Impact of Data Processing and DTM Resolution on Determining of Small Erosional Landforms

Abstract Small erosional landforms are characterised by a dynamics closely related to the occurrence and changes in precipitation and water flowing down the slopes. Triggered by water, the erosion processes are controlled by the other environmental conditions like slope gradient, lithology, land cover and land use. Studying the changes in the topography gives information about the spatiotemporal dynamics of erosion and can contribute to a more effective assessment of erosion susceptibility and mitigation measures at the earliest stage of the process development. Usually in the initial stages, the changes in the topography are hardly noticeable and using high resolution digital terrain models (DTMs) is of high importance. In this relation, the aim of the current research is to determine to what extent the resolution of the models influences the results of delineating the flow lines, rills and gullies. For this purpose, a terrain survey was carried out and data was acquired by UAS (uncrewed aerial system) DJI Phantom 4RTK. DTMs in horizontal resolution of 0.05, 0.1, 0.2, 0.5 and 1 m are created and analysed. Special attention is given to the analysis of surface curvature as an indicator for flow convergence and divergence. The research is done on a slope area covered mainly by grass and some rare bushes and trees. Despite the observed variations, the results show a general trend of decrease in the flow length with decreasing DTMs resolution. Considering the plan curvature and concave areas, the differences are smallest between the models with cell size 0.1 and 0.2 m.

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FURTHER ADVENTURES IN MARS DTM QUALITY: SMOOTHING ERRORS, SHARPENING DETAILS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-659-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 659-666

Author(s):

R. L. Kirk ◽

D. Mayer ◽

B. L. Redding ◽

D. M. Galuszka ◽

R. L. Fergason ◽

...

Keyword(s):

High Resolution ◽

Horizontal Resolution ◽

Error Performance ◽

Data Set ◽

Digital Terrain ◽

Terrain Models ◽

Landing Sites ◽

Imaging Science ◽

Resolution Imaging ◽

Slope Estimation

Abstract. We have used high-precision, high-resolution digital terrain models (DTMs) of the NASA Mars Science Laboratory and Mars 2020 rover landing sites based on mosaicked images from the Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (MRO HiRISE) camera as a reference data set to evaluate DTMs based on Mars Express High Resolution Stereo Camera (MEX HRSC) images. The Next Generation Automatic Terrain Extraction (NGATE) matcher in the SOCET SET/GXP® commercial photogram- metric system produces DTMs with relatively good (small) horizontal resolution but high error, and results are terrain dependent, with poorer resolution and smaller errors on smoother surfaces. Multiple approaches to smoothing the NGATE DTMs give very similar tradeoffs between resolution and error. Smoothing the NGATE DTMs with a 5x5 lowpass filter is near optimal in terms of both combined resolution-error performance and local slope estimation, but smoothing with a single pass of an area-based matcher, which has been the standard approach for generating planetary DTMs at the U.S. Geological Survey to date results in similar errors and only slightly worse resolution. DTMs from the HRSC team processing pipeline fall within this same trade space but are less sensitive to terrain roughness. DTMs produced with the Ames Stereo Pipeline also fall in this space at resolutions intermediate between NGATE and the team pipeline. Although DTM resolution and error each vary by a factor of 2, the product of resolution and error is much more consistent, varying by &leq;20% across multiple image sets and matching algorithms. Refinement of the stereo DTM by photoclinometry can yield significant quantitative improvement in resolution and some improvement in error (improving their product by as much as a factor of 2), provided that albedo variations over distances smaller than the stereo DTM resolution are not too severe.

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How Well Do We Know Europa’s Topography? An Evaluation of the Variability in Digital Terrain Models of Europa

Remote Sensing ◽

10.3390/rs13245097 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5097

Author(s):

Michael T. Bland ◽

Randolph L. Kirk ◽

Donna M. Galuszka ◽

David P. Mayer ◽

Ross A. Beyer ◽

...

Keyword(s):

Horizontal Resolution ◽

Data Sets ◽

Digital Terrain Models ◽

Matching Accuracy ◽

Extraterrestrial Life ◽

Correlation Kernel ◽

Digital Terrain ◽

Terrain Models ◽

Ice Shell ◽

Pixel Scale

Jupiter’s moon Europa harbors one of the most likely environments for extant extraterrestrial life. Determining whether Europa is truly habitable requires understanding the structure and thickness of its ice shell, including the existence of perched water or brines. Stereo-derived topography from images acquired by NASA Galileo’s Solid State Imager (SSI) of Europa are often used as a constraint on ice shell structure and heat flow, but the uncertainty in such topography has, to date, not been rigorously assessed. To evaluate the current uncertainty in Europa’s topography we generated and compared digital terrain models (DTMs) of Europa from SSI images using both the open-source Ames Stereo Pipeline (ASP) software and the commercial SOCET SET® software. After first describing the criteria for assessing stereo quality in detail, we qualitatively and quantitatively describe both the horizontal resolution and vertical precision of the DTMs. We find that the horizontal resolution of the SOCET SET® DTMs is typically 8–11× the root mean square (RMS) pixel scale of the images, whereas the resolution of the ASP DTMs is 9–13× the maximum pixel scale of the images. We calculate the RMS difference between the ASP and SOCET SET® DTMs as a proxy for the expected vertical precision (EP), which is a function of the matching accuracy and stereo geometry. We consistently find that the matching accuracy is ~0.5 pixels, which is larger than well-established “rules of thumb” that state that the matching accuracy is 0.2–0.3 pixels. The true EP is therefore ~1.7× larger than might otherwise be assumed. In most cases, DTM errors are approximately normally distributed, and errors that are several times the derived EP occur as expected. However, in two DTMs, larger errors (differences) occur and correlate with real topography. These differences primarily result from manual editing of the SOCET SET® DTMs. The product of the DTM error and the resolution is typically 4–8 pixel2 if calculated using the RMS image scale for SOCET SET® DTMs and the maximum images scale for the ASP DTMs, which is consistent with recent work using martian data sets and suggests that the relationship applies more broadly. We evaluate how ASP parameters affect DTM quality and find that using a smaller subpixel refinement kernel results in DTMs with smaller (better) resolution but, in some cases, larger gaps, which are sometimes reduced by increasing the size of the correlation kernel. We conclude that users of ASP should always systematically evaluate the choice of parameters for a given dataset.

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ANALYSIS OF MULTI-YEAR DYNAMICS OF THE NDVI GROWING INDEX IN THE IREMEL NATURAL PARK FOR LOCAL FOREST MANAGEMENT USING DIGITAL TERRAIN MODELS

Астраханский вестник экологического образования ◽

10.36698/2304-5957-2020-19-3-140-149 ◽

2020 ◽

Vol 19 (3) ◽

pp. 140-149

Author(s):

Inber Yaparov ◽

◽

Ildar Vildanov ◽

Elza Bashirova ◽

Rinat Gumerov ◽

...

Keyword(s):

Forest Management ◽

Digital Terrain Models ◽

Digital Terrain ◽

Natural Park ◽

Terrain Models

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Lidar-Based Morphometry of Conical Hills in Temperate Karst Areas in Slovenia

Remote Sensing ◽

10.3390/rs13142668 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2668

Author(s):

Tamás Telbisz

Keyword(s):

Empirical Distribution ◽

Inverse Gaussian ◽

Digital Terrain ◽

Terrain Models ◽

Starting Point ◽

Significant Difference ◽

Karst Areas ◽

Hill Area ◽

The Hill ◽

Comprehensive Study

Conical hills, or residual hills, are frequently mentioned landforms in the context of humid tropical karsts as they are dominant surface elements there. Residual hills are also present in temperate karsts, but generally in a less remarkable way. These landforms have not been thoroughly addressed in the literature to date, therefore the present article is the first attempt to morphometrically characterize temperate zone residual karst hills. We use the methods already developed for doline morphometry, and we apply them to the “inverse” topography using LiDAR-based digital terrain models (DTMs) of three Slovenian sample areas. The characteristics of hills and depressions are analysed in parallel, taking into account the rank of the forms. A common feature of hills and dolines is that, for both types, the empirical distribution of planform areas has a strongly positive skew. After logarithmic transformation, these distributions can be approximated by Inverse Gaussian, Normal, and Weibull distributions. Along with the rank, the planform area and vertical extent of the hills and dolines increase similarly. High circularity is characteristic only of the first-rank forms for both dolines and hills. For the sample areas, the the hill area ratios and the doline area ratios have similar values, but the total extent of the hills is slightly larger in each case. A difference between dolines and hills is that the shapes of hills are more similar to one another than those of dolines. The reason for this is that the larger, closed depressions are created by lateral coalescence, while the hills are residual forms carved from large blocks. Another significant difference is that the density of dolines is much higher than that of hills. This article is intended as a methodological starting point for a new topic, aiming at the comprehensive study of residual karst hills across different climatic areas.

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IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

Remote Sensing ◽

10.3390/rs13122417 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2417

Author(s):

Savvas Karatsiolis ◽

Andreas Kamilaris ◽

Ian Cole

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Aerial Imagery ◽

Aerial Images ◽

Surface Model ◽

Large Area ◽

Digital Terrain ◽

Terrain Models ◽

Architectural Features ◽

Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

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