Fusion Of LiDAR Data Research Articles

Collision-avoidance systems PRORETA: Situation analysis and intervention control

After a discussion of passive and active safety systems for automobiles and accident statistics it follows that a further progress in the reduction of accidents can be especially expected by next-generation driver-assistance systems with a sequence of warnings and active interventions. PRORETA is an Industry–University research project with the goal to develop steps towards accident-free driving. The first project considers two vehicles moving in the same direction. The other vehicle is detected by a fusion of LIDAR and camera data providing the system with relative speeds and distance. If the driver does not react to an obstacle on the own lane, the system automatically triggers an emergency braking and/or swerving to avoid a collision. This includes e.g. a fast and precise evasive trajectory control by automatic steering.The second project is dedicated to vehicles moving in opposite directions performing an overtaking maneuver on rural roads. The own vehicle detects the velocities and distances to the preceding and oncoming vehicle by RADAR and lane markings, etc. with a camera. The measured data of the two sensors undergo a sensor fusion with Kalman filters. The overtaking maneuver is predicted by using the measured data of all three vehicles. If an accident-free overtaking is in danger, warnings are given to the driver and if the driver does not react in time a full braking of the own vehicle is triggered such that the driver can turn back behind the overtaken vehicle.The contribution gives a survey on the developed strategies and some basic calculated features and control systems. Measured data are shown and give an impression of the driving experiments on the runway of an air field.

AERIAL IMAGES AND LIDAR DATA FUSION FOR DISASTER CHANGE DETECTION

Abstract. Potential applications of airborne LiDAR for disaster monitoring include flood prediction and assessment, monitoring of the growth of volcanoes and assistance in the prediction of eruptions, assessment of crustal elevation changes due to earthquakes, and monitoring of structural damage after earthquakes. Change detection in buildings is an important task in the context of disaster monitoring, especially after earthquakes. Traditionally, change detection is usually done by using multi-temporal images through spectral analyses. This provides two-dimensional spectral information without including heights. This paper will describe the capability of aerial images and LiDAR data fusion for rapid change detection in elevations, and methods of assessment of damage in made-made structures. In order to detect and evaluate changes in buildings, LiDAR-derived DEMs and aerial images from two epochs were used, showing changes in urban buildings due to construction and demolition. The proposed modelling scheme comprises three steps, namely, data pre-processing, change detection, and validation. In the first step for data pre-processing, data registration was carried out based on the multi-source data. In the second step, changes were detected by combining change detection techniques such as image differencing (ID), principal components analysis (PCA), minimum noise fraction (MNF) and post-classification comparison (P-C) based on support vector machines (SVM), each of which performs differently, based on simple majority vote. In the third step and to meet the objectives, the detected changes were compared against reference data that was generated manually. The comparison is based on two criteria: overall accuracy; and commission and omission errors. The results showed that the average detection accuracies were: 78.9%, 81.4%, 82.7% and 82.8% for post-classification, image differencing, PCA and MNF respectively. On the other hand, the commission and omission errors of the results improved when the techniques were combined compared to the best single change detection method. The proposed combination of techniques gives a high accuracy of 92.2% for detection of changes in buildings. The results show that using LiDAR data in the detection process improves the accuracy of feature detection by 14.9% compared with using aerial photography alone.

The SKY-Scanner time-critical decision support system for surveillance and risk evaluation during landing and take-off

This paper is devoted to an experimental work conducted within the FP6 SKY-Scanner project. The aim of the project is to develop new lidar (laser radar, LIght Detection And Ranging) equipment. Radar and lidar data fusion is a requirement. One work package was devoted to decision support model design and development. The proof of the concept decision support system (DSS) being developed adopts lidar capabilities, facilitates the tasks of the ATC controller in the aerodrome traffic zone (ATZ) and contributes to aircraft surveillance. The DSS is being designed as a framework for the operational implementation of norms. Factors such as separation between aircraft, track, glide path, altitude, etc. are subject to regulation in air traffic control (ATC). Legal sources are different: the rules of the air, airport procedure charts, etc. The scientific contribution is viewed from the standpoint of computational modelling. The risk model considered is a discrete representation of a (linear) deviation from the normative value. The traffic light visualization is used: the red light signifies intolerable, the yellow – tolerable, and the green – acceptable risk. The proposed method is illustrated on the Naples Capodichino airport sample day data.

Remote measurement of river morphology via fusion of LiDAR topography and spectrally based bathymetry

ABSTRACTThis study developed and evaluated a hybrid approach to remote measurement of river morphology that combines LiDAR topography with spectrally based bathymetry. Comparison of filtered LiDAR point clouds with surveyed cross‐sections indicated that subtle features on low‐relief floodplains were accurately resolved by LiDAR but that submerged areas could not be detected due to strong absorption of near‐infrared laser pulses by water. The reduced number of returns made the active channel evident in a LiDAR point density map. A second dataset suggested that pulse intensity also could be used to discriminate land from water via a threshold‐based masking procedure. Fusion of LiDAR and optical data required accurate co‐registration of images to the LiDAR, and we developed an object‐oriented procedure for achieving this alignment. Information on flow depths was derived by correlating pixel values with field measurements of depth. Highly turbid conditions dictated a positive relation between green band radiance and flow depth and contributed to under‐prediction of pool depths. Water surface elevations extracted from the LiDAR along the water's edge were used to produce a continuous water surface that preserved along‐channel variations in slope. Subtracting local flow depths from this surface yielded estimates of the bed elevation that were then combined with LiDAR topography for exposed areas to create a composite representation of the riverine terrain. The accuracy of this terrain model was assessed via comparison with detailed field surveys. A map of elevation residuals showed that the greatest errors were associated with underestimation of pool depths and failure to capture cross‐stream differences in water surface elevation. Nevertheless, fusion of LiDAR and passive optical image data provided an efficient means of characterizing river morphology that would not have been possible if either dataset had been used in isolation. Copyright © 2011 John Wiley & Sons, Ltd.

Leaf area and clumping indices for a boreal mixed-wood forest: lidar, hyperspectral, and Landsat models

The leaf area index (LAI) and the clumping index (CI) provide valuable insight into the spatial patterns of forest canopies, the canopy light regime and forest productivity. This study examines the spatial patterns of LAI and CI in a boreal mixed-wood forest, using extensive field measurements and remote sensing analysis. The objectives of this study are to: (1) examine the utility of airborne lidar (light detection and ranging) and hyperspectral data to model LAI and clumping indices; (2) compare these results to those found from commonly used Landsat vegetation indices (i.e. the normalized difference vegetation index (NDVI) and the simple ratio (SR)); (3) determine whether the fusion of lidar data with Landsat and/or hyperspectral data will improve the ability to model clumping and LAI; and (4) assess the relationships between clumping, LAI and canopy biochemistry. Regression models to predict CI were much stronger than those for LAI at the site. Lidar was the single best predictor of CI (r 2 > 0.8). Landsat NDVI and SR also had a moderately strong predictive performance for CI (r 2 > 0.68 with simple linear and non-linear regression forms), suggesting that canopy clumping can be predicted operationally from satellite platforms, at least in boreal mixed-wood environments. Foliar biochemistry, specifically canopy chlorophyll, carotenoids, magnesium, phosphorus and nitrogen, was strongly related to the clumping index. Combined, these results suggest that Landsat models of clumping could provide insight into the spatial distribution of foliar biochemistry, and thereby photosynthetic capacity, for boreal mixed-wood canopies. LAI models were weak (r 2 < 0.4) unless separate models were used for deciduous and coniferous plots. Coniferous LAI was easier to model than deciduous LAI (r 2 > 0.8 for several indices). Deciduous models of LAI were weaker for all remote sensing indices (r 2 < 0.67). There was a strong, linear relationship between foliar biochemistry and LAI for the deciduous plots. Overall, our results suggest that broadband satellite indices have strong predictive performance for clumping, but that airborne hyperspectral or lidar data are required to develop strong models of LAI at this boreal mixed-wood site.

EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion

Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain.

Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps

Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral data (IRS 1C LISS III), for the prediction of forest stem volume at plot level in a complex mountain area (Province of Trento, Southern Italian Alps), characterized by different tree species, complex morphology (i.e. altitude ranges from 65 m to 3700 m above sea level), and a range of different climates (from the sub-Mediterranean to Alpine type). 799 sample plots were randomly distributed over the 3000 km 2 of the forested areas of the Trento Province. From each plot, a set of variables were extracted from both LiDAR and multispectral data. A regression analysis was carried out considering two data sources (LiDAR and multispectral) and their combination, and dividing the plot areas into groups according to their species composition, altitude and slope. Experimental results show that the combination of LiDAR and IRS 1C LISS III data, for the estimation of stem volume, is effective in all the experiments considered. The best developed models comprise variables extracted from both of these data sources. The RMSE% on an independent validation set for the stem volume estimation models ranges between 17.2% and 26.5%, considering macro sets of tree species (deciduous, evergreen and mixed), between 17.5% and 29.0%, considering dominant species plots, and between 15.5% and 21.3% considering altitude and slope sets.

Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion

In this paper, we explored fusion of structural metrics from the Laser Vegetation Imaging Sensor (LVIS) and spectral characteristics from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) for biomass estimation in the Sierra Nevada. In addition, we combined the two sensors to map species-specific biomass and stress at landscape scale. Multiple endmember spectral mixture analysis (MESMA) was used to classify vegetation from AVIRIS images and obtain sub-pixel fractions of green vegetation, non-photosynthetic vegetation, soil, and shade. LVIS metrics, AVIRIS spectral indices, and MESMA fractions were compared with field measures of biomass using linear and stepwise regressions at stand (1 ha) level. AVIRIS metrics such as water band indices and shade fractions showed strong correlation with LVIS canopy height (r 2 = 0.69, RMSE = 5.2 m) and explained around 60% variability in biomass. LVIS variables were found to be consistently good predictors of total and species specific biomass (r 2 = 0.77, RMSE = 70.12 Mg/ha). Prediction by LVIS after species stratification of field data reduced errors by 12% (r 2 = 0.84, RMSE = 58.78 Mg/ha) over using LVIS metrics alone. Species-specific biomass maps and associated errors created from fusion were different from those produced without fusion, particularly for hardwoods and pines, although mean biomass differences between the two techniques were not statistically significant. A combined analysis of spatial maps from LVIS and AVIRIS showed increased water and chlorophyll stress in several high biomass stands in the study area. This study provides further evidence that lidar is better suited for biomass estimation, per se, while the best use of hyperspectral data may be to refine biomass predictions through a priori species stratification, while also providing information on canopy state, such as stress. Together, the two sensors have many potential applications in carbon dynamics, ecological and habitat studies.

Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules

This paper presents a method for mapping fuel types using LiDAR and multispectral data. A two-phase classification method is proposed to discriminate the fuel classes of the Prometheus classification system, which is adapted to the ecological characteristics of the European Mediterranean basin. The first step mapped the main fuel groups, namely grass, shrub and tree, as well as non-fuel classes. This phase was carried out using a Support Vector Machine (SVM) classification combining LiDAR and multispectral data. The overall accuracy of this classification was 92.8% with a kappa coefficient of 0.9. The second phase of the proposed method focused on discriminating additional fuel categories based on vertical information provided by the LiDAR measurements. Decision rules were applied to the output of the SVM classification based on the mean height of LiDAR returns and the vertical distribution of fuels, described by the relative LiDAR point density in different height intervals. The final fuel type classification yielded an overall accuracy of 88.24% with a kappa coefficient of 0.86. Some confusion was observed between fuel types 7 (dense tree cover presenting vertical continuity with understory vegetation) and 5 (trees with less than 30% of shrub cover) in some areas covered by Holm oak, which showed low LiDAR pulses penetration so that the understory vegetation was not correctly sampled.

Information fusion of Lidar range and intensity data for automatic building recognition

Lidar has proved to be a promising data source for various mapping and 3D modelling of buildings in urban areas. Therefore, many researchers have been trying to study and develop automatic building recognition algorithms based on Lidar data. But, according to the adjacency of buildings and other objects in urban areas, especially trees, the performance of obtained results from most of these algorithms is still dependent to several assumptions and simplifications. In this article, a multi-agent methodology has been proposed for automatic building recognition based on the decision level fusion of textural and spatial information extracted from Lidar range and intensity products. In this multi-agent methodology, two different groups of object recognition agents are defined for building and tree detection in parallel and the algorithm has two different operational levels based on the types of contextual information. In the first level, both object recognition agents decide about the types of objects in the study area based on textural information, and the candidates of building and tree regions will be generated. In the second operational level, building recognition and tree recognition agents perform some operations in macro level in order to modify the candidates of building and tree regions based on spatial information. The evaluation of obtained results confirms the high capabilities of proposed multi-agent algorithm to decrease the conflicts in the field of automatic building recognition in complex urban areas.

A Two-step Displacement Correction Algorithm for Registration of Lidar Point Clouds and Aerial Images without Orientation Parameters

This paper presents a novel linear registration algorithm for lidar point clouds and aerial images without orientation parameters. First, preprocessing is conducted to classify the lidar point clouds into ground points, building points, and aboveground, non-building points. After preprocessing, the algorithm consists of two sequential steps, i.e., Tilt Displacement Correction and Height Displacement Correction. As the kernel of the proposed registration algorithm, the mathematical model for Height Displacement Correction is a set of linear formulas analytically deduced from the rigorous geometric function of a single image. The proposed registration algorithm does not require any orientation parameters for the image, which greatly lowers the requirements for image acquisition. Due to the model's linearity, the proposed algorithm is computationally efficient. Our experimental results demonstrate that the proposed algorithm can register aerial images vvithout orientation parameters at the same accuracy level of space resection based on collinear equations. This result fulfills the requirement for the fusion of lidar range data and aerial images in most large-scale urban modeling applications.

Erratum to: Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data

Erratum to: Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data

Collision avoidance systems PRORETA: situation analysis and intervention control

After a discussion of passive and active safety systems for automobiles and accident statistics it follows that a further progress in the reduction of accidents can be especially expected by next generation driver assistance systems with a sequence of warnings and active interventions. PRORETA is an Industry-University research project with the goal to develop steps towards accident free driving. The first project considers two vehicles moving in the same direction. The other vehicle is detected by a fusion of LIDAR and camera data providing the system with relative speeds, distance and locations. If the driver does not react to an obstacle on the own lane, the system automatically triggers an emergency braking and/or swerving to avoid a collision. This includes e.g. a fast and precise evasive trajectory control by automatic steering.The second project is dedicated to vehicles in opposite direction performing an overtaking maneuver on rural roads. The own vehicle detects the velocities and distances to the preceding and oncoming vehicle by RADAR and lane markings etc. with a camera. The measured data of the two sensors undergo a sensor fusion with Kalman filters. The overtaking maneuver is predicted by using the measured data of all three vehicles. If an accident free overtaking is in danger, warnings are given to the driver and if the driver does not react a full braking of the own vehicle is fired such that the driver can turn back behind the overtaken vehicle. The contribution describes the developed strategies and some basic calculated features and control systems. Measured data is shown and some videos give an impression of the driving experiments on the runway of an air field.

Fusion of multiple image types for the creation of radiometrically-accurate synthetic scenes

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multi-spectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG's ability to generate scenes "on demand." To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, we have developed a comprehensive workflow aimed at reducing the man-in-the-loop requirements for many aspects of synthetic hyperspectral scene construction. Through a fusion of 3D lidar data with passive imagery, we have been able to partially-automate many of the required tasks in the creation of high-resolution urban DIRSIG scenes. This paper presents a description of these techniques.

Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data

Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies.

Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data

To safeguard the goals of flood protection and nature development, a river manager requires detailed and up‐to‐date information on vegetation structures in floodplains. In this study, remote‐sensing data on the vegetation of a semi‐natural floodplain along the river Waal in the Netherlands were gathered by means of a Compact Airborne Spectrographic Imager (CASI; spectral information) and LiDAR (structural information). These data were used to classify the floodplain vegetation into eight and five different vegetation classes, respectively. The main objective was to fuse the CASI and LiDAR‐derived datasets on a pixel level and to compare the classification results of the fused dataset with those of the non‐fused datasets. The performance of the classification results was evaluated against vegetation data recorded in the field. The LiDAR data alone provided insufficient information for accurate classification. The overall accuracy amounted to 41% in the five‐class set. Using CASI data only, the overall accuracy was 74% (five‐class set). The combination produced the best results, raising the overall accuracy to 81% (five‐class set). It is concluded that fusion of CASI and LiDAR data can improve the classification of floodplain vegetation, especially for those vegetation classes which are important to predict hydraulic roughness, i.e. bush and forest. A novel measure, the balance index, is introduced to assess the accuracy of error matrices describing an ordered sequence of classes such as vegetation structure classes that range from bare soil to forest.

Mapping surface fuel models using lidar and multispectral data fusion for fire behavior

Wild-land fires have become intense and more frequent all over the world. Improving the accuracy of mapping fuel models is essential for fuel management decisions and explicit fire behavior prediction for real-time support of suppression tactics and logistics decisions. The overall aim of this paper is to develop the use of lidar (LIght Detection and Ranging) remote sensing to accurately and effectively assess fuel models in East Texas. More specific goals include: (1) developing lidar derived products and the methodology to use them for assessing fuel models; (2) investigating the use of several techniques for data fusion of lidar and multispectral imagery for assessing fuel models; (3) investigating the gain in fuels mapping accuracy when using lidar as opposed to QuickBird imagery alone; and (4) producing spatially explicit digital fuel maps. Estimates of fuel models were compared with in-situ data collected over 62 plots. We employ a unique approach to classify fuel models using a combination of lidar height bins and multispectral image data. Different image processing approaches for fusing lidar and multispectral data, such as the Minimum Noise Fraction (MNF) and Principle Component Analysis (PCA), were used to improve the overall accuracy of image classification. Supervised image classification methods provided better accuracy (90.10%) with the fusion of airborne lidar data with QuickBird data than with QuickBird imagery alone (76.52%). According to our results, lidar derived data provide accurate estimates of surface fuel parameters efficiently and accurately over extensive areas of forests. This study demonstrates the importance of using accurate maps of fuel models derived using new lidar remote sensing techniques.

Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction

This paper aims to present a new approach for automatic extraction of building footprints in a combination of the IKONOS imagery with pan-sharpened multi-spectral bands and the low-sampled (∼ 0.1 points/m 2) airborne laser scanning data acquired from the Optech's 1020 ALTM (Airborne Laser Terrain Mapper). Initially, a laser point cluster in 3D object space was recognized as an isolated building object if all the member points were similarly attributed as building points by investigating the height property of laser points and the normalized difference vegetation indices (NDVI) driven from IKONOS imagery. As modelling cues, rectilinear lines around building outlines collected by either data-driven or model-driven manner were integrated in order to compensate the weakness of both methods. Finally, a full description of building outlines was accomplished by merging convex polygons, which were obtained as a building region was hierarchically divided by the extracted lines using the Binary Space Partitioning (BSP) tree. The system performance was evaluated by objective evaluation metrics in comparison to the Ordnance Survey's MasterMap®. This evaluation showed the delineation performance of up to 0.11 (the branching factor) and the detection percentage of 90.1% (the correctness) and the overall quality of 80.5%.

Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

AbstractFlood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, digital surface models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub‐metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. This paper describes the development of a LiDAR post‐processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post‐processing produces a digital terrain model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR‐derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ∼1 m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a two‐dimensional finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features (such as buildings and roads) and taller vegetation features (such as trees and hedges). This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA

Abstract The principal study objective was to explore the feasibility of using small-footprint lidar data and multispectral imagery to estimate forest volume and biomass on small (0.017-ha) plots. In addition, the spatial dependency of residuals between ground-measured and lidar-estimated variables was investigated. The lidar data set was acquired over deciduous and pine stands in the southeastern United States. Individual trees were identified on the lidar-derived canopy height model by local maximum focal filtering with both square and circular windows of variable size. The size of the dynamically varying window was based on the height of the canopy and the taxonomic group as derived from coregistered multispectral optical data. Lidar-measured parameters at an individual tree level (height, crown diameter) were used with regression models and cross validation to estimate plot-level field inventory data, including volume, biomass, basal area, and diameter at breast height (dbh). Maximum R2 values for estimating biomass were 0.32 for deciduous trees (RMSE 44 Mg/ha) and 0.82 for pines (RMSE 29 Mg/ha). When estimating volume, maximum R2 values for deciduous trees were 0.39 (RMSE 52.84 m3/ha) and 0.83 for pines (RMSE 47.9 m3/ha). Calculation of Moran's I coefficient for each regression model revealed a lack of significant autocorellation of the residuals at 0.05 level. Both model fit and prediction for volume and biomass models for deciduous and pine plots indicated that the circular window shape is more appropriate to locate individual trees with lidar. Using the fused data set, lidar and optical imagery, as opposed to using lidar data alone, always improved biomass and volume estimates for pines and in some cases for deciduous plots as well. FOR. SCI. 50(4):551–565.