scholarly journals INDEX OF FIELD SURVEY OF THE TERRITORY FOR MEDIUM-SCALE MAPPING

2021 ◽  
Vol 26 (4) ◽  
pp. 65-72
Author(s):  
Igor D. Makhatkov ◽  
Keyword(s):  
Large Scale ◽  
Field Survey ◽  
Remote Sensing Data ◽  
Secondary Data ◽  
Distance Measures ◽  
Medium Scale ◽  
Digital Elevation ◽  
Thematic Information ◽  
Elevation Model

The quality of thematic mapping mostly depends on the representativeness of the field survey of the territory. In large-scale mapping geostatistical techniques can be used to design survey spatial sampling. In medium-scale mapping, the sampling scheme is traditionally based on experience, common views about the thematic diversity of the territory and available cartographic data. In the digital map-ping of thematic variables secondary data is used. There are mainly remote sensing data and derivatives of a digital elevation model. Spatial extrapolation of thematic information of points is somehow connected with the use of distance measures between studied and unstudied points in the space of secondary variables. This measure is proposed to be used as a numerical measure of the survey quality of the entire mapping space. This article provides numerical experiments to illustrate the behavior of this index, as well as proposes possible ways of using the index to control fieldwork in order to improve the spatial model of thematic variables.

scholarly journals Influence of Quality of Remote Sensing Data on Vegetation Passability by Terrain Vehicles

2020 ◽  
Vol 9 (11) ◽  
pp. 684
Author(s):  
Josef Rada ◽  
Marian Rybansky ◽  
Filip Dohnal
Keyword(s):  
Remote Sensing Data ◽  
Surface Model ◽  
Remote Sensors ◽  
Digital Elevation ◽  
Methods Of Evaluation ◽  
Elevation Model ◽  
Images Processing ◽  
Forest Economic ◽  
Wheeled Vehicles

The article studied databases of vegetation created from remote sensors, outcome of analyses of Cross-Country Movement in forests, and quality of utilized data. The aim was to combine various databases of forests and get statistics of best data by using different methods of evaluation. Passability in forests is mainly conducted with analysis of driving between trees. The most suitable datasets in the Czech Republic are Forest Economic Plan and Digital Elevation Model 5th generation combined with Digital Surface Model 1st generation. Accuracy and usability of databases were compared with digital model of surface created from orthophoto images. Processing of data is the most important part that influences quality of statistical and map results. Studied characteristics of input databases and applied methods also have considerable influence on results of analysis of forest passability. The outcome substantially varies for personnel armored vehicles and wheeled vehicles mostly due to their movement capabilities.

New methodical approaches to mapping of geosystems (by the example of Baikalian Siberia’s geosystems)

2018 ◽  
Vol 937 (7) ◽  
pp. 23-34 ◽  
Author(s):  
I.N. Vladimirov
Keyword(s):  
Global Climate ◽  
Remote Sensing Data ◽  
Field Research ◽  
Scientific Basis ◽  
Nature Management ◽  
Climate Data ◽  
Structural Dynamic ◽  
Digital Elevation ◽  
North Asia ◽  
Elevation Model

The article considers a new approach to landscape mapping based on the synthesis of remote sensing data of high and medium spatial resolution, a digital elevation model, maps of various thematic contents, a set of global climate data, and materials of field research. The map of the Baikalian’s Siberia geosystems is based on the principles of the multistage regional-typological and structural-dynamic classification of geosystems proposed by Academician V.B. Sochava. The structure of the geosystems of the Baikalian Siberia is characterized by great complexity, both in the set of natural complexes and in the degree of their contrast. The regional classification range covers the geosystems inherent in different subcontinents of Asia and reflects their interpenetration, being a unique landscape-situational example of Siberian nature within North Asia. The map of the geosystems of the Baikalian Siberia reflects the main structural and dynamic diversity of geosystems in the region in the systems of their geographic and genetic spatial structures. These landscape cartographic studies fit into a single system of geographic forecasting and create a new fundamental scientific basis for developing recommendations for optimizing nature management in the Baikal region within the framework of implementing state environmental policy.

scholarly journals Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
José P. Granadeiro ◽  
João Belo ◽  
Mohamed Henriques ◽  
João Catalão ◽  
Teresa Catry
Keyword(s):  
Large Scale ◽  
Exposure Period ◽  
Intertidal Area ◽  
Digital Elevation ◽  
Geographical Bias ◽  
Novel Method ◽  
Tidal Stage ◽  
Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

scholarly journals Estimation of the HEC-HMS model parameters in data-scarce regions. Application to the Ouergha watershed (Sebou, Morocco)

2021 ◽  
Vol 314 ◽  
pp. 05002
Author(s):  
Hasna Moumni ◽  
Karima Sebari ◽  
Laila Stour ◽  
Abdellatif Ahbari
Keyword(s):  
Remote Sensing ◽  
Hydrological Model ◽  
Model Parameters ◽  
Flow Data ◽  
Random Parameters ◽  
Quality Of Data ◽  
Initial Values ◽  
Digital Elevation ◽  
Elevation Model

The availability, accessibility and quality of data are significant obstacles to hydrological modelling. Estimating the initial values of the hydrological model´’ ’s parameters is a laborious and determining task requiring much attention. Geographic information systems (GIS) and spatial remote sensing are prometting tools for processing and collecting data. In this work, we use an innovative approach to estimate the HEC-HMS hydrological model parameters from the soil map of Africa (250m), the land use map GLC30, the depth to bedrock map, the digital elevation model and observed flow data. The estimation approach is applied to the Ouergha basin (Sebou, Morocco). The proposed approach’s interest is to feed the HEC-HMS hydrological model with initial values of parameters close to the study area reality instead of using random parameters.

Estimating the ice thickness of the Müller Ice Cap using an inversion of the shallow ice approximation

2021 ◽  
Author(s):  
Ann-Sofie Priergaard Zinck ◽  
Aslak Grinsted
Keyword(s):  
Remote Sensing Data ◽  
The Arctic ◽  
Ice Thickness ◽  
Potential Contribution ◽  
Mean Squared Errors ◽  
Ice Caps ◽  
Ice Cap ◽  
Regression Parameters ◽  
Digital Elevation ◽  
Elevation Model

<p><span>The ice thickness of the Müller Ice Cap, Arctic Canada, is estimated using regression parameters obtained from an inversion of the shallow ice approximation by the use of a single Operation IceBridge flight line in combination with the glacier outline, surface slope, and elevation. The model is compared with an iterative inverse method of estimating the bedrock topography using PISM as a forward model. In both models the surface elevation is given by the Arctic Digital Elevation Model. The root mean squared errors of the ice thickness on the ice cap is 131 m and 139 m for the shallow ice inversion and the PISM model, respectively. Including the outlet glaciers increases the root mean squared errors to 136 m and 396 m, respectively. </span></p><p><span>The simplicity of the shallow ice inversion model, combined with the good results and the fact that only remote sensing data is needed, means that there is a possibility of applying this model in a global glacier thickness estimate by using the Randolph Glacier Inventory. Most global glacier estimates only provide the volume and not the ice thickness of the glaciers. Hence, global ice thickness models is of great importance in quantifying the potential contribution of sea level rise from the glaciers and ice caps around the globe. </span></p>

Docenten onder druk? De invloed van schoolgrootte op de arbeidssatisfactie van docenten in het voortgezet onderwijs

2008 ◽  
Vol 24 (3) ◽  
Author(s):  
Brenda Vermeeren ◽  
Bas de Wit
Keyword(s):  
Job Satisfaction ◽  
Secondary Education ◽  
School Size ◽  
Large Scale ◽  
Case Study Research ◽  
Secondary Data ◽  
Teacher Job Satisfaction ◽  
Teaching Staff ◽  
Teacher Work

Teachers under Pressure? The influence of school size upon teacher job satisfaction in Dutch secondary education Teachers under Pressure? The influence of school size upon teacher job satisfaction in Dutch secondary education In the last decades, a visible and significant upscaling of educational organisations can be witnessed in nearly all Dutch educational sectors. The rise of large-scaled public service organisations is related with increased interest in the quality and 'humanity' of education. Many discussions about large scale education are connected with supposed problems concerning teacher work within schools. It is regularly taken for granted that scale expansion puts teaching staff under pressure, and it has become usual to blame school size for several problems (demotivated professionals, failing quality of education). However, these assumptions are highly controversial. The research of presented in this article intends to answer the question what influence school size has upon teacher job satisfaction in Dutch secondary education. Based on a secondary data-analysis and case study research, we demonstrate that the scale of educational organisations does not necessarily say a lot. In contrast with prevailing views about school size, working in large secondary schools does not by definition harm teacher job satisfaction. On the basis of these findings, we propose a more fine-tuned approach, which attends the organisational context of teachers' work and other factors which affect teacher job satisfaction.

scholarly journals Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Hiroyuki Miura
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
Large Scale ◽  
Satellite Images ◽  
Erosion Depth ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fusion Analysis ◽  
Optical Satellite Images ◽  
Debris Flow Disaster

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.

A new method for supporting interpretation of paleochannels in a large scale — Detrended Digital Elevation Model Interpretation

Geomorphology ◽  
2020 ◽  
Vol 369 ◽  
pp. 107374
Author(s):  
Shuyan Zhang ◽  
Yong Ma ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Fulong Chen ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Large Scale ◽  
New Method ◽  
Model Interpretation ◽  
Digital Elevation ◽  
Elevation Model

scholarly journals A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands

Geosciences ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 248 ◽  
Author(s):  
Mariaelena Cama ◽  
Calogero Schillaci ◽  
Jan Kropáček ◽  
Volker Hochschild ◽  
Alberto Bosino ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing Data ◽  
Gully Erosion ◽  
Entropy Model ◽  
Global Issues ◽  
Susceptibility Maps ◽  
Digital Elevation ◽  
Elevation Model ◽  
Color Composite

Soil erosion represents one of the most important global issues with serious effects on agriculture and water quality, especially in developing countries, such as Ethiopia, where rapid population growth and climatic changes affect widely mountainous areas. The Meskay catchment is a head catchment of the Jemma Basin draining into the Blue Nile (Central Ethiopia) and is characterized by high relief energy. Thus, it is exposed to high degradation dynamics, especially in the lower parts of the catchment. In this study, we aim at the geomorphological assessment of soil erosion susceptibilities. First, a geomorphological map was generated based on remote sensing observations. In particular, we mapped three categories of landforms related to (i) sheet erosion, (ii) gully erosion, and (iii) badlands using a high-resolution digital elevation model (DEM). The map was validated by a detailed field survey. Subsequently, we used the three categories as dependent variables in a probabilistic modelling approach to derive the spatial distribution of the specific process susceptibilities. In this study we applied the maximum entropy model (MaxEnt). The independent variables were derived from a set of spatial attributes describing the lithology, terrain, and land cover based on remote sensing data and DEMs. As a result, we produced three separate susceptibility maps for sheet and gully erosion as well as badlands. The resulting susceptibility maps showed good to excellent prediction performance. Moreover, to explore the mutual overlap of the three susceptibility maps, we generated a combined map as a color composite where each color represents one component of water erosion. The latter map yields useful information for land-use managers and planning purposes.

scholarly journals A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

2020 ◽  
Vol 12 (3) ◽  
pp. 561 ◽  
Author(s):  
Bruno Adriano ◽  
Naoto Yokoya ◽  
Hiroyuki Miura ◽  
Masashi Matsuoka ◽  
Shunichi Koshimura
Keyword(s):  
Disaster Response ◽  
Large Scale ◽  
Reference Data ◽  
Mass Movement ◽  
Recovery Planning ◽  
Additional Information ◽  
Digital Elevation ◽  
Before And After ◽  
Intensity Images ◽  
Elevation Model

The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.

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