Search

Abstract: Automatic ground filtering for Light Detection And Ranging (LIDAR) data is a critical process for Digital Terrain Model (DTM) and three-dimensional urban model generation. Although researchers have developed many methods to separate bare ground from other urban features, the problem has not been fully solved due to the similar characteristics possessed by ground and non-ground objects, especially on abrupt surfaces. Current methods can be grouped into two major categories: neighborhood-based approaches and directional filtering. In this study, following the direction of the second branch, we propose a new Multi-directional Ground Filtering (MGF) algorithm to incorporate a two-dimensional neighborhood in the directional scanning so as to prevent the errors introduced by the sensitivity to directions. Besides this, the MGF algorithm explores the utility of identifying pattern varieties in different directions across an image. The authors conducted a comprehensive test of the performance on fifteen study sites and compared our results to eight other publicized methods based on the Kappa coefficients calculated from the error matrices reported by ISPRS. Overall, the MGF filter produces a promising performance in both urban and forest areas. The size and shape of non-ground objects do not pose significant influence on the performance of the MGF algorithm. The fact that MGF algorithm is robust to two commonly required parameters, slope and elevation difference thresholds, has added practical merits to be adopted in different landscapes. [Copyright &y& Elsevier]

Terrestrial Light Detection And Ranging (LiDAR), also referred to as terrestrial laser scanning (TLS), has gained increasing popularity in terms of providing highly detailed micro-topography with millimetric measurement precision and accuracy. However, accurately depicting terrain under dense vegetation remains a challenge due to the blocking of signal and the lack of nearby ground. Without dependence on historical data, this research proposes a novel and rapid solution to map densely vegetated coastal environments by integrating terrestrial LiDAR with GPS surveys. To verify and improve the application of terrestrial LiDAR in coastal dense-vegetation areas, we set up eleven scans of terrestrial LiDAR in October 2015 along a sand berm with vegetation planted in Plaquemines Parish of Louisiana. At the same time, 2634 GPS points were collected for the accuracy assessment of terrain mapping and terrain correction. Object-oriented classification was applied to classify the whole berm into tall vegetation, low vegetation and bare ground, with an overall accuracy of 92.7% and a kappa value of 0.89. Based on the classification results, terrain correction was conducted for the tall-vegetation and low-vegetation areas, respectively. An adaptive correction factor was applied to the tall-vegetation area, and the 95th percentile error was calculated as the correction factor from the surface model instead of the terrain model for the low-vegetation area. The terrain correction method successfully reduced the mean error from 0.407 m to −0.068 m (RMSE errors from 0.425 m to 0.146 m) in low vegetation and from 0.993 m to −0.098 m (RMSE from 1.070 m to 0.144 m) in tall vegetation. [ABSTRACT FROM AUTHOR]

Abstract: A lack of reliable observations for canopy science research is being partly overcome by the gradual use of lidar remote sensing. This study aims to improve lidar-based canopy characterization with airborne laser scanners through the combined use of lidar composite metrics and machine learning models. Our so-called composite metrics comprise a relatively large number of lidar predictors that tend to retain as much information as possible when reducing raw lidar point clouds into a format suitable as inputs to predictive models of canopy structural variables. The information-rich property of such composite metrics is further complemented by machine learning, which offers an array of supervised learning models capable of relating canopy characteristics to high-dimensional lidar metrics via complex, potentially nonlinear functional relationships. Using coincident lidar and field data over an Eastern Texas forest in USA, we conducted a case study to demonstrate the ubiquitous power of the lidar composite metrics in predicting multiple forest attributes and also illustrated the use of two kernel machines, namely, support vector machine and Gaussian processes (GP). Results show that the two machine learning models in conjunction with the lidar composite metrics outperformed traditional approaches such as the maximum likelihood classifier and linear regression models. For example, the five-fold cross validation for GP regression models (vs. linear/log-linear models) yielded a root mean squared error of 1.06 (2.36) m for Lorey''s height, 0.95 (3.43) m for dominant height, 5.34 (8.51) m2/ha for basal area, 21.4 (40.5) Mg/ha for aboveground biomass, 6.54 (9.88) Mg/ha for belowground biomass, 0.75 (2.76) m for canopy base height, 2.2 (2.76) m for canopy ceiling height, 0.015 (0.02) kg/m3 for canopy bulk density, 0.068 (0.133) kg/m2 for available canopy fuel, and 0.33 (0.39) m2/m2 for leaf area index. Moreover, uncertainty estimates from the GP regression were more indicative of the true errors in the predicted canopy variables than those from their linear counterparts. With the ever-increasing accessibility of multisource remote sensing data, we envision a concomitant expansion in the use of advanced statistical methods, such as machine learning, to explore the potentially complex relationships between canopy characteristics and remotely-sensed predictors, accompanied by a desideratum for improved error analysis. [Copyright &y& Elsevier]

Terrestrial laser scanning (TLS) swings a tiny-footprint laser to resolve 3D structures rapidly and precisely, affording new opportunities for ecosystem studies, but its actual utility depends largely on efficacies of lidar analysis methods. To improve characterizing forest canopies with TLS, we forged a methodological paradigm that combines physics and statistics to derive foliage profile, leaf area index (LAI), and leaf angle distribution (LAD): We modeled laser–vegetation interactions probabilistically and then developed a maximum likelihood estimator (MLE) of vegetation parameters. Unlike classical gap-based algorithms, MLE explicitly accommodates laser scanning geometries, fully leverages raw laser ranging data, and simultaneously derives foliage profile and LAD. We evaluated MLE using both synthetic lidar data and real TLS scans at sites in Everglades National Park, USA. Estimated LAI differed between algorithms by an average of 26%. Compared to classical gap analyses, MLE derived foliage density profile and LAD more accurately. Also, MLE has a rigorous statistical foundation and generated error intervals better indicative of the true uncertainties of estimated canopy parameters—an aspect often overlooked but essential for credible use of lidar vegetation products. The theoretical justification and experimental evidence converge to suggest that classical gap methods are sub-optimal for exploiting tiny-footprint lidar data and MLE offers a paradigm-shifting alternative. We envision that MLE will further boost confident use of terrestrial lidar as a versatile tool for environmental applications, such as forest survey, ecological conservation, and ecosystem management. [ABSTRACT FROM AUTHOR]