Jennifer M. Wozencraft

Overview of the coastal zone mapping and imaging lidar (CZMIL): a new multisensor airborne mapping system for the U.S. Army Corps of Engineers

CZMIL is a new airborne mapping and imaging system designed to simultaneously produce high resolution 3D images of the beach and shallow water seafloor, and to achieve benthic classification and water column characterization. It is designed to have high performance in shallow, turbid waters. The Data Acquisition System (DAS) is composed of a new bathymetric lidar integrated with a commercial imaging spectrometer and digital metric camera. The Data Processing System (DPS) employs new algorithms and software designed to automatically produce environmental image products by combining data from the three sensors within a data fusion paradigm. CZMIL is specifically designed to meet the requirements of the USACE Coastal Mapping Program, and is scheduled to enter field trials in the spring of 2011.

Seafloor and Land Cover Classification Through Airborne Lidar and Hyperspectral Data Fusion

In 2007 the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) collected concurrent bathymetric lidar and hyperspectral imagery in Hilo Bay, Hawaii. The data were collected using the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) system. CHARTS is JALBTCX in-house survey capability that includes a SHOALS-3000 lidar instrument integrated with a CASI-1500 hyperspectral imager. CHARTS collects either 20-kHz topographic lidar data and 3-kHz bathymetric lidar data, each concurrent with digital RGB and hyperspectral imagery. Optech Internationals Rapid Environmental Assessment (REA) Processor is designed to integrate the bathymetric lidar and hyperspectral data streams, creating a product suite that includes maps of water depth, bottom reflectance, water column volume reflectance, a+bb (a measure of water column attenuation) derived from the bathymetric lidar data, spectral color-balanced mosaics of seafloor reflectance, spectral water column parameters, and seafloor and landcover classifications This paper will demonstrate the capability of Optech REA on the production dataset from Hilo Bay.

Requirements for the Coastal Zone Mapping and Imaging Lidar (CZMIL)

The U.S. Army Corps of Engineers (USACE) began developing airborne lidar bathymetry systems for coastal mapping applications in 1986, and fielded its first system in 1994. In the ensuing years, the Scanning Hydrographic Operational Airborne Lidar Survey research and development program led to the creation of the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), a robust federal government partnership in airborne lidar bathymetry; the USACE National Coastal Mapping Program, a program of mapping built around airborne lidar bathymetry and complementary airborne remote sensing technologies; and a healthy commercial field of airborne lidar bathymeter manufacturers and service providers. The Coastal Zone Mapping and Imaging Lidar (CZMIL) is a new USACE sensor development effort and a partnership among the JALBTCX, Optech International, and The University of Southern Mississippi. The goal of CZMIL is to produce an integrated lidar and imagery sensor suite and software package designed for highly automated generation of physical and environmental information products for the coastal zone. CZMIL is an opportunity to revamp existing hardware and software to address the turbidity and shallow water limitations of existing systems; improve environmental applications of the data; take advantage of advances in laser, scanner, and receiver technology, and in signal processing and data fusion algorithms; while maintaining accurate depth measurement capability. The CZMIL program has been underway since 2006, resulting in a detailed design of the CZMIL software and hardware. CZMIL fabrication will be complete in 2010 and fielded in USACE operations in 2011.

Towards fully autonomous visual inspection of dark featureless dam penstocks using MAVs

In the last decade, multi-rotor Micro Aerial Vehicles (MAVs) have attracted great attention from robotics researchers. Offering affordable agility and maneuverability, multi-rotor aircrafts have become the most commonly used platforms for robotics applications. Amongst the most promising applications are inspection of power-lines, cell-towers, large and constrained infrastructures and precision agriculture. While GPS offers an easy solution for outdoor autonomy, using on-board sensors is the only solution for autonomy in constrained indoor environments. In this paper, we present our results on autonomous inspection of completely dark, featureless, symmetric dam penstocks using cameras and range sensors. We use a hex-rotor platform equipped with an IMU, four cameras and two lidars. One of the cameras tracks features on the walls using the on-board illumination to estimate the position along the tunnel axis unobservable to range sensors while all of the cameras are used for panoramic image construction. The two lidars estimate the remaining degrees of freedom (DOF). Outputs of the two estimators are fused using an Unscented Kalman Filter (UKF). A moderately trained operator defines waypoints using the Remote Control (RC). We demonstrate our results from Carters Dam, GA and Glen Canyon Dam, AZ which include panoramic images for cracks and rusty spot detection and 6-DOF estimation results with ground truth comparisons. To our knowledge ours is the only study that can autonomously inspect environments with no geometric cues and poor to no external illumination using MAVs.

Autonomous Navigation and Mapping for Inspection of Penstocks and Tunnels With MAVs

In this paper, we address the estimation, control, navigation and mapping problems to achieve autonomous inspection of penstocks and tunnels using aerial vehicles with on-board sensing and computation. Penstocks and tunnels have the shape of a generalized cylinder. They are generally dark and featureless. State estimation is challenging because range sensors do not yield adequate information and cameras do not work in the dark. We show that the six degrees of freedom (DOF) pose and velocity can be estimated by fusing information from an inertial measurement unit (IMU), a lidar and a set of cameras. This letter discusses in detail the range-based estimation part while leaving the details of vision component to our earlier work. The proposed algorithm relies only on a model of the generalized cylinder and is robust to changes in shape of the tunnel. The approach is validated through real experiments showing autonomous and shared control, state estimation and environment mapping in the penstock at Center Hill Dam, TN. To our knowledge, this is the first time autonomous navigation and mapping has been achieved in a penstock without any external infrastructure such GPS or external cameras.

Fusion of hyperspectral and bathymetric laser data in Kaneohe Bay, Hawaii

Passive, hyperspectral image data and bathymetric lidar data are complimentary data types that can be used effectively in tandem. Hyperspectral data contain information related to water quality, depth, and bottom type; and bathymetric lidar data contain precise information about the depth of the water and qualitative information about water quality and bottom reflectance. The two systems together provide constraints on each other. For example, lidar-derived depths can be used to constrain spectral radiative transfer models for hyperspectral data, which allows for the estimation of bottom reflectance for each pixel. Similarly, depths can be used to calibrate models, which permit the estimation of depths from the hyperspectral data cube on the raster defined by the spectral imagery. We demonstrate these capabilities by fusing hyperspectral data from the LASH and AVIRIS spectrometers with depth data from the SHOALS bathymetric laser to achieve bottom classification and increase the density of depth measurements in Kaneohe Bay, Hawaii. These capabilities are envisioned as operating modes of the next-generation SHOALS system, CHARTS, which will deploy a bathymetric laser and spectrometer on the same platform.

Post-Katrina Land-Cover, Elevation, and Volume Change Assessment along the South Shore of Lake Pontchartrain, Louisiana, U.S.A.

Abstract Advances in remote-sensing technology have led to its increased use for posthurricane disaster response and assessment; however, the use of the technology is underutilized in the recovery phase of the disaster management cycle. This study illustrates an example of a postdisaster recovery assessment by detecting coastal land cover, elevation, and volume changes using 3 years of post-Katrina hyperspectral and light detection and ranging data collected along the south shore of Lake Pontchartrain, Louisiana. Digital elevation models and basic land-cover classifications were generated for a 34-km2 study area for 2005, 2006, and 2007. A change detection method was used to assess postdisaster land-cover, elevation, and volume changes. Results showed that the vegetation classes had area increases, whereas bare ground/roads and structures classes had area decreases. Overall estimated volume changes included a net volume decrease of 1.6 × 106 m3 in 2005 to 2006 and a net volume decrease of 2.1 × 106 m3 in 2006 to 2007 within the study area. More specifically, low vegetation and bare ground/roads classes had net volume increases, whereas medium and tall vegetation and structures classes had net volume decreases. These changes in land cover, elevation, and volume illustrate some of the major physical impacts of the disaster and ensuing recovery. This study demonstrates an innovative image fusion approach to assess physical changes and postdisaster recovery in a residential, coastal environment.

Integrated LiDAR and Hyperspectral

Integrating LiDAR data and hyperspectral imagery is an area of active research in remote sensing, inclusive of application for coastal and coral reef mapping. These two technologies can be combined in a number of different ways, and at a number of stages of processing to produce benthic classification maps. This chapter introduces the concept of data fusion, presents a data fusion model, and describes the different ways in which LiDAR and hyperspectral data can be integrated for benthic mapping. Examples are presented to first demonstrate data fusion during the preprocessing stage prior to classification, followed by data fusion performed during processing and classification. The chapter concludes with examples of how classification maps derived from LiDAR data and hyperspectral imagery individually can be combined in a postprocessing high-level fusion approach to produce an integrated benthic classification map.

High Resolution Coastal Data for Hawaii

JALBTCX has collected valuable datasets in the state of Hawaii in 1999, 2000, and 2007. Applications for the data include coral reef mapping, tsunami modeling, and nautical charting. In 1999 and 2000, SHOALS measured bathymetry on a 4- by 4-m grid for all of Maui, Oahu, and Kauai, the western shore of Hawaii, and the southern shore of Molokai. The data were delivered in XYZ ASCII format and paper map sheets. In 2007, CHARTS measured topography on a 1- by 1.5-m grid on the northern shores of Maui, Oahu, Kauai, Hawaii, and Molokai. The data were delivered in LAS format with several derived digital information products including first and last return DEMs, bare earth models, coverage maps, RGB orthomosaics and hyperspectral image mosaics. Advanced digital data products like the basic land cover classification and color-balanced mosaic were generated using a combination of the lidar data and hyperspectral imagery.

Modeling of Airborne Bathymetric Lidar Waveforms

ABSTRACT Kim, M.; Kopilevich, Y.; Feygels, V.; Park, J.Y., and Wozencraft, J., 2016. Modeling of airborne bathymetric lidar waveforms. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 18–30. Coconut Creek (Florida), ISSN 0749-0208. Modeling the optical power of the lidar return waveform is performed for the radiometrically calibrated CZMIL (Coastal Zone Mapping and Imaging Lidar, Optech, Inc.) data. For this purpose, a lidar waveform simulator was developed. The theory is described based on the receiver sensitivity function, the radiative transfer equation (RTE) via the Greens function, the optical reciprocity theorem, and the small angle approximation (SAA). The SAA-based RTE is solved for the radiance distribution using the Fourier transform method. Along with the numerical algorithms, the contribution was made on the air-water and water-bottom interface peaks in the bathymetric lidar waveforms. Lacking ground truth data, a simulated waveform that best fits CZMIL data was used to estimate the optimized environmental parameters. The estimated parameters were well within the plausible natural optical properties. Compared to other approaches based on the relative intensity waveform, the simulation was applied to the absolute calibrated power. Thus, the model can be used to predict the general performance of any bathymetric lidar. This research will help design an optimized system to achieve the maximum performance. The forward modeling capability will also provide opportunities to develop advanced waveform processing algorithms, such as surface peak modeling and scattering correction. Thus, the improved quality of bathymetric lidar data contributed by this research will promote the various coastal science applications in terms of improved data accuracy and extended coverage.