Full Waveform Lidar

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-tobiomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Mapping tree species diversity is increasingly important in the face of environmental change and biodiversity conservation. We explore a potential way of mapping this diversity by relating forest structure to tree species diversity in Gabon. First, we test the relation between canopy height, as a proxy for niche volume, and tree species diversity. Then, we test the relation between vertical canopy structure, as a proxy for vertical niche occupation, and tree species diversity. We use large footprint full-waveform airborne lidar data collected across four study sites in Gabon (Lopé, Mabounié, Mondah, and Rabi) in combination with in situ estimates of species richness (S) and Shannon diversity (H′). Linear models using canopy height explained 44% and 43% of the variation in S and H′ at the 0.25 ha resolution. Linear models using canopy height and the plant area volume density profile explained 71% of this variation. We demonstrate applications of these models by mapping S and H′ in Mo...

As an active remote sensing technique airborne laser scanning (ALS) is able to capture the topography with high precision even for densely forested areas. Due to the high pulse repetition frequency of up to 400 kHz a high sampling rate on the ground can be achieved, which allows the description of the terrain surface in decimeter scale. In this contribution two approaches to characterize terrain roughness are described. In the first approach the standard deviation of detrended terrain points is calculated. To achieve a high spatial resolution of the derived roughness layer a high terrain point density is essential, which requires especially in dense forested areas a very high sampling rate. In addition to the 3D position of backscattering objects, full-waveform ALS systems provide the width of each detectable echo, which provides information on the range distribution of scatterers within the laser footprint that contribute to one echo. It is therefore, an indicator for surface roughness and the slope of the target. In comparison to the roughness layer derived from the first approach using high point densities, the derived echo width image shows similar spatial patterns of terrain roughness even for moderate point densities. The results show that both the echo widths and the vertical distribution of terrain echoes are useful to derive reliable geometric terrain roughness layers of large areas.

Full-waveform airborne laser scanning (FWF-ALS) is an active remote sensing technique that records the entire backscattered waveform from transmitted laser pulses. By decomposing the backscatter signals into a series of echoes in a post-processing step, information about the geometric location and radiometric properties of each individual scatterer can be obtained. Even though research on small-footprint waveform data can still be considered to be only in its beginning, a number of benefits start to emerge. From a theoretical point of view, the value of FWF-ALS sensors lies in the fact that the measurement process is depicted in its entire complexity. Thus it is possible to physically model the measurements process. From a practical point of view, the combination of geometric and radiometric information offers new means for interpreting the derived 3D point cloud and for deriving vegetation and terrain information. In this contribution we discuss methods for processing full-waveform ALS data for improving the characterization of the terrain and vegetation structure. The results show that full-waveform information is particularly useful for characterizing low vegetation cover, terrain roughness, and different forest classes. Examples are drawn from study areas in eastern Austria.

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission has been designed to measure forest structure using lidar waveforms sampled as it orbits the Earth while aboard the International Space Station. In this paper, we report the results of a study using airborne measurements of large-footprint (LF) lidar to simulate GEDI observations and to verify its capability its to retrieve ground elevation, vegetation height and aboveground biomass (AGB) by comparing to airborne small-footprint (SF) lidar measurements. The study focused on tropical forests and used data collected during the NASA and ESA AfriSAR ground and airborne campaigns in the Lope National Park in Central Gabon. The measurements covered a gradient of successional stages of forest development with different height, canopy density and topography. The comparison of the two sensors shows that LF lidar waveforms and simulated waveforms from SF lidar are equivalent in their ability to estimate ground elevation (RMSE=0.5 m, bias=0.29 m) and maximum forest height (RMSE=2.99 m; bias=0.24 m) over the study area. The difference in the AGB estimated from both lidar instruments at the 1-ha spatial scale is small over the entire study area (RMSE=6.34 Mg  ha -1 , bias=11.27 Mg  ha -1 ) and the bias is attributed to the impact of ground slopes greater than 10-20 degrees on the LF lidar measurements of forest height. Ourresults verify the ability of GEDI LF lidar to measure the complex structure of humid tropical forests and to provide estimates of AGB comparable to SF.

In this contribution the complexity of the vertical vegetation structure, based on dense airborne laser scanning (ALS) point cloud data (25 echoes/m 2 ), is analyzed to calculate vegetation roughness for hydraulic applications. Using the original 3D ALS point cloud, three levels of abstractions are derived (cells, voxels and connections) to analyze ALS data based on a 1x1 m 2 raster over the whole data set. A voxel structure is used to count the echoes in predefined detrended height levels within each cell. In general, it is assumed that the number of voxels containing echoes is an indicator for elevated objects and consequently for increased roughness. Neighboring voxels containing at least one data point are merged together to connections. An additional height threshold is applied to connect vertical neighboring voxels with a certain distance in between. Thus, the connections indicate continuous vegetation structures. The height of the surface near or lowest connection is an indicator for hydrodynamic roughness coefficients. For cells, voxels and connections the laser echoes are counted within the structure and various statistical measures are calculated. Based on these derived statistical parameters a rule-based classification is developed by applying a decision tree to assess vegetation types. Roughness coefficient values such as Manning's n are estimated, which are used as input for 2D hydrodynamic-numerical modeling. The estimated Manning's values from the ALS point cloud are compared with a traditional Manning's map. Finally, the effect of these two different Manning's n maps as input on the 2D hydraulics are quantified by calculating a height difference model of the inundated depth maps. The results show the large potential of using the entire vertical vegetation structure for hydraulic roughness estimation.

Full-waveform LiDAR is an active remote sensing technique that provides the scattering properties of the targets i.e. amplitude and echo width (EW) in addition to 3D point clouds. The amplitude provides information on the target's reflectance and the EW is a measure for the range variation of scatterers within the laser footprint contributing to a single echo and is, therefore, an indicator for surface roughness. For the delineation of high vegetation (i.e. trees and bushes) and buildings the normalized digital surface model provides the main input. Based on a height threshold, areas covered with elevated objects can be classified. For the differentiation between buildings and high vegetation the surface roughness is used, where roofs are assumed to be smooth and vegetation rough. The surface roughness can be described with the standard deviation (SD) of detrended LiDAR points. However, for low LiDAR point densities and for very dense deciduous forests or building parts overgrown with tree crowns the separability is limited if only the SD is used. Within the presented approach additionally the EW, featuring small values for smooth roof areas, and the echo ratio (ER), a measure for the penetrability of a surface, are used. For the final delineation of high vegetation and building areas morphologic operations are applied. The classified areas are vectorized and a minimum mapping unit is applied. The presented workflow is implemented into the scientific software package OPALS (Orientation and Processing of Airborne Laser Scanning data). The results show that the EW, the SD and the ER are useful quantities to delineate high vegetation and building polygons based on full-waveform LiDAR point clouds with high accuracy. The high degree of automation and the scripting capability OPALS promise a high potential for operational large area LiDAR data processing. Examples from different test sites in Austria are shown and discussed.

Semi-arid ecosystems cover approximately 40% of the earth's terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using stateof-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem. Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differentiate key plant functional types (PFTs) using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81-0.86% and 0.60-0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees). viii Using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI's potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m -1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area resulted in a more even and less divergent ecosystem state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem's health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% ix uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. Overall, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This work enhances our understanding of the present state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2 and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance. x TABLE OF CONTENTS DEDICATION .

Semi-arid ecosystems cover approximately 40% of the earth's terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem. Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differe...

This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech), with a point density of 7.6 points per m 2 , while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59 m average difference and 0.78 m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission has been designed to measure forest structure using lidar waveforms sampled as it orbits the Earth while aboard the International Space Station. In this paper, we report the results of a study using airborne measurements of large-footprint (LF) lidar to simulate GEDI observations and to verify its capability its to retrieve ground elevation, vegetation height and aboveground biomass (AGB) by comparing to airborne small-footprint (SF) lidar measurements. The study focused on tropical forests and used data collected during the NASA and ESA AfriSAR ground and airborne campaigns in the Lope National Park in Central Gabon. The measurements covered a gradient of successional stages of forest development with different height, canopy density and topography. The comparison of the two sensors shows that LF lidar waveforms and simulated waveforms from SF lidar are equivalent in their ability to estimate ground elevation (RMSE=0.5 m, bias=0.29 m) and maximum forest height (RMSE=2.99 m; bias=0.24 m) over the study area. The difference in the AGB estimated from both lidar instruments at the 1-ha spatial scale is small over the entire study area (RMSE=6.34 Mg  ha-1 , bias=11.27 Mg  ha-1) and the bias is attributed to the impact of ground slopes greater than 10-20 degrees on the LF lidar measurements of forest height. Ourresults verify the ability of GEDI LF lidar to measure the complex structure of humid tropical forests and to provide estimates of AGB comparable to SF.

Accurate classification of plant functional types (PFTs) reduces the uncertainty in global biomass and carbon estimates. Airborne small-footprint waveform lidar data are increasingly used for vegetation classification and above-ground carbon estimates at a range of spatial scales in woody or homogeneous grass and savanna ecosystems. However, a gap remains in understanding how waveform features represent and ultimately can be used to constrain the PFTs in heterogeneous semi-arid ecosystems. This study evaluates lidar waveform features and classification performance of six major PFTs, including shrubs and trees, along with bare ground in the Reynolds Creek Experimental Watershed, Idaho, USA. Waveform lidar data were obtained with the NASA Airborne Snow Observatory (ASO). From these data we derived waveform features at two spatial scales (1 m and 10 m rasters) by applying a Gaussian decomposition and a frequency-domain deconvolution. An ensemble random forest algorithm was used to assess classification performance and to select the most important waveform features. Classification models developed with the 10 m waveform features outperformed those at 1 m (Kappa (κ) = 0.81-0.86 vs. 0.60-0.70, respectively). At 1 m resolution, lidar height features improved the PFT classification accuracy by 10% compared to the analysis without these features. However, at 10 m resolution, the inclusion of lidar derived heights with other waveform features decreased the PFT classification performance by 4%. Pulse width, rise time, percent energy, differential target cross section, and radiometrically calibrated backscatter coefficient were the most important waveform features at both spatial scales. A significant finding is that bare ground was clearly differentiated from shrubs using pulse width. Though the overall accuracy ranges between 0.72 and 0.89 across spatial scales, the two shrub PFTs showed 0.45-0.87 individual classification success at 1 m, while bare ground and tree PFTs showed high (0.72-1.0) classification accuracy at 10 m. We conclude that small-footprint waveform features can be used to characterize the heterogeneous vegetation in this and similar semi-arid ecosystems at high spatial resolution. Furthermore, waveform features such as pulse width can be used to constrain the uncertainty of terrain modeling in environments where vegetation and bare ground lidar returns are close in time and space. The dependency on spatial resolution plays a critical role in classification performance in tree-shrub co-dominant ecosystems.

The Belaga Formation is a deep marine turbidite interpreted in relation to a submarine fan and therefore a prospect worth evaluating. The scope of this paper will focus on the shales from the thick sedimentary sequence of the Belaga Formation (Upper Cretaceous to Eocene) consisting of five (5) members in Central Sarawak, Malaysia, of which attention is given to the depositional environment and clay minerals as factors to consider as predictive tools for organic matter richness and gas storage capacity using the spectral gamma ray method using Thorium (Th), Potassium (K) and Uranium (U) as indices for inferences. A mineralogical chart of Th/K (ppm/%) indicates that the dominating clay minerals of the shales are mixed-layer types. The Th/U as a redox indicator is used to assert that the shales are of anoxic conditions of deep-marine environments. Despite the relatively high insoluble Th values (9.93-20.13) ppm, the presence of U in substantial amounts, which only occurs in reducing conditions where it is preserved as a lower insoluble valence (U 4+) explains for the low Th/U values ranging between (1.86-4.57) ppm/ppm of the fifty-eight analyzed shale samples. The overall Th/U value of the evaluated shales remain less than 7, where Th/U < 7 is suggestive of marine sediments, grey and green shales, whereas Th/U < 2 is indicative of marine black shales of reducing conditions. The 30Ma interval of sediment deposition of the Belaga Formation Eocene recorded varied climatic fluctuations influential to clay minerals contained in the shales and ascribe the paleoweathering state of the shales, which from the standard imitative ternary plot of normalized K-U-Th of shales from Northern America address the shales of the Belaga Formation as of deep weathering residuum.

A frozen or wet road surface is a cause of skidding and accidents, so road surface condition is important information for driving safety. Some instruments and methods have been developed to investigate road surface conditions based on optical imagery, although an active sensor is needed, regardless of the time of day. Recently, the laser scanner, which acquires backscattering intensity data related to reflectivity, has become popular in various fields. There is a need to investigate road surface conditions (frozen, wet, or dry) using laser backscattering intensity. This study tries to analyze signal characteristics of laser backscattering intensity to detect frozen and wet surfaces on roads. An ice target with a 7 cm thickness was placed on a road surface, and a wet surface was made due to the melting ice. The ice target, wet surface, dry surface, and roadside vegetation were scanned using a laser scanner. As a result, backscattering signals from the top surface of the ice target we...

Mapping tree species diversity is increasingly important in the face of environmental change and biodiversity conservation. We explore a potential way of mapping this diversity by relating forest structure to tree species diversity in Gabon. First, we test the relation between canopy height, as a proxy for niche volume, and tree species diversity. Then, we test the relation between vertical canopy structure, as a proxy for vertical niche occupation, and tree species diversity. We use large footprint full-waveform airborne lidar data collected across four study sites in Gabon (Lopé, Mabounié, Mondah, and Rabi) in combination with in situ estimates of species richness (S) and Shannon diversity (H′). Linear models using canopy height explained 44% and 43% of the variation in S and H′ at the 0.25 ha resolution. Linear models using canopy height and the plant area volume density profile explained 71% of this variation. We demonstrate applications of these models by mapping S and H′ in Mo...

Two-dimensional (2-D) debris-flow mathematical modeling is a useful und widely used tool when assessing debris-flow hazard. A high accuracy for 2-D model input parameters is essential for obtaining acceptable simulation results, especially for potential debris flows without a recent history of activity. The numerical grid in the debris-flow transport area is usually generated from available topographic data. In Slovenia, DEM5 and DEM12.5 are publicly available data. However, the morphological accuracy of those datasets is questionable because of their development methods and their low resolution. A better solution is using LiDAR-derived data with their higher resolution and a lot of options for further improvements using different methods and algorithms. 2-D debris-flow simulation results using LiDAR data, compared to less accurate DEMs, are more accurate and more useful for debris-flow hazard mapping. The modelled debris-flow depths and velocities are more accurate and better follow field conditions. Although using high-resolution topographic data results in much longer computational time, this shouldn't be a problem for potential debris flows. For debris-flow endangered and populated torrential fans, modelling of the influence of built structures on flow is important. With adequate numerical modelling of such structures the obtained simulation results are more meaningful, with better expressed J. Sodnik (&) Tempos d.o.o.,

There are very little attempts of DEM evaluation in such a disturbed or discontinuous surface (e.g., in tillage area). Present study aims to evaluate common interpolation methods (triangulation, nearest neighbor, natural neighbor, minimum curvature, multiquadratic radial basis function (MRBF), ordinary kriging, and inverse distance weight) in representing the detail topography of two different tillage types, namely bench terrace and furrow. Evaluation procedure was conducted through a stepwise analysis by using combination between the accuracy level (coefficient of determination (R 2), mean error (ME) and standard deviation error (S)) and the shape similarity analysis. This study also shows the application of break-line function during the interpolation process in order to optimize some interpolation methods and the usage of drainage sink area as another step in evaluating DEM quality. To achieve the aim of this study, two field-size of dry-land agriculture (tegalan) were observed by using a set of total station Nikon DTM 322 with 3" angle accuracy. These plots, namely Tieng (1652 m²) and Buntu (673 m²), are situated in the upper part of Wonosobo District, Central Java Province, Indonesia. Tieng plot represents the bench terrace system embedded with stones on its terrace risers and showing relatively smooth ground surface. On the other side, Buntu plot shows the ridges and furrows system that lays perpendicularly to the contour lines. In terms of R², ME and S, there were slight differences in results between each method, except the multiquadratic radial basis function which was failed to generate terrace form in Tieng. The final result shows that triangulation is the best fit method followed by natural neighbour at representing the bench terraces in Tieng plot. In the case of furrow in Buntu plot, natural neighbour is the most accurate method. Despite its superiority at representing the bench terrace, triangulation has larger sink drainage area compared to natural neighbour. This study has confirmed the robustness of a stepwise analysis between quantitative and qualitative assessment techniques for DEM accuracy. A fine value of quantitative parameter does not necessarily mean that it will fairly possess a good spatial accuracy. Detain topography Digital elevation model Interpolation Tillage systems

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-tobiomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

A recent suite of new global-scale satellite sensors and regional-scale airborne campaigns are providing a wealth of remote sensing data capable of dramatically advancing our current understanding of the spatial distribution of forest structure and carbon stocks. However, a baseline for forest stature and biomass estimates has yet to be established for the wide array of available remote sensing products. At present, it remains unclear how the estimates from these sensors compare to one another in terrestrial forests, with a clear dearth of studies in high carbon density mangrove ecosystems. In the tallest mangrove forest on Earth (Pongara National Park, Gabon), we leverage the data collected during the AfriSAR campaign to evaluate 17 state-of-the-art sensor data products across the full range of height and biomass known to exist globally in mangrove forest ecosystems, providing a much-needed baseline for sensor performance. Our major findings are: (Houghton, Hall, Goetz) height estimates are not consistent across products, with opposing trends in relative and absolute errors, highlighting the need for an adaptive approach to constraining height estimates (Panet al., 2011); radar height estimates had the lowest calibration error and bias, with further improvements using LiDAR fusion (Bonan, 2008); biomass variability and uncertainty strongly depends on forest stature, with variation across products increasing with canopy height, while relative biomass variation was highest in low-stature stands (Le Quéréet al., 2017); a remote sensing product's sensitivity to variations in canopy structure is more important than the absolute accuracy of height estimates (Mitchardet al., 2014); locally-calibrated area-wide totals are more representative than generalized global biomass models for high-precision biomass estimates. The findings presented here provide critical baseline expectations for height and biomass predictions across the full range of mangrove forest stature, which can be directly applied to current (TanDEM-X, GEDI, ICESat-2) and future (NISAR, BIOMASS) global-scale forest monitoring missions.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-tobiomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Full Waveform Inversion (FWI) is a powerful tool for quantitative seismic imaging from wide-azimuth seismic data. The method is based on the minimization of the misfit between observed and simulated data. This amounts to the resolution of a large-scale nonlinear minimization problem. The inverse Hessian operator plays a crucial role in this reconstruction process. Accounting accurately for the e↵ect of this operator within the minimization scheme should correct for illumination deficits, restore the amplitude of the subsurface parameters, and help to remove artifacts generated by energetic multiple reflections. Conventional preconditioned gradient-based minimization methods only roughly approximate the e↵ect of this operator. We are interested in this study to another class of minimization methods, named as truncated Newton methods. These methods are based on the computation of the model update through a matrix-free conjugate gradient resolution of the Newton linear system. The aim of this study is to present a feasible implementation of this method for the FWI problem, based on a second-order adjoint state

Full Waveform Inversion (FWI) is a powerful tool for quantitative seismic imaging from wide-azimuth seismic data. The method is based on the minimization of the misfit between observed and simulated data. This amounts to the resolution of a large-scale nonlinear minimization problem. The inverse Hessian operator plays a crucial role in this reconstruction process. Accounting accurately for the e↵ect of this operator within the minimization scheme should correct for illumination deficits, restore the amplitude of the subsurface parameters, and help to remove artifacts generated by energetic multiple reflections. Conventional preconditioned gradient-based minimization methods only roughly approximate the e↵ect of this operator. We are interested in this study to another class of minimization methods, named as truncated Newton methods. These methods are based on the computation of the model update through a matrix-free conjugate gradient resolution of the Newton linear system. The aim of this study is to present a feasible implementation of this method for the FWI problem, based on a second-order adjoint state

National forest inventories in tropical regions are sparse and have large uncertainty in capturing the physiographical variations of forest carbon across landscapes. Here, we produce for the first time the spatial patterns of carbon stored in forests of Democratic Republic of Congo (DRC) by using airborne LiDAR inventory of more than 432,000 ha of forests based on a designed probability sampling methodology. The LiDAR mean top canopy height measurements were trained to develop an unbiased carbon estimator by using 92 1-ha ground plots distributed across key forest types in DRC. LiDAR samples provided estimates of mean and uncertainty of aboveground carbon density at provincial scales and were combined with optical and radar satellite imagery in a machine learning algorithm to map forest height and carbon density over the entire country. By using the forest definition of DRC, we found a total of 23.3 ± 1.6 GtC carbon with a mean carbon density of 140 ± 9 MgC ha −1 in the aboveground and belowground live trees. The probability based LiDAR samples capture variations of structure and carbon across edaphic and climate conditions, and provide an alternative approach to national ground inventory for efficient and precise assessment of forest carbon resources for emission reduction (ER) programs.

There are very little attempts of DEM evaluation in such a disturbed or discontinuous surface (e.g., in tillage area). Present study aims to evaluate common interpolation methods (triangulation, nearest neighbor, natural neighbor, minimum curvature, multiquadratic radial basis function (MRBF), ordinary kriging, and inverse distance weight) in representing the detail topography of two different tillage types, namely bench terrace and furrow. Evaluation procedure was conducted through a stepwise analysis by using combination between the accuracy level (coefficient of determination (R 2), mean error (ME) and standard deviation error (S)) and the shape similarity analysis. This study also shows the application of break-line function during the interpolation process in order to optimize some interpolation methods and the usage of drainage sink area as another step in evaluating DEM quality. To achieve the aim of this study, two field-size of dry-land agriculture (tegalan) were observed by using a set of total station Nikon DTM 322 with 3" angle accuracy. These plots, namely Tieng (1652 m²) and Buntu (673 m²), are situated in the upper part of Wonosobo District, Central Java Province, Indonesia. Tieng plot represents the bench terrace system embedded with stones on its terrace risers and showing relatively smooth ground surface. On the other side, Buntu plot shows the ridges and furrows system that lays perpendicularly to the contour lines. In terms of R², ME and S, there were slight differences in results between each method, except the multiquadratic radial basis function which was failed to generate terrace form in Tieng. The final result shows that triangulation is the best fit method followed by natural neighbour at representing the bench terraces in Tieng plot. In the case of furrow in Buntu plot, natural neighbour is the most accurate method. Despite its superiority at representing the bench terrace, triangulation has larger sink drainage area compared to natural neighbour. This study has confirmed the robustness of a stepwise analysis between quantitative and qualitative assessment techniques for DEM accuracy. A fine value of quantitative parameter does not necessarily mean that it will fairly possess a good spatial accuracy. Detain topography Digital elevation model Interpolation Tillage systems

In arable landscapes, the airborne detection of archaeological features is often reliant on using the properties of the vegetation cover as a proxy for sub-surface features in the soil. Under the right conditions, the formation of vegetation marks allows archaeologists to identify and interpret archaeological features. Using airborne Laser Scanning, based on the principles of Light Detection and Ranging (LiDAR) to detect these marks is challenging, particularly given the difficulties of resolving subtle changes in a low and homogeneous crop with these sensors. In this paper, an experimental approach is adopted to explore how these marks could be detected as variations in canopy biomass using both range and full waveform LiDAR data. Although some detection was achieved using metrics of the full waveform data, it is the novel multi-temporal method of using discrete return data to detect and characterise archaeological vegetation marks that is offered for further consideration. This method was demonstrated to be applicable over a range of capture conditions, including soils deemed as difficult (i.e., clays and other heavy soils), and should increase the certainty of detection when employed in the increasingly multi-sensor approaches to heritage prospection and management.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-tobiomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Successful implementation of projects under the REDD+ mechanism, securing payment for storing forest carbon as an ecosystem service, requires quantification of biomass. Airborne laser scanning (ALS) is a relevant technology to enhance estimates of biomass in tropical forests. We present the analysis and results of modeling aboveground biomass (AGB) in a Tanzanian rainforest utilizing data from a small-footprint ALS system and 153 field plots with an area of 0.06-0.12 ha located on a systematic grid. The study area is dominated by steep terrain, a heterogeneous forest structure and large variation in AGB densities with values ranging from 43 to 1147 Mg•ha −1 , which goes beyond the range that has been reported in existing literature on biomass modeling with ALS data in the tropics. Root mean square errors from a 10-fold cross-validation of estimated values were about 33% of a mean value of 462 Mg•ha −1. Texture variables derived from a canopy surface model did not result in improved models. Analyses showed that (1) variables derived from echoes in the lower parts of the canopy and (2) canopy density variables explained more of the AGB density than variables representing the height of the canopy.

Classifying vegetation points from 3D airborne laser scanner (ALS) point clouds is a challenge and focus of current research. In particular, low vegetation points are very difficult to identify. The basic problem is that so far the majority of ALS systems have provided only the 3D coordinates of scattering objects and most of the criteria used in classifying points had to rely on simple geometric characteristics of a point relative to its neighbourhood. Methods for ALS data processing could be much improved if ALS systems measure, in addition to the range, further physical observables which can be used for vegetation classification. New ALS systems, which record the full echo-waveform, may provide crucial information for the classification of vegetation points. In this paper we show that the additional features derived from the full-waveform data-the amplitude, the pulse width and the number of pulses-can be used to discriminate between vegetation and non-vegetation points without using geometry information. Thus, a truly three dimensional representations of the classified ALS points can be obtained. The classification algorithm is based on a decision tree technique. The applicability of this method is demonstrated on data collected by the RIEGL LMS-Q560 sensor over the Schönbrunn area of Vienna. The performance of the classification algorithm was checked manually on 500 points randomly distributed and on several test zones selected over the study area. We found an overall accuracy of 88.6% with a kappa coefficient of 0.8.

Early warning systems for debris flows are low cost measures for mitigating this kind of hazard. The early warning systems provide a timely alert for upcoming events in order to take protective measures, such as closing railways-roads, evacuating people from the threatened areas, and put rescue forces into readiness. These systems usually are sensor-based, and the alert time is the interval between the timing of the first detachment of debris flow by a sensor and its arrival into the threatened area. At the purpose of increasing the alert time, we propose an early warning system based on a model-cascade: nowcasting, hydrological- and triggering models. Nowcasting anticipates rainfall pattern that is transformed into runoff by the hydrological model. The triggering model estimates the volume of sediments that the runoff can entrain, and compares it with a critical threshold. If this is exceeded the alert is launched. The proposed early warning system is tested against the available d...

Topo-bathymetric lidar is a powerful tool to survey coastal ecosystems while ensuring data continuity between land and water regardless of the nature of the terrain, and allowing the collection of information up to several dozens of metres deep. This study analyzes the potential of full waveform lidar data to monitor key ecosystems for climate change mitigation: seagrasses. It proposes an original way of processing topo-bathymetric lidar waveforms to map their spatial repartition and extent in Corsica (France). Waveform statistical and shape parameters are computed and used to produce a map of seagrass meadows that reaches over 86% of overall accuracy. Seagrass height is also extracted, offering perspectives for structural complexity assessment and ecosystem services quantification.

In the analysis of the temporal evolution of landslides and of related hydrogeological hazards, terrestrial laser scanning (TLS) seems to be a very suitable technique for morphological description and displacement analysis. In this note we present some procedures designed to solve specific issues related to monitoring. A particular attention has been devoted to data georeferencing, both during survey campaigns and while performing statistical data analysis. The proper interpolation algorithm for digital elevation model generation has been chosen taking into account the features of the landslide morphology and of the acquired datasets. For a detailed analysis of the different dynamics of the hillslope, we identified some areas with homogeneous behaviour applying in a geographic information system (GIS) environment a sort of rough segmentation to the grid obtained by differentiating two surfaces. This approach has allowed a clear identification of ground deformations, obtaining detailed quantitative information on surficial displacements. These procedures have been applied to a case study on a large landslide of about 10 hectares, located in Italy, which recently has severely damaged the national railway line. Landslide displacements have been monitored with TLS surveying for three years, from February 2010 to June 2012. Here we report the comparison results between the first and the last survey.

Vegetation biomass is a globally important climate-relevant terrestrial carbon pool and also drives local hydrological systems via evapotranspiration. Vegetation biomass of individual vegetation types has been successfully estimated from active and passive remote sensing data. However, for many tasks, landscape-level biomass maps across several vegetation types are more suitable than biomass maps of individual vegetation types. For example, the validation of ecohydrological models and carbon budgeting typically requires spatially continuous biomass estimates, independent from vegetation type. Studies that derive biomass estimates across multiple vegetation or land-cover types to merge them into a single landscape-level biomass map are still scarce, and corresponding workflows must be developed. Here, we present a workflow to derive biomass estimates on landscape-level for a large watershed in central Chile. Our workflow has three steps: First, we combine field plotbased biomass estimates with spectral and structural information collected from Sentinel-2, TanDEM-X and airborne LiDAR data to map grassland, shrubland, native forests and pine plantation biomass using random forest regressions with an automatic feature selection. Second, we predict all models to the entire landscape. Third, we derive a land-cover map including the four considered vegetation types. We then use this land-cover map to assign the correct vegetation type-specific biomass estimate to each pixel according to one of the four considered vegetation types. Using a single repeatable workflow, we obtained biomass predictions comparable to earlier studies focusing on only one of the four vegetation types (Spearman correlation between 0.80 and 0.84; normalized-RMSE below 16 % for all vegetation types). For all woody vegetation types, height metrics were amongst the selected predictors, while for grasslands, only Sentinel-2 bands were selected. The land-cover was also mapped with high accuracy (OA = 83.1 %). The final landscape-level biomass map spatially agrees well with the known biomass distribution patterns in the watershed. Progressing from vegetation-type specific maps towards landscape-level biomass maps is an essential step towards integrating remote-sensing based biomass estimates into models for water and carbon management.

At the geothermal test site near Groß Schönebeck (NE German Basin), a new 3D seismic reflection survey was conducted to study geothermal target layers at around 4 km depth and 150 °C. We present a workflow for seismic facies classification and modelling which is applied to a prospective sandstone horizon within the Rotliegend formation. Signal attributes are calculated along the horizon using the continuous Morlet wavelet transform. We use a This article is protected by copyright. All rights reserved. 2 Bauer et al. short mother wavelet to allow for the temporal resolution of the relatively short reflection signals to be analyzed. Time-frequency domain data patterns form the input of a neural network clustering using self-organizing maps (SOM). Neural model patterns are adopted during iterative learning to simulate the information inherent in the input data. After training we determine a gradient function across the SOM and apply an image processing technique called watershed segmentation. The result is a pattern clustering based on similarities in wavelet transform characteristics. Three different types of wavelet transform patterns were found for the sandstone horizon. We apply seismic waveform modelling to improve the understanding of the classification results. The modelling tests indicate that thickness variations have a much stronger influence on the wavelet transform response of the sandstone horizon compared with reasonable variations of seismic attenuation. In our interpretation, the assumed thickness variations could be a result of variable paleo-topography during deposition of predominantly fluvial sediments. A distinct seismic facies distribution is interpreted as a system of thicker paleo-channels deposited within a deepened landscape. The results provide constraints for the ongoing development of the geothermal test site.

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy...

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per s...

In forest ecology, a snag refers to a standing, partly or completely dead tree, often missing a top or most of the smaller branches. The accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, and forest management. Therefore, an understanding of its availability and spatial distribution is required. So far, LiDAR remote sensing has been successfully used to assess live trees and their biomass, but studies focusing on dead trees are rare. The paper develops a methodology for retrieving individual dead trees in a mixed mountain forest using features that are derived from small-footprint airborne full waveform LIDAR data. First, 3D coordinates of the laser beam reflections, the pulse intensity and width are extracted by waveform decomposition. Secondly, 3D single trees are detected by an integrated approach, which delineates both dominate tree crowns and understory small trees in the canopy height model (CHM) using the watershed algorithm followed by applying normalized cuts segmentation to merged watershed areas. Thus, single trees can be obtained as 3D point segments associated with waveform-specific features per point. Furthermore, the tree segments are delivered to feature definition process to derive geometric and reflectional features at single tree level, e.g. volume and maximal diameter of crown, mean intensity, gap fraction, etc. Finally, the spanned feature space for the tree segments is forwarded to a binary classifier using support vector machine (SVM) in order to discriminate dead trees from the living ones. The methodology is applied to datasets that have been captured with the Riegl LMS-Q560 laser scanner at a point density of 25 points/m 2 in the Bavarian Forest National Park, Germany, respectively under leaf-on and leaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification experiments lead in the best case to an overall accuracy of 73% in a leaf-on situation and 71% in a leaf-off situation, if we assess the classification results using 5-fold cross-validation method with the help of reference data acquired by the field surveying.

Topo-bathymetric lidar is a powerful tool to survey coastal ecosystems while ensuring data continuity between land and water regardless of the nature of the terrain, and allowing the collection of information up to several dozens of metres deep. This study analyzes the potential of full waveform lidar data to monitor key ecosystems for climate change mitigation: seagrasses. It proposes an original way of processing topo-bathymetric lidar waveforms to map their spatial repartition and extent in Corsica (France). Waveform statistical and shape parameters are computed and used to produce a map of seagrass meadows that reaches over 86% of overall accuracy. Seagrass height is also extracted, offering perspectives for structural complexity assessment and ecosystem services quantification.

Topo-bathymetric lidar is a powerful tool to survey coastal ecosystems while ensuring data continuity between land and water regardless of the nature of the terrain, and allowing the collection of information up to several dozens of metres deep. This study analyzes the potential of full waveform lidar data to monitor key ecosystems for climate change mitigation: seagrasses. It proposes an original way of processing topo-bathymetric lidar waveforms to map their spatial repartition and extent in Corsica (France). Waveform statistical and shape parameters are computed and used to produce a map of seagrass meadows that reaches over 86% of overall accuracy. Seagrass height is also extracted, offering perspectives for structural complexity assessment and ecosystem services quantification.

In forest ecology, a snag refers to a standing, partly or completely dead tree, often missing a top or most of the smaller branches. The accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, and forest management. Therefore, an understanding of its availability and spatial distribution is required. So far, LiDAR remote sensing has been successfully used to assess live trees and their biomass, but studies focusing on dead trees are rare. The paper develops a methodology for retrieving individual dead trees in a mixed mountain forest using features that are derived from small-footprint airborne full waveform LIDAR data. First, 3D coordinates of the laser beam reflections, the pulse intensity and width are extracted by waveform decomposition. Secondly, 3D single trees are detected by an integrated approach, which delineates both dominate tree crowns and understory small trees in the canopy height model (CHM) using the watershed algorithm followed by applying normalized cuts segmentation to merged watershed areas. Thus, single trees can be obtained as 3D point segments associated with waveform-specific features per point. Furthermore, the tree segments are delivered to feature definition process to derive geometric and reflectional features at single tree level, e.g. volume and maximal diameter of crown, mean intensity, gap fraction, etc. Finally, the spanned feature space for the tree segments is forwarded to a binary classifier using support vector machine (SVM) in order to discriminate dead trees from the living ones. The methodology is applied to datasets that have been captured with the Riegl LMS-Q560 laser scanner at a point density of 25 points/m 2 in the Bavarian Forest National Park, Germany, respectively under leaf-on and leaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification experiments lead in the best case to an overall accuracy of 73% in a leaf-on situation and 71% in a leaf-off situation, if we assess the classification results using 5-fold cross-validation method with the help of reference data acquired by the field surveying.

In this paper, an improved Tornado method for filtering LiDAR (Light Detection and Ranging) point clouds is presented. The original method uses a vertical cone with a downward vertex and an upward base to remove the points within it as non-ground points. The remaining points are ground points. The cone moves on the ground surface over the entire region of the point cloud. In this work, the regions of the objects are predicted by extracting the vertical features that have points in the vertical plane or vertical column. Therefore, the tornado method is only used in regions that contain objects. In addition, our improved method uses a specific height for a tornado to reduce the Type I error in mountainous areas. Also, a cylinder surrounding the cone is used to reduce the distance calculations between the cone and the point cloud. The results show that this method is very effective and fast compared to the original method. It also has promising results for the Type I error. In addition, this method was tested on the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets and produced outstanding results. The results show that this method achieves high filtering accuracy. Moreover, the proposed method achieves an overall average error of 6.83%, which is lower than most other methods.

In this paper, a new building detection method based on a density of LIDAR point clouds is proposed. In this method, trees, vegetation, and any objects that have points in a vertical plane or column are removed. In the density-based method, a cube is utilized to calculate the density therein. For each point, the cube is used to determine the number of neighbouring points. The density is calculated in two cases: 3D and 2D space. In 3D space, the volumetric density is calculated using the cube. In 2D space, all points are projected onto the horizontal plane, and the surface density is calculated using a square. Next, the two densities are compared and the points with different values in both cases are removed. The method leads to promising results in the removal of vegetation and trees. Moreover, the results achieve more than 94% completeness and correctness at the per-area level.

In this paper, an improved Tornado method for filtering LiDAR (Light Detection and Ranging) point clouds is presented. The original method uses a vertical cone with a downward vertex and an upward base to remove the points within it as non-ground points. The remaining points are ground points. The cone moves on the ground surface over the entire region of the point cloud. In this work, the regions of the objects are predicted by extracting the vertical features that have points in the vertical plane or vertical column. Therefore, the tornado method is only used in regions that contain objects. In addition, our improved method uses a specific height for a tornado to reduce the Type I error in mountainous areas. Also, a cylinder surrounding the cone is used to reduce the distance calculations between the cone and the point cloud. The results show that this method is very effective and fast compared to the original method. It also has promising results for the Type I error. In addition, this method was tested on the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets and produced outstanding results. The results show that this method achieves high filtering accuracy. Moreover, the proposed method achieves an overall average error of 6.83%, which is lower than most other methods.

In this paper, a new building detection method based on a density of LIDAR point clouds is proposed. In this method, trees, vegetation, and any objects that have points in a vertical plane or column are removed. In the density-based method, a cube is utilized to calculate the density therein. For each point, the cube is used to determine the number of neighbouring points. The density is calculated in two cases: 3D and 2D space. In 3D space, the volumetric density is calculated using the cube. In 2D space, all points are projected onto the horizontal plane, and the surface density is calculated using a square. Next, the two densities are compared and the points with different values in both cases are removed. The method leads to promising results in the removal of vegetation and trees. Moreover, the results achieve more than 94% completeness and correctness at the per-area level.

The monitoring of shrublands plays a fundamental role, from an ecological and climatic point of view, in biodiversity conservation, carbon stock estimates, and climate-change impact assessments. Laser scanning systems have proven to have a high capability in mapping non-herbaceous vegetation by classifying high-density point clouds. On the other hand, the classification of low-density airborne laser scanner (ALS) clouds is largely affected by confusion with rock spikes and boulders having similar heights and shapes. To identify rocks and improve the accuracy of vegetation classes, we implemented an effective and time-saving procedure based on the integration of geometric features with laser intensity segmented by K-means clustering (GIK procedure). The classification accuracy was evaluated, taking into account the data unevenness (small size of rock class vs. vegetation and terrain classes) by estimating the Balanced Accuracy (BA range 89.15–90.37); a comparison with a standard geom...

The concept for a new multi-spectral canopy LIDAR (MSCL) instrument was tested by simulating return waveforms using models providing tree structure (TREEGROW) and leaf reflectance (PROSPECT). The proposed instrument will take measurements at four different wavelengths, which were chosen according to physiological processes altering leaf reflectance. The modelling was used to assess both the structural and physiological information content such a device could provide, especially if the normally structure-dominated return waveform would pick up small changes in reflectance at the leaf level. Multi-spectral waveforms were simulated for models of single Scots pine trees of different ages and at different stages of the growing season. It was shown that the LIDAR waveforms would not only capture the tree height information, but as would also pick up the seasonal and vertical variation of NDVI computed from two of the four MSCL wavelengths inside the tree canopy. It could be demonstrated that a new multi-wavelength LIDAR predictor variable could significantly improve the retrieval accuracy of photosynthetically active biomass opposed to using a single wavelength LIDAR alone. It remains unclear, however, if these findings would persist for forest stands; thus such experiments simulating more complex scenarios will be the next task in this modelling framework.