High Spatial Resolution LiDAR Data Research Articles

Mission Planning for Low Altitude Aerial Drones during Water Sampling

Mission planning for small uncrewed aerial systems (sUAS) as a platform for remote sensors goes beyond the traditional issues of selecting a sensor, flying altitude/speed, spatial resolution, and the date/time of operation. Unlike purchasing or contracting imagery collections from traditional satellite or manned airborne systems, the sUAS operator must carefully select launching, landing, and flight paths that meet both the needs of the remote sensing collection and the regulatory requirements of federal, state, and local regulations. Mission planning for aerial drones must consider temporal and geographic changes in the environment, such as local weather conditions or changing tidal height. One key aspect of aerial drone missions is the visibility of the aircraft and communication with the aircraft. In this research, a visibility model for low-altitude aerial drone operations was designed using a GIS-based framework supported by high spatial resolution LiDAR data. In the example study, the geographic positions of the visibility of an aerial drone used for water sampling at low altitudes (e.g., 2 m above ground level) were modeled at different levels of tidal height. Using geospatial data for a test-case environment at the Winyah Bay estuarine environment in South Carolina, we demonstrate the utility, challenges, and solutions for determining the visibility of a very low-altitude aerial drone used in water sampling.

Canopy palm cover across the Brazilian Amazon forests mapped with airborneLiDARdata and deep learning

AbstractThe Amazon region in Brazil contains c. 5% of the palm species of the world. However, palm cover at macroecological scales has not yet been quantified in this biome. Here, we used high spatial resolution LiDAR data, acquired from 610 flightlines over the Brazilian Amazon, to map canopy palm cover for the first time using a deep learning approach. The image segmentation model from U‐Net deep learning was selected for mapping palm segments using the LiDAR canopy height model (CHM) at 0.5‐m spatial resolution. To train and validate the model, we manually delineated 6971 canopy palm segments over 931.43 ha of forests on four training sites by inspecting their unique star‐shaped crown architecture in the CHM. The results indicated an accuracy of 80% to automatically map canopy palm area. The approach detected >1.1 million palm segments over the 480 000 ha sampled by LiDAR and roughly estimated 1.05 billion palm segments for the Brazilian Amazon. Palm cover was not evenly distributed over the Amazon, revealing undocumented hotspots of high cover (>5%) in eastern Amazon (Pará state), and confirming documented hotspots in southwest (Acre state) and north of the region (Roraima state). Palm segment height was strongly and positively correlated with forest height, where palm segments showed overall lower height. A higher canopy palm cover was observed over shorter forests, while the opposite was found over taller forests, where palms may not be visible from the canopy. Palm segments occurred more frequently at valleys but they were also observed in other landscapes, depending on site location and forest height. Our findings highlight the disproportional occurrence of palm cover in some Amazonian canopies. This fact should be taken into account to improve regional carbon cycle representation and promote initiatives of biodiversity conservation and bioeconomic use of these forests.

Hydrogeomorphological analysis for hydraulic public domain definition: case study in Carrión River (Palencia, Spain)

Fluvial spaces have a marked reduction at a global level because of anthropic processes, which have generated an increase in elements exposed to flooding. These spaces include natural flow courses and flood-prone lands. In Spain, natural channels are defined as Hydraulic Public Domain assets, whose delimitation is essential for their protection and flood risk management. However, demarcation of these areas with independent hydrological-hydraulic approaches has generated underestimates in the extension of water-covered lands, thus in recent years it has become clear that there is a need to integrate other variables, such as geology and fluvial geomorphology, which allow comprehension of river dynamics from processes that occurred in the past. Therefore, this study aims to contribute to a better understanding in the definition of areas belonging to natural channels through the development of a detailed hydrogeomorphological-historical method applied to Carrion river (Palencia, Spain). For this purpose, Digital Terrain Models were generated from high spatial resolution LIDAR data, on which erosive and sedimentary forms generated by water circulation were delimited, whose process was also supported in ephemeral and topographic evidences collected in field. With this input, depending on dynamic characteristics and river land vegetation, limits of the Hydraulic Public Domain were drawn. The products obtained are made up as optimized elements both for management of natural courses and for delimitation and zoning of new flooding areas by incorporating hydrogeomorphological criteria.

High-Precision Land-Cover-Land-Use GIS Mapping and Land Availability and Suitability Analysis for Grass Biomass Production in the Aroostook River Valley, Maine, USA

High-precision land-cover-land-use GIS mapping was performed in four major townships in Maine’s Aroostook River Valley, using on-screen digitization and direct interpretation of very high spatial resolution satellite multispectral imagery (15–60 cm) and high spatial resolution LiDAR data (2 m) and the field mapping method. The project not only provides the first-ever high-precision land-use maps for northern Maine, but it also yields accurate hectarage estimates of different land-use types, in particular grassland, defined as fallow land, pasture, and hay field. This enables analysis of potential land availability and suitability for grass biomass production and other sustainable land uses. The results show that the total area of fallow land in the four towns is 7594 hectares, which accounts for 25% of total open land, and that fallow plots equal to or over four hectares in size total 4870, or 16% of open land. Union overlay analysis, using the Natural Resources Conservation Service (NRCS) soil data, indicates that only a very small percentage of grassland (4.9%) is on “poorly-drained” or “very-poorly-drained” soils, and that most grassland (85%) falls into the “farmland of state importance” or “prime farmland” categories, as determined by NRCS. It is concluded that Maine’s Aroostook River Valley has an ample base of suitable, underutilized land for producing grass biomass.

Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation1

Greenberg, Jonathan Asher, Erin L. Hestir, David Riano, George J. Scheer, and Susan L. Ustin, 2012. Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation. Journal of the American Water Resources Association (JAWRA) 48(5): 939‐948. DOI: 10.1111/j.1752‐1688.2012.00664.xAbstract: Riparian vegetation provides shade from insolation to stream channels. A consequence of removing vegetation may be an increase in insolation that can increase water temperatures and negatively impact ecosystem health. Although the mechanisms of riparian shading are well understood, spatially explicit, mechanistic models of shading have been limited by the data requirements of precisely describing the three‐dimensional structure of a riparian corridor. Remotely acquired, high spatial resolution LiDAR data provide detailed three‐dimensional vegetation structure and terrain topography over large regions. By parameterizing solar radiation models that incorporate terrain shadowing with LiDAR data, we can produce spatially explicit estimates of insolation. As a case study, we modeled the relative change in insolation on channels in the Sacramento‐San Joaquin River Delta under current conditions and under a hypothesized deforested Delta using classified LiDAR, rasterized at a 1‐m resolution. Our results suggest that the removal of levee vegetation could result in a 9% increase in solar radiation incident on Delta waters, and may lead to water temperature increases. General, coarse‐scale channel characteristics (reach width, azimuth, levee vegetation cover, and height) only accounted for 72% of the variation in the insolation. This indicates that the detailed information derived from LiDAR data has greater explanatory power than coarser reach‐scale metrics often used for insolation estimates.

Quantifying sand storage capacity of large woody debris on beaches using LiDAR

The sedimentological role of large woody debris (LWD) on beaches is understudied and is relevant for the morphodynamics of sandy, high-energy beach–dune systems of the northeast Pacific Ocean. On the west coast of Canada, this debris consists largely of historical escape logs from the coastal logging industry. In areas with competent wind regimes, LWD can trap appreciable amounts of windblown sand in the backshore, which can alter beach–foredune sediment budgets and initiate incipient dune formation. As this additional store of sediment must be reworked first during high water events, it provides an important buffer that reduces erosion of established foredunes. This study examines the morphology and sand storage capacity of three backshore LWD deposits of varying morphologies on northeastern Graham Island, Haida Gwaii (Queen Charlotte Islands) British Columbia, Canada. A new method was developed using coincident high spatial resolution LiDAR data and digital orthophotographs to derive DEMs for distinct ground cover classes (sand, LWD). These DEMs were then used to quantify relative storage capacities of LWD and sand in the backshore. Significant amounts of sand are stored within and around LWD on beaches in the study region. Existing storage quantities (above HHWMT) range from 9.19 × 10 4 to 1.39 × 10 5 kg m − 1 beach width or ∼ 1.14 to 1.60 m storage depth. The same LWD deposits have a further potential storage capacity ranging from 1.04 to 1.70 × 10 4 kg m − 1 beach width or ∼ 0.21 to 0.28 m depth. The relative storage capacity of these features is reflected in the backshore morphology of each site, with sediment transport further into the backshore dependent upon the morphology and relative in-filling of the log debris jam. With this additional sediment storage, log debris could enhance development of large incipient dunes in the backshore thereby buffering against increasing storminess and gradual sea-level rise in the region. As the use of precise LiDAR data is increasing in geomorphic research, the development of a verifiable methodology for estimating beach–dune storage volumes is timely.