Lidar Point Density Research Articles

Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models

AbstractTopography and the computational mesh grid are fundamental inputs to all two‐dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter‐scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC‐RAS model with varying mesh grid resolutions (1 and 2 m) and drone‐based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel‐floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.

Towards complete tree crown delineation by instance segmentation with Mask R–CNN and DETR using UAV-based multispectral imagery and lidar data

Precise single tree delineation allows for a more reliable determination of essential parameters such as tree species, height and vitality. Methods of instance segmentation are powerful neural networks for detecting and segmenting single objects and have the potential to push the accuracy of tree segmentation methods to a new level. In this study, two instance segmentation methods, Mask R–CNN and DETR, were applied to precisely delineate single tree crowns using multispectral images and images generated from UAV lidar data. The study area was in Bavaria, 35 km north of Munich (Germany), comprising a mixed forest stand of around 7 ha characterised mainly by Norway spruce (Picea abies) and large groups of European beeches (Fagus sylvatica) with 181–236 trees per ha. The data set, consisting of multispectral images and lidar data, was acquired using a Micasense RedEdge-MX dual camera system and a Riegl miniVUX-1UAV lidar scanner, both mounted on a hexacopter (DJI Matrice 600 Pro). At an altitude of approximately 85 m, two flight missions were conducted at an airspeed of 5 m/s, leading to a ground resolution of 5 cm and a lidar point density of 560 points/m2. In total, 1408 trees were marked by visual interpretation of the remote sensing data for training and validating the classifiers. Additionally, 125 trees were surveyed by tacheometric means used to test the optimized neural networks. The evaluations showed that segmentation using only multispectral imagery performed slightly better than with images generated from lidar data. In terms of F1 score, Mask R–CNN with color infrared (CIR) images achieved 92% in coniferous, 85% in deciduous and 83% in mixed stands. Compared to the images generated by lidar data, these scores are the same for coniferous and slightly worse for deciduous and mixed plots, by 4% and 2%, respectively. DETR with CIR images achieved 90% in coniferous, 81% in deciduous and 84% in mixed stands. These scores were 2%, 1%, and 2% worse, respectively, compared to the lidar data images in the same test areas. Interestingly, four conventional segmentation methods performed significantly worse than CIR-based and lidar-based instance segmentations. Additionally, the results revealed that tree crowns were more accurately segmented by instance segmentation. All in all, the results highlight the practical potential of the two deep learning-based tree segmentation methods, especially in comparison to baseline methods.

Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data.

Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: had a determination of coefficient R2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables—. In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.

Influences of vegetation, model, and data parameters on forest aboveground biomass assessment using an area-based approach

Although aboveground biomass (AGB) estimation using area-based approaches (ABAs) and its application to forestry have been actively researched through three decades, this technology has been little operationalized in the Central European forest sector. That means specific recommendations are needed in order to apply ABA for forest biomass modelling in this region. The present study was directed to filling such gaps while examining the effect of input ABA parameters on AGB model quality in conditions of mixed mountainous forests in Central Europe. Specific objectives were to assess whether the strength of the AGB model can be impacted by 1) canopy conditions (leaf-on and leaf-off), 2) airborne LiDAR point density (2.5, 5.0, 7.5, 10.0 points/m2), 3) field methods to estimate AGB (with regeneration components or without), and 4) machine learning methods (AdaBoost, Random decision forest, multilayer neural network, and Bayesian ridge regression). The results show that canopy conditions and airborne LiDAR point densities did not affect the strength of the AGB model, but that model's strength was affected by the vegetation regeneration component in the field biomass reference and by the machine learning method tested for modelling. AdaBoost and random decision forest were the most successful methods. To evaluate the quality of an AGB model it is recommended to combine several individual evaluation functions into the model score. The study highlights several recommendations to follow when estimating AGB from ALS using an ABA in Central European forests.

High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA

Forest canopy height model (CHM) is useful for analyzing forest stocking and its spatiotemporal variations. However, high-resolution CHM with regional coverage is commonly unavailable due to the high cost of LiDAR data acquisition and computational cost associated with data processing. We present a CHM generation method using U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) LiDAR data for tree height measurement capabilities for entire state of Indiana, USA. The accuracy of height measurement was investigated in relation to LiDAR point density, inventory height, and the timing of data collection. A simple data exploratory analysis (DEA) was conducted to identify problematic input data. Our CHM model has high accuracy compared to field-based height measurement (R2 = 0.85) on plots with relatively accurate GPS locations. Our study provides an easy-to-follow workflow for 3DEP LiDAR based CHM generation in a parallel processing environment for a large geographic area. In addition, the resulting CHM can serve as critical baseline information for monitoring and management decisions, as well as the calculation of other key forest metrics such as biomass and carbon storage.

Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data

Crop height is a key structure parameter for the modelling of crop growth, healthy status, yield forecasting and biomass estimation. Unmanned aerial vehicle (UAV) LiDAR systems can quickly and precisely acquire vegetation structure information at a low cost. UAV LiDAR data are increasingly used in vegetation parameters estimation. In this study, we estimated maize and soybean heights using two methods, i.e., based on LiDAR-derived CHM and based on LiDAR variables. The results show that UAV LiDAR data can successfully estimate maize and soybean heights. We found that the method based on LiDAR variables can produce more accurate estimates than CHM-based method. The estimation model of combined maize and soybean had a better prediction performance than those of the specific maize and soybean. Moreover, the soybean height estimation models derived from both methods yielded the lowest prediction precision. We studied the influence of LiDAR point density on crop height estimates through reduced point density (0.25–420 points/m2). When LiDAR point density was less than 1 point/m2, the estimation precision for the specific maize and soybean dropped rapidly with the decrease of point density. However, the point density had no significant influence on crop height estimation precision while LiDAR point density was greater than or equal to 1 point/m2. Moreover, the original point density did not generate the highest estimation precision in our study. Therefore, high LiDAR point density may be not required for estimating vegetation parameters, and a good balance between the point density and data acquisition cost should be found.

Silvi-Net – A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data

Forest managers and nature conservationists rely on precise mapping of single trees from remote sensing data for efficient estimation of forest attributes. In recent years, additional quantification of dead wood in particular has garnered interest. However, tree-level approaches utilizing segmented single trees are still limited in accuracy and their application is therefore mostly restricted to research studies. Furthermore, the combined classification of presegmented single trees with respect to tree species and health status is important for practical use but has been insufficiently investigated so far. Therefore, we introduce Silvi-Net, an approach based on convolutional neural networks (CNNs) fusing airborne lidar data and multispectral (MS) images for 3D object classification. First, we segment single 3D trees from the lidar point cloud, render multiple silhouette-like side-view images, and enrich them with calibrated laser echo characteristics. Second, projected outlines of the segmented trees are used to crop and mask the MS orthomosaic and to generate MS image patches for each tree. Third, we independently train two ResNet-18 networks to learn meaningful features from both datasets. This optimization process is based on pretrained CNN weights and recursive retraining of model parameters. Finally, the extracted features are fused for a final classification step based on a standard multi-layer perceptron and majority voting. We analyzed the network’s performance on data captured in two study areas, the Chernobyl Exclusion Zone (ChEZ) and the Bavarian Forest National Park (BFNP). For both study areas, the lidar point density was approximately 55 points/m2 and the ground sampling distance values of the true orthophotos were 10 cm (ChEZ) and 20 cm (BFNP). In general, the trained models showed high generalization capacity on independent test data, achieving an overall accuracy (OA) of 96.1% for the classification of pines, birches, alders, and dead trees (ChEZ) - and 91.5% for coniferous, deciduous, snags, and dead trees (BFNP). Interestingly, lidar-based imagery increased the OA by 2.5% (ChEZ) and 5.9% (BFNP) compared to experiments only utilizing MS imagery. Moreover, Silvi-Net also demonstrated superior OA compared to the baseline method PointNet++ by 11.3% (ChEZ) and 2.2% (BFNP). Overall, the effectiveness of our approach was proven using 2D and 3D datasets from two natural forest areas (400–530 trees/ha), acquired with different sensor models, and varying geometric and spectral resolution. Using the technique of transfer learning, Silvi-Net facilitates fast model convergence, even for datasets with a reduced number of samples. Consequently, operators can generate reliable maps that are of major importance in applications such as automated inventory and monitoring projects.

Forest Road Detection Using LiDAR Data and Hybrid Classification

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2.

Combining graph-cut clustering with object-based stem detection for tree segmentation in highly dense airborne lidar point clouds

Single tree detection has been a major research topic when it comes to support of collecting automatic field inventory using lidar. All previous methods show under- and over-segmentation effects because the associated control parameters have a limited scope. This paper describes a novel integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection. The key idea is to replace the static stopping criterion, which is usually defined by trial and error or by a sensitivity analysis, here with a query for whether a stem position has been provided by the stem detection in the remaining cluster to be partitioned. The stem detection automatically detects tree stems by identifying vertical lines based on a hierarchical classification procedure. We evaluate both stem detection and integrated single tree segmentation on mixed temperate forest plots captured in a leaf-on situation. The highly dense airborne lidar data was acquired with an average point density of more than 200 points/m2. The forest areas, which are located in Eastern Austria, are characterized by a stem density of around 1000 stems/ha and a tree age between 15 and 63 years. We test our algorithms with reference data measured both by visual interpretation of the laser point clouds using a conventional field campaign. In our experiments, we confirm that the automatic stem detection technique successfully locates stems if the lidar point density of the stems is at least five points/m. The experimental results show that stem detection alone can detect more than 80% of the stems, with a precision of better than 70%. Moreover, we prove that the integration of the stem detection renders the graph-cut segmentation effectively independent of the stopping criterion and improves the overall accuracy of the tree segmentation as well. In terms of F-scores, the overall improvement is up to 15% and 6% for reference data from visual inspection and field measurements, respectively. Compared with the results of two existing tree segmentation methods that were applied to the same datasets, the accuracy improvement is also demonstrated by F-scores increased up to 22% and 5%, respectively. Very interestingly, the integrated tree segmentation considerably enhances the detection accuracy, especially in mixed and deciduous forest areas by more than 10% in the case of reference data provided by visual inspection.

Topological-based roof modeling from 3D point clouds

Automatic extraction of building roofs from remote sensing data is important for many applications including 3D city modeling, urban planning, disaster management, and simulations. In this paper, we propose an automatic workflow for roof reconstruction by polygonal models from classified high-density LIDAR data. Roof planes are initially delineated by a segmentation algorithm combining a robust Hough-based normal estimator and a region growing strategy. Then, each roof is modeled by a 2D α-shape mesh which is used to discover not only building outline but also all ridges defined by intersecting roof planes, without any geometrical calculations. The mesh directly encodes the topological relations between neighboring planes which allows us to build the final polygonal model straightforwardly. This topological approach makes our solution more simple and robust than existing methods which mostly extract the intersection lines by means of geometrical computations. Experimental results show that the proposed workflow offers a high success rate for extraction at plane level (94% completeness, 92.7% correctness, 90.8% quality) when LIDAR point density is sufficiently high.

Calculating macroalgal height and biomass using bathymetric LiDAR and a comparison with surface area derived from satellite data in Nova Scotia, Canada

Abstract The ability to map and monitor the macroalgal coastal resource is important to both the industry and the regulator. This study evaluates topo-bathymetric lidar (light detection and ranging) as a tool for estimating the surface area, height and biomass of Ascophyllum nodosum, an anchored and vertically suspended (floating) macroalga, and compares the surface area derived from lidar and WorldView-2 satellite imagery. Pixel-based Maximum Likelihood classification of low tide satellite data produced 2-dimensional maps of intertidal macroalgae with overall accuracy greater than 80%. Low tide and high tide topo-bathymetric lidar surveys were completed in southwestern Nova Scotia, Canada. Comparison of lidar-derived seabed elevations with ground-truth data collected using a survey grade global navigation satellite system (GNSS) indicated the low tide survey data have a positive bias of 15 cm, likely resulting from the seaweed being draped over the surface. The high tide survey data did not exhibit this bias, although the suspended canopy floating on the water surface reduced the seabed lidar point density. Validation of lidar-derived seaweed heights indicated a mean difference of 30 cm with a root mean square error of 62 cm. The modelled surface area of seaweed was 28% greater in the lidar model than the satellite model. The average lidar-derived biomass estimate was within one standard deviation of the mean biomass measured in the field. The lidar method tends to overestimate the biomass compared to field measurements that were spatially biased to the mid-intertidal level. This study demonstrates an innovative and cost-effective approach that uses a single high tide bathymetric lidar survey to map the height and biomass of dense macroalgae.

A Multi-Threshold Segmentation for Tree-Level Parameter Extraction in a Deciduous Forest Using Small-Footprint Airborne LiDAR Data

The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.

Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar

The distribution of snow cover is critical for predicting ecohydrological processes and underpins mountain water supplies in ranges like the Sierra Nevada in the Western United States. Many key water supply areas are covered by montane forests, which have substantial effects on the amount and timing of snowmelt. In-situ observations of snow-forest interactions have limited spatial coverage and remote sensing using optical sensors (e.g. MODIS) cannot observe snow cover below the canopy. In this study, we developed and verified a lidar-based method to detect snow cover under canopy, investigated how fractional snow covered area (fSCA) varies with topography in open versus under canopy areas and developed a correction factor that could be used to improve satellite-derived fSCA products. We developed our new method using three snow-on lidar overflights and verified it with in-situ distributed temperature sensor (DTS) observations at Sagehen Creek watershed in the Sierra Nevada, California, USA. DTS validation of lidar classifications showed excellent agreement at 85–96%, including high agreement and large number of returns in under canopy locations. The lidar-derived fSCA observations generally showed earlier snow disappearance under the canopy than in open positions, which is consistent with relatively warm temperatures and greater longwave radiation. However, in contrast to expectations, areas with high solar exposure (i.e. high southwestness) exhibited higher fSCA under the canopy. Results indicated that the k factor (the ratio of under canopy fSCA to open fSCA) varied systematically with southwestness and elevation. Using this factor to correct the study domain fSCA indicated that the typical assumption that k = 1 could lead to an up to ~0.05 bias (in fSCA units) towards overestimation. However, within 10 and 100-m individual pixels the fSCA overprediction bias can be 25–30% for higher fSCA values. Although uncertainty would be reduced using higher snow-on lidar point densities, our method shows promise to improve the typical assumption that snow disappearance is identical in under the canopy and in the open (k = 1). Future applications of our lidar-based method at different sites with varying climate, topography and vegetation structure has the dual potential to expand understanding of snow-forest interactions in complex terrain and improve operational fSCA products.

STREAMS DO WORK: MEASURING THE WORK OF LOW-ORDER STREAMS ON THE LANDSCAPE USING POINT CLOUDS

Abstract. The mutable nature of low-order streams makes regular updating of surface water maps necessary for accurate representation. Low-order streams make up roughly half the streams in the conterminous United States by length, and small inaccuracies in stream head location can result in significant error in stream reach, order, and density. Reliable maps of stream features are vital for hydrologic modeling, ecosystem research, and boundary monitoring. High resolution digital elevation models derived from lidar data have shown promise in low order stream modeling yet forested high relief landscapes and low relief agricultural areas remain challenging. Here we present early results from research analyzing lidar point clouds to identify features and patterns that may be used in low-order stream identification and classification in challenging geographic conditions. This work has identified characteristics derived from point clouds that correlate with the presence of streams and stream heads and show promise for mapping small streams. In low topographic relief agricultural areas, cross sections collected at regular intervals along drainage channels extracted as 3D lines show a significant jump in value and variance of profile curvature standard deviation at stream heads. In high relief areas, observations show potential for stream mapping by identifying trends in riparian zone structure. Lidar return point density from riparian vegetation under 30 feet tall dips in the vicinity of intermittent stream heads. Also seen is an increase in point density above 60 feet downstream of stream heads. The trends found here likely reflect a change in vegetation structure relative to the presence of streams.

Retrieval of narrow-range LAI of at multiple lidar point densities: Application on Eucalyptus grandis plantation

Leaf area index (LAI) is an important forest structural parameter that can be used to characterize various biophysical processes such as photosynthesis, evapotranspiration, and carbon flux. Accurate monitoring of LAI therefore is crucial for efficient management of managed and natural vegetation ecosystems. Remote sensing techniques have proved useful in the quantification and monitoring of LAI in different vegetation types; however, most of the focus has been on vegetation with relatively large LAI ranges. This study aimed to investigate the utility of airborne light detection and ranging (lidar) data to estimate narrow-range LAI (min = 0.71, max = 1.56, mean = 1.08 ± 0.18) of intensively-managed Eucalyptus grandis plantations. The secondary aim of the study was to assess the effect of lidar point density on LAI retrieval. Reference LAI was quantified in 15 m radius sample plots (n = 46) using hemispherical photography. Akaike Information Criterion (AIC) regression was used to build candidate models that estimate LAI from lidar-derived height and density metrics. The correlations were investigated at different point densities, including the original (>6 points/m2) and reduced density levels (0.25–5 points/m2). Candidate models returned adjusted coefficient of determination (adj. R2) ranging between 0.65–0.83 (RMSE 7.0-10.0% of observed mean) depending on the number of predicting metrics included in the models. A model that had two non-collinear metrics was selected as a compromise model (adj. R2 = 0.67; RMSE = 9.7%); this model was comparable to the best model, which had many collinear metrics. Estimation accuracies were similar for lidar densities of the original, 2–5 points/m2 and less accurate for 0.25–1 point/m2. These findings demonstrated the capability of lidar in estimating observed LAI with low range and variation. The study also suggests the efficacy of moderate lidar point densities acquired at relatively low-cost surveys in attaining acceptable LAI estimation accuracy.

A semi-automated method to create a lidar-based hydro-flattened DEM

ABSTRACTThe response of water surfaces to light detection and ranging (lidar) pulses is unpredictable, which results in sparse lidar point density with varying intensity values. Due to the sparseness of the point cloud and lack of natural breaklines, lidar-derived digital elevation model (DEM) can produce unnatural surface over waterbodies. Such surfaces are not cartographically pleasing and can cause issues in the hydrologic and hydraulic modelling of a river. Hydro-flattening is the process of creating a lidar-derived DEM in which water surfaces appear and behave as they would in traditional topographic DEMs generated from photogrammetric digital terrain models. Hydro-flattened DEMs, created using lidar data, exclude the lidar points over waterbodies and include three-dimensional (3D) bank shorelines. In this article, a semi-automated method is presented for extracting bank shorelines for the purpose of creating lidar-derived hydro-flatten DEMs. Lidar point cloud and an approximate stream centreline are the primary data for this process. In the first step, a continuous bare ground surface (CBGS) is created by eliminating non-ground lidar points and by adding artificial underwater points. In the second step, the lowest elevation from the lidar point cloud within a radius distance from the river centreline is used to create a virtual water surface (VWS). This VWS is revised to consider water surface undulations such as ripples or waves, protruding underwater objects, etc. The revised VWS is then intersected with the CBGS to locate the two-dimensional (2D) bank shorelines. The 2D shorelines are assigned the elevations of the VWS and are used to produce a hydro-flattened DEM. The planimetric absolute mean separation of 0.94, 0.69, and 0.63 m for the three water surfaces is observed between the bank shoreline extracted using raw lidar points and a GPS (global positioning system) survey. The mean separation using vendor classified lidar points is 0.74, 0.67, and 0.64 m which are very similar to those using raw lidar.

Uncertainties in mapping forest carbon in urban ecosystems

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density – 5.8, 4.6, 2.3, and 1.2 pt s/m2, aerial optical NAIP (National Agricultural Imagery Program) imagery – 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7–11.0% of variation) than changing NAIP image resolution (causing 6.2–8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.

Impacts of LiDAR Sampling Methods and Point Spacing Density on DEM Generation

ABSTRACTLight Detection and Ranging (LiDAR) technology is gradually being adopted as the primary technique for surface structure derivation. There is a tendency for users of LiDAR data to want the highest density data possible, but such dense data sets increase overall costs including financial, processing, and storage without significant value added to the derived outputs, particularly in the case of digital elevation models (DEMs). In this study, we employed three sampling methods (systematic sampling, simple random sampling, and stratified random sampling) to select points from the original LiDAR data set that represented six different sparse densities. Using the sampled data sets, DEMs were generated and sensitivity analyses were conducted to explore whether an optimum LiDAR point sampling method and density could be identified. Despite the fact that significant differences in descriptive statistics were not observed for the three sampling strategies or point densities, there are evident differences among the derived DEMs, variograms, and viewsheds for very sparse samples (e.g., less than 2 percent of the original data set). In particular, the systematic and random sampling methods resulted in noticeable differences compared to the stratified random sampling approach. This research contributes to a better understanding of how LiDAR point sampling can be used to reduce data volume without compromising the final DEM quality.

Accuracy assessment of LiDAR-derived digital elevation models in a rural landscape with complex terrain

Digital elevation models (DEMs) are essential in many professional areas as they produce georeferenced elevation data that are critical for a wide range of studies, computations, decision-making processes, and derived products. Quality control thus becomes necessary to quantify the accuracy of the information provided. We assessed the accuracy of elevation data estimated by DEMs derived from LiDAR data representing diverse land cover types. For this purpose, we used the FUSION software and explored variations in accuracy in relation to the following factors: input data, interpolation methods, terrain slope, heterogeneity of land cover, and LiDAR point density. We selected and measured 1157 checkpoints by using total station and GPS techniques and following a stratified random design in order to validate the LiDAR-derived DEMs. We used robust estimators, nonparametric tests, and analysis of variance to examine the elevation errors. The study findings showed the following: (1) using the full set of LiDAR returns did not improve elevation accuracy relative to using the last-return data set; (2) using the minimum switch for interpolation did not improve accuracy compared to the default behavior of the interpolator; (3) land cover and slope significantly affected accuracy; (4) DEMs tended to underestimate elevation; and (5) the mean density of the returns classified as ground was significantly affected by land cover and slope factors.

Evaluating Lidar Point Densities for Effective Estimation of Aboveground Biomass

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m 2 , corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m 2 . Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m 2 , and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m 2 , our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.