44 research outputs found
Advanced Earth Observation for Humanitarian Information Extraction
No full textAdvanced Earth observation (EO) is increasingly recognized as an indispensible tool to support rigorous and robust decision making in crisis management. In crisis scenarios, EO data need to be streamed through time-critical workflows for delivering reliable and effective information to civil protection authorities. In this context, the over arching goal of this research is to closely examine the integral segments of routing EO-based rapid mapping workflows to understand prevailing shortfalls and to devise novel approaches to catalyze conditioned geoinformation delivery to cater increasing user demand. This study envisions three interconnected objectives, which are primarily fuelled by very high spatial resolution (VHSR) imagery, data fusion, image segmentation, and geographic object-based analysis (GEOBIA) framework. Focal study areas encapsulate natural and anthropogenic crises having occurred in the recent past: the 2010 earthquake-damaged areas in Haiti, the 2010 flood-impacted sites in Pakistan, and armed-conflicted areas and internally displaced persons (IDP) camps in Sri Lanka The first objective investigated how different data fusion algorithms perform whe
Advanced Earth Observation for Humanitarian Information Extraction
No full textAdvanced Earth observation (EO) is increasingly recognized as an indispensible tool to support rigorous and robust decision making in crisis management. In crisis scenarios, EO data need to be streamed through time-critical workflows for delivering reliable and effective information to civil protection authorities. In this context, the over arching goal of this research is to closely examine the integral segments of routing EO-based rapid mapping workflows to understand prevailing shortfalls and to devise novel approaches to catalyze conditioned geoinformation delivery to cater increasing user demand. This study envisions three interconnected objectives, which are primarily fuelled by very high spatial resolution (VHSR) imagery, data fusion, image segmentation, and geographic object-based analysis (GEOBIA) framework. Focal study areas encapsulate natural and anthropogenic crises having occurred in the recent past: the 2010 earthquake-damaged areas in Haiti, the 2010 flood-impacted sites in Pakistan, and armed-conflicted areas and internally displaced persons (IDP) camps in Sri Lanka
The first objective investigated how different data fusion algorithms perform when applied to VHSR satellite images that encompass ongoing- and post-crises scenes. The evaluation entailed twelve fusion algorithms. The spatial and spectral fidelities were assessed subjectively using fourteen quality indices. Ehlers, Wavelet, and High-pass filtering (HPF) fusion algorithms had the best scores for the majority of spectral quality indices. The University of New Brunswick and Gram-Schmidt fusion algorithms had the best scores for spatial metrics. The HPF algorithm emerged as the overall best performing fusion algorithm.
The second objective aimed to unravel the synergies of data fusion and image segmentation in the context of EO-based rapid mapping workflows. We statistically compared the quality image object candidates among twelve fused products and their original MS and PAN images. We have shown that the GEOBIA framework has the ability to create meaningful image objects during the segmentation process by compensating the fused image’s spectral distortions with the high-frequency information content that has been injected during fusion. We further questioned the necessity of the data fusion step in rapid mapping context. Bypassing time-intense data fusion steps helps to intensify EO-based rapid mapping workflows.
The third objective explored the efficacy of supervised empirical discrepancy measures for optimizing multiresolution segmentation (MRS) algorithm. I selected the Euclidean distance 2 (ED2) metric, a recently proposed supervised metric that measures dissimilarity between a reference polygon and an image object candidate, as a candidate to investigate the validity and efficacy of empirical discrepancy measures for finding the optimal scale parameter setting of the MRS algorithm. The discriminative capacity of the ED2 metric across different scales groups was tested using non-parametric statistical methods. My results showed that the ED2 metric significantly discriminates the quality of image object candidates at smaller scale values but it loses the sensitivity at larger scale values. This questions the meaningfulness of the ED2 metric in the MRS algorithms parameter optimization
Assessing the spatial fidelity of resolution-enhanced imagery using Fourier analysis: a proof-of-concept study
No full textEvaluating remote sensing image fusion algorithms for use in humanitarian crisis management
No full textClassifying and Georeferencing Indoor Point Clouds with ArcGIS
No full textThis study aimed to develop and apply a manual procedure for classifying and georeferencing indoor point clouds that we created using Paracosm’s PX-80 handheld three-dimensional laser scanner. We collected data for 11 buildings in Connecticut, USA and focused on classifying features-of-interest to public safety personnel (i.e., doors, windows, fire alarms, etc.). ArcGIS Desktop was used to manually digitize features that were easily identified in the point cloud and Paracosm’s Retrace was used to digitize small features for which imagery was needed for identification. We developed several tools in Python to facilitate point cloud classification and georeferencing. The procedure allowed accurate mapping of features as small as a sprinkler head. Point cloud classification and georeferencing for a 14 000 m2 building took 20–40 hours, depending on building characteristics and the types of features mapped. The methods can be applied in mapping a wide variety of features in indoor or outdoor environments
Permafrost thaw-related infrastructure damage costs in Alaska are projected to double under medium and high emission scenarios
No full textAbstract Infrastructure across the circumpolar Arctic is exposed to permafrost thaw hazards caused by global warming and human activity, creating the risk of damage and economic losses. However, losses are underestimated in existing literature due to incomprehensive infrastructure maps. Here, we mapped infrastructure from 0.5 m resolution satellite imagery of 285 Alaskan communities with a deep learning detection model. Combined with OpenStreetMap, we mapped a statewide Alaskan building footprint of 53 M m2 and a road network of 50,477 km. With deep learning, we expanded the OpenStreetMap building footprint by 47% statewide and 86% on discontinuous and continuous permafrost. Doubling the amount found in existing literature by using our improved map, we estimated that building and road losses due to permafrost thaw could cost Alaska 51B under the SSP245 and SSP585 scenarios, respectively. Finally, we highlight shortcomings in U.S. national risk assessments, which do not account for Alaskan permafrost hazards
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images
No full textThe logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census
Ice-Wedge Network Centerline and Polygon Area in the Bernard River Watershed, Banks Island Canada; 2010-2020
No full textThe evolution of ice-wedge polygon networks in tundra fire scars
No full textAbstract In response to increasing temperatures and precipitation in the Arctic, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowland landscapes, polygonal ice wedges are especially vulnerable, and their melting induces widespread subsidence triggering the transition from low-centered (LCP) to high-centered polygons (HCP) by forming degrading troughs. This process has an important impact on surface hydrology, as the connectivity of such trough networks determines the rate of drainage of an entire landscape (Liljedahl et al., 2016). While scientists have observed this degradation trend throughout large domains in the polygonal patterned Arctic landscape over timescales of multiple decades, it is especially evident in disturbed areas such as fire scars (Jones et al., 2015). Here, wildfires removed the insulating organic soil layer. We can therefore observe the LCP-to-HCP transition within only several years. Until now, studies on quantifying trough connectivity have been limited to local field studies and sparse time series only. With high-resolution Earth observation data, a more comprehensive analysis is possible. However, when considering the vast and ever-growing volumes of data generated, highly automated and scalable methods are needed that allow scientists to extract information on the geomorphic state and on changes over time of ice-wedge trough networks.
In this study, we combine very-high-resolution (VHR) aerial imagery and comprehensive databases of segmented polygons derived from VHR optical satellite imagery (Witharana et al., 2018) to investigate the changing polygonal ground landscapes and their environmental implications in fire scars in Northern and Western Alaska. Leveraging the automated and scalable nature of our recently introduced approach (Rettelbach et al., 2021), we represent the polygon networks as graphs (a concept from computer science to describe complex networks) and use graph metrics to describe the state of these (hydrological) trough networks. Due to a lack of historical data, we cannot investigate a dense time series of a single representative study area on the evolution of the network, but rather leverage the possibilities of a space-for-time substitution. Thus, we focus on data from multiple fire scars of different ages (up to 120 years between date of disturbance and date of acquisition). With our approach, we might infer past and future states of degradation from the currently prevailing spatial patterns showing how this type of disturbed landscape evolves over space and time. It further allows scientists to gain insights into the complex geomorphology, hydrology, and ecology of landscapes, thus helping to quantify how they interact with climate change
