Light Detection And Ranging Intensity Data Research Articles

Longitudinal Degradation of Pavement Marking Detectability for Mobile LiDAR Sensing Technology in Real-World Use

Recent advancements in vehicle automation and driver-assistance systems that detect pavement markings has increased the importance of the detectability of pavement markings through various sensor modalities across weather and road conditions. Among the sensing techniques, light detection and ranging (LiDAR) sensors have become popular for vehicle-automation applications. This study used low-cost mobile multi-beam LiDAR to assess the performance of several types of pavement marking materials installed on a limited-access highway in various conditions, and quantified the degradation in detection performance over three years. Four marking materials, HPS-8, polyurea, cold plastic, and sprayable thermoplastic, were analyzed in the current study. LiDAR reflectivity data extracted from a total of 210 passes through the test sections were analyzed. A new detectability score based on LiDAR intensity data was proposed to quantify the marking detectability. The results showed that the pavement marking detectability varied across the material types over the years. The results provide guidance for selecting materials and developing maintenance schedules when marking detectability by LiDAR is a concern.

Recognition of ecological vegetation fairy circles in intertidal salt marshes from UAV LiDAR point clouds

• Fairy circles are detected from 3D point clouds of UAV LiDAR for the first time. • A new model is proposed to correct the intensity data of UAV LiDAR. • Merely corrected intensity data can reliably be used for point cloud filtering. • Absent instantaneous UAV LiDAR scanning distances and scan angles are recovered. • The proposed method is promising for spatial patterns recognition in other scenes. Fairy circles (FC) are a type of spatial self-organized patterns that widely exist in various vegetation ecosystems and the accurate detection and quantitative characterization of these mysterious circles remain a technical challenge. In this study, vegetation FC in intertidal salt marshes are recognized from the derived reflectance information (backscattered intensity) and geometric quantities of light detection and ranging (LiDAR) carried on unmanned aerial vehicle (UAV). The specular effect on the UAV LiDAR intensity data over nadir regions of wet salt marshes is eliminated using the laser radar equation and Phong model where the absent distances and incidence angles are approximately retrieved on the basis of geometric and temporal relations in data collection. The FC are progressively recovered through three interconnected procedures. First, the retrieved reflectance information is used to discriminate the mudflat and vegetation points. Second, a spatial connectivity clustering algorithm is utilized on the extracted vegetation points to form individual spatially disconnected clusters. Finally, FC and regular vegetation are successfully recognized by jointly using the salient, size, and circularity features of the generated clusters. A multi-echo UAV LiDAR system is used for data collection at an intertidal salt marsh to assess the feasibility and prospects of the proposed method. Taking the manual detection results from the orthophoto generated by images of a UAV camera system as a reference, the missing detection rate, false detection rate, and area detection error of the proposed method are 6%, 9%, and 5%, respectively. Results suggest that UAV LiDAR is an extremely promising technique to characterize the geometric properties (e.g., location, size, and quantity) of FC from a holistic perspective.

Characterizing building materials using multispectral imagery and LiDAR intensity data

This paper addresses the underlying bottleneck of unknown materials and material characteristics for assessing the life cycle of an existing structure or considering interventions. This is done by classifying and characterizing common building materials with two readily accessible, remote sensing technologies: multispectral imaging and Light Detection and Ranging (LiDAR). A total of 142 samples, including concrete of 3 different water/cement ratios, 2 mortar types, and 2 brick types (each type fired at 3 different temperatures) were scanned using a 5-band multispectral camera in the visible, RedEdge, and Near Infrared range and 2 laser scanners with different wavelengths. A Partial Least Squares Discriminant Analysis model was developed to classify the main materials and then the subcategories within each material type. With multispectral data, an 82.75% average correct classification rate was achieved (improving to 83.07% when combined with LiDAR intensity data), but the effect was not uniformly positive. This paper demonstrates the potential to identify building materials in a non-destructive, non-contact manner to aid in in-situ facade material labelling.

Supervised spatial classification of multispectral LiDAR data in urban areas.

Multispectral LiDAR (light detection and ranging) data have been initially used for land cover classification. However, there are still high classification uncertainties, especially in urban areas, where objects are often mixed and confounded. This study investigated the efficiency of combining advanced statistical methods and LiDAR metrics derived from multispectral LiDAR data for improving land cover classification accuracy in urban areas. The study area is located in Oshawa, Ontario, Canada, on the Lake Ontario shoreline. Multispectral Optech Titan LiDAR data over the study area were acquired on 3 September 2014 in a single strip of 3 km2. Using the channels at 1,550 nm (C1), 1,064 nm (C2) and 532 nm (C3), LiDAR intensity data, normalized digital surface model (nDSM), pseudo normalized difference vegetation index (PseudoNDVI), morphological profiles (MP), and a novel hierarchical morphological profiles (HMP) were derived and used as features for the classification. A support vector machine classifier with a radial basis function (RBF) kernel was applied in the classification stage, where the optimal parameters for the classifier were selected by a grid search procedure. The combination of intensity, pseudoNDVI, nDSM and HMP resulted in the best land cover classification, with an overall accuracy of 93.28%.

Comparative Performances of Airborne LiDAR Height and Intensity Data for Leaf Area Index Estimation

Leaf area index (LAI) estimation based on remote sensing data has often relied on the use of spectral vegetation indices from optical data. However, it is difficult to accurately estimate LAI due to saturation of spectral signals. Light detection and ranging (LiDAR) systems have emerged as promising technologies for overcoming the saturation problem, and an increasing number of studies have been conducted on LAI estimation using LiDAR data. In this study, we compared the performance of LAI estimation using LiDAR height and intensity data, and explored the potential for estimating forest LAI using combined LiDAR height and intensity data. LAI estimation models were established using LiDAR height, intensity, and a combination of LiDAR height and intensity metrics based on a random forest regression algorithm. Our results show that the laser intercept index derived from LiDAR height or intensity data was the most important predictor for LAI. Field measurements of LAI at 64 sites were used to assess the power of various LiDAR metrics in predicting LAI. The results show that both LiDAR height and intensity metrics alone could reliably estimate forest LAI. However, compared to LiDAR intensity metrics [ $R^{{\rm 2}}\,= \,0.610$ with root mean squared error (RMSE) of 0.664], LiDAR height metrics had a better predictive power ( $R^{{\rm 2}}\,= \,0.765$ with RMSE of 0.562). Moreover, the combined LiDAR height and intensity metrics resulted in the highest LAI estimation accuracy ( $R^{{\rm 2}}\,= \,0.809$ with RMSE of 0.501). Therefore, the combination of LiDAR height and intensity data has a great potential for improving the LAI estimation accuracy.

Evaporite karst geohazards in the Delaware Basin, Texas: review of traditional karst studies coupled with geophysical and remote sensing characterization

Evaporite karst throughout the Gypsum Plain of west Texas is complex and extensive, including manifestations ranging from intrastratal brecciation and hypogene caves to epigene features and suffosion caves. Recent advances in hydrocarbon exploration and extraction has resulted in increased infrastructure development and utilization in the area; as a result, delineation and characterization of potential karst geohazards throughout the region have become a greater concern. While traditional karst surveys are essential for delineating the subsurface extent and morphology of individual caves for speleogenetic interpretation, these methods tend to underestimate the total extent of karst development and require surficial manifestation of karst phenomena. Therefore, this study utilizes a composite suite of remote sensing and traditional field studies for improved karst delineation and detection of potential karst geohazards within gypsum karst. Color InfraRed (CIR) imagery were utilized for delineation of lineaments associated with fractures, while Normalized Density Vegetation Index (NDVI) analyses were used to delineate regions of increased moisture flux and probable zones of shallow karst development. Digital Elevation Models (DEM) constructed from high-resolution LiDAR (Light Detection and Ranging) data were used to spatially interpret sinkholes, while analyses of LiDAR intensity data were used in a novel way to categorize local variations in surface geology. Resistivity data, including both direct current (DC) and capacitively coupled (CC) resistivity analyses, were acquired and interpreted throughout the study area to delineate potential shallow karst geohazards specifically associated with roadways of geohazard concern; however, detailed knowledge of the surrounding geology and local karst development proved essential for proper interpretation of resistivity inversions. The composite suite of traditional field investigations and remotely sensed karst delineations used in this study illustrate how complex gypsum karst terrains can be characterized with greater detail through the utilization of rapidly advancing technologies, especially in arid environments with low vegetation densities.

Temperature Compensation for Radiometric Correction of Terrestrial LiDAR Intensity Data

Correction of terrestrial Light Detection and Ranging (LiDAR) intensity data has been increasingly studied in recent years. The purpose is to obtain additional insight into the scanned environment that is not available from the geometric information alone. Radiometric correction, as the name implies, corrects the received intensity to standard reflectance values in the range of ( 0 , 1 ) . This correction typically compensates for the dependence of angle and distance. This paper presents an additional compensation for temperature that may be necessary for some LiDAR instruments such as the Faro Focus 3 D X 330 laser scanner. It is also shown that temperature compensation is not necessary for the Riegl VZ–400. Another important contribution of this work is the verification of a previously published radiometric correction in different environments. The correction was applied to two different Terrestrial Laser Scanner (TLS) instruments: a Faro Focus 3 D X 330 and Riegl VZ-400. Overall, the VZ-400, without temperature compensation, produced better results with a Root Mean Square (RMS) of the standard deviation of error being 0.053 and a RMS of the mean error of 0.036 compared to 0.069 and 0.046 for the Faro Focus 3 D X 330. It was found, for the case of the Faro device, that the temperature of the instrument played an important role in the accuracy of the results. The proposed temperature compensation method improved the RMS standard deviation of the error by 1.4 times and the RMS of the error by 2.6 times, compared to the uncompensated results.

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration.

In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

Surface characteristics modeling and performance evaluation of urban building materials using LiDAR data.

Analysis of light detection and ranging (LiDAR) intensity data to extract surface features is of great interest in remote sensing research. One potential application of LiDAR intensity data is target classification. A new bidirectional reflectance distribution function (BRDF) model is derived for target characterization of rough and smooth surfaces. Based on the geometry of our coaxial full-waveform LiDAR system, the integration method is improved through coordinate transformation to establish the relationship between the BRDF model and intensity data of LiDAR. A series of experiments using typical urban building materials are implemented to validate the proposed BRDF model and integration method. The fitting results show that three parameters extracted from the proposed BRDF model can distinguish the urban building materials from perspectives of roughness, specular reflectance, and diffuse reflectance. A comprehensive analysis of these parameters will help characterize surface features in a physically rigorous manner.

An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data

Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings. In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification. The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings; 2) pavement; 3) trees and shrubs; and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification. The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user's and producer's accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery.

THE EFFECTS OF LASER REFLECTION ANGLE ON RADIOMETRIC CORRECTION OF THE AIRBORNE LIDAR INTENSITY DATA

Abstract. Radiometric correction (RC) of the airborne Light Detection And Ranging (LiDAR) intensity data has been studied in the last few years. The physical model of the RC relies on the use of the laser range equation to convert the intensity values into the spectral reflectance of the reflected objects. A number of recent studies investigated the effects of the LiDAR system parameters (i.e. range, incidence angle, beam divergence, aperture size, automatic gain control, etc.) on the results of the RC process. Nevertheless, the condition of the object surface (slope and aspect) plays a crucial role in modelling the recorded intensity data. The variation of the object surface slope and aspect affects the direction as well as the magnitude of the reflected laser pulse which makes significant influence on the bidirectional reflectance distribution function. In this paper, the effects of the angle of reflection, which is the angle between the surface normal and the incidence laser pulse, on the RC results of the airborne LiDAR intensity data is investigated. A practical approach is proposed to compute the angle of reflection using the digital surface model (DSM) derived from the LiDAR data. Then, a comparison between the results of the intensity data after RC using the scan angle and RC using the angle of reflection is carried out. The comparison is done by converting the intensity data into equivalent image data and evaluating the classification results of the intensity image data. Preliminary findings show that: 1) the variance-to-mean ratio of the land cover features are significantly reduced while using the angle of reflection in the RC process; 2) 4% of accuracy improvement can be achieved using the intensity data corrected with the scan angle. The accuracy improvement increases to 8% when using the intensity data corrected with the angle of reflection. The research work practically justifies the use of the reflection angle in the RC process of airborne LiDAR intensity data.

PIXEL VS OBJECT-BASED IMAGE CLASSIFICATION TECHNIQUES FOR LIDAR INTENSITY DATA

Abstract. Light Detection and Ranging (LiDAR) systems are remote sensing techniques used mainly for terrain surface modelling. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which provides high separability in the reflected energy by the target. This phenomenon is investigated to use the LiDAR intensity data for land-cover classification. The goal of this paper is to investigate and evaluates the use of different image classification techniques applied on LiDAR intensity data for land cover classification. The two techniques proposed are: a) Maximum likelihood classifier used as pixel- based classification technique; and b) Image segmentation used as object-based classification technique. A study area covers an urban district in Burnaby, British Colombia, Canada, is selected to test the different classification techniques for extracting four feature classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas, from the LiDAR intensity data. Generally, the results show that LiDAR intensity data can be used for land cover classification. An overall accuracy of 63.5% can be achieved using the pixel-based classification technique. The overall accuracy of the results is improved to 68% using the object- based classification technique. Further research is underway to investigate different criteria for segmentation process and to refine the design of the object-based classification algorithm.

LAND COVER INFORMATION EXTRACTION USING LIDAR DATA

Abstract. Light Detection and Ranging (LiDAR) systems are used intensively in terrain surface modelling based on the range data determined by the LiDAR sensors. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which has high separability in the reflected energy from different targets. This characteristic is investigated to implement the LiDAR intensity data in land-cover classification. The goal of this paper is to investigate and evaluates the use of LiDAR data only (range and intensity data) to extract land cover information. Different bands generated from the LiDAR data (Normal Heights, Intensity Texture, Surfaces Slopes, and PCA) are combined with the original data to study the influence of including these layers on the classification accuracy. The Maximum likelihood classifier is used to conduct the classification process for the LiDAR Data as one of the best classification techniques from literature. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to test the different band combinations to extract four information classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas. The results show that an overall accuracy of more than 70% can be achieved using the intensity data, and other auxiliary data generated from the range and intensity data. Bands of the Principle Component Analysis (PCA) are also created from the LiDAR original and auxiliary data. Similar overall accuracy of the results can be achieved using the four bands extracted from the Principal Component Analysis (PCA).

Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction

Airborne light detection and ranging (LiDAR) systems are used to measure the range (distance from the sensor to the target) and the intensity data (the backscattered energy from the target). LiDAR has been used extensively to model the topography of the Earth surface. Nowadays, LiDAR systems operating in the near-infrared spectral range are also gaining high interest for land cover classification and object recognition. LiDAR system requires geometric calibration (GC) and radiometric correction (RC) in order to maximize the benefit from the collected LiDAR data. This paper evaluates the impact of the GC and the RC of the LiDAR data on land cover classification. The procedure includes the use of a quasi-rigorous method for the GC and the radar (range) equation for the RC of the LiDAR data. The geometric calibration procedure is used to adjust the coordinates of the point cloud by removing the impact of biases in the system parameters as well as deriving corrected ranges and scan angles (in the absence of the system’s raw measurements) for the RC process. The geometrically calibrated ranges and scan angles are then used to correct the intensity data from the atmospheric attenuation and background backscattering based on the radar (range) equation. The atmospheric attenuation, which has not been fully addressed in the previous literature, is modeled by considering the parameters of absorption as well as scattering derived from existing empirical models and public (free) molecular absorption database. A LiDAR dataset covering an urban area is used to evaluate the effect of the GC and RC of the LiDAR data on land cover classification. The results are evaluated using a true ortho-rectified aerial image acquired during the same flight mission. The classification results show an accuracy improvement of about 9.4–12.8% for the LiDAR data used after the GC and RC. The study provides a practical approach for the LiDAR system GC and RC which can be implemented by most of the data end users. The outcome from this research work is a data processing tool that maximizes the benefits of using the intensity data for object recognition and land cover classification, which will be quite important for LiDAR data users.

Airborne Laser Survey Specifications and Quality Management Protocols for Airport Obstruction Surveys

Airport obstruction surveys are conducted to identify obstacles (objects penetrating obstruction identification surfaces that may pose a hazard to air navigation) and to provide critical data for the safety of aircraft and passengers. The current practice of airborne light detection and ranging (LIDAR) technology for airport obstruction surveys is presented. Obstruction survey requirements and current federal standards related to airport airspace analysis and the airport-geographical information system (GIS) program are reviewed. The accuracy of airborne LIDAR surveys and obstruction analysis has been established in previous field studies conducted by the National Geodetic Survey (NGS). Commercial obstruction surveys have been conducted on many airports in North America with LIDAR configurations and survey specifications recommended by NGS. In most cases, LIDAR survey data can be collected efficiently during the day as well as at night. LIDAR point cloud and intensity data are used to identify obstacles for the entire survey area through computationally efficient office computer algorithms and complementary use of photogrammetry. In contrast, traditional photogrammetry-only methods are limited to identifying obstacles at discrete locations because of laborintensive data processing. In addition, LIDAR data can be processed into GIS shape files for seamless integration into an electronic airport layout plan. Multiuse of LIDAR data for both canopy and bare ground enables the generation of accurate digital elevation models, contours for engineering design, and planimetrics for GIS mapping. Mission planning, ground point spacing and density, and other key parameters considered in the latest sample LIDAR survey specifications published as ACRP Research Results Digest 10 are discussed.

A LiDAR survey of Stromboli volcano (Italy): Digital elevation model-based geomorphology and intensity analysis

LiDAR (Light Detection and Ranging) is a novel and very useful active remote sensing system which can be used to directly identify geomorphological features as well as the properties of materials on the ground surface. In this work, LiDAR data were applied to the study of the Stromboli volcano in Italy. LiDAR data points, collected during a survey in October 2005, were used to generate a Digital Elevation Model (DEM) and a calibrated intensity map of the ground surface. The DEM, derived maps and topographic cross-sections were used to complete a geomorphological analysis of Stromboli, which led to the identification of four main geomorphological domains linked to major volcanic cycles. Moreover, we investigated and documented the potential of LiDAR intensity data for distinguishing and characterizing different volcanic products, such as fallout deposits, epiclastic sediments and lava flows.

Lidar intensity for improved detection of inundation below the forest canopy

Wetland hydrology is an important factor controlling wetland function and extent, and should therefore be a vital part of any wetland mapping program. Broad-scale forested wetland hydrology has been difficult to study with conventional remote sensing methods. Airborne Light Detection and Ranging (LiDAR) is a new and rapidly developing technology. LiDAR data have mainly been used to derive information on elevation. However, the intensity (amplitude) of the signal has the potential to significantly improve the ability to remotely monitor inundation — an important component of wetland hydrology. A comparison between LiDAR intensity data collected during peak hydrologic expression and detailedin situ data from a series of forested wetlands on the eastern shore of Maryland demonstrate the strong potential of LiDAR intensity data for this application (>96% overall accuracy). The relative ability of LiDAR intensity data for forest inundation mapping was compared with that of a false color near-infrared aerial photograph collected coincident with the LiDAR intensity (70% overall accuracy; currently the most commonly used method for wetland mapping) and a wetness index map derived from a digital elevation model. The potential of LiDAR intensity data is strong for addressing issues related to the regulatory status of wetlands and measuring the delivery of ecosystem services.

Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area

Separating ground from nonground laser returns from airborne light detection and ranging (LiDAR) data is a key step in creating digital terrain models (DTMs). In this letter, bare-earth and forested surfaces are classified from LiDAR intensity data in a data set from central Idaho, U.S. Next, a Gaussian fitting (GF) method is applied to determine ground elevations from LiDAR elevation data according to the land-cover information. In comparison to ground-based reference data, the GF method generated an accurate DTM in this study area. Overall, the DTM underestimated the ground observations by approximately 31 cm. A combination of LiDAR intensity and elevation data may be effectively used to develop DTMs in similar terrain of relatively simple land-cover classes.

Land Cover Characteristics of Airborne LiDAR Intensity Data: A Case Study

Airborne light detection and ranging (LiDAR) systems provide precise geometric information over the surface of the Earth, as well as radiometric information. studies on LiDAR have primarily focused on topographic mapping and urban 3-D modeling. The radiometric characteristics of backscattered laser intensity are meanwhile not yet clearly understood. Recently, the interest in the radiometric properties of LiDAR has been rapidly expanding, and several works have performed the normalization or correction of laser intensity data. However, the effect of radiometric correction is still unclear, and a suitable radiometric correction method has yet to be proposed. This letter investigated the radiometric properties of LiDAR intensity data as a prerequisite procedure for the practical use of intensity. On the basis of a comparison with the surface reflectance measured by a portable spectroradiometer, it was found that the LiDAR intensity of vegetation was unusual. For land cover types other than vegetation, it was observed that range distance was the dominant factor of the intensity of LiDAR. Therefore, this letter suggests that the correction of LiDAR intensity in terms of range is effective to the land cover types except vegetation.

Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery

The airborne laser scanning LIDAR (LIght Detection And Ranging) provides high-resolution Digital Terrain Models (DTM) that have been applied recently to the characterization, quantification and monitoring of coastal environments. This study assesses the contribution of LIDAR altimetry and intensity data, topographically-derived features (slope and aspect), and multi-spectral imagery (three visible and a near-infrared band), to map coastal habitats in the Bidasoa estuary and its adjacent coastal area (Basque Country, northern Spain). The performance of high-resolution data sources was individually and jointly tested, with the maximum likelihood algorithm classifier in a rocky shore and a wetland zone; thus, including some of the most extended Cantabrian Sea littoral habitats, within the Bay of Biscay. The results show that reliability of coastal habitat classification was more enhanced with LIDAR-based DTM, compared with the other data sources: slope, aspect, intensity or near-infrared band. The addition of the DTM, to the three visible bands, produced gains of between 10% and 27% in the agreement measures, between the mapped and validation data (i.e. mean producer's and user's accuracy) for the two test sites. Raw LIDAR intensity images are only of limited value here, since they appeared heterogeneous and speckled. However, the enhanced Lee smoothing filter, applied to the LIDAR intensity, improved the overall accuracy measurements of the habitat classification, especially in the wetland zone; here, there were gains up to 7.9% in mean producer's and 11.6% in mean user's accuracy. This suggests that LIDAR can be useful for habitat mapping, when few data sources are available. The synergy between the LIDAR data, with multi-spectral bands, produced high accurate classifications (mean producer's accuracy: 92% for the 16 rocky habitats and 88% for the 11 wetland habitats). Fusion of the data enabled discrimination of intertidal communities, such as Corallina elongata, barnacles ( Chthamalus spp.), and stands of Spartina alterniflora and Phragmites australis, which presented misclassification when conventional visible bands were used alone. All of these results were corroborated by the kappa coefficient of agreement. The high classification accuracy found here, selecting data sources, highlights the value of integrating LIDAR data with multi-spectral imagery for habitat mapping in the intertidal complex fringe.