Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation for the ICESat-2 Mission

NASA's Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission is a decadal survey mission (2016 launch). The mission objectives are to measure land ice elevation, sea ice freeboard, and changes in these variables, as well as to collect measurements over vegetation to facilitate canopy height determination. Two innovative components will characterize the ICESat-2 lidar: 1) collection of elevation data by a multibeam system and 2) application of micropulse lidar (photon-counting) technology. A photon-counting altimeter yields clouds of discrete points, resulting from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of the returned points to reflectors of interest. The objective of this paper is to derive an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2 data, based on airborne observations with a Sigma Space micropulse lidar. The mathematical algorithm uses spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors, and geostatistical classification parameters and hyperparameters. Validation shows that ground and canopy elevation, and hence canopy height, can be expected to be observable with high accuracy by ICESat-2 for all expected beam energies considered for instrument design (93.01%-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp), and 72.85%-98.68% for 0.48 msp). The algorithm derived here is generally applicable for elevation determination from photoncounting lidar altimeter data collected over forested areas, land ice, sea ice, and land surfaces, as well as for cloud detection

Advances in Waveform and Photon Counting Lidar Processing for Forest Vegetation Applications

Full waveform (FW) and photon counting LiDAR (PCL) data have garnered greater attention due to increasing data availability, a wealth of information they contain and promising prospects for large scale vegetation mapping. However, many factors such as complex processing steps and scarce non-proprietary tools preclude extensive and practical uses of these data for vegetation characterization. Therefore, the overall goal of this study is to develop algorithms to process FW and PCL data and to explore their potential in real-world applications. Study I explored classical waveform decomposition methods such as the Gaussian decomposition, Richardson–Lucy (RL) deconvolution and a newly introduced optimized Gold deconvolution to process FW LiDAR data. Results demonstrated the advantages of the deconvolution and decomposition method, and the three approaches generated satisfactory results, while the best performances varied when different criteria were used. Built upon Study I, Study II applied the Bayesian non-linear modeling concepts for waveform decomposition and quantified the propagation of error and uncertainty along the processing steps. The performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels showed that the Bayesian decomposition could enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. In study III, we exploited FW LiDAR data to classify tree species through integrating machine learning methods (the Random forests (RF) and Conditional inference forests (CF)) and Bayesian inference method. Results of classification accuracy highlighted that the Bayesian method was a superior alternative to machine learning methods, and rendered users with more confidence for interpreting and applying classification results to real-world tasks such as forest inventory. Study IV focused on developing a framework to derive terrain elevation and vegetation canopy height from test-bed sensor data and to pre-validate the capacity of the upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available

An Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation of the ICESat-2 Mission

The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat-2 mission. A result relevant for instrument design is that even the two weaker beam classes considered can be expected to yield useful results for vegetation measurements (93.01-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp9) and 72.85% - 98.68% for 0.48 msp (msp4)). Resampling options affect results more than noise levels. The algorithm derived here is generally applicable for analysis of micropulse lidar altimeter data collected over forested areas as well as other surfaces, including land ice, sea ice and land surfaces

Advances in Waveform and Photon Counting Lidar Processing for Forest Vegetation Applications

Full waveform (FW) and photon counting LiDAR (PCL) data have garnered greater attention due to increasing data availability, a wealth of information they contain and promising prospects for large scale vegetation mapping. However, many factors such as complex processing steps and scarce non-proprietary tools preclude extensive and practical uses of these data for vegetation characterization. Therefore, the overall goal of this study is to develop algorithms to process FW and PCL data and to explore their potential in real-world applications. Study I explored classical waveform decomposition methods such as the Gaussian decomposition, Richardson–Lucy (RL) deconvolution and a newly introduced optimized Gold deconvolution to process FW LiDAR data. Results demonstrated the advantages of the deconvolution and decomposition method, and the three approaches generated satisfactory results, while the best performances varied when different criteria were used. Built upon Study I, Study II applied the Bayesian non-linear modeling concepts for waveform decomposition and quantified the propagation of error and uncertainty along the processing steps. The performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels showed that the Bayesian decomposition could enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. In study III, we exploited FW LiDAR data to classify tree species through integrating machine learning methods (the Random forests (RF) and Conditional inference forests (CF)) and Bayesian inference method. Results of classification accuracy highlighted that the Bayesian method was a superior alternative to machine learning methods, and rendered users with more confidence for interpreting and applying classification results to real-world tasks such as forest inventory. Study IV focused on developing a framework to derive terrain elevation and vegetation canopy height from test-bed sensor data and to pre-validate the capacity of the upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available

Mapping forest structure in Mississippi using LiDAR remote sensing

This study aimed at evaluating the agreement of spaceborne Light Detection and Ranging (lidar) ICESat-2 canopy height with Airborne Laser Scanning (ALS) derived canopy height to inform about the performance of ICESat-2 canopy height metrics and understand its uncertainties and utilities. The agreement was assessed for different forest types, physiographic regions, a range of percent canopy cover, and diverse disturbance histories. Results of this study suggest that best agreements are found using strong beam data collected at night for canopy height retrieval using ICESat-2. The ICESat-2 showed great potential for estimating canopy heights, particularly in evergreen forests with high canopy cover. Statistical models were developed using fixed-effects and mixed-effects modeling approaches to predict ALS canopy height metrics using ICESat-2 parameters and other attributes. Overall, ICESat-2 showed good agreement with ALS canopy height and showed its predictive ability to characterize canopy height. The outcome of this study will help the scientific community understand the capabilities and limitations of ICESat-2 canopy heights; the study also provides a new approach to obtain wall-to-wall ALS standard canopy height maps at landscape level

Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

The National Ecological Observatory Network (NEON) is a continental scale environmental monitoring initiative tasked with characterizing and understanding ecological phenomenology over a 30-year time frame. To support this mission, NEON collects ground truth measurements, such as organism counts and characterization, carbon flux measurements, etc. To spatially upscale these plot-based measurements, NEON developed an airborne observation platform (AOP), with a high-resolution visible camera, next-generation AVIRIS imaging spectrometer, and a discrete and waveform digitizing light detection and ranging (lidar) system. While visible imaging, imaging spectroscopy, and discrete lidar are relatively mature technologies, our understanding of and associated algorithm development for small-footprint full-waveform lidar are still in early stages of development. This work has as its primary aim to extend small-footprint full-waveform lidar capabilities to assess vegetation biophysical structure. In order to fully exploit waveform lidar capabilities, high fidelity geometric and radio-metric truth data are needed. Forests are structurally and spectrally complex, which makes collecting the necessary truth challenging, if not impossible. We utilize the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, which provides an environment for radiometric simulations, in order to simulate waveform lidar signals. The first step of this research was to build a virtual forest stand based on Harvard Forest inventory data. This scene was used to assess the level of geometric fidelity necessary for small-footprint waveform lidar simulation in broadleaf forests. It was found that leaves have the largest influence on the backscattered signal and that there is little contribution to the signal from the leaf stems and twigs. From this knowledge, a number of additional realistic and abstract virtual “forest” scenes were created to aid studies assessing the ability of waveform lidar systems to extract biophysical phenomenology. We developed an additive model, based on these scenes, for correcting the attenuation in backscattered signal caused by the canopy. The attenuation-corrected waveform, when coupled with estimates of the leaf-level reflectance, provides a measure of the complex within-canopy forest structure. This work has implications for our improved understanding of complex waveform lidar signals in forest environments and, very importantly, takes the research community a significant step closer to assessing fine-scale horizontally- and vertically-explicit leaf area, a holy grail of forest ecology

Application of Lidar Altimetry and Hyperspectral Imaging to Ice Sheet and Snow Monitoring

The Greenland Ice Sheet (GrIS) is of tremendous importance for climate change projections. The GrIS has contributed an estimated 10.8 mm to sea level rise since 1992, and that contribution is expected to increase in the coming decades. It is therefore essential to make routine measurements of ice, meltwater, and snow over the GrIS using satellite and airborne observations. Two prominent methods for ice sheet monitoring include lidar altimetry and hyperspectral imaging. Lidar altimetry is typically used to make fine-scale estimates of ice sheet surface height, whereas hyperspectral imaging is commonly utilized to infer snow or ice surface composition. In this dissertation, I use data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to examine light transmittance over the Greenland Ice Sheet. I first utilize ICESat-2 photon-counting data for the development of a retrieval algorithm for supraglacial lake depth, with validation from the Operation IceBridge airborne mission. This work was performed to support other depth retrieval efforts that struggle with attenuation in deep water. I then use hyperspectral radiative transfer models to perform a sensitivity analysis on snow grain size retrievals. Snow grain size is an important metric for snowpack evolution, but there are limited efforts to quantify potential errors in an existing inversion algorithm. Lastly, I used a combination of Operation IceBridge altimetry and AVIRIS-NG hyperspectral data to assess the impacts of snow grain size on surface heights derived from lidar altimetry. Results from the three studies indicate that lidar signals and ice reflectance in the near-infrared are highly sensitive to changes in surface media. Because it operates at 532 nm, the ICESat-2 laser penetrates through liquid water with minimal signal loss, but volumetric scattering within a snowpack may induce significant errors in surface heights derived from Operation IceBridge, especially at large snow grain sizes. The ICESat-2 laser is susceptible to noise from clouds and rough surface topography, so additional work is needed to accurately identify supraglacial lake beds and volumetric scattering caused by snow. Also, the near-infrared spectrum of snow is highly sensitive to changes in solar geometry and to the presence of dust, therefore increasing uncertainties in snow grain size retrievals. Co-dependencies between snowpack perturbations were not considered, but I speculate that snow particle shape and snow impurities will impact the angular distribution of radiation reflected from a snowpack. I expect that the research presented here will motivate the development of improved algorithms for supraglacial lake depth, snow grain size, and lidar altimetry bias.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169872/1/zhfair_1.pd

Spaceborne LiDAR Surveying and Mapping

Laser point cloud data have the characteristics of high elevation accuracy, fast processing efficiency, strong three-dimensional (3D) vision, and wide application fields. It will be one of the core datasets of the new generation national global topographic database. The rapid advancement of spaceborne laser earth observation technology allows the collection of global 3D point cloud data, which has brought a new breakthrough in the field of satellite-based earth observation, and its significant advantages of all-day time, high accuracy and high efficiency will lead the future development of space precise mapping technology. This chapter firstly introduces the principle and development status of satellite-based LiDAR technology, then presents the basic technical framework of satellite-based LiDAR 3D mapping, and analyzes the data processing methods of spaceborne photon point clouds, and finally, focuses on the application research in various fields including precise geolocation of combined with satellite images, fusion of multi-source topographic information, polar mapping, 3D objects reconstruction, and shallow sea topographic mapping, etc

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

ICESat-2 is the new generation of NASA’s ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean R 2 value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2