Assessment of Differential Forest Growth Following Disturbance in Minnesota, USA

We used LiDAR metrics and satellite imagery to examine regeneration on forested sites disturbed via harvest or natural means over a 44-year period. We tested the effectiveness of older low-density LiDAR elevation data in producing information related to existing levels of above ground biomass (AGB). To accomplish this, we paired the elevation data with a time series of wetness and greenness indices derived from Landsat satellite imagery to model changes in AGB for sites experiencing different agents of change. Current AGB was determined from high-density LiDAR acquired in northern Minnesota, USA. We then compared high-density LiDAR-based AGB and estimates modeled using Landsat and low-density LiDAR indices for 10,068 sites. Clear differences were found in standing AGB and accumulation rates between sites disturbed by different agents of change. Biomass accumulation following disturbance appears to decrease rapidly following an initial spike as stands 1asZX respond to newly opened growing space. Harvested sites experienced a roughly six-fold increase in the rate of biomass accumulation compared to sites subjected to stand replacing fire or insect and disease, and a 20% increase in productivity when compared to sites subjected to wind mediated canopy loss. Over time, this resulted in clear differences in standing AGB.

Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data De-Rived from sUAS Imagery

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of red-cockaded woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

Influence of Atmospheric Scattering on the Accuracy of Laser Altimetry of the GF-7 Satellite and Corrections

Satellite laser altimetry can obtain sub-meter or even centimeter-scale surface elevation data over large areas, but it is inevitably affected by scattering caused by clouds, aerosols, and other atmospheric particles. This laser ranging error caused by scattering cannot be ignored. In this study, we systematically combined existing atmospheric scattering identification technology used in satellite laser altimetry and observed that the traditional algorithm cannot effectively estimate the laser multiple scattering of the GaoFen-7 (GF-7) satellite. To solve this problem, we used data from the GF-7 satellite to analyze the importance of atmospheric scattering and propose an identification scheme for atmospheric scattering data over land and water areas. We also used a look-up table and a multi-layer perceptron (MLP) model to identify and correct atmospheric scattering, for which the availability of land and water data reached 16.67% and 26.09%, respectively. After correction using the MLP model, the availability of land and water data increased to 21% and 30%, respectively. These corrections mitigated the low identification accuracy due to atmospheric scattering, which is significant for facilitating satellite laser altimetry data processing.

Chronology of the Basalt Units Surrounding Chang’e-4 Landing Area

The Chang’e-4 (CE-4) lunar probe, the first soft landing spacecraft on the far side of the Moon, successfully landed in the Von Kármán crater on 3 January 2019. Geological studies of the landing area have been conducted and more intensive studies will be carried out with the in situ measured data. The chronological study of the maria basalt surrounding the CE-4 landing area is significant to the related studies. Currently, the crater size-frequency distribution (CSFD) technique is the most popular method to derive absolute model ages (AMAs) of geological units where no returned sample is available, and it has been widely used in dating maria basalt on the lunar surface. In this research, we first make a mosaic with multi-orbital Chang’e-2 (CE-2) images as a base map. Coupled with the elevation data and FeO content, nine representative areas of basalt units surrounding the CE-4 landing area are outlined and their AMAs are derived. The dating results of the nine basalt units indicate that the basalts erupted from 3.42 to 2.28 Ga ago in this area, a period much longer than derived by previous studies. The derived chronology of the above basalt units establishes a foundation for geological analysis of the returned CE-4 data.

Short communication: A new tool to define multiscale bedform characteristics from bed elevation data

Systematic identification and characterization of bedforms from bathymetric data are crucial in many studies focused on fluvial processes. Automated and accurate processing of bed elevation data is challenging where dune fields are complex, irregular and, especially, where multiple scales co-exist. Here, we introduce a new tool to quantify dune properties from bathymetric data representing multiple dune scales. A first step in the procedure is to decompose the bathymetric data based on a LOESS algorithm. Steep dune lee side slopes are accounted for by implementing objective breaks in the algorithm, accounting for discontinuities in the bed level profiles, often occurring at the toe of the lee side slope of dunes. The steep lee slopes are then approximated by fitting a sigmoid function. Following the decomposition of the bathymetric data, bedforms are identified based on zero-crossing, and the relevant properties are calculated. The approach to decompose bedforms adopted in the presented tool is particularly applicable where secondary dunes are large and thus filtering could easily lead to undesired smoothing of the primary morphology. Application of the tool to two bathymetric maps demonstrates that the decomposition and identification are successful, as the lee side slopes are better preserved.

The first sub-meter resolution digital elevation model of the Kruger National Park, South Africa

The use of digital elevation models has proven to be crucial in numerous studies related to savanna ecosystem research. However, the insufficient spatial resolution of the chosen input data is often considered to be a limiting factor when conducting local to regional scale ecosystem analysis. The elevation models and orthorectified imagery created in this study represent the first wall-to-wall digital elevation data sets produced for the Kruger National Park (KNP), South Africa, at very high spatial resolution. Using colour-infrared (CIR) aerial imagery from the archives of the Chief Directorate: National Geo-spatial Information (CDNGI), Department of Agriculture, Land Reform and Rural Development (DALRRD) aerial acquisition programme, we created digital surface models (DSMs), digital terrain models (DTMs) and CIR orthomosaics covering the entire KNP with a nominal ground sampling distance of 0.25 m. Elevation information was derived using state-of-the-art stereo matching algorithms that utilised semi-global matching (SGM) as a cost aggregation function throughout the image pairing, using the Enterprise software from CATALYST. The final products were validated against reference products, and showed excellent agreement with R² values of 0.99. Further, the validation of the DTM and DSM revealed median absolute vertical height error (LE90) across all sites of 1.02 m and 2.58 m, respectively. The orthomosaics were validated with in situ ground control points (GCPs) exhibiting a horizontal Circular Probable Error (CPE) of 1.37 m. The data resulting from this work will be distributed freely with the aim of fostering more scientific studies in the African science community and beyond.Conservation implications: Accurate information about terrain and surface height are crucial inputs to a variety of scientific analysis, which are essential in protected areas, such as flood prediction or fire hazard estimation. Elevation data sets and orthomosaics in very high resolution can therefore serve as a crucial tool to improve park management and foster positive implications on conservation efforts.

Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning

Unilateral corneal indices and topography maps are routinely used in practice, however, although there is consensus that fellow-eye asymmetry can be clinically significant, symmetry studies are limited to local curvature and single-point thickness or elevation measures. To improve our current practices, there is a need to devise algorithms for generating symmetry colormaps, study and categorize their patterns, and develop reference ranges for new global discriminative indices for identifying abnormal corneas. In this work, we test the feasibility of using the fellow eye as the reference surface for studying elevation symmetry throughout the entire corneal surface using 9230 raw Pentacam files from a population-based cohort of 4613 middle-aged adults. The 140 × 140 matrix of anterior elevation data in these files were handled with Python to subtract matrices, create color-coded maps, and engineer features for machine learning. The most common pattern was a monochrome circle (“flat”) denoting excellent mirror symmetry. Other discernible patterns were named “tilt”, “cone”, and “four-leaf”. Clustering was done with different combinations of features and various algorithms using Waikato Environment for Knowledge Analysis (WEKA). Our proposed approach can identify cases that may appear normal in each eye individually but need further testing. This work will be enhanced by including data of posterior elevation, thickness, and common diagnostic indices.

COMPARATIVE ANALYSIS FOR METHODS OF BUILDING DIGITAL ELEVATION MODELS FROM TOPOGRAPHIC MAPS USING GEOINFORMATION TECHNOLOGIES

The article considers the question of estimating the accuracy of interpolation methods for building digital elevation models using Soviet topographic maps. The territory of the Kursk region of the Russian Federation was used as the study area, because it is located on the Central Russian Upland and characterized by the complex structure of the vertical and horizontal dissection of the relief. Contour lines automatically obtained using a Python algorithm were used as the initial elevation data to build a digital elevation model. Digital elevation models obtained by thirteen different interpolation methods in ArcGIS and Surfer software were built and analyzed. Special attention is paid to the ANUDEM method, which allows to obtain hydrologically correct digital elevation models. Recommendations for the use of one or another method of interpolation are given. The results can be useful for professionals who use topographic maps in their work and deals with the design using digital elevation models.

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

The current study was planned to simulate runoff due to the snowmelt in the Lidder River catchment of Himalayan region under climate change scenarios. A basic degree-day model, Snowmelt-Runoff Model (SRM) was utilized to assess the hydrological consequences of change in climate. The SRM model performance during the calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found as 11.7, -10.1, -11.8, 1.96, and 8.6 during 2009-2014, respectively, while the R2 is 0.96, 0.92, 0.95, 0.90, and 0.94, respectively. The Dv and R2 values indicating that the simulated snowmelt runoff has a close agreement with the observed value. The simulated findings were also assessed under the different scenarios of climate change: a) increases in precipitation by +20 %, b) temperature rise of +2 °C, and c) temperature rise of +2 °C with a 20 % increase in snow cover. In scenario "b", the simulated results showed that runoff increased by 53 % in summer (April–September). In contrast, the projected increased discharge for scenarios "a" and "c" was 37 % and 67 %, respectively. In high elevation data-scarce mountain environments, the SRM is efficient in forecasting future water supplies due to the snowmelt runoff.