning technology cannot only greatly improve the safety and efficiency of underground survey but also assist in building Digital Mine database. It really provides a new method for underground survey.

Differential Radar Interferometry (DInSAR) is capable of all-weather and all time observation. It is an unprecedented and profound developing potential space technology with the superiority of continuous space coverage compared to other discrete measurements. The Envisat images from ESA were used in this paper to derive the co-seismic deformation interferograms caused by the Zhongba Ms6.7 earthquake on July 12, 2004 and Ms6.5 earthquake on April 8, 2005 occurred in Tibet. The results indicate that two earthquake events caused the deformation in an area of 20 km with a maximum LOS change about 19.0 cm and 30.5 cm respectively. Adopting isotropic elastic half-space dislocation model, we estimated that two earthquakes epicenter locate at E 83.71°, N 30.70° with a seismic moment tensor Mw6.1 and E 83.72°, N 30.52° with a seismic moment tensor Mw6.2, dominated by normal fault fracture with maximum slip 1 m and 1.4 m, located at the intersection of NW Brahmaputra fault zone and the near side NNE Palongcuo-Cangmucuo fault zone.

Wide Swath mode (WSM) is also called as ScanSAR, it is character is that synthetic aperture time is shared by circumjacent sub swaths and azimuth resolution is reduced so that large area deformation monitoring for earthquakes is realized. For imaging principle ScanSAR and IM mode are different, process of interferometry also have a lot of dissimilitudes such as coherence, co-registering, correction of atmosphere effects and geoid undulation. In order to make use of ScanSAR data to get wider deformation field, key techniques of ScanSAR interferometry are studied and analyzed in this paper. In the end, 405 km ×405 km deformation fields of Wenchuan and Yutian earthquake are gained by using ENVISAT ScanSAR data. With the application to ScanSAR interferometry in Wenchuan and Yutian earthquake, it is significant for Earth Science research such as large earthquakes and crustal motion.

Interferometric synthetic aperture radar (InSAR) is a very effective technique for measuring surface deformation, but the temporal and geometrical decorrelation and atmospheric disturbances can strongly compromise the accuracy of the results. Persistent scatterer InSAR (PS-InSAR) overcomes the decorrelation and atmospheric disturbances problem by identifying resolution elements whose echo is dominated by a single scatterer in a series of interferograms. The results obtained by PS-InSAR technique are not the field deformation information but persistent scatterers deformation. In this paper, PS-InSAR is used to investigate surface deformation caused by landslide hazards in Hanyuan reservoir resettlement, China. The subsidence map derived from Envisat ASAR data between May 2008 and Oct 2009 reveals the spatial extent of the deformations and subsidence velocity. The experimental results show that there are instable areas which are located in the Middle of new Hanyuan county. The average deformation rate of the instable areas is over - 20mm. The Upper right and Lower left of new Hanyuan county are relatively stable.

Land surface temperature (LST), vegetation index, and other surface characteristics that obtained from remote sensing data have been widely used to describe urban heat island (UHI) phenomenon, but through impervious surface area (ISA) to describe the phenomenon has only used in a few study areas in our country. In a high urbanization and high population density region like Jiangsu Province, a wide range of extraction of ISA to study its relationship with UHI is less. In this paper, we use multi-temporal remote sensing images as data sources, and extract ISA from it in a large-scale by using decision tree classifier (DTC) and linear spectral mixture analysis (LSMA). Then combine the average surface temperature from the sixth band of Landsat TM by mono-window algorithm for spatial analysis, to assess the change of the urban heat island temperature amplitude and its relationship with the urban development density, size and ecological environment. Finally we use statistical methods to analyze the relationship between ISA, LST and UHI. The results show that ISA has a positive correlation with surface temperature. The ratio of ISA is higher and the difference value of the temperature is larger, thus the UHI will be more obvious.

After the operation of GPS/IMU direct geo-referencing, segmentation, filtering, classification of scattered point data and aerial triangulation on airborne LiDAR(Light Detection and Ranging) data, the accurate and high-resolution DEM of the study area in the west part of Zengcheng city, Guangdong, China was constructed. In addition, unmanned aerial vehicle (UAV) images were used for ground objects identification. Landslides occur frequently in summer in the city because of heavy rainfall. The LiDAR data (point cloud) and the mosaic images were then combined to produce the suitability distribution maps by considering Several factors, such as slope gradient, slope aspect, on-the-spot investigation data etc The maps can then be used to analyze the potential risk of landslides and assess the risk level around some buildings. The experiment results show that the method based on LiDAR data and UAV images can rapidly and accurately survey the terrain of the study area and also provides useful information for architectural design.

This study aimed to estimate forest stand variables, such as mean height, mean crown diameter, mean diameter breast height (DBH), basal area, tree density, and aboveground biomass in coniferous tree species of Picea crassifolia stand in the Qilian Mountain, western China using low density small-footprint airborne LiDAR data. Firstly, LiDAR points were classified into ground points and vegetation points. Then the statistics of vegetation points, including height quantiles, mean height, and fractional cover was calculated. The stepwise multiple regression models were used to develop the equations relating the statistics of vegetation points to field inventory data and field-based estimates of biomass for each sample plot. The result shows that the mean height, biomass and basal area have a higher accuracy with R² of 0.830, 0.736 and 0.657, respectively, while the mean diameter breast height DBH, crown diameter and tree density have a lower accuracy with R² of 0.491, 0.356 and 0.403, respectively. Finally, the spatial forest stand variable maps were established using the stepwise multiple regression equations. These maps were very useful for updating and modifying forest base maps and forest register.

Microwave remote sensing has become an important way of soil moisture retrieval because of its better penetration into vegetation and soil layers, and its higher sensitivity to the content of soil moisture. Retrieval by active or passive method has a series of mature algorithms, and the combined active-passive methods are the recent research highlights. But the existing combined algorithms are not mature enough, with their weaker applicability and lower accuracy. This study aims to solve the problem of low accuracy, and proposes a new algorithm: based on the imitation of water-cloud model and IEM model, with a BP neural network, obtaining its own sensitive surface parameters from active and passive microwave remote sensing data, and developing a new combined active-passive retrieval model for soil moisture. Finally with testing and verifying in SMEX02 experiments, the model presented the results of higher accuracy.

In tidal flat terrain of the yellow sea radial sand ridges in eastern China, tidal creeks with water are regarded as the "blind area" of LiDAR surveys. These areas are also hard to be surveyed efficiently and cheaply by traditional surveying methods. To solve the problems of high cost and great effort, this paper researches a Back-Propagation neural network interpolation method, supported by LiDAR data and geomorphic unit classification. The interpolation model structure contains 2 hidden layers with 6 neurons in every layer. This research consists of the following steps: (1) geomorphic unit classification by analyzing dynamic geomorphology of tidal creeks, (2) terrain spatial regularity learning by analyzing a large set of LiDAR data, (3) model building based on the Back-Propagation neural network technique, (4) sample data training with similar tidal creek geomorphic unit data, (5) model structure and parameters determination, (6) testing by comparing the results with the survey data. The test results show that the developed methodology is effective in producing the terrain lacking LiDAR DEM in tidal flats.

Tropical forests represent a significant carbon store in the global carbon balance. Over recent years, substantial forests have been cut or burned. To investigate the changes of deforestation in tropical forests, ALOS PALSAR 50 m Orthorectified mosaic product (FBD) in Borneo from 2007 to 2009 was used, as SAR data can overcome the shortcoming of optical images. Pixel based unsupervised classification and object-oriented classification methods were applied to choose better method for classification. The comparison showed the object-oriented classification got the better result and its accuracy was higher. In the following step, object-oriented classification method was employed to monitor the changes of deforestation in Borneo during two years. The results indicated the deforestation increased sharply, especially the areas in the coastal and next to non-forest land cover/use. The change law of land cover/use in Borneo is forest to shrub, grassland and bare soil. There are also inverse changes from non-forest to forest by reforestation around settlement and water areas. Overall, forest decreases gradually and non-forest areas increases simultaneously in Borneo. This research indicates clearly that ALOS PALSAR Ortho-rectified FBD product can be used to classify land cover/use and two different time images could be employed in change detection without in situ data.

Jiaxing is 110 kilometers far away from Shanghai, the high speed railway and the planed maglev from Shanghai to Hangzhou cross Jiaxing city. It is very important to monitor ground subsidence in Jiaxing region for the safe running of the high speed railways. However, the traditional ground subsidence measurement methods, such as precise leveling and GPS, have a very low spatial resolution and are laborious. DInSAR technology is a new effective method for monitoring ground subsidence with high resolution and accuracy, but due to the influence of many kinds of noises, the interferograms cannot reveal ground deformation directly. In this work, small baseline subset (SBAS) method is applied to extract the ground subsidence information. SBAS can reduce the effects of topographic error and spatial decorrelation, and also increase the number of useable InSAR pairs. Totally 13 scenes of the Envisat SAR images covering Jiaxing region between 2008 and 2009 are chosen and processed. The results show that a funnel shaped ground subsidence appears at Pinghu city.

In this paper, the author use the DORIS software to conduct the differential interferometric processing of existing data and extracts ground deformation fields in different periods of Yaoqiao mining area, Xuzhou. After that, the analysis is carried out by combining with characteristics of ground exploitation subsidence, and the comparative analysis of land subsidence data obtained by conventional methods is conducted. The results show that: the land subsidence of Yaoqiao mining area is quite consistent to the exploitation subsidence rules; in addition, D-InSAR can monitor minor changes of exploitation subsidence boundaries in mining areas and the evolution trend of subsidence surface, and also can measure the subsidence speed. It has proved that the InSAR technology can conduct continuously effective dynamic monitoring of ground deformation in mining areas from this example. In terms of the regional continuous ground depression, DInSAR is much better than the traditional methods of measurement. The development of this new Earth observation technology has important practical significance in promoting the improvement of China's "digital mine" system and guiding the planning and preservation of buildings and road systems in mining areas.

Radar imagery is potentially useful for the identification, mapping, and measurement of streams, lakes, and wetlands. Many studies showed that comparison of two consecutive radar images is useful for determining flood extent. However, the use of radar data series for flood mapping is still rarely reported. The purpose of this study is to explore the use of ALOS-PALSAR imagery for observing the dynamics of the Mahakam River floodplain in Kalimantan, Indonesia, by incorporating field water level measurements. Water level measurements were carried out along the river, lakes and at two peatland locations, using arrays of pressure transducers. The first peatland (P1) is part of the Mahakam floodplain, representing open peat area dominated by shrub and reed. The second peatland (P2) represents a forest covered peatland. A series of PALSAR imagery (polarity: HH; pixel spacing: 50 m) covering the middle and lower Mahakam area in the years 2007 and 2008 was collected. A land use/land cover map was available from a previous analysis of PALSAR imagery. To analyze Radar backscatter behavior for different land cover types, several regions of interest were selected, based on the land cover classes. A number of land cover classes (medium shrub, high shrub, fern/grass, and secondary forest) were found to be sensitive to flooding, whereas in some other classes (peat forest, riverine forest and tree plantation) backscatter signatures remained almost unchanged with flood inundation. Correlations between water level and radar backscatter of the regions of interest were used to distinguish between three types of flooding signal, viz. flooding of low vegetation, flooding of high vegetation, and the boundary shift of lakes. An analysis of the relationship between radar backscatter and water levels was carried out in each of the regions of interest. For lakes and shrub covered peatland in P1, where the range of water level variation was high, a good water level-backscatter correlation was obtained. In peat forest covered peatland in P2, subject to a small range of water level variation, water level-backscatter correlations were poor.

Supported by NASA's Earth Surface and Interior (ESI) Program, we are producing a global set of Ground Control Points (GCPs) derived from the Ice, Cloud and land Elevation Satellite (ICESat) altimetry data. From February of 2003, to October of 2009, ICESat obtained nearly global measurements of land topography (± 86° latitudes) with unprecedented accuracy, sampling the Earth's surface at discrete ~50 m diameter laser footprints spaced 170 m along the altimetry profiles. We apply stringent editing to select the highest quality elevations, and use these GCPs to characterize and quantify spatially varying elevation biases in Digital Elevation Models (DEMs). In this paper, we present an evaluation of the soon to be released Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). Elevation biases and error statistics have been analyzed as a function of land cover and relief. The GMTED2010 products are a large improvement over previous sources of elevation data at comparable resolutions. RMSEs for all products and terrain conditions are below 7 m and typically are about 4 m. The GMTED2010 products are biased upward with respect to the ICESat GCPs on average by approximately 3 m.

One three-dimensional (3D) heritage reconstruction method based on structured light extraction and interpolation is proposed. Firstly, an image smoothing algorithm is presented to remove noise. And then a threshold method is designed to extract the laser line with a certain width. The center coordinates of the intersection line of gray-scale laser plane and the surface of artifacts can be obtained. Secondly, a calibration ruler is used to establish a mapping relation between the laser plane and the image plane. With the index table of mapping relationship, the corresponding relationship between laser plane and the 3D space can be represented by the rotating matrix. Finally, the mapping relation between images coordinates and 3D spatial coordinates can be used to obtain the 3D model of heritage relics. Experiment results show that the presented method is fast and accurate which can meet the requirement of heritage 3D construction.

Well located at the geometric center of China, Lanzhou is one of the typical valley-basin cities in the western China. In this study, we aim to resolve two questions: (1) from the spatial analysis, we want to know what changes have happened on urban landscape characteristics from 1976 to 2004? (2) What is the main driving force behind the changes? This research is based on American quick bird remote sensing image of 2004 with a high resolution of 0.61m and the topographical map of Lanzhou city. In addition, the detailed land use data and some cadastral data in 1976 are collected as well as the current land use data. Based on the calculated result, we analyse the change characteristics of the classlevel metrics and landscape-level metrics of the Lanzhou city. Our results show that the change trend in landscape associated with urbanization of Lanzhou city can be effectively captured by spatial analysis using the remote sensing image and GIS data processing technique, and by calculating several landscape pattern metrics, such as patch density, edge density, Shannon diversity index, contagion, etc.

Geological Logging plays a vital part in hydropower and traffic engineering construction. In view of the current problems existing in engineering geological logging, this paper proposes a new engineering geological logging approach, which integrates LiDAR techniques with digital close-range photogrammetry. In this approach, firstly, acquire digital images and point cloud of engineering excavation surface by high-resolution digital camera and three-dimensional laser scanner respectively. Secondly, project point data onto a raster image, after which, mark points will be extracted from point cloud raster image and digital images with the help of image processing technology. Lastly, calculate the exterior orientation elements of digital images by the close-range photogrammetric space resection algorithm, and then register point cloud with digital images by collinearity equation. As a result, two computer-aided logging methods are also presented: one is two-dimensional image logging method based on the single image projection transfer algorithm, the other is logging in three-dimensional virtual environment after three-dimensional reconstruction of engineering excavation surface. The practical applications have demonstrated that the approach proposed in this paper has a great improvement in efficiency and precision of geological logging.

Due to the geographical peculiarities of the sea islands, we often encounter the difficulty that some coastlines are hard to reach or to identify. Thus a specific measurement method cannot be implemented effectively, which affects the integrity and precision of island coastline surveying. In this paper, we use the digital elevation Model (DEM) by LIDAR date. However, The elevations acquired by an Airborne LIDAR system are ellipsoid heights, which is different from the National Vertical Datum 1985 in existing island coastline. For this reason, a regional quasi-geoid model is constructed with the geometric method, using the EGM2008 gravity field model and GPS/Leveling data. We manage to transform ellipsoid heights to normal heights from observations, and construct the DEM which is based on National Vertical Datum 1985. Finally island coastlines are extracted from the reference DEM and the associated tidal model and compared with that from GPS-RTK measurements.

Taiwan is located in an area where frequently changing weather conditions threaten the lives and properties of its residents. With global warming, concentrated rains and extreme weathers bring about more frequent flash floods and debris flows, and destroy more bridges. This study describes the first ever full-scale experiment to determine the lateral displacements of abandoned bridge piers during push-over tests. In addition to cable position transducers and a LVDT, a high speed 3D terrestrial laser scanner was placed in the riverbed to scan the test pier, the reaction pier, and the reference steel frame. The results showed that not only can the laser scanner detect and measure the pier displacements at specific points, but it can also provide the displacement measurements of the entire test pier from top to bottom, thus generating more measurement data of the pier deformation behavior for further analysis than the cable position transducers or the LVDT alone. Other benefits of using the laser scanner include the determination of the relative displacements between the test pier and the reaction pier and the 3D realistic visualization of the experiment site. It is believed that the terrestrial laser scanner can play important roles in future large-scale structural experiments.

It is the difficulties to radar image interpretation, present remote sensing investigation and assessment of geohazard is mainly dependent on the high-resolution optical images, resulting in limited ability to extract surface information. The main advantage of radar is that it provides superior penetration capability under any type of weather condition, and can be used in the day or night time, rich image information and so on for the risk assessment of landslide, especially in geohazard emergency; optical image cannot match this advantage. The use of the "5.12" earthquake-induced landslide hazard for the research prototype, elaborate unique advantages and technical support role of remote sensing technology in landslide investigation and risk assessment, from the basic terrain data acquisition, disaster background analysis, interpretation of landslide hazard, monitoring, mapping, etc. Use of airborne and satellite radar remote sensing and Multi-source data to composite analysis of hazard information, indicates that the better interpretation effect by field investigation. The research results of this paper have great reference value to emergency disaster prevention and reduction of occurred frequent and dangerous geohazard.

The southern yellow sea radial submarine sand ridges are in the central Jiangsu coast, where sediment dynamics is complex and the tidal ridges and channels are changing. The purpose of this paper is to model tidal flat terrain. Based on the regularity and variability characteristics of the tidal flats combined with remote sensing and LiDAR survey data, this research focuses on tidal flat terrain modeling with a neural network method. Firstly, the network structure and the parameters involved, such as weights and offset values of neurons, are determined by the BP Neural Network calculation using the 2006 LiDAR DEM in this area. Secondly, the characteristic lines, which are boundary lines of tidal basins, skeleton lines of tidal creeks and a series of waterlines, and so on are extracted from TM images of the no-data region similar to the area of study. Combining with survey data, the elevation data of characteristic lines are obtained. At last, the terrain of the region without elevation data is generated by the model. The test shows the terrain calculated by the model is very close to the surveyed terrain. The residual distribution is normal. The study is significant in getting a dynamic tidal flat terrain fast and efficiently.

It is very difficult to perform geomorphological analyses and modeling in the salt marshes because of fieldwork logistics, the tidal oscillation and variability, and the inherent dynamic nature of these environments. Recently novel technologies and methods introduce the capability to create high-resolution biophysical and elevation databases to quantitatively characterize salt marsh geomorphology in support of improved understanding of the evolution of intertidal systems. This study combines the use of high-resolution imagery obtained at low tides with a LIDAR-derived digital elevation mode (DEM) of Xiaoyangkou, a typical Jiangsu Coastal tidal estuarine system near the Jiangsu Radiate Sand Ridges, to quantify relationships among marsh features, their metrics, elevations, and tidal datum. The methods used to quantify features across an intertidal watershed in its entirety represent a significant advance in support of future development of process-driven models to explain these observations.

Conventional hydrologic analyses of digital elevation models (DEMs) perform well in areas of high topographic relief, where surface water flow is typically unidirectional, convergent, spatially static, and directed toward a single discharge point at the edge of a catchment. Such analyses do not perform well on landscapes with low topographic relief such as estuaries areas. where surface water flow is influenced by subtle topographic depressions and sea-land interaction, which may be bidirectional, divergent, and spatially dynamic in response to hydrologic forcing such as tides or variation in river discharge. We developed a framework for hydrologic analysis of low-relief landscapes using a high-resolution (0.2m) DEM derived from light detection and ranging (LIDAR) data collected over a Jiangsu Radiate Sand Ridges, China. Our approach assessed the pattern and characteristics of estuaries watershed and its hydrologic response drainage, where drainage boundaries were defined by subtle topographic divides across the Salt-marsh.

ICESat/GLAS data for a temperate forest in a hilly region were analysed regarding the potential to retrieve maximum canopy height using a direct approach. The GLAS height was derived by calculating the range of the waveform Signal Begin and the Ground Peak. The comparison with an inventory data base and airborne lidar based heights revealed that the GLAS height overestimates the reference heights by on average 5 m and 7 m, respectively (Std=6 m and Std=5 m). The GLAS waveforms were further investigated regarding the canopy structure and the ground surface topography. It was found that the waveforms are most influenced by the ground surface topography. The topography clearly affects the waveform shape and therefore also the accuracy of the GLAS height estimation. Waveforms corresponding to footprints with a mean slope up to 10° have a high potential to derive tree height. The correlation between the GLAS and reference heights is still fair for waveforms of moderate slope 10-15°. Higher slopes clearly challenge the analysis of the waveform structure and the derivation of forest parameters.

Laser altimeter measurements of forest stands with distinct structures and compositions have been acquired at 532 nm (green) and 1064 nm (near-infrared) wavelengths and parallel and perpendicular polarization states using the Slope Imaging Multi-polarization Photon Counting Lidar (SIMPL). The micropulse, single photon ranging measurement approach employed by SIMPL provides canopy structure measurements with high vertical and spatial resolution. Using a height distribution analysis method adapted from conventional, 1064 nm, full-waveform lidar remote sensing, the sensitivity of two parameters commonly used for above-ground biomass estimation are compared as a function of wavelength. The results for the height of median energy (HOME) and canopy cover are for the most part very similar, indicating biomass estimations using lidars operating at green and near-infrared wavelengths will yield comparable estimates. The expected detection of increasing depolarization with depth into the canopies due to volume multiplescattering was not observed, possibly due to the small laser footprint and the small detector field of view used in the SIMPL instrument. The results of this work provide pathfinder information for NASA's ICESat-2 mission that will employ a 532 nm, micropulse, photon counting laser altimeter.