In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test ...campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF) that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz). The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.
We present an investigation into different approaches for high-resolution mapping of near-field surface displacement for strike-slip earthquakes. Airborne laser scanning (ALS) and optical imagery are ...two common sources of earth observation data available to geoscientists for earthquake documentation and studies. Optical image correlation and point cloud differencing techniques are among the most widely used methods for retrieving displacement signals in the near field. We compare the performances of these techniques for estimating near-field deformation using pre and postevent high-resolution ALS and airborne imagery of the August 24, 2014 Mw 6.0 Napa, California earthquake. Estimates of deformation agree with field observations within a decimeter, at the expected accuracy level of the data. We show that the correlation of intensity images from ALS data can unveil the near-field deformation successfully and outperforms optical image correlation in vegetated areas as well as in the absence of geodetic markers (man-made structures). Furthermore, we illustrate that the point clouds generated with structure from motion perform comparably to ALS point clouds for retrieving the displacement signal in unvegetated areas. Overall, we conclude that ALS data are generally better than imagery for estimating near-field deformation regardless of the estimation methodology and that the iterative closest point algorithm was more effective at recovering the displacement signal.
Airborne light detection and ranging (lidar) data are widely used for high-resolution land cover mapping. The lidar elevation data are typically used as complementary information to passive ...multispectral or hyperspectral imagery to enable higher land cover classification accuracy. In this paper, we examine the capabilities of a recently developed multispectral airborne laser scanner, manufactured by Teledyne Optech, for direct classification of multispectral point clouds into ten land cover classes including grass, trees, two classes of soil, four classes of pavement, and two classes of buildings. The scanner, Titan MW, collects point clouds at three different laser wavelengths simultaneously, opening the door to new possibilities in land cover classification using only lidar data. We show that the recorded intensities of laser returns together with spatial metrics calculated from the three-dimensional (3D) locations of laser returns are sufficient for classifying the point cloud into ten distinct land cover classes. Our classification methods achieved an overall accuracy of 94.7% with a kappa coefficient of 0.94 using the support vector machine (SVM) method to classify single-return points and an overall accuracy of 79.7% and kappa coefficient of 0.77 using a rule-based classifier on multireturn points. A land cover map is then generated from the classified point cloud. We show that our results outperform the common approach of rasterizing the point cloud prior to classification by ~4% in overall accuracy, 0.04 in kappa coefficient, and by up to 16% in commission and omission errors. This improvement however comes at the price of increased complexity and computational burden.
Fox River is the main source of land-based pollutants that flows into the southern Green Bay of Lake Michigan. Evaluation of water quality is normally based on time consuming and expensive in situ ...measurements. Remotely sensed data is an efficient alternative for field monitoring because of its spatial and temporal coverage. In this study, remote sensing imagery combined with in situ measurements of water quality were used to estimate an empirical relationship between water surface reflectance and water quality parameters including water turbidity and Total Suspended Sediment (TSS). Surface reflectance values is obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The empirical equations were derived from data over summers 2011–13 and show high correlation coefficients of equal to 0.83 and 0.87 for TSS and turbidity respectively. The validity of the proposed equations was tested for summer 2014 data. The NRMSE for prediction of measured data by the proposed equations are 0.36 and 0.3 for TSS and turbidity. Remotely sensed data was also used to produce water quality maps to improve our understanding of the spatiotemporal variations of Fox River turbid plume. The proposed approach can be extended to other coastal regions of Great Lakes and provide a framework to study pollution transportation in coastal areas.
Impervious surfaces are land covers that do not allow water penetration. Water runoff from impervious surfaces can cause major flooding in extreme climates; therefore, mapping such surface covers in ...urban areas is of great importance for water resources, climatology, and urban studies. Automated land cover classification of the remotely sensed data is a necessary step before impervious surface maps are compiled. Very high resolution, passive satellite imagery is the modern source of data for land cover classification and impervious cover mapping. However, shadowed areas and relief displacement in urban areas significantly limit impervious surface mapping accuracy. Recently, multispectral airborne lidar sensors have become available which have the ability to scan the ground at three different laser wavelengths simultaneously. The multispectral point cloud has the capability of being used as the sole source of data for land cover classification because they can simultaneously provide both spectral and geometric information. This dissertation proposes a machine learning classification approach to classify the land cover into diverse classes that could be used for urban change studies, using multispectral lidar points directly such that the 3D information of the points is retained. Two methods to mitigate the multi-echo effect are proposed to reduce the influence of this effect on the spectral information of ALS returns. Furthermore, hybrid intensity correction schemes are devised and tested to improve the classification accuracy. Next, the impervious surface map product is created using the classified points and it is shown that this map yields higher accuracies compared to a similar map created from hyperspectral passive imagery in shadowed areas (by ~21%), areas obstructed by relief displacement (by ~19%), and areas obstructed by tree canopies (by ~40%.) This demonstrates the advantage of multispectral ALS data for automated, high-accuracy, and high-resolution impervious surface mapping.
Hydrodynamic flood modeling improves hydrologic and hydraulic prediction of storm events. However, the computationally intensive numerical solutions required for high-resolution hydrodynamics have ...historically prevented their implementation in near-real-time flood forecasting. This study examines whether several Deep Neural Network (DNN) architectures are suitable for optimizing hydrodynamic flood models. Several pluvial flooding events were simulated in a low-relief high-resolution urban environment using a 2D HEC-RAS hydrodynamic model. These simulations were assembled into a training set for the DNNs, which were then used to forecast flooding depths and velocities. The DNNs' forecasts were compared to the hydrodynamic flood models, and showed good agreement, with a median RMSE of around 2 mm for cell flooding depths in the study area. The DNNs also improved forecast computation time significantly, with the DNNs providing forecasts between 34.2 and 72.4 times faster than conventional hydrodynamic models. The study area showed little change between HEC-RAS' Full Momentum Equations and Diffusion Equations, however, important numerical stability considerations were discovered that impact equation selection and DNN architecture configuration. Overall, the results from this study show that DNNs can greatly optimize hydrodynamic flood modeling, and enable near-real-time hydrodynamic flood forecasting.
Classification of multispectral lidar point clouds Ekhtari, Nima; Glennie, Craig; Fernandez-Diaz, Juan Carlos
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),
2017-July
Conference Proceeding
Airborne Light Detection And Ranging (LiDAR) data are widely used for high-resolution land cover mapping. The LiDAR data are typically used as complementary information to passive multispectral or ...hyperspectral imagery to obtain higher land cover classification accuracy. In this paper, we examine the capabilities of a recently developed multispectral airborne laser scanner, manufactured by Teledyne Optech, for classification of multispectral point clouds into typical land cover classes. This scanner, Titan MW (multi-wavelength), collects point clouds using three different wavelength lasers simultaneously, hence opening the door to new possibilities in land cover classification using only LiDAR data. We show that the recorded intensities of returned laser pulses together with structural characteristics of the features on the Earth surface calculated from the 3D positions of returns are sufficient enough to classify the point cloud into 10 distinct land cover classes. We achieved an overall accuracy of 95.9% with a kappa coefficient of 0.95 using a Support Vector Machine (SVM) classifier to classify single-return points and an overall accuracy of 89.2% and kappa coefficient of 0.82 using a rule-based classifier on multi-return points.
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