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

2019 ◽  
Vol 19 (8) ◽  
pp. 1881-1893 ◽  
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.

scholarly journals Efficacy of using Radar Induced Factors in Landslide Susceptibility Analysis: case study of Koslanda, Sri Lanka

2019 ◽  
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 ◽  
Bivariate Analysis ◽  
Optical Remote Sensing ◽  
Analytic Hierarchy

Abstract. Through recent technological developments of radar and optical remote sensing in the areas of temporal, spectral, spatial, and global coverage, the availability of such images either at a low cost or free of charge, and the advancement of tools developed in image analysis techniques and GIS for spatial data analysis, a large variety of applications using remote sensing and GIS as tools are possible. Hence, this study aims to assess the efficacy of using Radar Induced Factors (RIF) in identifying landslide susceptibility using bivariate Information Value method (InfoVal method) and multivariate Multi Criteria Decision Analysis based on the Analytic Hierarchy Process statistical analysis. Using identified landslide causative factors, four landslide prediction models as bivariate without and with RIF, multivariate without and with RIF are generated. Twelve factors topographical, hydrological, geological, land cover and soil plus three RIF are considered. The prediction levels of susceptibility regions are distinguished and categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With integration of RIF, boundary detection between high and very low areas increased by 7 %, and 4 % respectively, and there is an improvement of 2.45 % prediction and 1.12 % validation performances of bivariate analysis than multivariate.

scholarly journals A Low-Cost Optical Remote Sensing Application for Glacier Deformation Monitoring in an Alpine Environment

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1750 ◽  
Author(s):  
Daniele Giordan ◽  
Paolo Allasia ◽  
Niccolò Dematteis ◽  
Federico Dell’Anese ◽  
Marco Vagliasindi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
Deformation Monitoring ◽  
Optical Remote Sensing ◽  
Alpine Environment ◽  
Remote Sensing Application ◽  
Sensing Application

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

2016 ◽  
Vol 9 (1) ◽  
pp. 63-77 ◽  
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.

Landslide Susceptibility Models Utilising Spatial Data Analysis Techniques. A Case Study from the Lower Deba Valley, Guipuzcoa (Spain)

2003 ◽  
Vol 30 (3) ◽  
pp. 267-279 ◽  
Author(s):  
Juan Remondo ◽  
Alberto González-Díez ◽  
José Ramón Díaz De Terán ◽  
Antonio Cendrero
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Spatial Data Analysis ◽  
Analysis Techniques

scholarly journals Ecological Environment Assessment in World Natural Heritage Site Based on Remote-Sensing Data. A Case Study from the Bayinbuluke

2019 ◽  
Vol 11 (22) ◽  
pp. 6385 ◽  
Author(s):  
Qin Liu ◽  
Zhaoping Yang ◽  
Fang Han ◽  
Hui Shi ◽  
Zhi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Ecological Status ◽  
Spatial Data Analysis ◽  
Ecological Environment ◽  
Species Structure ◽  
Heritage Sites ◽  
Environment Assessment ◽  
Heritage Site ◽  
Better Than

Ecological environment assessment would be helpful for a rapid and systematic understanding of ecological status and would contribute to formulate appropriate strategies for the sustainability of heritage sites. A procedure based on spatial principle component analysis was employed to measure the ecological status in Bayinbuluke; exploratory spatial data analysis and geo-detector model were introduced to assess the spatio-temporal distribution characteristics and detect the driving factors of the ecological environment. Five results are presented: (1) During 2007–2018, the average values of moisture, greenness, and heat increased by 51.72%, 23.10%, and 4.99% respectively, and the average values of dryness decreased by 56.70%. However, the fluctuation of each indicator increased. (2) The ecological environment of Bayinbuluke was improved from 2007 to 2018, and presented a distribution pattern that the heritage site was better than the buffer zone, and the southeast area was better than the northwest area. (3) The ecological environment presented a significant spatial clustering characteristic, and four types of spatial associations were proposed for assessing spatial dependence among the samples. (4) Elevation, protection partition, temperature, river, road, tourism, precipitation, community resident, and slope were statistically significant with respect to the changes in ecological status, and the interaction of any two factors was higher than the effect of one factor alone. (5) The remote-sensing ecological index (RSEI) could reflect the vegetation growth to a certain extent, but has limited ability to respond to species structure. Overall, the framework presented in this paper realized a visual and measurable approach for a detailed monitoring of the ecological environment and provided valuable information for the protection and management of heritage sites.

Use of GIS and Remote Sensing for Landslide Susceptibility Mapping

2019 ◽  
pp. 384-398 ◽  
Author(s):  
Arzu Erener ◽  
Gulcan Sarp ◽  
Sebnem H. Duzgun
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Gis And Remote Sensing ◽  
Data Layers

In recent years, geographical information systems (GISs) and remote sensing (RS) have proven to be common tools adopted for different studies in different scientific disciplines. GIS is defined as a set of tools for the input, storage, retrieval, manipulation, management, modeling, analysis, and output of spatial data. RS, on the other hand, can play a role in the production of a data and in the generation of thematic maps related to spatial studies. This study focuses on use of GIS and RS data for landslide susceptibility mapping. Five factors including normalized difference vegetation index (NDVI) and topographic wetness index (TWI), slope, lineament density, and distance to roads were used for the grid-based approach for landslide susceptibility mappings. Results of this study suggest that geographic information systems can effectively be used to obtain susceptibility maps by compiling and overlaying several data layers relevant to landslide hazards.

An Introduction to Spatial Data Analysis

2020 ◽  
Author(s):  
Martin Wegmann ◽  
Jakob Schwalb-Willmann ◽  
Stefan Dech
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Spatial Data Analysis ◽  
Data Sets ◽  
Data Handling ◽  
Book Covers

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

scholarly journals Evaluation Model of Remote Sensing Satellites Cooperative Observation Capability

2021 ◽  
Author(s):  
Zhonggang Zheng ◽  
Kun Fu ◽  
Qingmei Li
Keyword(s):  
Remote Sensing ◽  
Process Model ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Complex Problem ◽  
Optical Remote Sensing ◽  
Analytic Hierarchy ◽  
Number Of Factors ◽  
Analytic Hierarchical Process ◽  
Hierarchy Process

This paper proposed a new evaluation model based on analytic hierarchy process to quantitatively evaluate the capability of multi-satellite cooperative remote sensing observation. The analytic hierarchical process model is a combination of qualitative and quantitative analysis of systematic decision analysis method. According to the objective of the remote sensing cooperative observation mission, we decompose the complex problem into several levels and a number of factors, compare and calculate various factors in pairs, and obtain the combination weights of different schemes. The model can be used to evaluate the observation capability of resource satellites. Taking the optical remote sensing satellites such as China’s resource satellite series and GF-4 as examples, this paper verifies and evaluates the model for three typical tasks: point target observation, regional target observation and moving target continuous observation. The results show that the model can provide quantitative reference and model support for comprehensive evaluation of the collaborative observation capability of remote sensing satellites.

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