Urban land cover classification using airborne LiDAR ...

Urban land cover classification using airborne LiDAR data: a review Wai Yeung Yan∗, Ahmed Shaker, Nagwa El-Ashmawy Department of Civil Engineering, Ryerson University, Toronto, Ontario, Canada

Abstract Distribution of land cover has a profound impact on the climate and environment; mapping the land cover patterns from global, regional to local scales are important for scientists and authorities to yield better monitoring of the changing world. Satellite remote sensing has been demonstrated as an efficient tool to monitor the land cover patterns for a large spatial extent. Nevertheless, the demand on land cover maps at a finer scale (especially in urban areas) has been raised with evidence by numerous biophysical and socio-economic studies. This paper reviews the small-footprint LiDAR sensor – one of the latest high resolution airborne remote sensing technologies, and its application on urban land cover classification. While most of the early researches focus on the analysis of geometric components of 3D LiDAR data point clouds, there has been an increasing interest in investigating the use of intensity data, waveform data and multi-sensor data to facilitate land cover classification and object recognition in urban environment. In this paper, the advancement of airborne LiDAR technology, including data configuration, feature spaces, classification techniques, and radiometric calibration/correction are reviewed and discussed. The review mainly focuses on the LiDAR studies conducted during the last decade with an emphasis on identification of the approach, analysis of pros and cons, investigating the overall accuracy of the technology, and how the classification results can serve as an input for different urban environmental analysis. Finally, several promising directions for future LiDAR research are highlighted, in hope that it will pave the way for the applications of urban environmental modeling and assessment at a finer scale and a greater extent. Keywords: Airborne LiDAR, laser scanning, LiDAR intensity, land cover mapping, land cover classification, radiometric calibration, radiometric correction, full-waveform, urban environment, and urban analysis

1. Introduction Land cover is defined as the physical composition and characteristics of land elements on the Earth surface (Cihlar, 2000). Since the distribution of land cover has significant impact ∗

Corresponding author: Wai Yeung Yan Email address: [email protected] (Wai Yeung Yan)

Preprint submitted to Remote Sensing of Environment

November 13, 2014

on the climate and environment, mapping the land cover patterns from global, regional, to local scales are important for scientists and authorities to yield better monitoring of the changing world. The Climate Research Committee of the National Research Council (National Research Council, 2005) stressed that distribution of land cover has pronounced impact on the Earth’s radiation balancing, since any changes in land cover would affect the evaporation, transpiration and heat flux on the ground surface. For instance, tree canopies absorb solar radiation resulting in a reduction of land surface temperature on the ground. On the other hand, increase of impervious land cover in urban area (e.g., asphalt, concrete, paving stones, etc.) prevents infiltration of groundwater, which may cause potential flood hazard. Therefore, precise monitoring of land cover becomes indispensable for decision makers in dealing with public policy planning and Earth resources management. Satellite remote sensing has been demonstrated as an efficient tool to acquire the Earth’s topography for a large spatial extent. Remote sensors record the spectral reflectance of different land cover features from visible to infrared wavelength, and from moderate to very high spatial resolution. Since the launch of the first Earth observation remote sensing system - Landsat Multispectral Scanner System (or Landsat 1), scientists have extensively explored the use of satellite images to derive land cover patterns by different computeraided processing techniques. Classification techniques are commonly used to find out the land cover patterns based on the spectral signatures derived from the remote sensing images. Currently, national and international agencies have successfully created land cover classification systems and land cover maps at national scale, for instance, the United States Geological Survey’s (USGS) Global Land Cover Characteristics Database, European Environmental Agency’s (EEA) Coordination of Information on the Environment (CORINE), European Commission’s Joint Research Centres GLC2000, Canadian Council on Geomatics and Natural Resources Canada’s GeoBase (Johnson and Singh, 2003) (see Fig. 1), etc. These regional/global land cover maps were produced from the satellite remote sensing data such as AVHRR (Loveland et al., 2000), MODIS (Friedl et al., 2002), Landsat (Tucker et al., 2004) and SPOT (Bartholom´e and Belward, 2005). In addition, hierarchical land cover/use classification systems were established and reviewed by various national agencies, including USGS (Anderson et al., 1976) and United Nation (UN) / Food and Agriculture Organization (FAO) (Gregorio and Jansen, 2000). One of the recent challenges for remote sensing studies is to improve the spatial resolution of mapping products to support various urban studies at fine scale; thus, this has evoked a passionate discussion of “downscaling” in remote sensing research lately. For instance, thermal images acquired by satellite sensors such as ASTER, Landsat and SEVIRI, are usually with lower spatial resolution than multi-spectral images. To enhance the resolution of the derived land surface temperature (LST) image, land cover classification results such as vegetation cover (Bechtel et al., 2012; Zakˇsek and Oˇstir, 2012) and impervious surface (Essa et al., 2013, 2012) are commonly used for downscaling the result of LST based on techniques, such as sub-pixel estimation, spatial sharpening, image fusion or regression analysis (Atkinson, 2013). Therefore, the demand on land cover map at finer scale, especially in urban 2



Figure 1: An example of land cover GIS data available at Canadian GeoBase.

area, has been raised with evidence by numerous biophysical and socio-economic studies in urban heat island (Liu and Weng, 2009; Mitraka et al., 2012; Nichol, 2009), urban sprawl pattern (Bhatta et al., 2010; Jat et al., 2008; Sun et al., 2007), urban environmental quality (Li and Weng, 2007; Liang and Weng, 2011; Rahman et al., 2011b), urban rainfall-runoff modeling (Berezowski et al., 2012; Ravagnani et al., 2009; Thanapura et al., 2007), urban anthropogenic heat (Weng, 2009; Xu et al., 2008; Zhou et al., 2012), and urban air pollution (Jiang et al., 2008; Superczynski and Christopher, 2011; Xian, 2007). Very high spatial resolution optical satellite sensors, such as GeoEye and WorldView, now provide less than half-meter pixel resolution in the acquired remote sensing images. Theoretically, users can derive larger scale of land cover maps from these high resolution satellite images. Nevertheless, the problem of between-class spectral confusion and withinclass spectral variation in high spatial resolution imagery would degrade the separability among different land cover features. Though intelligent image segmentation and objectbased classification techniques have been proposed to replace pixel-based classification to deal with these data (Blaschke, 2010; Zhou and Troy, 2008), the effects of shadowing and relief displacement still pose considerable challenges in the derived products (Dare, 2005; Zhou et al., 2009). A survey conducted by Wilkinson (2005) addressed 574 classification experiments from 138 scientific papers over the past fifteen years. Surprisingly, the development of remote sensing image classification techniques did not show a significant upward trend in terms of the overall accuracy in the last two decades. Perhaps passive aerial/satellite remote 1

 

3

sensing technique reaches certain limits to produce a land cover map product at finer scale; therefore, one of the advancements to consider in the future is to divert the research from algorithmic development into multi-sensor data fusion (Benediktsson et al., 2007; Zhang, 2010). That need thus inspires researchers investigating the use of topographic airborne LiDAR data for land cover classification during the last decade. 2. The Development of Airborne LiDAR Technology Airborne LiDAR is a laser profiling and scanning system for bathymetric and topographic applications, which emerged commercially in mid-1990s. With the aid of direct geo-referencing technique, the laser scanning equipment installed in the aircraft collects a cloud of laser range measurements for calculating the 3D coordinates (xyz) of the survey area. In contrast to the 2D planimetric remote sensing data, the explicit LiDAR data point cloud describes the 3D topographic profile of the Earth surface. Other benefits of airborne LiDAR include no effects of relief displacement, penetration of tree canopy, and insensitivity to lighting conditions. Therefore, airborne LiDAR technique has been effectively used for generating digital terrain model (DTM), topographic mapping, construction of digital 3D city model, natural hazard assessment, etc. Fig. 2(a) shows an example of airborne LiDARderived surfaces fused with intensity data. The results are compared against the aerial photo depicted in Fig. 2(b). The effects of relief displacement and shadowing appeared in the aerial photo are mostly resolved in the airborne LiDAR data.

(a)

(b)

Figure 2: (a) Airborne LiDAR-derived surface fused with intensity image, and (b) high resolution aerial photo with effects of shadows and tilted buildings.

In 1999, the International Society for Photogrammetry and Remote Sensing (ISPRS) Journal of Photogrammetry and Remote Sensing published a special theme issue on airborne laser scanning (Wehr and Lohr, 1999b), which has made a significant impact on the LiDAR development in both industry and academic communities. One of the top cited papers in this theme issue, published by Ackermann (1999), pointed out the expected future development of airborne laser scanner in three different areas: 1) more and extended 4

applications, 2) consolidation and extension of LiDAR data processing methods, and 3) additional information about the surface characteristics analyzed from the returned signal. After a decade, the three arguments have been well proven by the efforts made by the manufacturers, industry, academician, professional bodies and end-users. Firstly, the rapid growth in the LiDAR market was highlighted by an industrial survey (Cary and Associates, 2009). The survey indicated that there has been an increase of 75% in the number of LiDAR systems in use, 53% in LiDAR operators, and 100% in the number of end users between 2005 and 2008. According to the same survey, such an upward trend is expected to continue in the next several years. The rapid development in LiDAR sensor and software can also be justified by the continuous reviews published by the GIM International (Lemmens, 2007a,b, 2009, 2010). With the rapid growth in both the demand and supply, a wide array of LiDAR applications can be found in various review papers regarding forestry modeling and analysis (Hyypp¨a et al., 2008; Lim et al., 2003; Wulder et al., 2012), habitat ecology (Bradbury et al., 2005), landslide investigation (Jaboyedoff et al., 2012), 3D building modeling (Wang, 2013), road extraction (Quackenbush et al., 2013), and snow depth measurement (Deems et al., 2013). Second, various parties have made significant contribution to the consolidation and extension of LiDAR data processing methods in both direct and indirect ways. Methods such as LiDAR data filtering (Meng et al., 2010; Sithole and Vosselman, 2004), DEM generation (Liu, 2008), system calibration (Skaloud and Lichti, 2006), radiometric calibration (Wagner, 2010), and full-waveform data modeling (Mallet and Bretar, 2009; Wagner et al., 2006) have been researched and examined. The American Society of Photogrammetry and Remote Sensing (APSRS) also provided a platform for the formulation of Log ASCII Standard (LAS) data format from version 1.1 to 1.4 (ASPRS, 2003a, 2005, 2008, 2010, 2011), and published guidelines and manual for manipulating the airborne LiDAR data (ASPRS, 2003b, 2004; Renslow, 2012). The ISPRS has recently offered three benchmark testing datasets for examining the algorithms and methods for urban object classification and 3D building reconstruction (Rottensteiner et al., 2013). In addition, a number of professional bodies have also organized and co-organized annual LiDAR conferences, such as ISPRS LS Workshop, SilviLaser, International LiDAR mapping forum, etc. All these efforts have stimulated the development of LiDAR data processing methods and algorithms. Lastly, additional information about the surface characteristics analyzed from the returned signal has also been investigated. Since commercial topographic LiDAR sensors usually utilize the near-infrared laser, which operates at wavelength 1064 nm or 1550 nm, high separability of spectral reflectance can always be found among different land cover materials in the near-infrared spectrum (see Fig. 3). In this regard, the peak laser energy (intensity data) backscattered from different objects can be used to distinguish different land cover features (Song et al., 2002). The latest development of waveform digitizer can further record the backscattered signal in nanosecond, resulting in a continuous high resolution 1-D waveform profile. As a result of researches in understanding the physical basis 5

Visible Range

0.6

LiDAR at 1.064 μm



Asphalt Road Brick Dry Grass Lime Stone Sea Water

Reflectance

0.5 0.4 0.3 0.2 0.1 0 0.4

0.6

0.8

1 1.2 1.4 Wavelength (μm)

1.6

1.8

2



Figure 3: Spectral reflectance of different materials across visible to infrared wavelengths.

of LiDAR signal returns (Coren and Sterzai, 2006; H¨ofle and Pfeifer, 2007; Kaasalainen et al., 2007a; Wagner, 2010), airborne LiDAR technology has been applied to study the land surface condition and analyze the land cover pattern since last decade. This paper reviews the latest development of airborne LiDAR technology, in particular, discrete return and full-waveform LiDAR sensors, and their applications on urban land cover classification and analysis. The review mainly focuses on the peer-review journal papers published under the category of “Remote Sensing” in ISI Web of Knowledge. Nevertheless, in order to form a comprehensive review, publications that have a significant impact on the topic were referenced including other research papers and international conferences (such as IEEE and ISPRS Conferences, Silvilaser, Lecture Notes in Computer Science, etc.). The rest of the paper is organized as follows. Section 3 provides a summary of the most notable studies on urban land cover classification regarding the LiDAR data specification and scanning configuration. Subsequently, Section 4 provides a detailed review of different LiDAR-derived features. The effects of different classification techniques, and radiometric calibration/correction on the classification accuracy are highlighted in Sections 5 and 6, respectively. Besides land cover classification, Section 7 presents numerous applications of urban object extraction and analysis. In Section 8, we highlight several promising areas for future research; and finally, the conclusions are drawn in Section 9. 3. Airborne LiDAR Data and Scanning Configuration Table 1 lists a number of representative studies that demonstrate the use of airborne LiDAR for urban land cover classification in terms of the scanning configuration, number of feature spaces used, data resolution, classification approach and overall accuracy. As shown 6

in Table 1, it is apparent that the LiDAR sensor development shows a gradual improvement with respect to the scanning configuration. For instance, the pulse rate (or pulse repetition frequency) as reported in early studies (Brennan and Webster, 2006; Charaniya et al., 2004; Lodha et al., 2007, 2006) ranges from 25 kHz to 40 kHz only. The pulse rate significantly increases to more than a double (82 kHz to 117 kHz) of its initial range as indicated by (Chehata et al., 2009; Guo et al., 2011; Habib et al., 2011; Mallet et al., 2008; Yan et al., 2012). In terms of flying height, the configuration of the majority studies varies from 300 m to 950 m so as to maintain a reasonable mean point density. The latest developed LiDAR sensor can further operate up to 1 to 4 km in order to serve a large area coverage and mapping purpose (Singh et al., 2012). Regarding the swath (scan) angle and flying speed, there is a lack of studies researching such parameters with an exception of the following maximum scan angles: 22.5◦ and 30◦ as presented in Alexander et al. (2010).

Table 1: A list of representative studies using airborne LiDAR for urban land cover classification. Case Study

Laser Scanning Configuration

Data Resolution

Feature Spaces

Classification Technique

Overall Accuracy

Charaniya et al. (2004) Brennan and Webster (2006) Lodha et al. (2006), Lodha et al. (2007) Im et al. (2008)

PR = 25 kHz; PS = 0.26 m; WL = 1064 nm BD = 0.45 mrad; FH = 950 m; PR = 40 kHz; SR = 17 Hz; WL = 1064 nm PR = 25 kHz; PS = 0.26 m; WL = 1064 nm

0.5 m

2 to 5

66% to 84%

1 m

5

0.5 m

5

4 Classes; Gaussian Mixture Model 7 to 10 Classes; ObjectOriented 4 Classes; AdaBoost and Support Vector Machines 5 Classes; Object-based Decision Tree 4 Classes; Support Vector Machines

PD = 15.3 pts/m2 ; PR = 50kHz; PS = 0.25 m 8 0.4 m; WL = 1064 nm Mallet et al. Dataset 1: FH = 500m; FP = 0.25 m; Waveform 8 (2008) PR = 100 kHz; PD = 2.5 pts/m2 ; WL = Data 1550nm Dataset 2: FH = 700 m; FP = 0.35 m; PR = 111 kHz; PD = 5 pts/m2 ; WL = 1550 nm Chehata et al. FH = 500 m; FP = 0.25 m; PR = 100 0.5 m 17 4 Classes; Random For(2009) kHz; PD = 2.5 pts/m2 ; WL = 1550 nm est Alexander et al. Dataset 1: BD = 0.5 mrad; FD = 0.475 Waveform 6 6 Classes; Decision Trees −1 (2010) Data m; FH = 950 m; FS = 65 ms ; PD = 0.5 to 0.8 pts /m2 ; PR = 50 kHz; SA = 22.5◦ ; WL = 1550 nm Dataset 2: BD = 0.5 mrad; FH = 300 m; FP = 0.15 m; SA = 30◦ ; WL = 1550 nm Niemeyer et al. PD = 2.9 and 3.7 pts/m2 ; WL = 1550 Waveform 11 3 Classes: Conditional (2011) nm Data Random Fields Guo et al. (2011) FH = 500 m; FP = 0.25 m; PR = 100 Waveform 3 to 12 4 Classes: Random 2 kHz; PD = 2.5 pts / m ; WL = 1550 nm Data Forests Habib et al. BD = 0.33 mrad; FH = 600 m; PR = 0.2 m 1 to 2 3 to 5 Classes; Maximum (2011), Yan et al. 83kHz; PD = 4 to 5 pts/m2 ; WL = 1064 Likelihood (ML) (2012) nm Singh et al. (2012) FH = 1676.4 m; PS = 1.4 m; WL = 1064 1/5/10 m 3 6 Classes: ML and Decinm sion Trees Buj´ an et al. BD = 0.2 mrad; FH = 1300 m; PR = 25 0.5 m 5 6 Classes; ML and CT 2 (2012) kHz; PD = 4 pts/m Classifier Zhou (2013) FH = 800 m to 1200 m; PR = 50 kHz; 1 m 3 to 5 4 Classes: Object-based SR = 36 Hz; PS = 1 to 1.5 m * BD = Beam Divergence; FH = Flying Height; FP = Footprint; FS = Flying Speed; PD = Point Density; PR = Pulse Rate; PS = Point Spacing; SR = Scan Rate; SA = Swath (Scan) Angle; WL = Wavelength

94% to 98% 90% to 92%

> 90% 80% to 90%

94.35% 73% to 92%

89% to 94% 82% to 96% 30% to 70%

53% to 64% > 96% > 90%

The data resolution refers to the resolution of interpolated surfaces, which is always found while using discrete return LiDAR data. It is noted that most of the studies used 0.5 m to 1 m data resolution, which deems to be a good trade-off between the mean point spacing and point density. Some recent studies such as Habib et al. (2011); Yan et al. (2012) attempted to increase the resolution to 0.2 m; the detailed scene provided by the LiDAR-derived surfaces may induce the problem of between-class confusion and within-class variation, leading to a relatively low accuracy. Despite the fact that a few researches assessed the effects of 7

data density, flying height, pulse repetition frequency and scan angle on building extraction (Morsdorf et al., 2008), biophysical vegetation products (Lohani and Singh, 2008), and forest canopy mapping (Chasmer et al., 2006; Hopkinson, 2007), recommendations toward the data specification and LiDAR scanning configuration for urban land cover classification have not yet being thoroughly examined. 4. Feature Spaces 4.1. LiDAR-derived Height Features The increasing awareness of airborne LiDAR for land cover classification and object recognition can be ascribed by the predominant height features derived. The breakthrough of airborne LiDAR sensor, which is capable of acquiring 3D topography data, adds a new dimension to the land cover classification. The LiDAR height (z value) information has demonstrated an importance for accurate delineation of elevated features from the bare Earth features, regardless of urban or natural environment. Commonly, LiDAR users interpolate the 3D LiDAR data point cloud to produce a digital surface model (DSM) layer that can increase the signature separabilities among different land cover classes. Accuracy improvements with 5% to 6% were reported in several literatures by incorporating LiDARderived height features on multispectral images (Hartfield et al., 2011; Priestnall et al., 2000). A transformed type of LiDAR-derived feature, i.e. normalized DSM (nDSM), which represents the above-ground feature only, can be generated by subtracting the DSM with digital elevation model (DEM). Charaniya et al. (2004) acquired a 10-m DEM provided by the USGS and a LiDAR-derived DSM to produce a normalized height layer. Nevertheless, the resolution of existing DEM does not usually match the resolution of LiDAR-derived DSM, which may result in an inaccurate determination of above-ground features. With the mature development of LiDAR data filtering techniques (Sithole and Vosselman, 2004; Zhang et al., 2003a), bare ground layer can be generated from the existing LiDAR data together with the normalized height feature. Such normalized height feature has demonstrated its usefulness in improving classification accuracy (Bartels and Wei, 2006; Brennan and Webster, 2006; Hartfield et al., 2011). Zhu and Toutin (2013) further utilized the nDSM to extract the slope component for identifying high-voltage electric wires from the other five land cover classes. A number of experiments have shown that the LiDAR-derived height features can significantly distinguish high vegetation (tree features) from low vegetation (Charaniya et al., 2004; Hecht et al., 2008; Huang et al., 2013) in urban areas. In the experiment of Huang et al. (2008), adding a LiDAR-derived nDSM together with a multi-spectral RGB image led to an accuracy improvement of 12% to 18% in classifying four urban land cover classes. Chen et al. (2009) further addressed the problem of spectral mixture of grass and shrub while using QuickBird image for land cover classification. By incorporating the nDSM, the classication accuracy of shrub and grassland was improved from 85.25% to 92.09%, and from 82.86% to 97.06%, respectively. In addition, they addressed that such height information can be useful to resolve the heterogeneous reflectance of buildings in urban environment. Apart from using the LiDAR-derived height features directly, Antonarakis et al. (2008) generated 8

the skewness and kurtosis models from the LiDAR elevation points, which aided in differentiating between natural and planted poplar riparian forests in their experiment. Some other transformed type of height features, such as the height variation (Charaniya et al., 2004), mean, variance and standard derivation of height in the first echo (Bartels and Wei, 2006), homogeneity, contrast, and entropy of height (Im et al., 2008) were also investigated for classification; however, the efficiency of such layers is not as obvious as the use of LiDAR height (DSM or nDSM) and intensity data. 4.2. LiDAR Intensity Data On the other hand, the radiometric component of LiDAR data, i.e. intensity (I value), can serve as an additional feature in the classification domain. For a discrete return LiDAR sensor, the laser intensity represents the peak amplitudes recorded in the laser beam return backscattered from the illuminated object, where the intensity is usually linearized into an 8 to 12 bit data scale. For the latest full-waveform LiDAR, the sensor not only records a discrete number of echoes, but it also digitizes the entire waveform of the emitted pulse and the backscattered echoes (Wagner, 2010) as illustrated in Fig. 4. Therefore, the intensity data was explored not only for characterizing the natural surface condition, such as Lava flow identification and aging (Mazzarini et al., 2007), wetland hydrology (Lang and McCarty, 2009), surface moisture (Garroway et al., 2011; Kaasalainen et al., 2010a) and rock properties (Burton et al., 2011), but also aiding in urban land cover classification. Power

Power Emitted Laser Pulse

Time

Time

Atmospheric Scattering

st

1 Return

Backscattered Intensity

Returned Laser Pulse(s)

nd

2 Return

rd

3 Return

Figure 4: Illustration of the laser pulse return signal and the recorded signal strength.

Using airborne LiDAR intensity data as a feature space for urban land cover classification was initially discussed in Song et al. (2002). They examined the airborne LiDAR intensity 9

data of asphalt road, grass, house roof and tree, where the intensity data is possible to provide sufficient separability for land cover classification. In the experiment of Charaniya et al. (2004), the intensity data demonstrated a critical role to separate two types of low elevated urban features, i.e. road and low vegetation. Brennan and Webster (2006) addressed that the intensity data is useful to distinguish the bright and dark structures, especially features of similar height but different reflectance properties. Im et al. (2008) conducted a sensitive analysis on eight different LiDAR-derived surfaces for land cover classification on three different sites. It was found that the overall accuracy was increased by 10% to 20% when the intensity data was included in the feature spaces in their experiment. Besides LiDAR data classification, the intensity data can also supplement the feature space with other remote sensing data. Zhou et al. (2009) demonstrated that the LiDAR intensity data can contribute to the classification of shaded areas in urban environment, which can compensate such drawbacks induced by using high resolution digital aerial image. MacFaden et al. (2012) adopted the mean intensity value as a criterion to distinguish the low-lying vegetation from impervious surface under the hierarchical structure of object-based image classification approach. Despite the proven functionality, the LiDAR intensity data has a certain drawback as well. For example, Yoon et al. (2008) and Yan et al. (2012) addressed that a large variance of intensity values can be found in the tree features due to the irregular geometry of the canopy surface. Intensity data has a certain level of noise (Minh and Hien, 2011; Song et al., 2002), which may degrade the classification performance. Furthermore, radiometric misalignment found in overlapping LiDAR data strips may degrade the classification accuracy (Brennan and Webster, 2006; Yan and Shaker, 2014), see Fig. 5. Therefore, radiometric calibration, correction and normalization (which will be covered in Section 6) should be applied in order to reduce the discrepancy within the intensity data. 4.3. Multiple-Return and Texture Features Besides LiDAR height and intensity features, there are a few studies exploiting the characteristics of multiple returns of LiDAR data to facilitate land cover classification. By investigating the first and the last LiDAR data returns, individual (height or intensity) features or the difference among these features can be derived to increase the feature spaces. Charaniya et al. (2004) suggested the difference between the first and last return as an additional feature, which leads to an accuracy improvement of roads and buildings by 5% to 6%. Bartels and Wei (2006) performed similar experiments and claimed that such feature can improve the accuracy; however, no individual quantitative measure of each feature was included in the paper. Brennan and Webster (2006) and Buj´an et al. (2012) adopted the multiple-return data as one of the criteria in the object-oriented decision tree classifier to distinguish permeable object (e.g. tree canopy) from non-permeable object (e.g. building, small house). This is possible since laser pulse can penetrate the tree canopy resulting in a multiple return from the leaves, stems, branches and the ground. Singh et al. (2012) performed an experiment that covers a large-area land cover mapping in the Mecklenburg County and Charlotte Metropolitan Area, North Carolina, USA. Results from the experiment unveiled that the difference in the first and last LiDAR pulse (together with the nDSM) contributes to the overall classification accuracy regardless of the classifier applied. Despite 10

(a)

(b)

Figure 5: An example of line striping noise appeared in overlapping LiDAR data strips: (a) Original intensity data, and b) Radiometrically corrected and normalized intensity data.

the above studies reporting the use of multiple-return data, the efficiency of such feature does not seem to be of universal significance. Unless the urban environment is mixed with building and tree in a complex and irregular distribution, the multiple-return data seems to be useful in some sense; otherwise, incorporation of nDSM and intensity can be used to distinguish these two common types of urban land cover. Texture features, such as Gray-Level Co-Occurrence Matrix (GLCM) measures (Haralick et al., 1973), are commonly adopted in satellite remote sensing for improving the classification accuracy (Shaban and Dikshit, 2001; Smits and Annoni, 1999; Zhang et al., 2003b). Texture analysis takes into consideration of the distribution and variation of neighborhood pixel data; hence, the spatial properties of classes could be incorporated as one of the classification criteria. Therefore, a few studies generated texture from LiDAR data for classification. Im et al. (2008) generated four GLCM measures (i.e. homogeneity, contrast, entropy and correlation) from the LiDAR height data to facilitate three different land cover classification scenarios. Nevertheless, these texture features did not seem to significantly improve the classification performance when compared to the LiDAR-derived height features and intensity data. Samadzadegan et al. (2010) proposed to generate four texture measures (mean, entropy, standard deviation and homogeneity) for classifying tree, building and ground. The results showed that only the entropy texture yields improving the overall accuracy among the aforementioned texture measures. Huang et al. (2011) extracted four GLCM textures (homogeneity, angular second moment, entropy and dissimilarity) from the LiDAR DSM image. The results showed that the homogeneity texture generated using a 19 × 19 window can significantly contribute to a Support Vector Machine (SWM) based 11

classifier. In spite of these trials, a lack of study can be found to conclude the significance of texture features toward airborne LiDAR data classification. 4.4. Multi-Sensor Data Fusion Due to the lack of spectral information of airborne LiDAR data, land cover classification can be improved considerably through the multi-sensor data fusion approach. To perform the multi-sensor data fusion, two criteria must be fulfilled: 1) the aerial/satellite image data has to be registered to the same coordinate system as the airborne LiDAR data, and 2) the spatial resolution of both dataset should be identical or matched, if image-based classification technique is adopted. The first criterion is usually achieved by using the onboard aerial image acquired during the same LiDAR survey. Under such circumstance, direct geo-referencing technique can be applied to both image and LiDAR data. Some studies reported the use of very high resolution image such as QuickBird (Chen et al., 2009) and WorldView (Kim and Kim, 2014; Minh and Hien, 2011) image together with airborne LiDAR data for land cover classification. In this case, ortho-rectification (or geo-referencing) has to be performed as pre-processing on the satellite image. The second criterion can be addressed by employing a downscaling process to match the spatial resolution between the aerial/satellite image data and airborne LiDAR data. Singh et al. (2012) incorporated the Landsat TM with 619 tiles of airborne LiDAR data, and assessed the best resolution (1m, 5m, 10m, and 30m) for large area land cover mapping. The results found that a 5-m image resolution deems to be a better trade-off between the classification accuracy and the computational process. Multi-sensor data fusion is commonly found in a hierarchical object-oriented classification approach (Forzieri et al., 2012, 2013). Decision (prior knowledge) rules are built through using the feature spaces generated from the aerial/satellite image and LiDAR data (Huang et al., 2008). For instance, Chen et al. (2009) derived Normalized Difference Water Index (NDWI) and Spectral Shape Index (SSI) from a QuickBird image to differentiate shadow and water bodies as well as NDVI to classify the vegetation. On the other hand, the nDSM derived from the airborne LiDAR data was used to distinguish the elevated objects (building) from ground. Similar design of classification trees can be found in Sasaki et al. (2012) and Buj´an et al. (2012). Hartfield et al. (2011) reported that the classification accuracy of multi-spectral image together with the Normalized Difference Vegetation Index (NDVI) for 8 classes was 84%. By adding the LiDAR-derived nDSM, it increases the accuracy to 89.2%, since the nDSM data eliminates the confusion between the herbaceous and trees/shrubs classes. Zhou et al. (2009) further addressed that multi-sensor data can yield better classification to extract the shadow, which could always be found in optical remote sensing data under urban environment. Guan et al. (2013) reported a combination of RGB orthoimage and LiDAR data for classification. Due to the lack of NIR band, they used the red band of the orthoimage and intensity data captured by LiDAR sensor to generate a LiDAR-NDVI that outperforms in separating the grass cover from ground. MacFaden et al. (2012) integrated very high resolution multispectral orthoimage data (0.152 m) and airborne LiDAR data (point spacing = 0.57 m) for urban tree canopy and land cover mapping. They demon12

strated the use of NDVI and nDSM that are critical to delineate to buildings through using a sequence of algorithms in detecting, erroneous edges. With respect to the success of these case studies, seems to be a feasible solution, especially for large area land cover

those tree features next evaluating, and resizing multi-sensor data fusion mapping.

4.5. Waveform-Derived Features Apart from the multi-echo LiDAR-derived features, full-waveform LiDAR provides extraordinary capability in generating high dense topographic surface aiding in data interpretation and land cover classification. With the onboard equipped waveform digitizer, airborne LiDAR sensor is capable of recording the entire waveform of the backscattered laser pulse signal within nanosecond (ns), resulting in a 1-D signal profile (Mallet and Bretar, 2009). Since the recorded waveform is usually composed of a number of backscattered echoes, signal deconvolution process should be carried out in order to extract waveform features for classification. Wagner et al. (2006) proposed to decompose the waveform signal into a mixture of Gaussian components, where the technique subsequently serves as a standard decomposition and modeling approach. Nevertheless, due to the complexity of terrain and different LiDAR system settings, the symmetric assumption of Gaussian decomposition may not reflect the backscattered signal in reality. Therefore, various new techniques for robust estimation of the backscattered waveform geometry were proposed afterwards. Chauve et al. (2007) investigated two standard extension of Gaussian: Lognormal and generalized Gaussian functions for improving the LiDAR waveform modeling. The generalized Gaussian modeling approach was demonstrated to improve the peak point detection by introducing additional parameters. Mallet et al. (2010) proposed three parametric functions (including generalized Gaussian, Nakagami, and Burr) based on the Reversible Jump Monte Carlo Markov Chain for fitting both symmetric and asymmetric echoes for the full-waveform data. Roncat et al. (2012) proposed a least-squares B-spline curve fitting approach to model the emitted and received waveform, which does not require initial values in Gaussian decomposition approach. As shown in Table 1, early studies utilized discrete multi-return LiDAR sensor, where the first attempt of using full-waveform data type for classification appeared in 2008 (Mallet et al., 2008). Various waveform features can be extracted from the Gaussian decomposition function to serve land cover classification. Commonly, the waveform amplitude, number of echoes, echo width, and the difference between the first and last echo pulse are tested for urban land cover classification (Alexander et al., 2010; Chehata et al., 2009; Neuenschwander et al., 2009; Niemeyer et al., 2011). Although some attempts derived more than 10 to 18 features from the waveform data, only a few of the features can significantly contribute in distinguishing different land cover classes. In the experiment of Mallet et al. (2008), it was shown that the echo width can significantly distinguish the vegetation (especially tree) from paved areas or built-up features. Chehata et al. (2009) also echoed the findings of Mallet et al. (2008) on the use of echo width to distinguish trees from other land features. Besides that, they highlighted the echo amplitude is always found high in rooftop, gravel and cars, but low in asphalt and tar street; where the echo shape is shown to be very low and very high in roof and vegetation, respectively. They pointed out that the height difference, echo 13

amplitude and echo width are useful in their classification tasks. Despite the fact that the pulse width is significantly affected by the surface roughness, Lin and Mills (2010) showed that it has less noisy characteristics than the intensity data. Therefore, the pulse width is of particular valuable interest to identify the grass cover on the ground. In addition to the amplitude, elevation, echo width, and echo numbers, Alexander et al. (2010) investigated the backscatter cross section derived based on the radar (range) equation for classification. It was found that the backscattered cross section outperforms in separating low ground features, i.e. road and grass. Vaughn et al. (2011) transformed the waveform data into frequency domain by using Fast Fourier Transform to aid in classifying five different tree species with an overall accuracy 75%. In view of all these trials, airborne LiDAR data classification seems to be comparatively predominant in fine scale land cover classification. Neuenschwander et al. (2009) compared the results of full-waveform LiDAR data and QuickBird multi-spectral image for classifying 7 land cover classes. The classification result of LiDAR data yielded an overall accuracy of 85.8% compared to 71.2% using QuickBird image. 5. Classification Techniques Urban landscape is usually composed of a complex combination of both built-up and natural objects including, but not limited to, paved roads, buildings, bridges, vehicles, fences, railways, trees, and grass cover. Depending on the goal of classification, there are three to seven common land cover classes that existing studies look for (see Table 1). Various classification techniques were reported in the literature to classify and extract these land cover features from the LiDAR data. Since most of the initial studies transformed the LiDAR data point cloud into image data, traditional supervised pixel-based classification techniques such as neural network (Minh and Hien, 2011; Nguyen et al., 2005; Priestnall et al., 2000), Gaussian mixture modeling (Charaniya et al., 2004), maximum likelihood (Bartels and Wei, 2006; Hodgson et al., 2003), and rule-based classification (Huang et al., 2008), etc. were directly applied. Given the nugget effect of salt-and-pepper noises in the pixel-based classification results, object-based image segmentation and classification approach seems to be more appropriate. The object-based approach relies on a user-defined hierarchical structure to classify the segmented objects, and the technique has proven to outperform the traditional pixel-based classifiers not only on satellite images (Blaschke, 2010), but also airborne LiDAR data (Chen et al., 2009; El-Ashmawy et al., 2011; Huang et al., 2008; Minh and Hien, 2011; Sasaki et al., 2012). Various studies reported that an overall accuracy of over 80% can be achieved using object-based technique on LiDAR-derived surfaces (Antonarakis et al., 2008; Brennan and Webster, 2006; Im et al., 2008; MacFaden et al., 2012; O’Neil-Dunne et al., 2013; Zhou, 2013). Despite the desirable accuracy, most of these trials mainly used discrete return airborne LiDAR data to generate image data, which is fairly straightforward. In view of the increased feature dimension of LiDAR waveform data, classification techniques have become complicated and hybrid. Full-waveform LiDAR data requires pre-processing of waveform fitting and decomposition in order to derive waveform-based features for classification. The high dimension and 14

complex feature spaces require an intelligent classification technique to accurately determine the land cover classes. Recently, SVM has gained high interest not only in satellite imagery (Mountrakis et al., 2011), but also in airborne LiDAR data with some promising results (Bretar et al., 2009; Mallet et al., 2008; Samadzadegan et al., 2010). Although SVM has demonstrated the ability to work well with high dimension data, the method does not consider the spatial correlation in class labelling. For instance, a LiDAR data point can classify the distinct point as paved road, even if all surrounding data points are classified as “water bodies”. Therefore, classifiers such as Markov Random Fields (MRF) and Conditional Random Fields (CRF), which incorporate contextual information of neighborhood data, are examined to reduce the inhomogeneous classification results of waveform data. The experiment conducted by Niemeyer et al. (2011) using CRF demonstrated an overall accuracy ranging from 89.3% to 94.3%. In comparison to the accuracy provided by SVM (74.3% to 80.3%) and MRF (87.1% to 93.8%), the CRF yielded consistenly better results. In addition, CRF classifier can overcome the limitation of misclassification of features with similar reflectance (e.g. rooftop and ground features), which may occur when using SVM and MRF. Similar experiments and results can also be found in Cao et al. (2012) and Niemeyer et al. (2012). Nevertheless, CRF method is computational expensive in terms of training time. The study of Niemeyer et al. (2011) reported that it took 202 minutes and 252 minutes to train the CRF and MRF, respectively, with a study area of 1.93 ha. Some emerging research attempted to use ensemble learning (or multiple classifier systems) such as AdaBoost (Lodha et al., 2007; Nourzad and Pradhan, 2014), Cascade Binary Classifier (Carlberg et al., 2009), Weighted Majority Voting (Samadzadegan et al., 2010), and Random Forests (Chehata et al., 2009; Guo et al., 2011) for both discrete return and waveform LiDAR data, which still produced desirable classification accuracy (80% to 90%). 6. Effects of Radiometric Calibration, Correction and Normalization Discrete return LiDAR intensity data or waveform backscattered coefficient of fullwaveform LiDAR data has been demonstrated as a viable approach for land cover classification. Therefore, radiometric calibration and correction of the LiDAR intensity data can be performed, either in empirical or physical way (H¨ofle and Pfeifer, 2007), to improve the classification results. The empirical approach does not consider the physical properties of the laser backscattered energy. Instead, it introduces statistical methods to minimize the noise in the intensity data, such as median filter (Buj´an et al., 2012; Song et al., 2002). Fang and Huang (2004) introduced a discrete wavelet transform approach for noise reduction in LiDAR signal; the results demonstrated that the proposed method outperforms the traditional digital filters by improving the signal-to-noise ratio. Lai et al. (2005) investigated a mean filtering algorithm to fuse the LiDAR intensity range data and remove different types of signal noise. The results showed that the proposed filtering algorithm merely improves the quality of the data. On the other hand, the physical approach relies on the use of radar (range) equation (Jelalian, 1992), which was first proposed for radiometric correction of LiDAR intensity data by Coren and Sterzai (2006); H¨ofle and Pfeifer (2007); Kaasalainen et al. (2007a). The correction process aims at converting the recorded intensity data into the 15

spectral reflectance of the illuminated object by studying the physical properties of the parameters such as flying height (Vain et al., 2009), range (Kaasalainen et al., 2009b), incidence angle (Abed et al., 2012; Kaasalainen et al., 2011a; Kukko et al., 2008), sensor aperture size (Kaasalainen and Kaasalainen, 2008), surface moisture (Kaasalainen et al., 2010a), automatic gain control on the backscattered intensity (Vain et al., 2010), reflection model (Jutzi and Gross, 2010), and atmospheric attenuation (Yan et al., 2012). Absolute calibration approaches were also developed by investigating different ground reference artificial targets (Kaasalainen et al., 2009a) and natural targets (Vain et al., 2009). Although airborne LiDAR intensity data has been used directly in various early studies without any radiometric pre-processing (Brennan and Webster, 2006; Charaniya et al., 2004; Song et al., 2002; Yoon et al., 2008), the effects of radiometric calibration and correction on land cover classification was recently demonstrated. Korpela et al. (2010) conducted tree classification using discrete-return LiDAR data. Accuracy improvement of 6% to 9% was achieved after normalizing the range and automatic gain control of the intensity data. In the experiment of Gatziolis (2011), the classification results using original intensity data for classifying conifers, mixed forests and hardwoods reached to only 44.4%. However, after applying a range-based normalization, the overall accuracy bloomed up to 75.6% (increase 31.2%). Kaasalainen et al. (2011b) compared the LiDAR intensity data for a number of external calibration targets with reference to the in situ reflectance measurements captured by a near-infrared digital camera. The goodness of fit (R2 ) was increased from 0.52 to 0.73 after radiometric calibration. Yan et al. (2012) compared three different classification scenarios in an urban area by using LiDAR intensity data. The results found that the overall accuracy was improved by 9.4% to 12.8% after applying both geometric calibration and radiometric correction. Habib et al. (2011) performed similar research using LiDAR intensity data together with a DSM and, an accuracy improvement of 7.5% was achieved in a 4-class scenario. Yan and Shaker (2014) proposed a normalization model to adjust the radiometric misalignment of overlapping LiDAR intensity data based on a Gaussian mixture modeling and sub-histogram matching technique. The findings showed an improvement of classification accuracy ranging from 5.7% to 16.5% with various classification scenarios. 7. Airborne LiDAR Applications in Urban Environment Airborne LiDAR technology has been proliferated, especially in urban environment, due to its uniqueness over other remote sensing data. Besides land cover classification, numerous studies have demonstrated the use of airborne LiDAR data for a variety of urban object recognition, extraction, and analysis. Under this section, five applications that utilize airborne LiDAR data to facilitate urban infrastructure and environmental analysis are identified. Though 2D/3D building recognition, extraction and reconstruction are among one of the hottest topics, it was intentionally disregarded in this section, since there are numerous literatures that have already covered this area (Brenner, 2005; Dorninger and Pfeifer, 2008; Haala and Kada, 2010; Musialski et al., 2013; Wang, 2013). 16

7.1. Urban Morphology and Green Analysis Through airborne LiDAR data classification and object recognition, the results can contribute different perspectives to the control of urban planning and monitoring. Examples are not limited to urban impervious surface extraction (Germaine and Hung, 2011; Hodgson et al., 2003), urban environmental quality assessment (Garcia-Gutierrez et al., 2011) and urban change detection (Stal et al., 2013; Teo and Shih, 2013). In view of these studies, the majority of contribution from airborne LiDAR toward urban environment can be categorized in terms of urban morphology and green analysis. Yu et al. (2010) proposed an object-based method to process airborne LiDAR data for the building density information. The method consists of a sequence of numerical operations: generating nDSM, extracting building objects, deriving object attributes, associating objects with the corresponding land lots, and computing building density indicators at land lot and urban district scales. Lu et al. (2011) delineated building boundaries from the LiDAR data and developed a volumetric approach for population estimation in regions with heterogeneous housing characteristics. The experiments demonstrated a high correlation (R2 > 0.8) in the population estimation. Helbich et al. (2013) utilized airborne LiDAR data and solar radiation simulation to assess the geometry of hedonic houses (i.e. cardinal direction, floor height and adjacent vicinity) in a complex urban environment. The results can be acted as a quantitative measure for real estate and hedonic house price modeling. Gonzalez-Aguilera et al. (2013) derived both quantitative and qualitative urban parameters, such as building heights, areas, volumes, coverage ratio, floor area ratio, etc. from the LiDAR extracted building models. These planning parameters are desired for city design and policy analysis. Estimation of urban green volume provides information for ecologically orientated city planning and environmentally sustainable development. Hecht et al. (2008) estimated the urban green volume using single pulse LiDAR-derived nDSM. An adaptive cylinder construction based on fuzzy logic technique was applied to model the deciduous trees, which can be used to derive a green volume index (GVI). Yao and Wei (2013) proposed the AdaBoost classifier to extract individual trees in urban environment, and the accuracy of extracted trees are within the accuracy of 0.65 m in the horizontal position and 0.12 m for the tree height. Huang et al. (2013) proposed an object-based method for automated estimation of green volume by using airborne LiDAR and imagery data. The method consists of following steps: generating and ltering the nDSM, extracting the nDSM of urban vegetation based on the NDVI, locating the local maxima points, segmenting the vegetation objects of individual tree crowns and grassland, and calculating the urban green volume of each vegetation object. Both results successfully demonstrated the quantity and spatial distribution of estimated urban green volume, which provide a cue to the reduction of urban heat island effects. 7.2. Urban Flood Risk Modeling Flood risk modeling is one of the major tasks for stormwater management agencies to address the impact of catastrophic rainfall events on infrastructure damages and human lives. 17

Such task requires high quality geospatial data to extract topographic information (e.g. sub-catchment area and width, percentage of imperviousness, surface slope, land cover/use information, etc.) that serves as an input for rainfall-runoff simulation. Simulation model, such as the United States Environmental Protection Agency (EPA) Storm Water Management Model (SWMM), performs volumetric calculation of overflow with respect to the existing urban drainage system and topography, and estimates the potential flood extent with different rainfall scenarios. Indeed, there have been considerable discussions on developing detailed distributed model (high resolution model) over traditional lumped model (low resolution model) since a finer resolution of the model gives a high degree of confidence in assigning hydrologic parameters of individual subcatchment (Khakbaz et al., 2012). Therefore, airborne LiDAR technology can fulfill the requirement for providing high resolution 3D topographic data that matches the need for micro-scale flood risk modeling. The majority of existing studies mainly involve LiDAR data filtering to separate ground and non-ground features, and subsequently use the nDSM, intensity and multi-sensor data to perform surface classification (Mason et al., 2007, Tsubaki2010). Webster et al. (2004, 2006) utilized LiDAR-derived DEM to perform flood simulation modeling for coastal urban areas caused by the storm-surge events. Both studies demonstrated how to make use of the flood simulation results for 3D visualization and producing the flood-risk maps. Casas et al. (2006) compared the DTM generated from three different data sources (contour data, GPS and LiDAR) for hydraulic flood modeling. They stressed that the results of modeling mainly depend on the DTM accuracy, among which the LiDAR-derived DTM produced the least root-mean-square error in terms of elevation accuracy and estimated flood areas. Tsubaki and Fujita (2010) used LiDAR data to generate unstructured grid that represents the complex urban landscape, and estimated the water depth by performing different inundation simulations. Recently, Arrighi et al. (2013) further made use of the developed techniques and census data to generate a damage model for the area of St. Croce district in Florence, Italy, and estimated the potential losses in terms of the official per capita income. 7.3. Mapping Power Transmission Lines Mapping the power transmission lines is one of the powerful applications of airborne LiDAR because such task is hard to be achieved using manual surveying technique. Monitoring the power transmission line spans and the immediate surroundings are critical for the purpose of maintenance, thermal rating, upgrading, and vegetation management (Lu and Kieloch, 2008). McLaughlin (2006) proposed a two-stage algorithm to extract the power transmission lines from airborne LiDAR data with an initial classification of ground features and subsequent extraction of transmission line spans. The method has been proven to correctly identify 86.9% of those LiDAR data points laid on the transmission lines and extracted 72.1% of the individual transmission line spans. Lu and Kieloch (2008) provided a list of recommendation based on conductor temperature, wind speed, solar radiation, electrical load, etc. for accurate modeling of transmission lines using aerial LiDAR survey. Li et al. (2012) proposed an improved object recognition method by fusing information from airborne LiDAR and imagery data, and multiple visual feature descriptors (color and texture) for au18

tomated power line corridor monitoring. Jwa and Sohn (2012) proposed a catenary curve model by initially identifying power line candidate LiDAR points, and progressively growing them for modeling a complete power line. The experiment demonstrated a satisfactory success rate of 96% with less than 5.2 cm in 3D power line modeling accuracy. Kim and Sohn (2013) further proposed a sphere-based volumetric approach for feature extraction of land cover classification. A random forest classifier together with 21 features was used to classify the 3D LiDAR data scene for detecting power lines and pylons. The accuracy was reported over 90% in the test of classification performance. 7.4. Modeling GPS/Airport Signal Obstruction Airborne LiDAR has made evolution to traditional surveying measurements by having the high posting density of data points in 3D. This has significantly aided in modeling artificial (e.g. GPS signal, airport signal) signal obstructions in a complex urban environment. Parrish et al. (2005) performed various testings with different airborne laser scanning configuration for detecting airport obstructions. With a 20◦ forward tilt angle, a 0.2 mrad beam divergence, and a flying height of 750 m of airborne LiDAR survey, all obstructions were detected and the vertical accuracy achieved was 0.88 m when compared to the traditional field-surveyed obstructions. Parrish and Nowak (2009) further investigated the use of full-waveform LiDAR data for detecting vertical objects in an airport by formulating a comprehensive processing workflow. A reduction of processing time with 46% and 38% was recorded in the entire obstruction survey and manual analysis, respectively, as compared with the previous LiDAR obstruction survey. Uddin et al. (2011) reviewed and summarized the airborne LiDAR survey method, accuracy and current practice for airport obstruction survey. Fusion of LiDAR survey data with aerial photogrammetry can further improve the time and cost, and produce multi-use value-added geospatial product for airport management. On the other hand, post-processed airborne LiDAR data product can aid to evaluate the signal obstruction and satellite availability for GPS users. Taylor et al. (2007) developed a prototype of mission planning for assessing the satellite availability with various forms of DSM. The study proved that airborne LiDAR provides the most appropriate dataset for modeling and predicting GPS satellites availability in terms of reliability, productivity and accuracy. Lohani and Kumar (2008) developed a model to predict the Geometric Dilution of Precision (GDOP) value and the probability of its occurrence at a point in space and time using airborne LiDAR data and the ultra-rapid product available from the International GPS Service. Li et al. (2008) integrated LiDAR data and 2D building footprints for visualization and prediction of GPS multi-path effect in urban areas by implementing a ray-tracing model under a 3D-GIS platform. These studies successfully demonstrated the feasibility and effectiveness of using airborne LiDAR data, which acquires a detail 3D topographic profile, to predict the satellite availability and influence of multi-path signals. 7.5. Solar Radiation Assessment As airborne LiDAR permits accurate probing of the topography in complex urban environment, the data captured can be used to assess the potential solar radiation, which can 19

advocate the development of substantial and renewable energy. The LiDAR-derived DSM can be used to determine the size, slope and exposition of individual building roof plane, and potential installation of photovoltaics (Voegtle et al., 2005; Yu et al., 2009). Nevertheless, complex roof structures with objects (dormers, windows, chimneys, antennas, etc.) may cause disturbance in extracting correct roof plane using DSM. Therefore, some attempts were reported to develop algorithms for roof top identification and extraction, and perform solar potential analysis based on the LiDAR data point cloud (Jochem et al., 2009). Yu et al. (2009) and Jakubiec and Reinhart (2013) presented two successful case studies with large scale solar radiation assessment in urban built-up environment in downtown Houston, Texas, and the City of Cambridge, Massachusetts, respectively. Apart from considering the solar radiation to the roof top, the shadowing effect caused by the surrounding features may also influence the level of solar coverage. Therefore, Lukaˇc et al. (2013) determined a new method that combines the extracted urban topography from the airborne LiDAR data together with the pyranometer measurements of global and diffuse solar irradiances. The method considers the shadowing effects from the surrounding features (e.g. tall trees), and the results has proven to improve overall accuracy in comparison to the actual power plant ˇ measurement. Since the above process is computational intensive, Lukaˇc and Zalik (2013) further proposed a Graphics Processing Units (GPUs) based Compute Unified Device Architecture (CUDA) technology for solar potential estimation. The proposed method was shown to be several times faster with the same degree of accuracy than a multi-core CPU-based approach. 8. The Way Ahead With respect to the increased number of publications and case studies, both the industry and academician have successfully contributed to the development of new LiDAR sensors and data processing algorithms; such trends will undoubtedly carry on in the very near future. Although airborne LiDAR has successfully demonstrated its effectiveness in urban land cover classification and object recognition, what achieved thus far are by no means the last word in LiDAR research. The demand of detailed land cover features and the increased of LiDAR data resolution would lead to the heterogeneity and complexity in land cover classification. There are still various outstanding issues that have to be overcome, and a number of forthcoming research areas require further investigations. 8.1. Multi / Hyperspectral LiDAR Sensor In view of the majority of existing airborne LiDAR sensors operating in single wavelength, the invention of multi / hyperspectral airborne LiDAR sensor is desired and is likely to bloom in the coming few years. There are a few experimental multi-wavelength and hyperspectral LiDAR sensors being developed in laboratory as reported in recent literatures. In spite of a few attempts investigated the potential use of an ultraviolet laser (355 nm) for developing a medium footprint LiDAR (Allouis et al., 2011; Cuesta et al., 2010), the Finnish Geodetic Institute first reported the design of a small footprint multi / hyperspectral LiDAR sensor using supercontinuum laser source with wavelength from 600 nm to 2000 nm 20

(Chen et al., 2010; Hakala et al., 2012; Kaasalainen et al., 2007b). Woodhouse et al. (2011) reported a multispectral canopy LiDAR demonstrator project by testing a tunable laser operated at four wavelengths (531 nm, 550 nm, 660 nm, and 780 nm). The ultimate goal of these developments can offer significant contribution in studying the forest structural and physiological information properties through building different biochemical and biophysical indices, such as NDVI (Chen et al., 2010; Morsdorf et al., 2009); Photochemical Reflectance Index (Woodhouse et al., 2011); Water Concentration Index (Hakala et al., 2012); Modified Chlorophyll Absorption Ratio Index (Hakala et al., 2012); a dual-wavelength (1063 nm and 1545 nm) simple/normalized radio index (Gaulton et al., 2013). Kaasalainen et al. (2010b) attempted to use the backscattered intensity data for automatic target identification and surface classification; examples are not limited to the differentiation of needles, branches and background in the 3D tree structure (Suomalainen et al., 2011). Although most of the preliminary studies focused on forest canopy modeling and monitoring (Bo et al., 2011; Hancock et al., 2012; Morsdorf et al., 2009; Wallace et al., 2012a; Wei et al., 2012), the introduction of multi / hyperspectral LiDAR sensor would provide new feature spaces to further enhance the capability of studying land surface condition, i.e. land cover classification. Recently, Wang et al. (2014) utilized dual wavelength LiDAR data collected by two different sensors to classify six land cover classes; the overall accuracy produced by a SVM classifier reached to 97.4%. Substantial research efforts are still required to develop an universal radiometric calibration and correction model for the collected multi / hyperspectral LiDAR intensity data (Briese et al., 2012; Roncat et al., 2012). 8.2. Unmanned Aerial Vehicle Unmanned aerial vehicle (UAV) was introduced for remote area investigation and mapping initially for military purpose. With the development of low-cost and light-weight sensors, UAV system is capable of carrying GPS, IMU and camera to serve a variety of civil applications, such as agricultural mapping, vegetation monitoring and rangeland study. Due to the restriction of 2D planmetric imaging data, a few recent trials have been reported of installing LiDAR sensor on the UAV payload. Nagai et al. (2009) presented a design of UAV LiDAR system and direct geo-referencing technique to process the collected LiDAR data for digital 3D modeling. Lin et al. (2011) presented a small helicopter system (4.5 kg), that is able to lift up a payload of 7 kg, including GPS/IMU, LiDAR, CCD camera and thermal camera, to serve fine scale mapping applications, such as tree height estimation, pole detection, road extraction and DTM generation. Lin et al. (2013) investigated the combined use of data collected from UAV imaging system and mobile mapping system for land cover classification in an urban environment. Owing to the high mobility, repeated surveys can be easily performed under different environment so as to serve ad-hoc request or rapid response, such as ice surface observation (Crocker et al., 2012), vegetation control (Ax et al., 2013), and forestry monitoring (Wallace et al., 2012b). As these initial trials demonstrated promising results toward different applications, the introduction of such portable sensor opens a new door for many research directions, such as system calibration, sensor modeling, data fusion with onboard camera, on-line data processing, etc. 21

8.3. Fine-Scale Urban Analysis and Applications Although the review has covered a wide range of land cover classification studies using airborne LiDAR data, further investigation on the use of land cover information derived is lacking. It is expected that the forthcoming studies will look deep into the classification results for different fine/micro-scale urban analysis and applications. For instance, airborne LiDAR data can be used to classify the vegetation cover and impervious surface in dense high rise urban environment (Mason et al., 2007; Tsubaki and Fujita, 2010). Since high resolution satellite image is not able to determine the surface condition during the leaf-on season, airborne LiDAR data can be used to determine the land cover and topography (e.g. dimension and slope of sub-catchment), and a micro-scale urban hydrologic model can be constructed for potential flood assessment as reported in section 7.2. Another interesting clue could be found in air ventilation assessment (AVA) of urban environment (Ng, 2009; Yim et al., 2009). Such task has drawn high attention by local planning authority since AVA has significant influences on the human comfort, especially in high rise built-up urban area. AVA involves computing the skyview factor, solar radiation, urban morphology, surface roughness and land cover pattern. Therefore, airborne LiDAR data can provide predominant high resolution data to derive all these parameters (Ng et al., 2011), which serve as an input for modeling the site wind availability. A by-product of such task could lead to the development of urban climatic map for a city scale; and various examples can be found in Germany, Hong Kong and Japan (Ren et al., 2011). 8.4. Distribution and Visualization Platform Driven by the promotion and distribution of LiDAR data and its derived products, a fast and robust rendering and visualization platform needs to be developed and delivered without restriction to the operating platform, including web-based information system, smart phone, or even wearable device. Substantial research efforts are required to build an efficient data streaming and dissemination platform to display the 3D LiDAR data so as to facilitate the end users for data exploration and interpretation. Some initial attempts are found in Kuder and Zalik (2011) and Lewis et al. (2012), who developed a web-based visualization platform for LiDAR data. Burwell et al. (2012) developed a prototype that displays LiDAR data point cloud in a 3D virtual reality platform with a head-mounted display and joystick for navigation. Such kind of visualization platform is foreseeable to be applied in a variety of large scale 3D urban applications, such as 3D city modeling (Lafarge and Mallet, 2012), 3D cadastre development and management (Tse and Gold, 2003), and pipeline management (Lewis et al., 2012). Development of a national/global 3D land cover map with high spatial and temporal resolution is also desired and it should be beneficial as a result of the proposed platform. 8.5. Further Development of LiDAR Data Processing Algorithms The invention of high end LiDAR sensors, the need of large and fine scale urban analysis, and the development of distribution and visualization platform - all provoke the need for further effort in developing LiDAR data processing algorithms. The bulky size of LiDAR data point cloud and complex file structure (especially for the foreseeable multi / hyperspectral 22

LiDAR waveform data) would impose certain computational burden. Recently, initialization ˇ ˇ toward data compression (Lipuˇs and Zalik, 2012; Mongus and Zalik, 2011), data structure and file handling (Elseberg et al., 2013), high performance computing framework (Han et al., ˇ 2009; Lee et al., 2011) and GPU-based processing (Lukaˇc and Zalik, 2013; Oryspayev et al., 2012) have been addressed and researched. Some other attempts have also been found to use compressed LiDAR data for land cover classification (Laky et al., 2010; Toth et al., 2010) and digital 3D modeling (Jang et al., 2011). Nevertheless, these initial trials presented only dealed with a small dataset and restricted to a specific problem domain. Obviously, more efforts are essential to encourage and develop intelligent data processing tools that are compatible with multi-platform and open source standard. For instance, Isenburg (2013) successfully developed a free prototype, named LASzip, for lossless compression of LiDAR data in LAS format. Such prototype is able to compress the LAS files into compact LAZ files that are only 7% to 20% of the original file size, and it is among a few successful LiDAR data processing tools that are widely adopted by the industry and research community. 9. Conclusions The use of satellite remote sensing in land cover classification has been well researched since early 1970s. However, the detailed scene provided by very high resolution satellite imagery raises several challenges to derive accurate land cover products, especially in urban environment. In view of the demand of land cover maps at finer scale with evidence by numerous biophysical and socio-economic studies, airborne LiDAR data has been explored for urban land cover classification during the last decade. This paper attempts to fill the current void of airborne LiDAR reviews by placing a focus on urban land cover classification. The merits of using airborne LiDAR data for urban land cover classification can be summarized as follows: • The LiDAR-derived height data adds a new dimension toward the classification feature space for distinguishing elevated features (such as building, tree) from ground features, where such process cannot be achieved using single-scene satellite remote sensing image. • The LiDAR intensity data collects the backscattered laser energy at the NIR wavelength (usually 1064 nm or 1550 nm), where high separability of common urban land cover features can be found. It is a very useful cue for distinguishing between manmade and ground features. • The LiDAR waveform data provides a high resolution 1-D radiometric profile, which can reveal certain hidden information of the backscattered object. For instance, tree features always come up with a large echo width of LiDAR waveform, while asphalt ground would return a sharp and narrow peak echo. • Fusion of high resolution (satellite or aerial) remote sensing and airborne LiDAR data can achieve mutual benefits for compensating the lack of 3D topography and multispectral information from each other. 23

• The drawback of high resolution satellite image classification, including the lack of 3D topography data, relief displacement, shadowing effect, etc. can be overcome by using airborne LiDAR data, which is feasible to develop an accurate land cover product and serves a variety of urban studies. Although a wide range of applications have been demonstrated in urban environment using airborne LiDAR data, it is believed that more extended applications, especially in micro-scale modeling of urban environment and ecosystem, are likely to be grown in the coming future. With the introduction of multi-sensor data, such as mobile mapping system, UAV, and multi / hyperspectral LiDAR sensor, more efforts in data calibration, data integration, data fusion, data retrieval, data dissemination, data compression, and data classification would be essential to efficiently exploit the data for land cover classification at a finer scale and a greater extent. 10. Acknowledgements This research work is supported by the Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the GEOIDE Canadian Network of Excellence, Strategic Investment Initiative (SII) project SII P-IV # 72. The authors would like to acknowledge Prof. Ayman Habib (Purdue University) for providing the aerial image and LiDAR data shown in Fig. 2, Dr. Joe Liadsky (Optech Inc.) for providing the original LiDAR data shown in Fig. 5, and Ms. Moh Moh Lin Khin (Ryerson University) and Mr. Prathees Mahendrarajah (McMaster University) for their assistance in proofreading the manuscript. Helpful comments from the three anonymous referees are much appreciated. References Abed, F. M., Mills, J. P., Miller, P. E., 2012. Echo amplitude normalization of full-waveform airborne laser scanning data based on robust incidence angle estimation. IEEE Transactions on Geoscience and Remote Sensing 50 (7), 2910–2918. Ackermann, F., 1999. Airborne laser scanning - present status and future expectations. ISPRS Journal of Photogrammetry and Remote Sensing 54 (2-3), 64–67. Alexander, C., Tansey, K., Kaduk, J., Holland, D., Tate, N. J., 2010. Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing 65 (5), 423–432. Allouis, T., Durrieu, S., Chazette, P., Bailly, J.-S., Cuesta, J., V´ega, C., Flamant, P., Couteron, P., 2011. Potential of an ultraviolet, medium-footprint LiDAR prototype for retrieving forest structure. ISPRS Journal of Photogrammetry and Remote Sensing 66 (6), S92–S102. Anderson, J., Hardy, E., Roach, J., Witmer, R., 1976. A land use and land cover classification system for use with remote sensor data. US Geological Survey Professional Paper 964 USGS, Washington, D.C. Antonarakis, A., Richards, K., Brasington, J., 2008. Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment 112 (6), 2988–2998. Arrighi, C., Brugioni, M., Castelli, F., Franceschini, S., Mazzanti, B., 2013. Urban micro-scale flood risk estimation with parsimonious hydraulic modelling and census data. Natural Hazards and Earth System Science 13 (5), 1375–1391. ASPRS, 2003a. ASPRS LIDAR Data Exchange Format Standard, Version 1.0. http://asprs.org/a/ society/committees/standards/asprs_las_format_v10.pdf.

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