Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density

Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.

Modelling vertical error in LiDAR-derived digital elevation models

The National Ecological Observatory Network (NEON) is the first-ever continental-scale ecological observatory and is designed to collect and provide open data to the scientific community for the next 30 years. NEON is at a very exciting moment in its history – after almost two decades of design and planning by committed and visionary scientists and engineers, the construction of 47 terrestrial and 34 aquatic field sites is nearly complete. At this moment, nearly 300 field scientists are out in deserts, grasslands, forests, and streams taking measurements that will turn into over 180, freely available, NEON data products. The collection and delivery of these data are designed to enable transformative research on US ecosystems at previously unimaginable spatial and temporal scales. Most ecologists, including myself, entered this field because of a fascination with the causes and complexities of ecological interactions. I began my career studying small mammals, plant reproductive strategies, and bird foraging behavior. I was thrilled to be in the field, recording the patterns of nature near my undergraduate institution in the rolling tallgrass prairies of eastern Kansas. My observations made their way from pencil to paper to high stacks of punch cards sent through large, noisy computers. Fast forward 30 years, and we now have access to large datasets and complex statistical models to synthesize ecological observations in the hope of achieving broad-scale understanding and ecological forecasts. NEON is an NSF-funded large research infrastructure project designed to provide a rich set of resources to scientists. As such, it is vital that members of the scientific community stay engaged throughout the project's lifetime. In the years since NEON was originally imagined, the perception of how closely ecologists have been involved has fluctuated, with some voicing the opinion that NEON was moving forward without sufficient scholarly input. My top priority, as NEON's new Director and Chief Scientist, is to improve and strengthen NEON engagement with the scientific community. To begin, we solicited feedback to inform a strategic plan to guide our engagement activities, not just at headquarters but across all our field sites. Recent feedback demonstrated strong interest in using NEON's data and infrastructure but also some important concerns that need to be addressed. For such a large public investment, I believe that it is essential to show the unique value of the NEON project. For example, rather than being redundant or competitive with other funded programs, NEON provides complementary and comprehensive data from the soils to the atmosphere from an extensive suite of field sites. The data products are publicly available and can be used in conjunction with additional observations or with ongoing or newly proposed experiments at those sites. These resources provide tremendous added value to our research capabilities for addressing novel and societally relevant questions. Feedback from the community has also revealed that many potential users have not searched through our currently available data products: indeed, 38% of respondents to a recent survey said that they did not know if NEON resources are easy to access or whether they are of high quality. In my short time at NEON, I have been impressed with the rigor of the quality assurance and quality control processes in place, starting from when data collection is planned, through implementation of detailed collection protocols, to processing and delivery. All along the path from field to portal, the data are validated and checked to ensure high quality for the user community. In response to feedback, we are actively developing detailed information to make those procedures more transparent to all users. I am excited to be in my new position because I am passionate about ecology, and I believe that NEON is a vast resource for the scientific community. The observatory will facilitate research and education by our community (and beyond) in ways that we have not yet imagined and will propel our knowledge forward. I envision NEON as a hub of innovative research synthesis on ecological responses to environmental change, and as a nucleus of ecological science for the next generation of ecologists. To repeat an often-cited phrase, whatever you thought about NEON, think again. This is a tremendous opportunity for the ecological community, and I invite you to join us at this exciting time. Visit the NEON website (www.neonscience.org) to learn more about ways you can engage and provide your feedback. We want to hear from you and support your exciting work…this is your observatory! SHARON K COLLINGE Chief Scientist/Observatory Director, National Ecological Observatory Network, Boulder, CO

Abstract. Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

Light detection and ranging (LiDAR) is an active remote sensing technology that can be used to directly acquire three-dimensional forest information, and it has great potential to replace traditional time-consuming field surveys. We aimed to determine the accuracy with which enhanced airborne LiDAR with high density (mean, 47 points m−2) can replicate detailed vertical foliage arrangement and three forest structural indices, i.e. number of layers (NSTRAT), sum of the percent vegetation cover (PCVC), and foliage height diversity (FHD), all of which are useful for characterizing avian habitats. Thirteen 40-m-long lines were set in an urban forest area, and LiDAR-based and field-based cross-sectional profiles with 1 × 1-m meshes were compared. In addition, we performed random LiDAR point thinning to determine whether the replications were possible with lower point densities. The 1 × 1-m mesh vertical foliage arrangements were replicated with high accuracy by using LiDAR (mean producer's accuracy, 0.756; mean user's accuracy, 0.810) before the point reduction. All the three indices were also replicated with high accuracy (<0.12 of the relative root mean square error for the 1:1 relationship). Even when the mean point density was reduced to 2.4 points m−2, the three indices could be quantitatively estimated with high accuracy by using simple linear regression analyses (>0.8 in R 2). For NSTRAT and FHD, numerical replication was possible with high accuracy (<0.2 and <0.1, respectively, of the relative root mean square error for the 1:1 relationship), even when the mean point density was reduced to 2.4 points m−2. In conclusion, enhanced high-density LiDAR seems to be highly suitable as a substitute for field surveys for delineating detailed vertical forest structure in order to characterize forest avian habitats. Moreover, lower-density LiDAR data could replicate the forest structural indices, suggesting its useful low-cost application in quantitative avian habitat models.

The variation in point cloud density is driven by many different factors. This variation is expected to affect the quality of the information extracted from the point clouds, however, the extent to which these variations impact the ability to accurately extract and assess geometric features of highways from point cloud data are unknown. This paper investigates the impacts of point density reduction on the extraction and assessment of four critical geometric features. The density of light detection and ranging (LiDAR) data was first reduced and the different features were extracted at varying levels of point density and on a selection of different highway segments in Alberta, Canada. The information obtained at lower point density was then compared to what was obtained at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e., reducing the point density to as low as 10% of the original data (30 ppm2 on the pavement surface) yielded results comparable to what was obtained at 100% density (300 ppm2) In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed. These findings are critical for transportation agencies considering the adoption of LiDAR technology to manage elements of their infrastructure and for researchers developing data processing tools and algorithms for the semantic segmentation of transportation features from remotely sensed point clouds.

The point density is a preeminent parameter on airborne laser scanner surveys. It is not only related to accuracy but costs and savings. The lack of uniformity of the point density across the survey is well-known in the scientific community. This paper analyzes the behaviour of the point density derived by an oscillating mirror laser scanner on different single strips on flat bare ground in order to estimate a meaningful mean density value. The variation of the point density at both extreme ends of the oscillating mirror scan is meaningful. It will be demonstrated that excluding the extreme sectors across the strip corresponding to 1/8 of the swath width (12.5% of the sampling area, half in each side) for the computation of the mean density value is enough to satisfy light detection and ranging (LiDAR) specifications for national level surveys.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

AimRapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).LocationEastern USA.Time period2017–2018.Taxa studiedTrees.MethodsWith a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.ResultsModels using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.Main conclusionsWe found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

The National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO2. EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities.

We developed an empirical model to estimate aboveground carbon density with variables derived from airborne Light Detection and Ranging (LiDAR) in tropical seasonal forests in Cambodia, and assessed the effects of LiDAR pulse density on the accurate estimation of aboveground carbon density. First, we tested the applicability of variables used for estimating aboveground carbon density with the original LiDAR pulse density data (26 pulse m−2). Aboveground carbon density was regressed against variables derived from airborne LiDAR. Three individual height variable models were developed along with a canopy density model, and three other models combined canopy height and canopy density variables. The influence of forest type on model accuracy was also assessed. Next, the relationship between pulse density and estimation accuracy was investigated using the best regression model. The accuracy of the models were compared based on seven LiDAR point densities consisting of 0.25, 1, 2, 3, 4, 5 and 10 pulse m−2. The best model was obtained using the single mean canopy height (MCH) model (R2 = 0.92) with the original pulse density data. The relationship between MCH and aboveground carbon density was found to be consistent under different forest types. The differences between predicted and measured residual mean of squares of deviations were less than 1.5 Mg C ha−1 between each pulse density. We concluded that aboveground carbon density can be estimated using MCH derived from airborne LiDAR in tropical seasonal forests in Cambodia even with a low pulse density of 0.25 pulse m−2 without stratifying the study area based on forest type.

Soil microbial communities play critical roles in various ecosystem processes, but studies at a large spatial and temporal scale have been challenging due to the difficulty in finding the relevant samples in available data sets as well as the lack of standardization in sample collection and processing. The National Ecological Observatory Network (NEON) has been collecting soil microbial community data multiple times per year for 47 terrestrial sites in 20 eco‐climatic domains, producing one of the most extensive standardized sampling efforts for soil microbial biodiversity to date. Here, we introduce the neonMicrobe R package—a suite of downloading, preprocessing, data set assembly, and sensitivity analysis tools for NEON’s newly published 16S and ITS amplicon sequencing data products which characterize soil bacterial and fungal communities, respectively. neonMicrobe is designed to make these data more accessible to ecologists without assuming prior experience with bioinformatic pipelines. We describe quality control steps used to remove quality‐flagged samples, report on sensitivity analyses used to determine appropriate quality filtering parameters for the DADA2 workflow, and demonstrate the immediate usability of the output data by conducting standard analyses of soil microbial diversity. The sequence abundance tables produced by neonMicrobe can be linked to NEON’s other data products (e.g., soil physical and chemical properties, plant community composition) and soil subsamples archived in the NEON Biorepository. We provide recommendations for incorporating neonMicrobe into reproducible scientific workflows, discuss technical considerations for large‐scale amplicon sequence analysis, and outline future directions for NEON‐enabled microbial ecology. In particular, we believe that NEON marker gene sequence data will allow researchers to answer outstanding questions about the spatial and temporal dynamics of soil microbial communities while explicitly accounting for scale dependence. We expect that the data produced by NEON and the neonMicrobe R package will act as a valuable ecological baseline to inform and contextualize future experimental and modeling endeavors.

Topographical technology by Airborne LIDAR (Light Detection and Ranging) generates a precise points cloud with a density of several points per square meter, LIDAR data processing is a crucial step to be used. Extraction of 3D information in automatic way and especially in urban areas from LIDAR data is one of the most difficult problems in computer vision; it is also a necessary step for implementation of several applications that require a high level interpretation of LASER data. Therefore, there is recently an increased interest in this research field and a vast literature. The problematic discussed in this article lies in the differentiation between the sets of points that represent a specified layer of information (construction, vegetation, roads, lines, etc.). This step is called segmentation. The aim of this study is to provide a set of automatic segmentation techniques tailored to different types of 3D data and proposes a methodology to classify LIDAR data with a maximum degree of automaticity using only point cloud data.

Airborne Light Detection and Ranging (LiDAR) topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and landscape classification, 3D urban modeling, river bank management, agricultural crop studies, etc. In this paper, we focus mainly on the use of LiDAR data in terrain modeling/Digital Elevation Model (DEM) generation. Technological advancements in building LiDAR sensors have enabled highly accurate and highly dense LiDAR point clouds, which have made possible high resolution modeling of terrain surfaces. However, high density data result in massive data volumes, which pose computing issues. Computational time required for dissemination, processing and storage of these data is directly proportional to the volume of the data. We describe a novel technique based on the slope map of the terrain, which addresses the challenging problem in the area of spatial data analysis, of reducing this dense LiDAR data without sacrificing its accuracy. To the best of our knowledge, this is the first ever landscape-driven data reduction algorithm. We also perform an empirical study, which shows that there is no significant loss in accuracy for the DEM generated from a 52% reduced LiDAR dataset generated by our algorithm, compared to the DEM generated from an original, complete LiDAR dataset. For the accuracy of our statistical analysis, we perform Root Mean Square Error (RMSE) comparing all of the grid points of the original DEM to the DEM generated by reduced data, instead of comparing a few random control points. Besides, our multi-core data reduction algorithm is highly scalable. We also describe a modified parallel Inverse Distance Weighted (IDW) spatial interpolation method and show that the DEMs it generates are time-efficient and have better accuracy than the one’s generated by the traditional IDW method.

Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion