scholarly journals Land-Surface Quantitative Analysis to Investigate the Spatial Distribution of Gravitational Landforms along Rocky Coasts

2021 ◽  
Vol 13 (24) ◽  
pp. 5012
Author(s):  
Daniela Piacentini ◽  
Francesco Troiani ◽  
Davide Torre ◽  
Marco Menichetti
Keyword(s):  
Spatial Distribution ◽  
Quantitative Analysis ◽  
Land Surface ◽  
Ad Hoc ◽  
Airborne Lidar ◽  
Visual Spatial ◽  
Comparison Results ◽  
San Bartolo ◽  
Rocky Coasts ◽  
Rocky Coast

The increasing availability of high-quality digital elevation models (DEMs) has been associated with a growing interest in developing quantitative analyses aimed at taking advantage of these detailed, updated, and promising digital datasets. Land-surface quantitative (LSQ) analysis is valuable for describing the land-surface topography and performing measures of the signature of specific geomorphic processes, taking into account site-specific geological contexts and morphoclimatic settings, proving to be particularly effective in transitional environments, such as rocky coasts. This paper presents the results of research aimed at investigating the spatial distribution of gravitational landforms along rocky coasts, by means of LSQ analysis based on a DEM with a ground resolution of 2 m, derived from airborne LiDAR (light detection and ranging) surveys. The study area is at Mt. San Bartolo (Northern Marche, Italy) and characterized by a sea cliff diffusely affected by gravitational phenomena of different sizes and types. Geomorphological and geological field data, interpretations of remotely sensed datasets derived from ad hoc unmanned aerial vehicle (UAV) flights, and DEM-derived hillshades were also adapted to support LSQ analysis. In detail, four morphometric variables (slope, roughness, terrain ruggedness index, and elevation standard deviation) were computed and the outputs evaluated based on visual–spatial inspections of derived raster datasets, descriptive statistics, and joint comparison. Results reveal the best performing variables and how combined interpretations can support the identification and mapping of zones characterized by varying spatial distribution of gravitational landforms of different types. The findings achieved along the Mt. San Bartolo rocky coast confirm that an approach based on land-surface quantitative analysis can act as a proxy to efficiently investigate gravitational slope processes in coastal areas, especially those that are difficult to reach with traditional field surveys.

scholarly journals Investigating Performance Of Airborne Lidar Data Filtering With Triangular Irregular Network (TIN) Algorithm

2014 ◽  
Vol XL-7 ◽  
pp. 199-202 ◽  
Author(s):  
M. Uysal ◽  
N. Polat
Keyword(s):  
Urban Area ◽  
Rural Area ◽  
Land Surface ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar System ◽  
Filtering Algorithm ◽  
Topographic Features ◽  
Comparison Results ◽  
Triangular Irregular Network

Digital Elevation Model (DEM) is an important topographic product and essential demand for many applications. Traditional methods for creating DEM are very costly and time consuming because of land surveying. In time, Photogrammetry has become one of the major methods to generate DEM. Recently, airborne Light Detection and Ranging (LIDAR) system has become a powerful way to produce a DEM due to advantage of collecting three-dimensional information very effectively over a large area by means of precision and time. <br><br> Airborne LIDAR system collects information not only from land surface but also from every object between plane and terrain that can reflect the laser beam. So filtering out nonground points from raw point clouds is the major step of DEM generation. There are many filtering algorithm due to several factors that affect the filtering prosedures. The performanses of these filters change based on the topographic features of area.One of these algorithm is called Triangular Irregular Network (TIN). <br><br> In this study the TIN algorithm is used to filter Lidar point cloud that are collected from two different sites. While one of these sites is a rural area, the other site is an urban area; therefore these sites have different topographic features. In addition, the reference DEMs are available for these sites. In order to test the performance of TIN algorithm, the Lidar point clouds are filtered and used to generate DEM for the sites. Finally, the generated DEM are compared with the reference DEM for each site. The comparison results show that the TIN filtering algorithm perform more effectively in urban area than rural area in terms of correlations with reference DEMs.

scholarly journals Integrated Field Surveying and Land Surface Quantitative Analysis to Assess Landslide Proneness in the Conero Promontory Rocky Coast (Italy)

2020 ◽  
Vol 10 (14) ◽  
pp. 4793
Author(s):  
Francesco Troiani ◽  
Salvatore Martino ◽  
Gian Marco Marmoni ◽  
Marco Menichetti ◽  
Davide Torre ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Time Scale ◽  
Land Surface ◽  
Slope Instability ◽  
Space And Time ◽  
Coastal Slope ◽  
Long Time ◽  
Landslide Processes ◽  
Rocky Coast

Rock slopes involved in extensive landslide processes are often characterized by complex morphodynamics acting at different scales of space and time, responsible for different evolutionary scenarios. Mass Rock Creep (MRC) is a critical process for long-term geomorphological evolution of slopes and can likewise characterize actively retreating coastal cliffs where, in addition, landslides of different typologies and size superimpose in space and time to marine processes. The rocky coast at the Conero promontory (central Adriatic Sea, Italy) offers a rare opportunity for better understanding the predisposing role of the morphostructural setting on coastal slope instability on a long-time scale. In fact, the area presents several landslides of different typologies and size and state of activity, together with a wide set of landforms and structural features effective for better comprehending the evolution mechanisms of slope instability processes. Different investigation methods were implemented; in particular, traditional geomorphological and structural field surveys were combined with land surface quantitative analysis based on a Digital Elevation Model (DEM) with ground-resolution of 2 m. The results obtained demonstrate that MRC involves the entire coastal slope, which can be zoned in two distinct sectors as a function of a different morphostructural setting responsible for highly differentiated landslide processes. Therefore, at the long-time scale, two different morphodynamic styles can be depicted along the coastal slopes that correspond to specific evolutionary scenarios. The first scenario is characterized by MRC-driven, time-dependent slope processes involving the entire slope, whereas the second one includes force-driven slope processes acting at smaller space–time scales. The Conero promontory case study highlights that the relationships between slope shape and structural setting of the deforming areas are crucial for reaching critical volumes to induce generalized slope collapse as the final stage of the MRC process. The results from this study stress the importance of understanding the role of morphostructures as predisposing conditions for generalized slope failures along rocky coasts involved in MRC. The findings discussed here suggest the importance of the assessment of the slope instability at the long time scale for a better comprehension of the present-day slope dynamics and its major implications for landslide monitoring strategies and the hazard mitigation strategies.

scholarly journals PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Grzegorz Bokota ◽  
Jacek Sroka ◽  
Subhadip Basu ◽  
Nirmal Das ◽  
Pawel Trzaskoma ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Cell Nucleus ◽  
Ad Hoc ◽  
Multiple Use ◽  
Entry Barrier ◽  
Multi Scale ◽  
3D Microscopy ◽  
Nuclear Structures ◽  
Bulk Processing ◽  
Microscopy Images

Abstract Background Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation. Results In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. Conclusions In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

scholarly journals Use of LST images from MODIS/AQUA sensor as an indication of frost occurrence in RS

2015 ◽  
Vol 19 (10) ◽  
pp. 920-925 ◽  
Author(s):  
Débora de S. Simões ◽  
Denise C. Fontana ◽  
Matheus B. Vicari
Keyword(s):  
Spatial Distribution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Rio Grande ◽  
Rio Grande Do Sul ◽  
Weather Stations ◽  
Aqua Satellite ◽  
South Of Brazil ◽  
June July ◽  
Minimum Air Temperature

ABSTRACTAlthough frost occurrence causes severe losses in agriculture, especially in the south of Brazil, the data of minimum air temperature (Tmin) currently available for monitoring and predicting frosts show insufficient spatial distribution. This study aimed to evaluate the MDY11A1 (LST – Land Surface Temperature) product, from the MODIS sensor on board the AQUA satellite as an estimator of frost occurrence in the southeast of the state of Rio Grande do Sul, Brazil. LST images from the nighttime overpass of the MODIS/AQUA sensor for the months of June, July and August from 2006 to 2012, and data from three conventional weather stations of the National Institute of Meteorology (INMET) were used. Consistency was observed between Tmin data measured in weather stations and LST data obtained from the MODIS sensor. According to the results, LSTs below 3 ºC recorded by the MODIS/AQUA sensor are an indication of a favorable scenario to frost occurrence.

scholarly journals The seven sisters DANCe

2018 ◽  
Vol 612 ◽  
pp. A70 ◽  
Author(s):  
J. Olivares ◽  
E. Moraux ◽  
L. M. Sarro ◽  
H. Bouy ◽  
A. Berihuete ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Model Comparison ◽  
Precise Determination ◽  
Model Parameters ◽  
Data Sets ◽  
Adequate Model ◽  
Comparison Results ◽  
Bayesian Evidence ◽  
Mass Segregation

Context. Membership analyses of the DANCe and Tycho + DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims. We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods. We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results. We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11.5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King’s model, although the Generalised King model introduced here has slightly better fitting properties. Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions. We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11.5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.

scholarly journals Spatial distribution pattern of Tarsius Lariang in lore lindu national park

2019 ◽  
Vol 13 (2) ◽  
pp. 606 ◽  
Author(s):  
Abdul Rosyid ◽  
Yanto Santosa ◽  
I Nengah Surati Jaya ◽  
M. Bismark ◽  
Agus P. Kartono
Keyword(s):  
Spatial Distribution ◽  
Distribution Pattern ◽  
Land Surface ◽  
National Park ◽  
Spatial Distribution Pattern ◽  
Systematic Sampling ◽  
Affecting Factors ◽  
Factors Affecting ◽  
Insect Abundance ◽  
Lore Lindu National Park

<p><span>Tarsius lariang (T. lariang) is an endemic species in Lore Lindu National Park (LLNP). Available information regarding T. lariang is limited to only morphological, anatomical, cytogenetic, and voices issues. Knowledge for its geospatial characteristics such as spatial preferences and spatial distribution is rare. The main objective of this study is to identify the spatial distribution pattern of T. lariang in LLNP. An additional objective is to identify the environmental factors affecting its spatial distribution patterns. Field observation for distribution pattern was done at the observation plot that were using systematic sampling with random start. Furthermore, the density estimation in each point was calculated using Triangle Count and Concentration Count method, while insect abundance was estimated using light traps sample data. Finally, spatial pattern was estimated using nearest neighbor index, while the environmental affecting factors were identified by using spatial analysis and correlation analyses. From 45 observation points, the T. lariang distribution pattern was clumped. It is also recognized that the significant factors affecting the spatial distribution were insect abundance, proximity from the commercially utilized land, and land surface temperature.</span></p><p> </p>

scholarly journals Determination of land surface reflectance using the AATSR dual-view capability

2014 ◽  
Vol 7 (7) ◽  
pp. 7451-7494
Author(s):  
L. Sogacheva ◽  
P. Kolmonen ◽  
T. H. Virtanen ◽  
E. Rodriguez ◽  
A.-M. Sundström ◽  
...  
Keyword(s):  
Land Surface ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Surface Reflectance ◽  
Imaging Spectrometer ◽  
Comparison Results ◽  
Moderate Resolution ◽  
The Difference ◽  
Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995–2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite.

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