Michael J. Starek

Modeling and analysis of landscape evolution using airborne, terrestrial, and laboratory laser scanning

Current laser scanning (Lidar, light detection and ranging) technologies span a wide range of survey extent and resolutions, from regional airborne Lidar mapping and terrestrial Lidar field surveys to laboratory systems utilizing indoor three-dimensional (3D) laser scanners. Proliferation in Lidar technology and data collection enables new approaches for monitoring and analysis of landscape evolution. For example, repeat Lidar surveys that generate a time series of point cloud data provide an opportunity to transition from traditional, static representations of topography to terrain abstraction as a 3D dynamic layer. Three case studies are presented to illustrate novel techniques for landscape evolution analysis based on time series of Lidar data: (1) application of multiyear airborne Lidar surveys to a study of a dynamic coastal region, where the change is driven by eolian sediment transport, wave-induced beach erosion, and human intervention; (2) monitoring of vegetation growth and the impact of landscape structure on overland flow in an agricultural field using terrestrial laser scanning; and (3) investigation of landscape design impacts on overland water flow and other physical processes using a tangible geospatial modeling system. The presented studies demonstrate new insights into landscape evolution in different environments that can be gained from Lidar scanning spanning 1.0–0.001 m resolutions with geographic information system analysis capabilities.

Space-Time Cube Representation of Stream Bank Evolution Mapped by Terrestrial Laser Scanning

Terrestrial laser scanning (TLS) is utilized to monitor bank erosion along a stream that has incised through historic millpond (legacy) sediment. A processing workflow is developed to generate digital terrain models (DTMs) of the banks surface from the TLS point cloud data. Differencing of the DTMs reveals that the majority of sediment loss stems from the legacy sediment layer. The DTM time series is stacked into a voxel model to form a space-time cube (STC). The STC provides a compact representation of the banks spatiotemporal evolution captured by the TLS scans. The continuous STC extends this approach by generating a voxel model with equal temporal resolution directly from the point cloud data. Novel visualizations are extracted from the STCs to explore patterns in surface evolution. Results show that erosion is highly variable in space and time, with large-scale erosion being episodic due to bank failure within legacy sediment.

Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Abstract. This paper explores the potential of using unmanned aircraft system (UAS)-based visible-band images to assess cotton growth. By applying the structure-from-motion algorithm, the cotton plant height (ph) and canopy cover (cc) information were retrieved from the point cloud-based digital surface models (DSMs) and orthomosaic images. Both UAS-based ph and cc follow a sigmoid growth pattern as confirmed by ground-based studies. By applying an empirical model that converts the cotton ph to cc, the estimated cc shows strong correlation (R2=0.990) with the observed cc. An attempt for modeling cotton yield was carried out using the ph and cc information obtained on June 26, 2015, the date when sigmoid growth curves for both ph and cc tended to decline in slope. In a cross-validation test, the correlation between the ground-measured yield and the estimated equivalent derived from the ph and/or cc was compared. Generally, combining ph and cc, the performance of the yield estimation is most comparable against the observed yield. On the other hand, the observed yield and cc-based estimation produce the second strongest correlation, regardless of the complexity of the models.

Small-scale UAS for geoinformatics applications on an island campus

Texas A&M University Corpus-Christi is undergoing a major university expansion on its island campus. The universitys Conrad Blucher Institute for Surveying and Science was tasked with providing the spatial information needs related to this expansion. To meet this objective, a small-scale Unmanned Aircraft System (UAS) equipped with a small-format digital camera is utilized to acquire aerial imagery in support of campus facilities. A processing and analysis workflow is implemented to evaluate the quality of geospatial data products derived from the system. Results show a mean planimetric accuracy of 6 cm when using control points. Vertical accuracy is evaluated by comparing UAS-derived elevations to airborne lidar and RTK GPS. The mean absolute difference is approximately 13 cm relative to lidar and 3.7 cm relative to RTK GPS. Results provide insight into the capabilities and challenges in utilizing UAS equipped with small-format digital cameras for survey-grade 3D mapping.

Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Abstract. A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.

UAS imaging for automated crop lodging detection: a case study over an experimental maize field

Lodging has been recognized as one of the major destructive factors for crop quality and yield, particularly in corn. A variety of contributing causes, e.g. disease and/or pest, weather conditions, excessive nitrogen, and high plant density, may lead to lodging before harvesting season. Traditional lodging detection strategies mainly rely on ground data collection, which is insufficient in efficiency and accuracy. To address this problem, this research focuses on the use of unmanned aircraft systems (UAS) for automated detection of crop lodging. The study was conducted over an experimental corn field at the Texas A and M AgriLife Research and Extension Center at Corpus Christi, Texas, during the growing season of 2016. Nadir-view images of the corn field were taken by small UAS platforms equipped with consumer grade RGB and NIR cameras on a per week basis, enabling a timely observation of the plant growth. 3D structural information of the plants was reconstructed using structure-from-motion photogrammetry. The structural information was then applied to calculate crop height, and rates of growth. A lodging index for detecting corn lodging was proposed afterwards. Ground truth data of lodging was collected on a per row basis and used for fair assessment and tuning of the detection algorithm. Results show the UAS-measured height correlates well with the ground-measured height. More importantly, the lodging index can effectively reflect severity of corn lodging and yield after harvesting.

Airborne lidar point cloud-based below-canopy line-of-sight visibility estimator

Abstract Point cloud data collected by small-footprint lidar scanning systems have proven effective in modeling the forest canopy for extraction of tree parameters. Although line-of-sight visibility (LOSV) in complex forests may be important for military planning and search-and-rescue operations, the ability to estimate LOSV from lidar scanners is not well developed. A new estimator of below-canopy LOSV (BC-LOSV) by addressing the problem of estimation of lidar under-sampling of the forest understory is created. Airborne and terrestrial lidar scanning data were acquired for two forested sites in order to test a probabilistic model for BC-LOSV estimation solely from airborne lidar data. Individual crowns were segmented, and allometric projections of the probability model into the lower canopy and stem regions allowed the estimation of the likelihood of the presence of vision-blocking elements for any given LOSV vector. Using terrestrial lidar scans as ground truth, we found an approximate average absolute difference of 20% between BC-LOSV estimates from the airborne and terrestrial point clouds, with minimal bias for either over- or underestimates. The model shows the usefulness of a data-driven approach to BC-LOSV estimation that depends only on small-footprint airborne lidar point cloud and physical knowledge of tree phenology.

Temporal Estimates of Crop Growth in Sorghum and Maize Breeding Enabled by Unmanned Aerial Systems

We comprehensively validated the use of UAS in sorghum and maize breeding programs. Temporal estimates of plant growth will allow researchers to elucidate new phenotypes. The stage of the breeding pipeline dictates the applicability of UAS platforms. The implementation of UAS is demonstrated in different crop species. Monetary and time costs should be considered before implementation of UAS.

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the “k” clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point- and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes.