•Remote sensing data supports research on reclaimed post-mining sites.•Key factors affecting SOC, TN, and SWS in post-mining ecosystems were identified.•DTM significantly impacts SOC, while TN is ...mainly influenced by NIR and NDVI.•TWI and CHM are particularly important for SWS assessment in post-mining ecosystems.
The estimation of Soil Organic Carbon (SOC), Total Nitrogen (TN), and Soil Water Storage (SWS) is crucial in comprehending ecosystem services and environmental sustainability. It plays a crucial role in guiding sustainable restoration strategies and supporting the long-term health of post-mining sites. Remote sensing technology provides valuable tools for modelling and mapping soil properties in reclaimed post-mining sites efficiently and cost-effectively. This study aimed to utilize remote sensing data to estimate SOC, TN, and SWS in a reclaimed post-mining site. Field data was collected from 130 research plots to obtain reference data for SOC, TN, and SWS from the Sonica hard coal post-mine spoil heap. Remote sensing data were: airborne laser scanning (ALS) point clouds and Planets cope satellite imageries. Generalized Additive Models (GAM) were used to develop predictive models. Wall-to-wall predictions of analyzed variables were performed. The results identified topographic and remote sensing indicators that significantly influence SOC, TN, and SWS. Digital Terrain Model (DTM), aspect, and blue spectral band are variables that explain SOC storage, with a significant influence of DTM, ranging from −8 to 18 Mg ha−1. TN was explained by DTM, Canopy Height Model (CHM), blue and Near Infrared (NIR) spectral bands, and Normalized Difference Vegetation Index (NDVI), mainly influenced by NIR and NDVI, ranging from −1.1 to 0.8 and −0.9 to 1.4 Mg ha−1, respectively. The values of Topographic Wetness Index (TWI), aspect, CHM, blue and NIR spectral bands explained SWS, highlighting their importance in assessing soil water dynamics in post-mining landscapes, with TWI and CHM being particularly influential, ranging from −2 to 5.1 and −6 to 2 mm, respectively. However, caution is advised when predicting SOC and TN using remote sensing in post-mining sites due to geogenic carbon considerations.
Small unmanned aircraft systems (sUAS) are a relatively new type of aerial platform for acquiring high-resolution remote sensing measurements of Earth surface processes and landforms. However, ...despite growing application there has been little quantitative assessment of sUAS performance. Here we present results from a field experiment designed to evaluate the accuracy of a photogrammetrically-derived digital terrain model (DTM) developed from imagery acquired with a low-cost digital camera onboard an sUAS. We also show the utility of the high-resolution (0.1m) sUAS imagery for resolving small-scale biogeomorphic features. The experiment was conducted in an area with active and stabilized aeolian landforms in the southern Canadian Prairies. Images were acquired with a Hawkeye RQ-84Z Areohawk fixed-wing sUAS. A total of 280 images were acquired along 14 flight lines, covering an area of 1.95km2. The survey was completed in 4.5h, including GPS surveying, sUAS setup and flight time. Standard image processing and photogrammetric techniques were used to produce a 1m resolution DTM and a 0.1m resolution orthorectified image mosaic. The latter revealed previously un-mapped bioturbation features. The vertical accuracy of the DTM was evaluated with 99 Real-Time Kinematic GPS points, while 20 of these points were used to quantify horizontal accuracy. The horizontal root mean squared error (RMSE) of the orthoimage was 0.18m, while the vertical RMSE of the DTM was 0.29m, which is equivalent to the RMSE of a bare earth LiDAR DTM for the same site. The combined error from both datasets was used to define a threshold of the minimum elevation difference that could be reliably attributed to erosion or deposition in the seven years separating the sUAS and LiDAR datasets. Overall, our results suggest that sUAS-acquired imagery may provide a low-cost, rapid, and flexible alternative to airborne LiDAR for geomorphological mapping.
•In 4.5h we collected airborne imagery and ground data to produce a 1m DTM.•The accuracy of the sUAS DTM is equivalent to a bare Earth LiDAR DTM.•Small-scale biogeomorphic features in 0.1m imagery were not visible in 1m imagery.
Impact crater cataloging is an important tool in the study of the geological history of planetary bodies in the Solar System, including dating of surface features and geologic mapping of surface ...processes. Catalogs of impact craters have been created by a diverse set of methods over many decades, including using visible or near infra-red imagery and digital terrain models.
I present an automated system for crater detection and cataloging using a digital terrain model (DTM) of Mars — In the algorithm craters are first identified as rings or disks on samples of the DTM image using a convolutional neural network with a UNET architecture, and the location and size of the features are determined using a circle matching algorithm. I describe the crater detection algorithm (CDA) and compare its performance relative to an existing crater dataset. I further examine craters missed by the CDA as well as potential new craters found by the algorithm. I show that the CDA can find three–quarters of the resolvable craters in the Mars DTMs, with a median difference of 5–10% in crater diameter compared to an existing database.
A version of this CDA has been used to process DTM data from the Moon and Mercury (Silburt et al., 2019). The source code for the complete CDA is available at https://github.com/silburt/DeepMoon, and Martian crater datasets generated using this CDA are available at https://doi.org/10.5683/SP2/MDKPC8.
•Trained a neural network CDA to identify and locate craters.•Applied the neural network to the MOLA/HRSC blended DTM.•Performance of the CDA is comparable to expert human classifiers.•Reproduced human-generated crater distribution on a global DTM.
•Methods for quantifying pedodiversity (PD) as related to elevation have been proposed.•The clear relationship between PD and elevation was described with polynomial model.•Concept of potential ...pedodiversity (PPD) (PD at each cell) has also been proposed.•Variogram of PPD revealed its spatial periodicity.•Larger portion of spatial variability a periodicity of PPD was explained by polynomial fit.
This paper discusses the study of pedodiversity quantification as related to elevation. It attempts to answer what is the magnitude of pedodiversity change with increasing or decreasing elevation. The proposed methods include segmentation of the study area (country of Czechia) so that segments of either equal elevation increments, or equal area extent are delineated. Based on soil taxa, commonly used pedodiversity indices in terms of diversity index (Shannon’s entropy) are calculated for each segment. Furthermore, a concept of potential pedodiversity (PPD) have been proposed (it can be defined as a pressure of the surroundings on the soil unit change at a particular soil point location), with the aim to enable a pixel-to-pixel analysis. The size of the pixel surroundings was optimized for the most contrasted PPD pattern. It has been found that the pedodiversity tends to decrease with increasing elevation, and that such relationship can be described with a polynomial model (5-order) more likely than with a linear one. For the majority of the area, however, the linear fit may be a good approximation. To further visualize the effect of elevation on the PPD, experimental sample variogram and residual (variogram of residuals of the fitted model) variograms were examined. In addition to the difference in variogram sills, which can be attributed to the portion of variance explained by the fitted model, the elevation also had a significant impact on the reduction of periodicity in the spatial structure of pedodiversity. In this respect, the polynomial model confirmed its superiority over the linear one again.
Unmanned Aerial Vehicle (UAV) systems, sensors, and photogrammetric processing techniques have enabled timely and highly detailed three-dimensional surface reconstructions at a scale that bridges the ...gap between conventional remote-sensing and field-scale observations. In this work 29 rotary and fixed-wing UAV surveys were conducted during multiple field campaigns, totaling 47 flights and over 14.3 km2, to document permafrost thaw subsidence impacts on or close to road infrastructure in the Northwest Territories, Canada. This paper provides four case studies: (1) terrain models and orthomosaic time series revealed the morphology and daily to annual dynamics of thaw-driven mass wasting phenomenon (retrogressive thaw slumps; RTS). Scar zone cut volume estimates ranged between 3.2 × 103 and 5.9 × 106 m3. The annual net erosion of RTS surveyed ranged between 0.35 × 103 and 0.39 × 106 m3. The largest RTS produced a long debris tongue with an estimated volume of 1.9 × 106 m3. Downslope transport of scar zone and embankment fill materials was visualized using flow vectors, while thermal imaging revealed areas of exposed ground ice and mobile lobes of saturated, thawed materials. (2) Stratigraphic models were developed for RTS headwalls, delineating ground-ice bodies and stratigraphic unconformities. (3) In poorly drained areas along road embankments, UAV surveys detected seasonal terrain uplift and settlement of up to 0.5 m (>1700 m2 in extent) as a result of injection ice development. (4) Time series of terrain models highlighted the thaw-driven evolution of a borrow pit (6.4 × 105 m3 cut volume) constructed in permafrost terrain, whereby fluvial and thaw-driven sediment transfer (1.1 and 3.9 × 103 m3 a−1 respectively) was observed and annual slope profile reconfiguration was monitored to gain management insights concerning site stabilization. Elevation model vertical accuracies were also assessed as part of the case studies and ranged between 0.02 and 0.13 m Root Mean Square Error. Photogrammetric models processed with Post-processed Kinematic image solutions achieved similar accuracies without ground control points over much larger and complex areas than previously reported. The high resolution of UAV surveys, and the capacity to derive quantitative time series provides novel insights into permafrost processes that are otherwise challenging to study. The timely emergence of these tools bridges field-based research and applied studies with broad-scale remote-sensing approaches during a period when climate change is transforming permafrost environments.
A common geomorphometric object in geospatial analysis is the contour-based network (CBN). Unlike grid- and patch-based networks, CBN partitions an entire terrain surface vertically. Traditional ...methods connect streamline segments with consecutive contours according to orthogonality or minimum-distance measure. This causes three-dimensional inconsistency (not all straight segments intersect orthogonally with a contour). Therefore, segments deviate from the exact streamline. The steepest descent methodology seems applicable; however, it was designed for optimization rather than tracking. A trajectory would oscillate at narrow hills and valleys, and tracing terminates once a convergence criterion follows. This occurs irrespective of proximity to an exact stationary point, but no method for continuation exists. Thus, the author devised a framework for a three-dimensionally consistent CBN. The curvature vector and tolerance help achieve good reproductivity, storage efficiency, and adjustable precision. The proposed method advances inward of the exact curve and continues moving over stationary points, adhering to the original terrain surface. A complete CBN is composed via gleaning the streamline curves by including contours identified by another method. The resulting object emanates smooth curves along with three-dimensional consistency. A demonstrative experiment is conducted using a lower-resolution digital terrain model dataset; the results highlight the significance of the devised framework.
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Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive ...triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%.
We report on the results of the Mars Express High-Resolution Stereo Camera (HRSC) experiment pertaining to one of its major aims, mapping the surface of Mars by high-resolution digital terrain models ...(DTM, up to 50
m grid spacing) and orthoimages (up to 12.5
m resolution). We introduce the specifications and characteristics of these data products and give an overview of the procedures that have been developed and are applied for their derivation. We also address the performance characteristics of the mapping project related to different aspects of internal accuracy, accuracy with respect to the global reference system, and regional aspects. Using adaptive processing techniques for terrain reconstruction and a revised approach to the improvement of orientation data, a mean precision of the resulting 3D points of about 12
m is obtained, exceeding the mean ground resolution of the stereo images. Using Mars Orbiter Laser Altimeter (MOLA) data, the HRSC models are firmly tied to the global reference system at the scale of the HRSC DTM grid spacing in the lateral dimension, and to within few meters vertically. HRSC high-resolution DTMs are typically generated using a grid size of about 2 times the mean ground resolution, but usually not larger than 3 times the mean ground resolution, and not smaller than 3 times the precision of the integrated 3D points derived from stereo image analysis. Statistically, every grid cell is based on at least one measured 3D point. Thus, horizontal DTM resolution is well established with regard to the precision and density of the derived 3D points, while the concurrent aim of a detailed terrain representation at maximum possible resolution is pursued. Comparison with the DTM derived from MOLA data allows us to identify specific advancements related to this updated view of Martian topography. We also address the mapping performance of HRSC in comparison to MOLA with respect to latitude and to different surface types and morphologies. Finally, comparison with MOLA highlights typical complementarities of the two different approaches for mapping planetary surfaces.
Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an ...unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM) photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP) technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM) required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.
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