Geoinformatics Production for Urban Disasters Risk Reduction: A Zero Cost Solution

Zou, Zhichong; Lin, Xiaofeng

doi:10.1007/978-3-642-45025-9_32

Cited by 5 publications

(9 citation statements)

References 10 publications

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“…The mean shift algorithm provides an efficient way to simplify (Zou and Lin, 2013) and also helps to open the original image into a multi-scale space to allow the incorporation of scale into classification (Vu, 2012). In our study, Monteverdi (GUI version OTB) is employed for mean shift segmentation.…”

Section: Results Of Mean Shift Segmentation Based Ge Images In Elevenmentioning

confidence: 99%

“…Segmentation produces homogenous clusters or objects each of which is associated with additional attributes such as mean, variance, shape index, textural measures, etc. depending on the segmentation methods (Zou and Lin, 2013) and is the process of completely partitioning a scene (e.g., a remote sensing image) into non-overlapping regions (segments) in scene space (e.g., image space) (Schiewe, 2002). Image segmentation is a key component to object-based classification (Clark Labs, 2018;Humboldt State University, 2015) and still is considered to be a very crucial prerequisite, because its quality largely affects the final performance of geoobjects recognition (Su and Zhang, 2017).…”

Section: Concepts Of Segmentation Algorithmsmentioning

confidence: 99%

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Spatiotemporal Change of Urban Agriculture Using Google Earth Imagery: A Case of Municipality of Nakhonratchasima City, Thailand

Jantakat¹,

Juntakut²,

Plaiklang³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Presently, urban agriculture (UA) is an important part of the urban ecosystem and a key factor that can help in the urban environmental management. Therefore, this paper studies a spatial-temporal analysis of UA areas and types in Municipality of Nakhonratchasima City (MNC), Thailand. This UA types referred land use classification system of Land Development Department (LDD). Google Earth images acquired in the years of 2007, 2011, 2015 and 2018 were used to examine UA change with segmentation-based classification method in QGIS to classify Google Earth images into thematic maps. Moreover, this study showed different spatiotemporal change patterns, composition and rates in the study area and indicates the importance of analyzing UA change. Therefore, the results of this classification consisted of eleven classes – abandoned paddy field, rice paddy, abandoned field crop, mixed field crop, cassava, betel palm, mixed orchard, coconut, rose apple, truck crop, and fish farm. Truck crop had the greatest cover in study area while floricultural covered the minimal space over periods of study. The UA change analysis over time for entire study areas provides an overall picture of change trends. Furthermore, the UA change at census sector scale gives new insights on how human-induced activities (e.g., built-up areas and roads) affect UA change patterns and rates. This research indicates the necessity to implement change detection for better understanding the UA change patterns and rates.</p>

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Section: Results Of Mean Shift Segmentation Based Ge Images In Elevenmentioning

confidence: 99%

Section: Concepts Of Segmentation Algorithmsmentioning

confidence: 99%

Spatiotemporal Change of Urban Agriculture Using Google Earth Imagery: A Case of Municipality of Nakhonratchasima City, Thailand

Jantakat¹,

Juntakut²,

Plaiklang³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Although there are limited features compared to the commercial geo-data and geo-software resources, free geo-data and open source geo-software have the advantages of allowing further developments by users to meet practical needs with flexibility. The detailed approach can be found in Zou and Lin (2013).…”

Section: Discussionmentioning

confidence: 99%

Zero cost solutions of geo-informatics acquisition, collection and production for natural disaster risk assessment

2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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The global and local impacts of climate change and natural disasters cause great casualties and economic losses each year. Catastrophic events bring more damage to urban areas than rural places because of the high density of human population, property and the infrastructures. Geo-informatics is critical for all stages of disaster management including disaster prevention, preparedness, emergency decision making, disaster relief, rebuilding and recovery. It is critical for risk assessment of natural disasters. Since all hazard risk components are location-specific and vary spatially, accurate risk assessment relies on geo-informatics. Geo-informatics as the basis of decision-making information has proved to be critical and essential to natural, technological and manmade disaster risk assessments. Commercial sources of geo-informatics are usually expensive, especially for those developing countries and regions where living standards are low but natural disasters occur more frequently and cause large losses. The expense of geo-informatics acquisition, collection and production varies vastly with different update frequency, imagery quality, and the total amount of information carried. In contrast with commercial sources, free sources of geo-data and geo-software have limited comparable features. However, they have the flexibility to add new features by further development. Based on a current study of urban natural disaster risk assessment and vulnerability mapping which aims at improving community safety and resilience to natural hazards, this paper shares our experience on zero cost solutions of geo-informatics acquisition, collection, and semi-automatic production procedures by using free internet resources of Google Maps, Google Earth, and free and/or open source software including QGIS (Quantum GIS), GRASS, SAGA, Monteverdi, Sextante GIS and Orfeo Toolbox. In order to enrich attributes to meet the requirements of risk assessment, methods of combining the acquired geo-informatics with other formats of geo-data (e.g., Digital Elevation Model, raster layers, vector layers, and Microsoft Excel files) are introduced. The purpose of this paper is to share our experience on zero-cost geo-informatics acquisition, collection and production solutions with urban planning managers, emergency managers, risk researchers and people with similar concerns. The geo-informatics developed from this research forms a basis for disaster vulnerability measurement which is an important part for urban disaster risk assessment.

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“…OTB is used worldwide because it extracts spatial features, segments, and classifies high-resolution imagery (Huang and Zhang, 2008). This software is efficient, and free for research, teaching, and even commercial use (Zou and Lin, 2013).…”

Section: Generation Of the Orthomosaic And The Canopy Height Modelmentioning

confidence: 99%

“…Thus, it identifies geographical features by using scale and homogeneity parameters obtained from reflectance in RGB and from elevation (Huang and Zhang, 2008). In addition, mean-shift segmentation also helps to open the original image into a multi-scale space to allow scale to be incorporated into classification (Zou and Lin, 2013).…”

Section: Generation Of the Orthomosaic And The Canopy Height Modelmentioning

confidence: 99%

Structural attributes estimation in a natural tropical forest fragment using very high-resolution imagery from unmanned aircraft systems

2022

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Structural attributes are fundamental biophysical parameters of forest, useful for ecological and environmental monitoring and planning. Canopy height is an important input for the estimation of several biophysical parameters as aboveground biomass and carbon stock, and can be related with forest degradation, deforestation, and emission reduction. Thus, an accurate canopy height estimation is a crucial issue in climate change studies and REDD+ initiatives. VHR imagery from unmanned aircraft systems has been studied as a low cost mean for canopy height estimation at local scales, but the accuracy in the estimation is a factor that determines its utility. We evaluated the ability of VHR imagery from unmanned aircraft systems to derive structural attributes, specifically tree-crown area and height, in a natural tropical forest fragment located in the foothills of the Andes Mountains, in the humid tropical forests of the region known as Biogeographic Chocó, South America. The region is one of the most biodiverse areas of the world and has a high level of endemism, but it is also at higher risk of natural-resource loss. We used a structure from motion approach to derive canopy height models of the forest fragment, and we applied mean-shift algorithms to identify single tree crowns. The accuracy assessment was performed using reference data derived from field campaigns and visually interpretation of VHR imagery. The estimated root-mean-square error of the population of vertical errors for the canopy height model was 3.6 m. The total accuracy for delineating tree crowns was 73.9%. We found that using VHR imagery, specific trees and canopy gaps can be identified and easily monitored, which is an important step in conservation programs. We also discuss the usefulness of these findings in the context of fragmented forests and the tradeoffs between the price of a LIDAR system and the accuracy of this approach.

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Geoinformatics Production for Urban Disasters Risk Reduction: A Zero Cost Solution

Cited by 5 publications

References 10 publications

Spatiotemporal Change of Urban Agriculture Using Google Earth Imagery: A Case of Municipality of Nakhonratchasima City, Thailand

Spatiotemporal Change of Urban Agriculture Using Google Earth Imagery: A Case of Municipality of Nakhonratchasima City, Thailand

Zero cost solutions of geo-informatics acquisition, collection and production for natural disaster risk assessment

Structural attributes estimation in a natural tropical forest fragment using very high-resolution imagery from unmanned aircraft systems

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