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The mapping and change detection of land cover and land use are essential for urban management. The aim of this study was to map and monitor the spatial and temporal change in urban land cover and land use in Davidson County, Tennessee in the periods of 2013, 2016, and 2020. The urban land cover and land use categories were classified and mapped using Random Forest algorithm. A combination of Landsat Operational Land Imager (OLI) satellite data with Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and derived Topographic Position Index (TPI) were used in the classification and monitoring of urban land cover and land use change. The urban land cover and land use types were mapped with average overall accuracies of about 87% in 2020, 85% in 2016 and 2013. The overall accuracy increased by around 8%, 9%, and 6% in 2020, 2016, and 2013 classifications respectively when lidar-DEM and derived TPI were added to Landsat OLI satellite data in the classification relative to standalone Landsat OLI. Total change occurred in about 63% of Davidson County between 2016 and 2020 with significant net gains and losses among land cover and land use types. This information could support land use planning. [ABSTRACT FROM AUTHOR]

Three-dimensional reconstruction from a single image has excellent future prospects. The use of neural networks for three-dimensional reconstruction has achieved remarkable results. Most of the current point-cloud-based three-dimensional reconstruction networks are trained using nonreal data sets and do not have good generalizability. Based on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago ()data set of large-scale scenes, this article proposes a method for processing real data sets. The data set produced in this work can better train our network model and realize point cloud reconstruction based on a single picture of the real world. Finally, the constructed point cloud data correspond well to the corresponding three-dimensional shapes, and to a certain extent, the disadvantage of the uneven distribution of the point cloud data obtained by light detection and ranging scanning is overcome using the proposed method. [ABSTRACT FROM AUTHOR]

Although, using the imaging and LiDAR sensors for simultaneous data collection reduced the collection time, mobilizing two sets of aircraft and equipment would make the collection process faster, resulting in faster data output. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING May 2021 313 MAPPING KUWAIT OIL FIELDS WITH LEICA LIDAR AND IMAGING SENSOR SYSTEM Majeed Al-Muwail, Faisal Al-Bous, Nasir Osman, Ramesh Mahishi Murthy Operations Technical Services (South & East Kuwait), Kuwait Oil Company, Kuwait 314 May 2021 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING Introduction The Infrastructure Master Plan (IMP) section of the Operations Support Group in Kuwait Oil Company (KOC) is responsible for developing a Master Plan to manage all the oil field's surface footprint. KOC selected the Area of Interest (AOI) based on the geographic distribution of KOC services/ facilities all over the State of Kuwait. [Extracted from the article]

Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identi- fication system (LPIS) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and -2 time series. The temporal evolution of crop types was modeled with a linearchain conditional random field, trained with time series of multi-modal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km2) which show different crop types, various parcel sizes, and agricultural practices: .the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark positive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual crops. [ABSTRACT FROM AUTHOR]

In order to eliminate the influences of surface roughness and vegetation on radar signals in the vegetation-covered soil moisture estimation, the present paper proposes a combining method based on modified particle swarm optimization (MPSO) and back-propagation (BP) neural network algorithm. This method combines optical and radar data at the field scale and uses MPSO to optimize the weight of the neural network. An effective inertia weight is introduced in the MPSO and an implicit relationship between backscatter coefficient and soil moisture is established. Experimental results show that the combining method produces better accuracy than other inversion methods with R² of 72.2% and Root Mean Square Error (RMSE) of 0.033 cm³/cm³, respectively. Meanwhile, the estimated accuracy of surface soil moisture using radar and optical data simultaneously is much higher than that using only a single data source as input with R² of 0.827 and RMSE of 0.029 cm³/cm³. Therefore, the combining method can effectively improve the accuracy of soil moisture retrieval and provide support for large-scale agricultural monitoring. [ABSTRACT FROM AUTHOR]

Synchronization of airborne laser scanning devices is a critical process that directly affects data accuracy. This process can be more challenging with low-cost airborne laser scanning (ALS) systems because some device connections from off-theshelf sensors are less stable. An alternative to synchronization is performing a postprocessing clock correction. This article presents a technique for postprocessing synchronization (offline) that estimates clock differences based on the correlation between the signals from the global navigation satellite system (GNSS) trajectory and the light detection and ranging (lidar) range, followed by refinement with a least-squares method. The correlation between signals was automatically estimated considering the planned flight maneuvers, in a flat terrain, to produce altimetric trajectory variations. Experiments were performed with an Ibeo LUX laser unit integrated with a NovAtel SPAN-IGM-S1 inertial navigation system that was transported by an unmanned aerial vehicle (UAV). The planimetric and altimetric accuracies of the point cloud obtained with the proposed postprocessing synchronization technique were 28 cm and 10 cm, respectively, at a flight height of 35 [ABSTRACT FROM AUTHOR]

The classification and mapping accuracy of urban land cover and land use has always been a critical topic and several auxiliary data have been used to improve the classification accuracy. However, to the best of our knowledge, there is limited knowledge of the addition of airborne Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and Topographic Position Index (TPI) for urban land cover and land use classification and mapping. The aim of this study was to explore the addition of airborne lidar-DEM and derived TPI to reflect data of Landsat Operational Land Imager (OLI) in improving the classification accuracy of urban land cover and land use mapping. Specifically, this study explored the mapping accuracies of urban land cover and land use classifications derived using: 1) standalone Landsat OLI satellite data; 2) Landsat OLI with acquired airborne lidar-DEM; 3) Landsat OLI with TPI; and 4) Landsat OLI with airborne lidar-DEM and derived TPI. The results showed that the addition of airborne lidar-DEM and TPI yielded the best overall urban land cover and land use classification accuracy of about 88%. The findings in this study demonstrated that both lidar-DEM and TPI had a positive impact in improving urban land cover and land use classification. [ABSTRACT FROM AUTHOR]

The article highlights some of the systems and methods that directly benefit environmental professionals and students who are focused on imaging and geospatial information for improved understanding, management and monitoring of soil resources. It profiles 5 key emergent geospatial technologies including airborne topograhic lidar, proximal sensing of soil properties and unmanned aerials systems (UAS). It cites the need to strengthen educational, outreach and professional certification programs.

The combination of RapidEye satellite imagery and light detection and ranging (lidar) derivatives was assessed for mapping land-cover within a mountaintop coal surface mine complex in the southern coalfields of West Virginia, USA. Support vector machines (SVM), random forests (RF), and boosted classification and regression trees (CART) algorithms were used. Incorporation of the lidar-derived data increased map accuracy in comparison to using only the five imagery bands, and SVM generally produced a more accurate classification than the ensemble tree algorithms based on overall map accuracy, Kappa statistics, allocation disagreement, quantity disagreement, and McNemar's test of statistical significance. Based on measures of predictor variable importance within the ensemble tree classifiers, the normalized digital surface model (nDSM) was found to be more useful than first return intensity data for differentiating the classes. [ABSTRACT FROM AUTHOR]

Elevation error in the Intermap X-band airborne Interferometric Synthetic Aperture Radar DTM data set is defined in a 260-hectare mixed land-cover area using external dual-frequency GPS and bare-earth lidar point-cloud validation data. Absolute elevation error is reported globally, and within land-cover classes characterized by distinctive vegetation canopy densities and depths that are considered to have the potential to affect X-band DTM elevation error in distinctive ways. Observed global and land-cover specific elevation errors are subsequently compared with an external study where land-cover dependent errors were quantified within four ]idar data sets that overlapped the IFSAR DTM validation area. The results of these absolute and comparative results are subsequently used to make recommendations regarding the potential of lntermap bare-earth IFSAR DTM data in environmental modeling applications elsewhere, and the scope for using the data in conjunction with, and as an alternative to airborne lidar data is discussed. [ABSTRACT FROM AUTHOR]

Terrestrial lidar (TLS) is an emerging technology for deriving forest attributes, including conventional inventory and canopy characterizations. However, little is known about the influence of scanner specifications on derived forest parameters. We compared two TLS systems at two sites in British Columbia. Common scanning benchmarks and identical algorithms were used to obtain estimates of tree diameter, position, and canopy characteristics. Visualization of range images and point clouds showed clear differences, even though both scanners were relatively high-resolution instruments. These translated into quantifiable differences in impulse penetration, characterization of stems and crowns far from the scan location, and gap fraction. Differences between scanners in estimates of effective plant area index were greater than differences between sites. Both scanners provided a detailed digital model of forest structure, and gross structural characterizations (including crown dimensions and position) were relatively robust; but comparison of canopy density metrics may require consideration of scanner attributes. [ABSTRACT FROM AUTHOR]

Airborne laser scanning technology (lidar) makes it easy to collect large amounts of points that sample the terrain beneath. The ASPRS LAS Specification is the standard file format for storing and distributing this data. However, managing the amount of data becomes challenging for surveys acquiring billions of points. We describe a lossless compression scheme for lidar in binary LAS format. Our compressed LAZ files are only 7 to 25 percent of the original size. Our encoding and decoding speeds are several million points per second on average desk- top computers. Compression is streaming and decompression supports random-access. A reference implementation with a LGPL license is available at http://laszip.org. On a national scale, the compression savings of storing LAZ instead of LAS can be measured in Petabytes of data, i.e., data that no longer need to be hosted, backed up, and served for download. Techniques like LASzip are imperative in order to manage lidar elevation for an entire nation. [ABSTRACT FROM AUTHOR]

Lidar systems have been proven as a cost-effective tool for the collection of high density and accurate point cloud over physical surfaces. The collected point cloud does not exhibit homogenous point distribution due to the characteristics of the scanning system and/or the physical properties of the scanned surfaces. In order to effectively process the lidar point clouds, local point density variations should be quantified and taken into account for the definition of processing parameters. In this paper, new approaches are presented for the estimation of local point density indices while considering the 3D relationship among lidar points, the physical properties of the reflecting surfaces, and the noise level in the datasets collected by different laser scanners. The impact of considering the estimated local point density variations on the quality of lidar data segmentation results is then investigated by performing a quality control procedure. Quantitative evaluation of segmentation results highlights the efficacy of utilizing the estimated local point density indices for the derivation of more accurate segmentation. [ABSTRACT FROM AUTHOR]

Mapping vegetation height over large areas presents a problem of scale: height varies with the individual tree or stand, but the resolution of available datasets is too low to characterize this variability sufficiently for many applications. We address this problem by fusing 1 km resolution canopy height data derived from satellite-based laser altimetry with higher-resolution land-cover data, resulting in 30 m resolution estimates of canopy height. These are downscaled further to 1 m resolution by simulating individual trees. A web service architecture is used, which allows processing to occur on demand without preprocessing large datasets. We compared the resulting canopy volumes to reference airborne lidar data from 262 randomly located 1 km² areas within nine study sites. Results at 30 m resolution show an RMSE of 33 percent of the mean reference volume and an R² of 0.77; at 1 m the RMSE is 66 percent and the R² is 0.38. [ABSTRACT FROM AUTHOR]

As airborne laser scanning (ALS) gains wider adoption to support forest operations in Canada, the consistency and quality of derivative products that support long-term monitoring and planning are becoming a key issues for managers. The Canadian Consortium for Lidar Environmental Applications Research (C-CLEAR) has supported almost 200 projects across Canada since 2000, with forest-related studies being a dominant theme. In 2010 and 2011, field operations were mobilized to support 13 ALS projects spanning almost the full longitudinal gradient of Canada's forests. This paper presents case studies for seven plus an overview of some best practices and data processing workflow tools that have resulted from these consortium activities. Although the projects and research teams are spread across Canada, the coordination and decade of experience provided through C-CLEAR have brought common methodological elements to all. It is clear that operational, analytical and reporting guidelines that adhere to community accepted standards are required if the benefits promised by ALS forestry are to be realized. A national Lidar Institute that builds upon the C-CLEAR model and focuses on developing standards, guidelines, and certified training would address this need. [ABSTRACT FROM AUTHOR]

Lidar has been established over the past few years as a mainstream tool for the acquisition of three dimensional point data. Besides the conventional mapping missions, lidar has proven to be very useful for a wide spectrum of applications such as forestry, urban planning, structural deformation analysis, and reverse engineering. In the context of a nationwide dataset, it is safe to assume that multiple laser scanners are under different conditions to collect the data. Current registration and segmentation algorithms assume homogeneity in the local point density and accuracy which is an invalid assumption that cannot be tolerated. As a consequence of the wide range of lidar sensors currently available, it is becoming crucial to develop algorithms for the registration and segmentation of lidar data with significantly varying characteristics (i.e., varying point density and accuracy). In this paper, a methodology for the optimal registration and segmentation of heterogeneous lidar data is presented. An example of integrating airborne and terrestrial laser scans is presented and followed by a discussion of the pros of the integration process. [ABSTRACT FROM AUTHOR]

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m²), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. For example, from the top of canopy, we predicted canopy height (r² = 0.87), canopy cover (r² = 0.83), basal area (r² = 0.82), shrub cover (r² = 0.62), shrub height (r² = 0.59), combined fuel loads (r² = 0.48), and fuel bed depth (r² = 0.35). While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and <50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management. [ABSTRACT FROM AUTHOR]

A canopy height model (CHM) derived from lidar data can be segmented to obtain individual tree crowns. However, branches, tree crowns, and tree clusters usually have similar shapes and overlapping sizes. This causes current individual tree crown delineation methods for CHMs to work less effectively on closed canopy deciduous or mixed wood forests consisting of various-sized tree crowns. Based on mult-scale analysis and segmentation, an innovative tree crown delineation method was developed in this study. In this method, the scale levels of target tree crowns are first morphologically determined; the CHM is filtered at the multiple scale levels; and local maxima within each filtered CHM are taken as markers to segment the original CHM using the marker-controlled watershed method. After tree crown segments are selected from the multiple resulting segmentation maps and integrated together, a complete tree crown map is generated. In an experiment on natural forests in Ontario, Canada, the proposed method yielded crown maps having a good consistency with manual and visual interpretation. For instance, when compared to a manually delineated forest map, the automated method correctly delineated about 69 percent, 65 percent, and 73 percent of the tree crowns from plots of closed canopy coniferous, deciduous, and mixed wood forests, respectively. [ABSTRACT FROM AUTHOR]

Soil moisture gradients and nutrient fertility are used to classify forest types in Ontario, Canada based on ecological land classification (ELC). An existing ]idar dataset for the Romeo Malette Forest near Timmins, Ontario was used to derive three terrain indices (topographic wetness index (TWl), percent elevation index (PEI), and canopy height model (CHM)) at varying resolutions (2 m, 5 m, 10 m and 20 m) to determine the resolution that best characterizes soil moisture patterns in a boreal forest landscape. Depression removal algorithms were examined to determine how they affect the TWI, and thus, soil moisture estimation. This paper stresses the importance of gathering data at a resolution that is sufficient for mapping fine-scale basin features to accurately model soil moisture in forested environments. The results of this research indicate that 5 m resolution data provided the best overall relationship with measured seasonal soil moisture. More generally, the results indicate that high spatial resolution variables (i.e., 2 m, 5 m) may be more suited to modeling soil moisture trends at shallow depths (0 to 15 era), while coarser resolutions (i.e., 10 m, 20 m) may be more adept at resolving trends over greater depths (0 to 40 cm). [ABSTRACT FROM AUTHOR]

Mobile lidar (light detection and ranging) data collection is a rapidly emerging technology in which multiple georeferenced sensors (e.g., laser scanners, cameras) are mounted on a moving vehicle to collect real world data. The photorealistic modeling of large-scale real world scenes such as urban environments has become increasingly interesting to the vision, graphics, and photogrammetry communities. In this paper, we present an automatic approach to window and facade detection from mobile lidar data. The proposed method combines bottom-up with top-down strategies to extract facade planes from noisy lidar point clouds. The window detection is achieved through a two-step approach: potential window point detection and window localization. The facade pattern is automatically inferred to enhance the robustness of the window detection. Experimental results on six datasets result in 71.2 percent and 88.9 percent in the first two datasets, 100 percent for the rest four datasets in terms of completeness rate, and 100 percent correctness rate for all the tested datasets, which demonstrate the effectiveness of the proposed solution for planar facades with rectilinear windows. The application potential includes generation of building facade models with street-level details and texture synthesis for producing realistic occlusion-free facade texture. [ABSTRACT FROM AUTHOR]

We developed a neural network based approach to identify urban tree species at the individual tree level from lidar and hyperspectral imagery. This approach is capable of modeling the characteristics of multiple spectral signatures within each species using an internally unsupervised engine, and is able to catch spectral differences between species using an externally supervised system. To generate a species-level map for an urban forest with high spatial heterogeneity and species diversity, we conducted a treetop-based species identification. This can avoid the problems of double-sided illumination, shadow, and mixed pixels, encountered in the crown-based species classification. The study indicates lidar data in conjunction with hyperspectral imagery are not only capable of detecting individual trees and estimating their tree metrics, but also identifying their species types using the developed algorithm. The integration of these two data sources has great potential to take the place of traditional field surveys. [ABSTRACT FROM AUTHOR]

A large amount high-resolution satellite imagery (HRSI) has been available in the commercial market because of its value in creating accurate base maps for various applications. As massive amounts of HRSI are acquired globally by satellites with short revisit times, automated but accurate georegistration is still required despite advances in precise orbit tracking and estimation. Motivated by the attractive properties of airborne lidar data, such as their high resolution and accuracy, this study proposes a new automated method for refining the HRSI with rational polynomial coefficients (RPCs) using airborne lidar information. By projecting the lidar intensity return into the HRSI space, the image matching complexity is reduced to a simple, 2D case. The true challenge is in overcoming the difference between the HRSI and the lidar intensity return to allow for reliable matching. To this end, this paper proposes a new method based on simple relative edge cross correlation (RECC) with a screening method to prevent false matching. To make the approach more robust, data snooping was added for a final detection of outliers. Experiments were performed using three Kompsat-2 images and the potential of the approach was confirmed, showing sub-pixel accuracy. [ABSTRACT FROM AUTHOR]

Very large three-dimensional (313) point datasets are increasingly common, such as from Light Detection and Ranging (lidar). Increasingly, there are attempts to exploit these 3D point data sets beyond mere visualization. However, current Spatial Information Systems provide only limited 3D support. Even commercial systems advertising in-built, 3D data types provide only minimal functionality. Specifically, there is no effective means of indexing large 3D point datasets, which is crucial for efficient analysis and engineering use. Also, many datasets are information rich (e.g., contain color or some other associated semantic information), which has yet to be fully exploited. This paper presents the implementation in a commercial spatial database of a spatial indexing technique using an octree data structure and highlights its advantages for sparse, as well as uniformly distributed, aerial lidar data. The implementation outperforms an existing r-tree index within the software, and offers additional functionality of attribute-based 3D grouping. [ABSTRACT FROM AUTHOR]

The article discusses a research on coastal wetlands monitoring in the U.S. by the National Coastal Wetlands Inventory (NCWI), which aims to improve the tools for controlling invasive species. It says that the research used methods which include remote sensing, multi-temporal Landsat satellite imagery, Light Detection and Ranging (lidar), and Advanced Synthetic Aperture Radar (ASAR). It adds that the result of the research will help in the accuracy of wetland inventories.

This paper presents a cell-based approach for computing the deformation of a monitored object by analyzing point cloud data from terrestrial lidar. This approach can automatically generate an informative deformation description (called "deformation map") with distinctive deformation characteristics for different partial areas. The approach consists of three major computing steps: (a) "split" the space of the monitored object is divided into 3D uniform cells, (b) "detect" deformation parameters for each cell (called "meta-deformation") are estimated by comparing the point clouds in the cell sampled at Epochs I and II, and (c) "merge" the adjacent cells with similar meta-deformation are combined together in a partial area with a consistent "sub-deformation." The main contributions of this paper are: (a) a hybrid deformation model for incremental and corn preh ensive deformation representation, including meta deformation, sub-deformation and deformation map, (b) a systematic procedure of "split-detect-merge" to automatically and gradually estimate the hybrid deformation model, and (c) a complete validation with a couple of synthetic and real datasets. [ABSTRACT FROM AUTHOR]

The article discusses the use of laser waveforms in remote sensing and the collection of geospatial data. The author first explains the use of laser pulses for topographic Light Detection and Ranging (lidar), and then goes on to assess the implementation of the Full Waveform Digitization (FWD) module to lidar systems. He explains how to analyze waveform data through the analysis of backscatter and echo amplitude.

Navigation applications, among other location-based applications, that rely primarily on Global Navigation Satellite System (GNSS) are subject to positioning uncertainties. This paper presents an integrated GNSS (IGNSS) QOS prediction methodology that can provide navigation applications with quality of GNSS positions on prospective roads in a selected route ahead of time. As a part of the methodology, this paper discusses the technique for simulating signal paths using lidar data and signal propagation models for predicting levels of satellite visibility, positional availability, and positional accuracy. Experiments were conducted, and the predicted results by IGNSS QOS were evaluated against reference data (GPS coordinates) at various environment settings. Evaluation results indicated that IGNSS QOS can predict positioning quality with reasonably a high level of confidence in open sky locations and with some uncertainties in obstructed locations. [ABSTRACT FROM AUTHOR]

The article examines the LAS 1.4 specification for the point cloud file format. LAS acts a transmitter of laser point cloud data (Lidar). After examining the initial philosophy behind lidar processing, which revolves around laser pulses, and the initial LAS 1.0 format, the author provides information on waveform data, point data records, and variable length records (VLR).

Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3D point data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diame- ter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Moun- tains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees ("recall"), 94 percent of the segmented trees were correct ("precision "), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data. [ABSTRACT FROM AUTHOR]

Relating less directly to the physical reflectance from remote sensors, land-use analysis is comparably more challenging than land-cover studies, especially for residential land-uses. This research presents an object-oriented approach to detect residential land use of buildings directly from lidar data, aerial photography, and road maps to enhance urban land- use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then uses a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on seven land-use characteristics of buildings. Experiments based on the 8.25 km2 study site located in Austin, Texas proved the possibility and efficiency of directly detecting and identifying residential buildings from remote sensing images with 81.1 percent of residential buildings correctly classified. [ABSTRACT FROM AUTHOR]

This applications paper presents the methods used to create a seamless topobathy digital terrain model (DTM) at 50-foot resolution intended to support hurricanes storm surge modeling in Tampa Bay, Florida. Lidar, bathymetry, and various breakline data were integrated using the Terrain Data Set structure in ArcGIS®. The use of the Terrain Data Set structure allowed for embedding large data sets (such as lidar points) and archiving them after DTM creation while maintaining topographic analysis capabilities. The bathymetric data, natively referenced to Mean Sea Level (MSL), were converted to North American Vertical Datum of 1988 (NAVD88) using an inverse distance weighted average offset from the three nearest NOAA tidal benchmark stations; results of this conversion were within 6.1 centimeters of those produced by NOAA VDatum software in a quality control test area. This methodology can therefore be used in coastal regions of other countries. [ABSTRACT FROM AUTHOR]

Pinyon-juniper woodlands comprise the third most common land-cover type in the United States and have been documented to have drastically increased both in density and extent in recent decades. We explored Landsat-5 TM and Light Detection and Ranging (lidar) data, individually and fused together, for estimating sub-pixel juniper cover. Linear spectral unmixing (LSU), Constrained Energy Minimization (CEM), and Mixture Tuned Matched Filtering (MTMF) techniques were compared along with spectral-lidar fusion approaches. None of the Landsat-5 TM-derived estimates were significantly correlated with field-measured juniper cover (n = 100), while lidar-derived estimates were strongly correlated (R2 = 0.74, p-value <0.001). Fusion of these estimates produced superior results to both classifications individually (R2 = 0.80, p-value <0.001). The MTMF technique performed best, while a multiple regression-based fusion was the best approach to combining the two data sources. Future studies can use the best sub-pixel classification and fusion approach to quantify changes in associated ecosystem properties such as carbon. [ABSTRACT FROM AUTHOR]

This study presents a novel approach based on the tensor voting framework for extracting building features from airborne lidar data. Geometric features of lidar points are represented by a tensor field in this paper. For the extraction of roof patches, a region-growing method with principal features is developed from the properties of eigenvalues and eigen vectors of the tensor field. Additionally, three new indicators for the strength of features are presented to reduce the effect of the number of points on feature identification, and a supervised method is proposed to determine the threshold of planar feature strength for the region-growing. The extraction of ridge and edge lines from the segmented roof patches is also discussed. Experiments based on airborne lidar data are described to demonstrate the effectiveness of the proposed method, with those the results compared with the PGA method. [ABSTRACT FROM AUTHOR]

This research investigated the applicability of lidar data for estimating population at the census block level using a volumetric approach. The study area, near the urban downtown area of Denver, Colorado, was selected since it includes dense distribution of different types of residential buildings. A modified morphological building detection algorithm was proposed to extract buildings from the lidar-derived surfaces. The extraction results showed that the modified morphological building detection algorithm can effectively recover building pixels occluded by nearby trees. The extracted buildings were further refined to residential buildings using parcel data. Two approaches (i.e., area- and volume-based) to population estimation were investigated at the census block level. Four regression models (i.e., simple linear regression, multiple linear regression, regression tree using one variable, and regression tree using multiple variables) were used to identify the relationship between census population and the area or volume information of the residential buildings. The volume-based models over- whelmingly outperformed the area-based models in the study area, and the models using multiple variables yielded more accurate estimation than the single variable models. The volume-based regression tree model using multiple variables yielded the most accurate estimations: R2 = 0.89, RMSE = 21 people, and RRMSE = 26.8 percent in the calibration site; and R2 = 0.80, RMSE = 27 people, and RRMSE = 30.1 percent in the validation site. As the results show, the volumetric approach using lidar remote sensing is effective for population estimation in regions with heterogeneous housing characteristics. [ABSTRACT FROM AUTHOR]

The article discusses the developments related to the aerial lidar technologies in the U.S. It highlights the Lidar Guidelines and Base Specification of the U.S. Geological Survey National Geospatial Program (NGP) which plays a role in aerial lider data accuracy. It also highlights the American Reinvestment and Recovery Act (ARRA) which was responsible in funding the NGP projects.

The article offers information regarding the discovery of ancient city of Sacapu Angamuco in Mexico through the application of Lidar. It says that the architectural remains found include foundations of domestic dwellings and other structure. It states the application of Lidar for archaeological surveys for ruins conservation and preservation through the documentation of the existing conditions of the ruins.

This paper uses GeoEye-1 imagery and airborne lidar (Light Detection and Ranging) data to map buildings and their rubble in Port-au-Prince caused by the Haiti earthquake on 12 January 2010. This is achieved by performing an objectbased one-class-at-a-time land cover classification of the image and lidai data using spectral, textural and height information. Classification accuracy is about 87 percent overall, and approximately 80 percent for buildings and rubble, Comparison of manually-selected 200 actual damaged buildings within an area of two sq. km in the city center shows an accuracy of over 90 percent for building and rubble mapping. 3D building models for approximately 55,000 buildings covering an area of 30 sq. km over Port-auPrince were generated. It is found that most of the damage is to the concrete and masonry structures in the well planned areas of the city and very little damage to the shelters and the temporary type houses with metal sheet roofs. The study demonstrates that fusing optical imagery and lidar data can effectively map the nature, severity, extent and damage patterns caused by earthquakes in densely populated urban areas like Port-au-Prince. [ABSTRACT FROM AUTHOR]

As third-party lidar software manufacturers are increasingly adding support for full-waveform data, a common question is which algorithm(s) to implement. To this end, a new approach is needed to compare and contrast various lidar waveform processing strategies from a practical, operational perspective. Quality and type of information output, processing speed, suitability for particular applications, robustness against poor parameter selection, and more subjective measures related to user experience are of interest. This paper describes a new empirical method of comparing range extraction and discrimination performance of different algorithms, based on a ranging-lab setup with multiple, adjustable screen targets, with precisely-measured separations. We present the results of comparing three different algorithms described in the scientific literature. The results show distinct differences and also indicate that there is no "one-size-fits-all" approach: the choice of a specific algorithm and adjustable parameter settings are highly application-dependent. [ABSTRACT FROM AUTHOR]

A method is presented for the co-registration of overlapping elevation surfaces based on local slope analysis. Comparison and statistical analysis of local slope versus local elevation difference between overlapping surfaces allows for the estimation of both vertical and horizontal offsets between the two surfaces. This method is then used to re-align the flightlines of a Light Detection and Ranging (lidar) dataset collected in southern Idaho resulting in a dataset with significantly higher accuracy than the original. The relative horizontal accuracy is doubled, with a final value of approximately 25 to 30 cm, while the relative vertical accuracy is improved by several centimeters to a final value of approximately 6 to 7 cm. [ABSTRACT FROM AUTHOR]

A potential solution to reduce high acquisition costs for airborne lidar (light detection and ranging) data is to combine lidar transects and optical satellite imagery to characterize forest vertical structure. Although multiple regression is typically used for such modeling, it seldom fully captures the complex relationships between forest variables. In an effort to improve these relationships, this study investigated the potential of Support Vector Regression (svR), a machine learning technique, to generalize (lidar-measured) forest canopy height from four lidar transects (representing 8.8 percent, 17.6 percent, 26.4 percent and 35.2 percent area of the site) to the entire study area using QuickBird imagery. The best estimated canopy height was then linked with field measurements to predict actual canopy height, above-ground biomass (AGB) and volume. GEOgraphic Object-Based Image Analysis (GEOBIA) was used to generate all estimates at a small tree/cluster level with a mean object size (Mos) of 0.04 ha for conifer and deciduous trees. Results show that for all lidar transect samples, SVR models achieved better performance for estimating canopy height than multiple regression. By using SVR and a single lidar transect (i.e., 8.8 percent of the study area), the following relationships were found between predicted and field-measured canopy height (R2: 0.81; RMSE: 4.0 m), AGB (R2: 0.76; RMSE: 63.1 Mg/ha) and volume (R2: 0.64; P.MSE: 156.9 m3/ha). [ABSTRACT FROM AUTHOR]

The height and shape of shrub canopies are critical measurements for characterizing shrub steppe rangelands. Remote sensing technologies might provide an efficient method to acquire these measurements across large areas. This study compared point-cloud and rasterized lidar data to field-measured sagebrush height and shape to quantify the correlation between field-based and lidar-derived estimates. The results demonstrated that discrete return, small-footprint lidar with high point density (9.46 points/m²) can provide strong predictions of true sagebrush height (R² of 0.84 to 0.86), but with a consistent underestimation of approximately 30 percent. Our results provided the first successful lidar-based descriptors of sagebrush shape with R² values of 0.65, 0.74, and 0.78 for respective predictions of shortest canopy diameter, longest canopy diameter, and canopy area. Future studies can extend lidar-derived shrub height and shape measurements to canopy volume, cover, and biomass estimates. [ABSTRACT FROM AUTHOR]

The article discusses the significant increase of demand of the North American Electric Realibility Corp. (NERC) for airborne lidar mapping of transmission corridors. It notes that to address such concern, production companies need to scale up their production operations. The move of GeoCue Corp. in offering an example of commercial solution that addresses the integration of product management solution while improving the overall efficiency is also highlighted.

This paper presents a method to estimate forest parameters and surface topography from NASA's Geosciences Laser Altimeter System (GLAS). Their potential use as observational inputs to models is demonstrated using a wind-risk model for the UK, ForestGALES. Relative heights above ground were used as biophysical parameter estimators. Top Height was estimated with R² = 0.73, RMSE = 4.5 m. Diameter at breast height estimates differed for conifer-dominated stands (R² = 0.72, RMSE = 0.07 m) and for stands containing mostly broadleaves (R² = 0.41, RMSE = 0.11 m). Ground elevation estimation produced R² = 0.99 7, RMSE = 2.2 m. These three parameters were applied to ForestGALES for stand-level assessment of wind-throw risk. Stability is sensitive to small differences in tree dimensions, and therefore vegetation parameters require greater accuracy than those currently retrievable from GLAS to more reliably determine risk of wind-throw. Future satellite lidar missions such as NASA's DESDynI sensor aim to produce improved vegetation parameter estimation plus greater spatial coverage which would offer more appropriate inputs for forestry models. [ABSTRACT FROM AUTHOR]

The goal for this paper is to increase awareness of patents far analysis methods that use airborne laser scanning (ALS) data in estimating forest inventories. US patents that have been applied for or issued are identified and critically reviewed. They are discussed in the context of the current controversy on whether methods patents, particularly software patents, are being granted too liberally. The patent process should promote innovation, but may sometimes discourage innovation and hinder scientific research. Some patent claims may overlap prior art (earlier public information), or may be obvious extensions of known methods. Actions are identified by which researchers may be able to help limit the scope of pending patents or to identify prior art that could be useful in re-examinations of existing patents. [ABSTRACT FROM AUTHOR]

This study developed a new method to derive forest type information from large-footprint lidar data based on full waveform analysis. For this purpose, the raw waveform was decomposed into Gaussian components, and canopy return and ground return of the waveforms were separated. Two types of metrics hypothesized to have relationship with forest types were derived from the canopy return part of the waveform. The first type of metrics is quantile-based metrics reflecting the vertical distribution of canopy return energy, and the second type is statistical characteristics of the Gaussian components of canopy return part. Support Vector Machine classification was applied to different combinations of the metrics to find their relationship with different forest types. The results showed that the second type of metrics, indicating the canopy stratum characteristics, showed great promise in separating broad-leaved and needle-leaved forests with the accuracy ranging from 88.68 percent to 90.57 percent and Kappa statistic from 0.7406 to 0.7868. [ABSTRACT FROM AUTHOR]

This study investigates the potential of discrete return light detection and ranging (lidar) data to characterize forest succession in a mixed mature forest in central Ontario using indices applied to the lidar point cloud. Derived indices include statistical indices, predicted Lorey's height (R² = 0,86; RSME = 2.36 m) and quadratic mean diameter- at-breast-height (R² = 0.68; RMSE = 1.21 cm), canopy density indices and an information theory based complexity index. To assess how well these indices are able to capture the vertical structure of forest stands, they are compared to Oliver and Larson's (1996) four stages of forest stand development. Best subsets regressions indicated that no single index is able to separate all four stages adequately. However, the predicted Lorey's height index is optimal for separating early from mid succession stages (p <.0001) and a combination of height and complexity indices performed best to discriminate between mid- and late-succession stages (p <.0001). [ABSTRACT FROM AUTHOR]

A novel approach for improving the consistency of intensity measurements using range-based normalization is introduced. The normalization is data-driven, can be fully automated, and involves scaling differences in observed intensity between returns collocated in space but registered to different laser scanning swaths. The scaling is proportional to the overall rate of attenuation f of laser energy. The utility of this approach for applications of lidar over forests was evaluated by examining classification results of broad cover types obtained using observed and normalized intensity measurements in an Oregon study area. The normalization was more effective for single returns, leading to a 53 percent reduction in intensity coefficient of variation between observed and range-normalized measurements. The poor cover type classification accuracy (44.4 percent; kappa 0.167) obtained by using observed intensities of first returns improved substantially to 75.6 percent (kappa 0.624) when using above-ground, single returns and f = 2.04. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-level morphological active contour (MMAC) algorithm to identify and delineate tree crowns in mountainous forest based on rasterized airborne lidar data. MMAC is a generalized tree crown mapping algorithm which can accommodate multiple heads in a crown as well as overlapping crowns. The MMAC algorithm comprises three steps: bottom up erosion (BUE) which identifies stand candidates, top down dilation (TDD) which estimates the crown periphery, and an active contour model (ACM) which delineates crown contours. Three sample plots were selected in Alishan National Scenic Area, Taiwan, (predominantly alder, sugi, and red cypress) for evaluation of the algorithm. When compared with ground survey data, the algorithm achieved an average detection accuracy of 24 percent omission error and 13 percent commission error in identifying individual trees in mountainous forest stands. Detection accuracy is potentially related to stand density. [ABSTRACT FROM AUTHOR]