The use of drones has become standard practice in the geospatial field. Come learn from experts on the capabilities of modern UAS sensors, including calibration, advanced processing approaches, new regulations, and real-world applications.
1:00 PM – 1:15 PM – UAS-Derived Hyperspectral & Lidar Data Metrics for Accurate Estimation of Sugarcane Yields
The goal of sugarcane producers is to maximize yields, while minimizing the cost of production, thereby insuring a profitable venture. This goal, while straight-forward for annual crops, is much more complicated for perennial crops that are harvested over several years. Sugarcane yield (kg sugar/ha) is determined by multiplying two different yield components, the cane yield (Mg stalks/ha) and the sugar yield of the stalks, which is expressed as the theoretically recoverable sugar (TRS) (kg sugar/Mg stalks). These two different yield components depend on a wide variety of factors including crop age, variety, soil type, soil fertility, water availability, pest and diseases and climate factors. In addition, sugarcane is harvested over a time frame ranging from 3-5 months in Louisiana, USA and the yield variation can be significant over this extended time frame. These combined factors increase the grower’s uncertainty as to the potential yield of a given field. The main objective of this research project is to develop a new methodology for accurately estimating sugarcane yields prior to harvest using emerging sUAS technology with hyperspectral and LiDAR sensors. If accurate yield estimates were available for the two yield components of interest (cane and sugar yields) the growers could develop harvest schedules that could maximize yields and production efficiency. Currently available technology has only attempted to estimate one of the yield components (cane yield). It is the goal in this project to utilize LiDAR structural data to estimate cane yield and hyperspectral data to estimate sugar yields, thereby supplying sugarcane growers with the complete yield picture. A Resonon Pika-L pushbroom hyperspectral sensor with spectral range of 400nm -1000nm and spectral resolution of 2.1nm mounted on a DJI M600 drone and Riegl VUX-1UAV LiDAR sensor on Microdrone 3000LR drone was employed in this study. The site for the study was located on a commercial sugarcane farm in Paincourtville, LA, USA. The hyperspectral radiance data is converted to reflectance values using the in-situ calibration tarp (12% and 24% reflectivity) near the mission area. Since the employed sUAS is flown at very low altitudes (60 to 120 m) the atmospheric effects (scattering and absorption) will be negligible and should be very close to surface reflectance. Most of the analysis was performed on the derived reflectance image and georectified using GPS/IMU data from the airframe to register the pixels to real world coordinates. Similarly, multiple LiDAR echo returns were processed to obtain dense point clouds that is capable of penetrating canopy covers. Yield estimates were obtained by harvesting selected rows from the field in 30-m sections utilizing a chopper harvester and field wagon equipped with load cells. Sugarcane stalk samples collected during harvest were used to determine TRS at the USDA juice quality lab. Several vegetation indices were derived from the hyperspectral image data cube (280 bands). We found through analysis that the Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) and Vogelmann Red Edge Index (VERI) are the two most appropriate indices to model sucrose accumulation in the plant as evident by the yellowing of the leaves (ripening of the stalks) due to the diminishing chlorophyll content from the leaf to the plant. MCARI2 and VERI was then correlated with field measurements of both cane and sugar yields. The indices were also compared to estimates obtained from a yield monitor mounted on the chopper harvester. A regression analysis of the MCARI2 and VERI indices with sugar yields gives a R value of 0.71 and 0.83 respectively. LiDAR point clouds were successfully classified to model the bare earth, transition zone from stalk to leaves and top of the sugarcane. We are able to accurately predict the crop heights and plant density from classified LiDAR point cloud. A regression analysis of LiDAR point cloud with cane yield gives a r2 value of 0.89. The results from these evaluations show that fusing hyperspectral and LiDAR data have potential as an alternative to estimate sugarcane yields prior to harvest; however, additional research is needed to refine yield estimate procedures and increase the area covered by each flight.
Balaji Ramachandran, Nicholls State University
1:15 PM – 1:30 PM – Transforming SCAT with UAS: Oil Spill Response in Marsh Environments
The Shoreline Cleanup Assessment Technique (SCAT) was created as a systematic way to survey shorelines affected by spilled oil. SCAT begins in the early stages of a response to assess initial shoreline conditions and continues throughout cleanup activities until final signoff. Traditionally, SCAT teams composed of representatives from agencies and organizations involved in the response are sent to survey shorelines by boat or on foot. Not all shorelines are easily accessible by these methods. For example, sensitive marsh environments are difficult to impossible to walk on and shallow water and tidal flats often prevent boats from getting close enough to the marsh edge to see beyond a few feet.
New methods for conducting SCAT surveys were employed during a recent oil spill response to a wellhead blowout in the marshes of Louisiana’s Pass A Loutre State Wildlife Management Area. After initial overflights were conducted from traditional aircraft to define the incident impact area, multiple UAS missions were flown over the site to collect mosaic imagery of all the shorelines. In just fourteen hours over two days, imagery mosaics were generated that covered 1800 acres of marsh complex. The information collected from these mosaics was used to inform Incident Command and Trustees (USCG, State (LOSCO, LADEQ, LADNR), USFWS, and NOAA) and to direct response operations. Additionally, SCAT isn’t typically concerned with oil on water, but the UAS mosaics successfully captured floating oil, streamers, and sheens.
Due to the success of the initial UAS missions, it was decided that the SCAT process would be conducted in the same manner, by collecting UAS imagery over the impacted shorelines. The imagery was processed into mosaics using Pix4Dreact while still in the field collecting imagery. In some cases, using a satellite Internet connection, the mosaics were uploaded and sent to the command post before leaving the field. Trained SCAT teams reviewed the imagery and characterized any oil present, much in the same way as they would using a paper form in the field, although this time it was done by editing an Esri Enterprise feature service while viewing the UAS mosaics in ArcGIS Pro. In addition to rapid imagery collection, this method reduced further marsh damage by eliminating the need for foot surveys and vessels accessing the sensitive marsh habitat.
Jennifer Horsman, RPI
1:30 PM – 1:45 PM – Changing Waters: A GIS approach to Dam Breach Mitigation and Management
Dams play a critical role in supporting economic growth, ensuring water security, and enhancing climate resilience. However, many of the nation’s dams are aging and in need of modernization. More than 75% of U.S. dams are over 50 years old, with the majority privately owned—particularly smaller, earthen, or agricultural dams. Over 15,000 dams are classified as high-hazard potential, yet many still lack Emergency Action Plans (EAPs). A cornerstone of dam safety is the ability to respond rapidly to failures or imminent threats with adequate resources to protect life and property. This underscores the urgent need for accurate dam breach modeling. This presentation highlights case studies from South Carolina and demonstrates a scalable workflow that integrates hydrologic and hydraulic (H&H) modeling with GIS-based analysis. We simulated flood extents, mapped inundation zones, and assessed at-risk infrastructure to support emergency planning and update flood risk zones. Designed for integration with web-based GIS platforms, this workflow offers a practical, cost-effective, and resource-efficient approach to leveraging geospatial technology for emergency preparedness, disaster response, infrastructure resilience, and sustainable development.
Avantika Ramekar, Prairie Engineers
1:45 PM – 2:00 PM – The Full Picture: Surface & Subsurface Mapping with UAS-Based Magnetometry and Photogrammetry in Geologic Exploration
Geologic exploration increasingly depends on integrating surface and subsurface data to form a complete understanding of the terrain and underlying structures. This case study highlights how UAS-based magnetometry and photogrammetry were deployed in Nevada’s Wassuk Range to support early-stage mineral exploration in complex terrain.
Using a drone-mounted magnetometer system, we acquired high-resolution magnetic data to detect subsurface geologic features, complemented by photogrammetric mapping to generate detailed surface models. Together, these tools created a comprehensive spatial dataset that reduced the need for interpolation and enhanced our ability to interpret geophysical anomalies within their topographic and structural context.
This presentation will walk through the project workflow, from mission planning and data acquisition to processing and integration in software platforms, illustrating how airborne remote sensing is reshaping field exploration. We will also discuss the operational challenges encountered, including flying in steep, remote terrain, optimizing flight paths for magnetic data coverage, and maintaining accuracy and safety in challenging environments.
This case study demonstrates the power of coupling surface imaging with subsurface geophysical sensing to guide decision-making and reduce uncertainty. The key takeaway for geologists, mining professionals, and land managers is clear: higher-resolution data leads to more confident interpretations and better base mapping in exploration.
As drone-based systems become more accessible and capable, this integrated approach offers a cost-effective and logistically agile solution for projects in areas where traditional ground surveys are limited by terrain, access, or budget.
Michael Detwiler, Wood Rodgers, Inc.
2:00 PM – 2:15 PM – Accurate Characterization of Coastal Ridges by Fusing Hyperspectral and Lidar Data
This is a collaborative ecological field study between the Geomatics Program and Biological Sciences to characterize ridges along the Louisiana coast in USA. The primary objective of the study is to fuse data from hyperspectral and lidar sensors for accurate ridge characterization. The study sites include Isle de Jean Charles in the Terrebonne Basin, along with the Elmer’s Island Refuge, BTNEP Maritime Ridge, and Grand Isle State Park within the Barataria Basin in South Louisiana. These ridges were mapped for evaluation using uncrewed aerial systems (UAS) mounted with lidar and hyperspectral sensors. To fulfill this objective, we have conducted four types of surveys simultaneously. First a field vegetation sampling plot was georeferenced using a high precision Real-Time Network (RTN) GNSS Survey. This was followed by multiple missions using our MicroDrone MD3000LR quadcopter with Riegl Lidar VUX-1UAV sensor to capture the digital surface model (DSM) of the ridge and use lidar intensities in cover classification. On the same day several missions were flown using our DJI Pro M600 hexacopter with RESONON Pika-L Hyperspectral sensor (spectral wavelength range 400NM-1000NM with individual band sensitivity of 2.1NM) to collect data on floral and faunal cover class. This was followed by a low altitude flight along random transects using a DJI Mavic Pro for ground truthing and quality control. The data collection was completed over habitats in their Fall-2022 / Winter-2023 phases of growth. This data was compared to the same habitats during their Spring-2023 / Summer-2023 phase of growth, allowing a comparison between different seasons across different habitats. The overall goal of this multidisciplinary project is to fill data gaps that have been identified as a limitation to the ongoing refinement and implementation of the Louisiana Coastal Master Plan (LA-CMP) in regard to ridge restoration. Among the different image classification employed Spectral Angle Mapper (SAM) classification technique shows promise for classifying hyperspectral data cubes. The overall accuracy ranging from 81 to 85 percent with certain target coastal restoration species showing individual accuracies in the higher 90 percent ranges. The results show lidar data has allowed us to resolve the elevation changes from the crown of a forested ridge to the adjacent intertidal marsh. Fusion of hyperspectral and lidar intensities has allowed us to resolve the vegetative cover classes including the canopy created by trees compared to the cover created by coastal plants.
Balaji Ramachandran, Nicholls State University
2:15 PM – 2:30 PM – Advanced Lidar Workflows for Forest Inventory, Furnace Inspection, and Stockpile Analysis
The integration of aerial and terrestrial LiDAR sensors has significantly enhanced data acquisition capabilities for forestry, industrial, and structural monitoring applications. This study presents the results of a Proof of Concept (POC) conducted in operational areas of Gerdau, a major steel manufacturer in Brazil. The workflow employed the TrueView 540 aerial LiDAR sensor mounted on an unmanned aircraft system and the TVGO 116s terrestrial SLAM sensor, jointly processed and co-registered in LP360 software. The investigation encompassed five core components: (1) forest inventory using Individual Tree Segmentation (ITS) for automated extraction of tree count, height, and geolocation; (2) Diameter at Breast Height (DBH) estimation through trunk classification and cross-section analysis; (3) Digital Terrain Model (DTM) generation using AI-based ground classification; (4) structural assessment of industrial furnaces, including 3D analysis, profile sections, orthomosaic generation, and deformation detection—particularly on roof surfaces; and (5) volumetric modeling of wood and charcoal stockpiles using TIN-based surface differencing. Contour lines were additionally produced to support engineering and earthwork planning. The results demonstrated high precision and strong consistency between aerial and terrestrial datasets, confirming the effectiveness of an integrated LiDAR acquisition and processing workflow for large-scale forestry and industrial environments.
Thadeu Lugarini, GeoCue Group
