Use and categorization of Light Detection and Ranging vegetation metrics in avian diversity and species distribution research

Artificial light at night is a top predictor of bird migration stopover density

As billions of nocturnal avian migrants traverse North America, twice a year they must contend with landscape changes driven by natural and anthropogenic forces, including the rapid growth of the artificial glow of the night sky. While airspaces facilitate migrant passage, terrestrial landscapes serve as essential areas to restore energy reserves and often act as refugia-making it critical to holistically identify stopover locations and understand drivers of use. Here, we leverage over 10 million remote sensing observations to develop seasonal contiguous United States layers of bird migrant stopover density. In over 70% of our models, we identify skyglow as a highly influential and consistently positive predictor of bird migration stopover density across the United States. This finding points to the potential of an expanding threat to avian migrants: peri-urban illuminated areas may act as ecological traps at macroscales that increase the mortality of birds during migration.

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R² = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25-65cm) and segmented (25-40cm, 40-50cm and 50-65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R² from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.

Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey

The third Dutch national airborne laser scanning flight campaign (AHN3, Actueel Hoogtebestand Nederland) conducted between 2014 and 2019 during the leaf-off season (October-April) across the whole Netherlands provides a free and open-access, country-wide dataset with ∼700 billion points and a point density of ∼10(-20) points/m². The AHN3 point cloud was obtained with Light Detection And Ranging (LiDAR) technology and contains for each point the x, y, z coordinates and additional characteristics (e.g. return number, intensity value, scan angle rank and GPS time). Moreover, the point cloud has been pre-processed by 'Rijkswaterstraat' (the executive agency of the Dutch Ministry of Infrastructure and Water Management), comes with a Digital Terrain Model (DTM) and a Digital Surface Model (DSM), and is delivered with a pre-classification of each point into one of six classes (0: Never Classified, 1: Unclassified, 2: Ground, 6: Building, 9: Water, 26: Reserved [bridges etc.]). However, no detailed information on vegetation structure is available from the AHN3 point cloud. We processed the AHN3 point cloud (∼16 TB uncompressed data volume) into 10 m resolution raster layers of ecosystem structure at a national extent, using a novel high-throughput workflow called 'Laserfarm' and a cluster of virtual machines with fast central processing units, high memory nodes and associated big data storage for managing the large amount of files. The raster layers (available as GeoTIFF files) capture 25 LiDAR metrics of vegetation structure, including ecosystem height (e.g. 95^th percentiles of normalized z), ecosystem cover (e.g. pulse penetration ratio, canopy cover, and density of vegetation points within defined height layers), and ecosystem structural complexity (e.g. skewness and variability of vertical vegetation point distribution). The raster layers make use of the Dutch projected coordinate system (EPSG:28992 Amersfoort / RD New), are each ∼1 GB in size, and can be readily used by ecologists in a geographic information system (GIS) or analytical open-source software such as R and Python. Even though the class '1: Unclassified' mainly includes vegetation points, other objects such as cars, fences, and boats can also be present in this class, introducing potential biases in the derived data products. We therefore validated the raster layers of ecosystem structure using >180,000 hand-labelled LiDAR points in 100 randomly selected sample plots (10 m × 10 m each) across the Netherlands. Besides vegetation, objects such as boats, fences, and cars were identified in the sampled plots. However, the misclassification rate of vegetation points (i.e. non-vegetation points that were assumed to be vegetation) was low (∼0.05) and the accuracy of the 25 LiDAR metrics derived from the AHN3 point cloud was high (∼90%). To minimize existing inaccuracies in this country-wide data product (e.g. ships on water bodies, chimneys on roofs, or cars on roads that might be incorrectly used as vegetation points), we provide an additional mask that captures water bodies, buildings and roads generated from the Dutch cadaster dataset. This newly generated country-wide ecosystem structure data product provides new opportunities for ecology and biodiversity science, e.g. for mapping the 3D vegetation structure of a variety of ecosystems or for modelling biodiversity, species distributions, abundance and ecological niches of animals and their habitats.

An overview of remote monitoring methods in biodiversity conservation

Conservation of biodiversity is critical for the coexistence of humans and the sustenance of other living organisms within the ecosystem. Identification and prioritization of specific regions to be conserved are impossible without proper information about the sites. Advanced monitoring agencies like the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) had accredited that the sum total of species that are now threatened with extinction is higher than ever before in the past and are progressing toward extinct at an alarming rate. Besides this, the conceptualized global responses to these crises are still inadequate and entail drastic changes. Therefore, more sophisticated monitoring and conservation techniques are required which can simultaneously cover a larger surface area within a stipulated time frame and gather a large pool of data. Hence, this study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring is highlighted. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.

UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV-borne and airborne imagery to those derived from low- (1–2 pts/m2) and high-density (ca. 20 pts/m2) ALS-scanning for the precise local-scale modelling of woody vegetation structures (the number and height of trees/shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100% for UAV-based models; however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV-based models compared to 95.8% for the high-density ALS model. The percentage mean absolute errors (%MAE) of shrub/tree heights derived from UAV data ranged between 12.2 and 23.7%, and AImg height accuracy was relatively lower (%MAE: 21.4–47.4). Combining UAV-borne or AImg-based digital surface models (DSM) with ALS-based digital terrain models (DTMs) significantly improved the nDSM height accuracy (%MAE: 9.4–13.5 and 12.2–25.0, respectively) but failed to significantly improve the detection of the number of individual shrubs/trees. The height accuracy and detection success using low- or high-density ALS did not differ. Therefore, we conclude that UAV-borne imagery has the potential to replace custom ALS in specific local-scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS-based digital terrain models can further improve the success of the use of such data.

A multidisciplinary approach to locating clandestine gravesites in cold cases: Combining geographic profiling, LiDAR, and near surface geophysics

By nature, clandestine burials are difficult to locate, an issue that can complicate the legal process, and interrupt the natural grief process of the family. The purpose of this paper is to present a three-step process to search for clandestine graves using (1) geographic profiling, (2) light detection and ranging (LiDAR), and (3) near surface geophysics. Each process incrementally decreases the geographic area being searched, while increasing the level of detail provided to investigators. Using two well-known Australian cases and one experimental study, this paper will demonstrate how (1) can highlight potential search areas, (2) can further narrow down the location of potential burial sites within these search areas, and (3) can assist with locating the clandestine grave. Although each technique on its own can successfully locate graves, combining the techniques can provide the most efficient approach to locate those who are missing and buried.

Detectability of the Critically Endangered Araucaria angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR

The Brazilian Atlantic Forest is a global biodiversity hotspot and has been extensively mapped using satellite remote sensing. However, past mapping focused on overall forest cover without consideration of keystone plant resources such as Araucaria angustifolia. A. angustifolia is a critically endangered coniferous tree that is essential for supporting overall biodiversity in the Atlantic Forest. A. angustifolia’s distribution has declined dramatically because of overexploitation and land-use changes. Accurate detection and rapid assessments of the distribution and abundance of this species are urgently needed. We compared two approaches for mapping Araucaria angustifolia across two scales (stand vs. individual tree) at three study sites in Brazil. The first approach used Worldview-2 images and Random Forest in Google Earth Engine to detect A. angustifolia at the stand level, with an accuracy of >90% across all three study sites. The second approach relied on object identification using UAV-LiDAR and successfully mapped individual trees (producer’s/user’s accuracy = 94%/64%) at one study site. Both approaches can be employed in tandem to map remaining stands and to determine the exact location of A. angustifolia trees. Each approach has its own strengths and weaknesses, and we discuss their adoptability by managers to inform conservation of A. angustifolia.

Disentangling LiDAR Contribution in Modelling Species–Habitat Structure Relationships in Terrestrial Ecosystems Worldwide. A Systematic Review and Future Directions

Global biodiversity is threatened by unprecedented and increasing anthropogenic pressures, including habitat loss and fragmentation. LiDAR can become a decisive technology by providing accurate information about the linkages between biodiversity and ecosystem structure. Here, we review the current use of LiDAR metrics in ecological studies regarding birds, mammals, reptiles, amphibians, invertebrates, bryophytes, lichens, and fungi (BLF). We quantify the types of research (ecosystem and LiDAR sources) and describe the LiDAR platforms and data that are currently available. We also categorize and harmonize LiDAR metrics into five LiDAR morphological traits (canopy cover, height and vertical distribution, understory and shrubland, and topographic traits) and quantify their current use and effectiveness across taxonomic groups and ecosystems. The literature review returned 173 papers that met our criteria. Europe and North America held most of the studies, and birds were the most studied group, whereas temperate forest was by far the most represented ecosystem. Globally, canopy height was the most used LiDAR trait, especially in forest ecosystems, whereas canopy cover and terrain topography traits performed better in those ecosystems where they were mapped. Understory structure and shrubland traits together with terrain topography showed high effectiveness for less studied groups such as BLF and invertebrates and in open landscapes. Our results show how LiDAR technology has greatly contributed to habitat mapping, including organisms poorly studied until recently, such as BLF. Finally, we discuss the forthcoming opportunities for biodiversity mapping with different LiDAR platforms in combination with spectral information. We advocate (i) for the integration of spaceborne LiDAR data with the already available airborne (airplane, drones) and terrestrial technology, and (ii) the coupling of it with multispectral/hyperspectral information, which will allow for the exploration and analyses of new species and ecosystems.

Coupling urban 3-D information and circuit theory to advance the development of urban ecological networks

Ongoing, rapid urban growth accompanied by habitat fragmentation and loss challenges biodiversity conservation and leads to decreases in ecosystem services. Application of the concept of ecological networks in the preservation and restoration of connections among isolated patches of natural areas is a powerful conservation strategy. However, previous approaches often failed to objectively consider the impacts of complex 3-D city environments on ecological niches. We used airborne lidar-derived information on the 3-D structure of the built environment and vegetation and detailed land use and cover data to characterize habitat quality, niche diversity, and human disturbance and to predict habitat connectivity among 38 identified habitat core areas (HCAs) in Nanjing, China. We used circuit theory and Linkage Mapper to create a landscape resistance layer, simulate habitat connectivity, and identify and prioritize important corridors. We mapped 64 links by using current flow centrality to evaluate each HCA's contribution and the links that facilitate intact connectivity. Values were highest for HCA links located in the west, south, and northeast of the study area, where natural forests with complex 3-D structures predominate. Two smaller HCA areas had high centrality scores relative to their extents, which means they could act as important stepping stones in connectivity planning. The mapped pinch-point regions had narrow and fragile links among the HCAs, suggesting they require special protection. The barriers with the highest impact scores were mainly located at the HCA connections to Purple Mountain and, based on these high scores, are more likely to indicate important locations that can be restored to improve potential connections. Our novel framework allowed us to sufficiently convey spatially explicit information to identify targets for habitat restoration and potential pathways for species movement and dispersal. Such information is critical for assessing existing or potential habitats and corridors and developing strategic plans to balance habitat conservation and other land uses based on scientifically informed connectivity planning and implementation.

Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain

As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) employs a micro-pulse multi-beam photon counting approach to produce photon data for measuring global terrain. Few studies have assessed the accuracy of different ATLAS channels in retrieving ground topography in forested terrain. This study aims to assess the accuracy of measuring ground topography in forested terrain using different ATLAS channels and the correlation between laser intensity parameters, laser pointing angle parameters, and elevation error. The accuracy of ground topography measured by the ATLAS footprints is evaluated by comparing the derived Digital Terrain Model (DTM) from the ATL03 (Global Geolocated Photon Data) and ATL08 (Land and Vegetation Height) products with that from the airborne Light Detection And Ranging (LiDAR). Results show that the ATLAS product performed well in the study area at all laser intensities and laser pointing angles, and correlations were found between the ATLAS DTM and airborne LiDAR DTM (coefficient of determination––R2 = 1.00, root mean squared error––RMSE = 0.75 m). Considering different laser intensities, there is a significant correlation between the tx_pulse_energy parameter and elevation error. With different laser pointing angles, there is no significant correlation between the tx_pulse_skew_est, tx_pulse_width_lower, tx_pulse_width_upper parameters and the elevation error.

Standardizing Ecosystem Morphological Traits from 3D Information Sources

3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits - height, cover, and structural complexity - that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources - satellites, aircrafts, drones, or ground data - allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported.