cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12853985.v1
title: A distributed framework for extracting maritime traffic patterns

description: All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels’ trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.
DOI: 10.6084/m9.figshare.8197595
title: Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships

description: Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations.
DOI: 10.6084/m9.figshare.8197595.v1
title: Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships

description: Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations.
DOI: 10.6084/m9.figshare.7957343
title: Individual Movement - Sequence Analysis Method (IM-SAM): characterizing spatio-temporal patterns of animal habitat use across landscapes

description: We present methodological advances to a recently developed framework to study sequential habitat use by animals using a visually-explicit and tree-based Sequence Analysis Method (SAM), derived from molecular biology and more recently used in time geography. Habitat use sequences are expressed as annotations obtained by intersecting GPS movement trajectories with environmental layers. Here, we develop IM-SAM, where we use the individual reference area of use as the reference spatial context. To assess IM-SAM’s applicability, we investigated the sequential use of open and closed habitats across multiple European roe deer populations ranging in landscapes with contrasting structure. Starting from simulated sequences based on a mechanistic movement model, we found that different sequential patterns of habitat use were distinguished as separate, robust clusters, with less variable cluster size when habitats were present in equal proportions within the individual reference area of use. Application on real roe deer sequences showed that our approach effectively captured variation in spatio-temporal patterns of sequential habitat use, and provided evidence for important behavioral processes, such as day-night habitat alternation. By characterizing sequential habitat use patterns of animals, we may better evaluate the temporal trade-offs in animal habitat use and how they are affected by changes in landscapes.
DOI: 10.6084/m9.figshare.7957343.v1
title: Individual Movement - Sequence Analysis Method (IM-SAM): characterizing spatio-temporal patterns of animal habitat use across landscapes

description: We present methodological advances to a recently developed framework to study sequential habitat use by animals using a visually-explicit and tree-based Sequence Analysis Method (SAM), derived from molecular biology and more recently used in time geography. Habitat use sequences are expressed as annotations obtained by intersecting GPS movement trajectories with environmental layers. Here, we develop IM-SAM, where we use the individual reference area of use as the reference spatial context. To assess IM-SAM’s applicability, we investigated the sequential use of open and closed habitats across multiple European roe deer populations ranging in landscapes with contrasting structure. Starting from simulated sequences based on a mechanistic movement model, we found that different sequential patterns of habitat use were distinguished as separate, robust clusters, with less variable cluster size when habitats were present in equal proportions within the individual reference area of use. Application on real roe deer sequences showed that our approach effectively captured variation in spatio-temporal patterns of sequential habitat use, and provided evidence for important behavioral processes, such as day-night habitat alternation. By characterizing sequential habitat use patterns of animals, we may better evaluate the temporal trade-offs in animal habitat use and how they are affected by changes in landscapes.
DOI: 10.6084/m9.figshare.12909630
title: Analyzing ice dynamics using Sentinel-1 data at the Solheimajoküll Glacier, Iceland

description: The climate in southern Iceland has warmed over the last 70 years, resulting in accelerated glacier dynamics at the Solheimajoküll glacier. In this study, we compare glacier terminus locations from 1973 to 2018, to changes in climate across the study area, and we derive ice-surface velocities (2015–2018) from satellite remote-sensing imagery (Sentinel-1) using the offset-tracking method. There have been two regional temperature trends in the study period: cooling (1973–1979) and warming (1980–2018). Our results indicate a time lag of about 20 years between the onset of glacier retreat (−53 m/year since 2000) and the inception of the warming period. Seasonally, the velocity time series suggest acceleration during the summer melt season since 2016, whereas glacier velocities during accumulation months were constant. The highest velocities were observed at high elevations where the ice-surface slope is the steepest. We tested several scenarios to assess the hydrological time response to glacier accelerations, with the highest correlations being found between one and 30 days after the velocity estimates. Monthly correlation analyses indicated inter-annual and intra-annual variability in the glacier dynamics. Additionally, we investigate the linkage between glacier velocities and meltwater outflow parameters as they provide useful information about internal processes in the glacier. Velocity estimates positively correlate with water level and negatively correlate with water conductivity between April and August. There is also a disruption in the correlation trend between water conductivity and ice velocity in June, potentially due to a seasonal release of geothermal water.
DOI: 10.6084/m9.figshare.12909630.v1
title: Analyzing ice dynamics using Sentinel-1 data at the Solheimajoküll Glacier, Iceland

description: The climate in southern Iceland has warmed over the last 70 years, resulting in accelerated glacier dynamics at the Solheimajoküll glacier. In this study, we compare glacier terminus locations from 1973 to 2018, to changes in climate across the study area, and we derive ice-surface velocities (2015–2018) from satellite remote-sensing imagery (Sentinel-1) using the offset-tracking method. There have been two regional temperature trends in the study period: cooling (1973–1979) and warming (1980–2018). Our results indicate a time lag of about 20 years between the onset of glacier retreat (−53 m/year since 2000) and the inception of the warming period. Seasonally, the velocity time series suggest acceleration during the summer melt season since 2016, whereas glacier velocities during accumulation months were constant. The highest velocities were observed at high elevations where the ice-surface slope is the steepest. We tested several scenarios to assess the hydrological time response to glacier accelerations, with the highest correlations being found between one and 30 days after the velocity estimates. Monthly correlation analyses indicated inter-annual and intra-annual variability in the glacier dynamics. Additionally, we investigate the linkage between glacier velocities and meltwater outflow parameters as they provide useful information about internal processes in the glacier. Velocity estimates positively correlate with water level and negatively correlate with water conductivity between April and August. There is also a disruption in the correlation trend between water conductivity and ice velocity in June, potentially due to a seasonal release of geothermal water.
DOI: 10.6084/m9.figshare.12436583.v1
title: Simulation case study of displacement monitoring using network derived positioning

description: Network-based real-time kinematic (NRTK) GNSS (Global Navigation Satellite System) is among the most commonly used surveying technique in many countries for various applications that requires a single GNSS receiver. The main principle of this system is to generate reliable error models that can mitigate dispersive (e.g. ionospheric delay) and non-dispersive (e.g. tropospheric delay and orbit bias) errors, which are the main sources of the degradation of positioning accuracy. In this study, the performance of NRTK positioning for deformation and landslide monitoring is investigated using a simulation apparatus. For this purpose, 20 simulated two-dimensional and vertical displacements are conducted within a two-month period in 2019. The 24-h NRTK data with 1-s sampling intervals are obtained using a CHC N72 GNSS reference receiver for each displacement. The 24-h NRTK data are mutually subdivided into 12-, 6-, 3- and 1-h non-overlapping sessions to investigate the effect of observation time on monitoring displacement performance. Two filtering methods – namely, averaging the raw observations and averaging the observations derived from Kalman filtering – are chosen for each session. The displacements obtained from the filtered NRTK observations are compared to the simulated (true) displacements. Another experiment is carried out to monitor the displacement in real time using first-order low-pass and moving-average filters. The NRTK data are collected during the displacement for a real-time experiment. The results of the experiments indicate that 1-sigma horizontal and vertical Root Mean Square errors (RMSEs) of displacements between the filtered NRTK data and true displacements are determined to be 1.5 and 5.4 mm, respectively, using 24-h data. The results also suggest that no significant difference is observed between the filtering methods for most of the points. The RMSE of the two-dimensional displacement direction between the azimuth angle derived from the filtered NRTK data and the azimuth angle of true direction is found to be 15° for 3 mm displacement using 24-h data. For detecting the displacements visually in real time, the minimum magnitude of displacements needs to be 7 mm and 10 mm for the horizontal and vertical components, respectively, to distinguish the displacements from the noise.
DOI: 10.6084/m9.figshare.11902350
title: Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh

description: Since August 2017, more than 744,400 stateless Rohingya refugees – an ethnic Muslim minority group from the Rakhine State – have entered Bangladesh to escape serious crimes against humanity conducted by the Myanmar Army. Extensive level of deforestation and hill cutting activities took place in Cox’s Bazar District (CBD) in Bangladesh to accommodate them. The refugee camps are sitting on hills and loose soil and are highly vulnerable to rainfall-triggered landslides. Notably in June 2017, landslides in the same region killed at least 160 people. From this perspective, the study aims to develop a localised landslide early warning system (EWS) for the Rohingya refugees and their host communities in CBD. A novel method, combining landslide inventory and susceptibility maps, rainfall thresholds and dynamic web-based alert system, has been introduced to develop the landslide early warning system (EWS) by applying advanced geoinformation techniques. Results suggest that approximately 5,800 hectares of forest land cover disappeared due to the 2017 Rohingya influx. Land cover changes through hill cutting andslope modifications, and unplanned urbanisation are predominantly responsible for slope failures and consecutive 5-day periods of rainfall between 95–220 mm could initiate landslides in high susceptible areas. The EWS would support the local authorities and international organisations in reducing disaster risks and saving lives from landslides in a humanitarian context.
DOI: 10.6084/m9.figshare.12436583
title: Simulation case study of displacement monitoring using network derived positioning

description: Network-based real-time kinematic (NRTK) GNSS (Global Navigation Satellite System) is among the most commonly used surveying technique in many countries for various applications that requires a single GNSS receiver. The main principle of this system is to generate reliable error models that can mitigate dispersive (e.g. ionospheric delay) and non-dispersive (e.g. tropospheric delay and orbit bias) errors, which are the main sources of the degradation of positioning accuracy. In this study, the performance of NRTK positioning for deformation and landslide monitoring is investigated using a simulation apparatus. For this purpose, 20 simulated two-dimensional and vertical displacements are conducted within a two-month period in 2019. The 24-h NRTK data with 1-s sampling intervals are obtained using a CHC N72 GNSS reference receiver for each displacement. The 24-h NRTK data are mutually subdivided into 12-, 6-, 3- and 1-h non-overlapping sessions to investigate the effect of observation time on monitoring displacement performance. Two filtering methods – namely, averaging the raw observations and averaging the observations derived from Kalman filtering – are chosen for each session. The displacements obtained from the filtered NRTK observations are compared to the simulated (true) displacements. Another experiment is carried out to monitor the displacement in real time using first-order low-pass and moving-average filters. The NRTK data are collected during the displacement for a real-time experiment. The results of the experiments indicate that 1-sigma horizontal and vertical Root Mean Square errors (RMSEs) of displacements between the filtered NRTK data and true displacements are determined to be 1.5 and 5.4 mm, respectively, using 24-h data. The results also suggest that no significant difference is observed between the filtering methods for most of the points. The RMSE of the two-dimensional displacement direction between the azimuth angle derived from the filtered NRTK data and the azimuth angle of true direction is found to be 15° for 3 mm displacement using 24-h data. For detecting the displacements visually in real time, the minimum magnitude of displacements needs to be 7 mm and 10 mm for the horizontal and vertical components, respectively, to distinguish the displacements from the noise.
DOI: 10.6084/m9.figshare.11902350.v1
title: Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh

description: Since August 2017, more than 744,400 stateless Rohingya refugees – an ethnic Muslim minority group from the Rakhine State – have entered Bangladesh to escape serious crimes against humanity conducted by the Myanmar Army. Extensive level of deforestation and hill cutting activities took place in Cox’s Bazar District (CBD) in Bangladesh to accommodate them. The refugee camps are sitting on hills and loose soil and are highly vulnerable to rainfall-triggered landslides. Notably in June 2017, landslides in the same region killed at least 160 people. From this perspective, the study aims to develop a localised landslide early warning system (EWS) for the Rohingya refugees and their host communities in CBD. A novel method, combining landslide inventory and susceptibility maps, rainfall thresholds and dynamic web-based alert system, has been introduced to develop the landslide early warning system (EWS) by applying advanced geoinformation techniques. Results suggest that approximately 5,800 hectares of forest land cover disappeared due to the 2017 Rohingya influx. Land cover changes through hill cutting andslope modifications, and unplanned urbanisation are predominantly responsible for slope failures and consecutive 5-day periods of rainfall between 95–220 mm could initiate landslides in high susceptible areas. The EWS would support the local authorities and international organisations in reducing disaster risks and saving lives from landslides in a humanitarian context.
DOI: 10.6084/m9.figshare.13083796.v2
title: A data-mining approach towards damage modelling for <i>El Niño</i> events in Peru

description: Compound natural hazards like <i>El Niño</i> events cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after the <i>El Niño</i> event 2017 – which caused intense rainfall, ponding water, flash floods and landslides – enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.
DOI: 10.6084/m9.figshare.13083796.v1
title: A data-mining approach towards damage modelling for <i>El Niño</i> events in Peru

description: Compound natural hazards like <i>El Niño</i> events cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after the <i>El Niño</i> event 2017 – which caused intense rainfall, ponding water, flash floods and landslides – enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.