BANCO: An SVG-based approach to create Web sites for the management of remote sensing, spatial and non-spatial data

Unmanned Aerial System (UAS) is an efficient tool to bridge the gap between high expensive satellite remote sensing, manned aerial surveys, and labors time consuming conventional fieldwork techniques of data collection. UAS can provide spatial data at very fine (up to a few mm) and desirable temporal resolution. Several studies have used vegetation indices (VIs) calculated from UAS based on optical- and MSS-datasets to model the parameters of biophysical units of the Earth surface. They have used different techniques of estimations, predictions and classifications. However, these results vary according to used datasets and techniques and appear very site-specific. These existing approaches aren’t optimal and applicable for all cases and need to be tested according to sensor category and different geophysical environmental conditions for global applications. UAS remote sensing is a challenging and interesting area of research for sustainable land management.

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PREFACE – ISPRS Geospatial Week 2019

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-1-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 1-1

Author(s):

G. Vosselman ◽

S. J. Oude Elberink ◽

M. Y. Yang

Keyword(s):

Remote Sensing ◽

Data Quality ◽

Spatial Data ◽

Laser Scanning ◽

Spatial Information ◽

Smart Cities ◽

International Workshop ◽

Crowd Sourcing ◽

Spatial Data Quality ◽

Information Sciences

Abstract. The ISPRS Geospatial Week 2019 is a combination of 13 workshops organised by 30 ISPRS Working Groups active in areas of interest of ISPRS. The Geospatial Week 2019 is held from 10–14 June 2019, and is convened by the University of Twente acting as local organiser. The Geospatial Week 2019 is the fourth edition, after Antalya Turkey in 2013, La Grande Motte France in 2015 and Wuhan China in 2017.The following 13 workshops provide excellent opportunities to discuss the latest developments in the fields of sensors, photogrammetry, remote sensing, and spatial information sciences: <ul> <li>C3M&amp;GBD – Collaborative Crowdsourced Cloud Mapping and Geospatial Big Data</li> <li>CHGCS – Cryosphere and Hydrosphere for Global Change Studies</li> <li>EuroCow-M3DMaN – Joint European Calibration and Orientation Workshop and Workshop onMulti-sensor systems for 3D Mapping and Navigation</li> <li>HyperMLPA – Hyperspectral Sensing meets Machine Learning and Pattern Analysis</li> <li>Indoor3D</li> <li>ISSDQ – International Symposium on Spatial Data Quality</li> <li>IWIDF – International Workshop on Image and Data Fusion</li> <li>Laser Scanning</li> <li>PRSM – Planetary Remote Sensing and Mapping</li> <li>SarCon – Advances in SAR: Constellations, Signal processing, and Applications</li> <li>Semantics3D – Semantic Scene Analysis and 3D Reconstruction from Images and ImageSequences</li> <li>SmartGeoApps – Advanced Geospatial Applications for Smart Cities and Regions</li> <li>UAV-g – Unmanned Aerial Vehicles in Geomatics</li> </ul> Many of the workshops are part of well-established series of workshops convened in the past. They cover topics like UAV photogrammetry, laser scanning, spatial data quality, scene understanding, hyperspectral imaging, and crowd sourcing and collaborative mapping with applications ranging from indoor mapping and smart cities to global cryosphere and hydrosphere studies and planetary mapping.In total 143 full papers and 357 extended abstracts were submitted by authors from 63 countries. 1250 reviews have been delivered by 295 reviewers. A total of 81 full papers have been accepted for the volume IV-2/W5 of the International Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Another 289 papers are published in volume XLII-2/W13 of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.The editors would like to thank all contributing authors, reviewers and all workshop organizers for their role in preparing and organizing the Geospatial Week 2019. Thanks to their contributions, we can offer an excessive and varying collection in the Annals and the Archives.We hope you enjoy reading the proceedings.George Vosselman, Geospatial Week Director 2019, General Chair Sander Oude Elberink, Programme Chair Michael Ying Yang, Programme Chair

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Forest areas remote monitoring within the limits of transport objects impact

Automobile Roads and Road Construction ◽

10.33744/0365-8171-2021-109-109-118 ◽

2021 ◽

pp. 109-118

Author(s):

Dmytro Liashenko ◽

◽

Dmytro Pavliuk ◽

Vadym Belenok ◽

Vitalii Babii ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Remote Monitoring ◽

Forest Cover ◽

Landsat Imagery ◽

Remote Sensing Data ◽

Transport Infrastructure ◽

Nature Management ◽

Collecting Methods ◽

Remote Sensing Methods

The article studies the issues of using remote sensing data for the tasks of ensuring sustainable nature management in the territories within the influence of transport infrastructure objects. Peculiarities of remote monitoring for tasks of transport networks design and in the process of their operation are determined. The paper analyzes the development of modern remote sensing methods (satellite imagery, the use of mobile sensors installed on cars or aircraft). A brief overview of spatial data collecting methods for the tasks of managing the development of territories within the influence of transport infrastructure (roads, railways, etc.) has made. The article considers the experience of using remote sensing technologies to monitor changes in the parameters of forest cover in the Transcarpathian region (Ukraine) in areas near to highways, by use Landsat imagery.

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A REVIEW OF 3D GIS FOR USE IN CREATING VIRTUAL HISTORIC DUBLIN

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

10.5194/isprs-archives-xlii-2-w9-249-2019 ◽

2019 ◽

Vol XLII-2/W9 ◽

pp. 249-254

Author(s):

A. Chenaux ◽

M. Murphy ◽

S. Pavia ◽

S. Fai ◽

T. Molnar ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Complex Analysis ◽

Remote Sensing Data ◽

Public Works ◽

3D Gis ◽

Building Information ◽

Urban Rural ◽

Interactive 3D ◽

Under Construction

Abstract. This paper illustrates how BIM integration with GIS is approached as part of the workflow in creating Virtual Historic Dublin. A design for a WEB based interactive 3D model of historic buildings and centres in Dublin City (Virtual Historic Dublin City) paralleling smart city initiates is now under construction and led by the National Monuments at the Office of Public Works in Ireland. The aim is to facilitate the conservation and maintenance of historic infrastructure and fabric and the dissemination of knowledge for education and cultural tourism using an extensive Historic Building Information Model. Remote sensing data is now processed with greater ease to create 3D intelligent models in Historic BIM. While the use of remote sensing, HBIM and game engine platforms are the main applications used at present, 3D GIS has potential to form part of the workflow for developing the Virtual Historic City. 2D GIS is now being replaced by 3D spatial data allowing more complex analysis to be carried out, 3D GIS can define and depict buildings, urban rural centres in relation to their geometry topological, semantic and visualisation properties. The addition of semantic attributes allows complex analysis and 3D spatial queries for modelling city and urban elements. This analysis includes fabric and structural elements of buildings, relief, vegetation, transportation, water bodies, city furniture and land use.

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Understanding Urban Growth in Beijing-Tianjin-Hebei Region over the Past 100 Years Using Old Maps and Landsat Data

Remote Sensing ◽

10.3390/rs13163264 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3264 ◽

Cited By ~ 1

Author(s):

Shuang Li ◽

Zhongqiu Sun ◽

Yafei Wang ◽

Yuxia Wang

Keyword(s):

Remote Sensing ◽

Urban Area ◽

Spatial Data ◽

Urban Expansion ◽

Remote Sensing Images ◽

The Past ◽

Old Maps ◽

The World ◽

The City

Studying urban expansion from a longer-term perspective is of great significance to obtain an in-depth understanding of the process of urbanization. Remote sensing data are mostly selected to investigate the long-term expansion of cities. In this study, we selected the world-class urban agglomeration of Beijing-Tianjin-Hebei (BTH) as the study area, and then discussed how to make full use of multi-source, multi-category, and multi-temporal spatial data (old maps and remote sensing images) to study long-term urbanization. Through this study, we addressed three questions: (1) How much has the urban area in BTH expanded in the past 100 years? (2) How did the urban area expand in the past century? (3) What factors or important historical events have changed the development of cities with different functions? By comprehensively using urban spatial data, such as old maps and remote sensing images, geo-referencing them, and extracting built-up area information, a long-term series of urban built-up areas in the BTH region can be obtained. Results show the following: (1) There was clear evidence of dramatic urban expansion in this area, and the total built-up area had increased by 55.585 times, from 126.181 km2 to 7013.832 km2. (2) Continuous outward expansion has always been the main trend, while the compactness of the built-up land within the city is constantly decreasing and the complexity of the city boundary is increasing. (3) Cities in BTH were mostly formed through the construction of city walls during the Ming and Qing dynasties, and the expansion process was mostly highly related to important political events, traffic development, and other factors. In summary, the BTH area, similarly to China and most regions of the world, has experienced rapid urbanization and the history of such ancient cities should be further preserved with the combined use of old maps.

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Spatial Data Lake for Smart Cities: From Design to Implementation

AGILE: GIScience Series ◽

10.5194/agile-giss-1-8-2020 ◽

2020 ◽

Vol 1 ◽

pp. 1-15

Author(s):

Rodrique Kafando ◽

Rémy Decoupes ◽

Lucile Sautot ◽

Maguelonne Teisseire

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Smart Cities ◽

Real Data ◽

Heterogeneous Data ◽

Geographic Information ◽

Sensor Data ◽

Remote Sensing Images ◽

Thematic Information ◽

General Data

Abstract. In this paper, we propose a methodology for designing data lake dedicated to Spatial Data and an implementation of this specific framework. Inspired from previous proposals on general data lake Design and based on the Geographic information – Metadata normalization (ISO 19115), the contribution presented in this paper integrates, with the same philosophy, the spatial and thematic dimensions of heterogeneous data (remote sensing images, textual documents and sensor data, etc). To support our proposal, the process has been implemented in a real data project in collaboration with Montpellier Métropole Méditerranée (3M), a metropolis in the South of France. This framework offers a uniform management of the spatial and thematic information embedded in the elements of the data lake.

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Land and Forest Management by Land Use/ Land Cover Analysis and Change Detection Using Remote Sensing and GIS

Journal of Landscape Ecology ◽

10.1515/jlecol-2016-0005 ◽

2016 ◽

Vol 9 (1) ◽

pp. 63-77 ◽

Cited By ~ 4

Author(s):

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Management ◽

Land Cover ◽

Spatial Data ◽

Management Strategies ◽

Geographical Information ◽

Spatial Data Analysis ◽

Study Region ◽

Rugged Terrain

Abstract Remote sensing and Geographical Information System (GIS) are the most effective tools in spatial data analysis. Natural resources like land, forest and water, these techniques have proved a valuable source of information generation as well as in the management and planning purposes. This study aims to suggest possible land and forest management strategies in Chakia tahsil based on land use and land cover analysis and the changing pattern observed during the last ten years. The population of Chakia tahsil is mainly rural in nature. The study has revealed that the northern part of the region, which offers for the settlement and all the agricultural practices constitutes nearly 23.48% and is a dead level plain, whereas the southern part, which constitute nearly 76.6% of the region is characterized by plateau and is covered with forest. The southern plateau rises abruptly from the northern alluvial plain with a number of escarpments. The contour line of 100 m mainly demarcates the boundary between plateau and plain. The plateau zone is deeply dissected and highly rugged terrain. The resultant topography comprises of a number of mesas and isolated hillocks showing elevation differences from 150 m to 385 m above mean sea level. Being rugged terrain in the southern part, nowadays human encroachment are taking place for more land for the cultivation. The changes were well observed in the land use and land cover in the study region. A large part of fallow land and open forest were converted into cultivated land.

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The flood resilience dashboard: community resilience

10.5194/egusphere-egu21-14277 ◽

2021 ◽

Author(s):

Ian McCallum ◽

Stefan Velev ◽

Finn Laurien ◽

Reinhard Mechler ◽

Adriana Keating ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Forest Cover ◽

Community Resilience ◽

Mobile Technologies ◽

Capacity Assessment ◽

Night Time ◽

Web Mapping ◽

Crowdsourced Data ◽

The World

Communities around the world in flood-prone regions are increasingly aware of the benefits of using spatial data to better understand their predicament. With the advent of web mapping, free and open satellite data and the proliferation of mobile technologies, the possibilities for both understanding and improving community resilience are on the rise.Here we present the &#8220;Flood Resilience Dashboard&#8221;, which is designed to put geo-spatial flood resilience data into the hands of practitioners. The objective is to provide a platform for practitioners in the Zurich Flood Resilience Alliance which gives access to both community resilience data and freely available, peer reviewed flood risk data, which can be used for decision support at scale. This data will include among others the Zurich Flood Resilience Measurement for Communities (FRMC) data, Vulnerability Capacity Assessment (VCA) maps, remote sensing derived information on flooding and other biophysical datasets (e.g. forest cover, water extent), modelled risk information, satellite imagery (e.g. night-time lights), crowdsourced data and more. Using two case studies, we illustrate how the above-mentioned datasets help to better understand community resilience. When co-developed with communities, these examples could potentially be scaled up and applied to similar regions around the world.

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Efficacy of using radar-derived factors in landslide susceptibility analysis: case study of Koslanda, Sri Lanka

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-1881-2019 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1881-1893 ◽

Cited By ~ 1

Author(s):

Ahangama Kankanamge Rasika Nishamanie Ranasinghe ◽

Ranmalee Bandara ◽

Udeni Gnanapriya Anuruddha Puswewala ◽

Thilantha Lakmal Dammalage

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Landslide Susceptibility ◽

Prediction Models ◽

Low Cost ◽

Information Value ◽

Spatial Data Analysis ◽

Optical Remote Sensing ◽

Analytic Hierarchy ◽

Technological Developments

Abstract. Through the recent technological developments of radar and optical remote sensing in (i) the areas of temporal, spectral, spatial, and global coverage; (ii) the availability of such images either at a low cost or free of charge; and (iii) the advancement of tools developed in image analysis techniques and GIS for spatial data analysis, there is a vast potential for landslide studies using remote sensing and GIS as tools. Hence, this study aimed to assess the efficacy of using radar-derived factors (RDFs) in identifying landslide susceptibility using the bivariate information value method (InfoVal method) and the multivariate multi-criteria decision analysis based on the analytic hierarchy process statistical analysis. Using identified landslide causative factors, four landslide prediction models – bivariate with and without RDFs as well as multivariate with and without RDFs – were generated. Twelve factors such as topographical, hydrological, geological, land cover and soil plus three RDFs are considered. The weight of index for landslide susceptibility is calculated by using the landslide failure map, and susceptibility regions are categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With the integration of RDFs, boundary detection between high- and very-low-susceptibility regions are increased by 7 % and 4 % respectively.

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