scholarly journals Prioritizing Flood-Prone Areas Using Spatial Data in the Province of New Brunswick, Canada

Geosciences ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 478
Author(s):  
Sheika Henry ◽  
Anne-Marie Laroche ◽  
Achraf Hentati ◽  
Jasmin Boisvert
Keyword(s):  
New Brunswick ◽  
Spatial Data ◽  
Flood Hazard ◽  
Economic Damage ◽  
Comprehensive Overview ◽  
Multi Criteria Evaluation ◽  
Flood Exposure ◽  
Detailed Assessment ◽  
Flood Prone Areas ◽  
Provincial Scale

Over the years, floods have caused economic damage that has impacted development in many regions. As a result, a comprehensive overview of flood-prone areas at the provincial scale is important in order to identify zones that require detailed assessment with hydrodynamic models. This study presents two approaches that were used to prioritize flood-prone areas at the provincial scale in New Brunswick, Canada. The first approach is based on a spatial multi-criteria evaluation (SMCE) technique, while the second approach pertains to flood exposure analysis. The results show the variation in the identified flood-prone areas and, depending on the methodology and scenario used, prioritization changes. Therefore, a standard methodology might not be feasible and should be developed based on the objective of the study. The results obtained can be useful for flood risk practitioners when making decisions about where to commence detailed flood hazard and risk assessment.

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

Mapping wildlife: integrating stakeholder knowledge with modelled patterns of deer abundance by using participatory GIS

2009 ◽  
Vol 36 (7) ◽  
pp. 553 ◽  
Author(s):  
Z. Austin ◽  
S. Cinderby ◽  
J. C. R. Smart ◽  
D. Raffaelli ◽  
P. C. L. White
Keyword(s):  
Community Involvement ◽  
Spatial Data ◽  
Spatial Scales ◽  
Stakeholder Participation ◽  
Landscape Scale ◽  
Scientific Data ◽  
Future Research ◽  
Economic Damage ◽  
Participatory Gis ◽  
Wild Deer

Context. Some species that are perceived by certain stakeholders as a valuable resource can also cause ecological or economic damage, leading to contrasting management objectives and subsequent conflict between stakeholder groups. There is increasing recognition that the integration of stakeholder knowledge with formal scientific data can enhance the information available for use in management. This is especially true where scientific understanding is incomplete, as is frequently the case for wide-ranging species, which can be difficult to monitor directly at the landscape scale. Aims. The aim of the research was to incorporate stakeholder knowledge with data derived from formal quantitative models to modify predictions of wildlife distribution and abundance, using wild deer in the UK as an example. Methods. We use selected predictor variables from a deer–vehicle collision model to estimate deer densities at the 10-km square level throughout the East of England. With these predictions as a baseline, we illustrate the use of participatory GIS as a methodological framework for enabling stakeholder participation in the refinement of landscape-scale deer abundance maps. Key results. Stakeholder participation resulted in modifications to modelled abundance patterns for all wild deer species present in the East of England, although the modifications were minor and there was a high degree of consistency among stakeholders in the adjustments made. For muntjac, roe and fallow deer, the majority of stakeholder changes represented an increase in density, suggesting that populations of these species are increasing in the region. Conclusions. Our results show that participatory GIS is a useful technique for enabling stakeholders to contribute to incomplete scientific knowledge, especially where up-to-date species distribution and abundance data are needed to inform wildlife research and management. Implications. The results of the present study will serve as a valuable information base for future research on deer management in the region. The flexibility of the approach makes it applicable to a range of species at different spatial scales and other wildlife conflict issues. These may include the management of invasive species or the conservation of threatened species, where accurate spatial data and enhanced community involvement are necessary in order to facilitate effective management.

Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment 

2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hazard Assessment ◽  
Water Depth ◽  
Flood Hazard ◽  
Research Centre ◽  
Hazard Mapping ◽  
Hazard Maps ◽  
Flood Hazard Assessment ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i.e.</span></span><span xml:lang="EN-US" data-contrast="auto"><span> land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-</span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein “DEM-based methods”) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km</span></span><sup><span xml:lang="EN-US" data-contrast="auto"><span>2</span></span></sup><span xml:lang="EN-US" data-contrast="auto"><span>, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i</span></span><span xml:lang="EN-US" data-contrast="auto"><span>) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.</span></span><span> </span></p>

scholarly journals Delineating flood prone areas using a statistical approach

2016 ◽  
Author(s):  
Ivan Marchesini ◽  
Mauro Rossi ◽  
Paola Salvati ◽  
Marco Donnini ◽  
Simone Sterlacchini ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Learning Algorithm ◽  
Flood Hazard ◽  
Environmental Data ◽  
Training Data ◽  
Hazard Maps ◽  
Elevation Data ◽  
Terrain Elevation ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.

scholarly journals GIS-Based Multi-Criteria Evaluation for Potential Inland Aquaculture Site Selection in the George Town Conurbation, Malaysia

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1174
Author(s):  
Puteri Nur Atiqah Bandira ◽  
Mohd Amirul Mahamud ◽  
Narimah Samat ◽  
Mou Leong Tan ◽  
Ngai Weng Chan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Site Selection ◽  
Ad Hoc ◽  
Buffer Zone ◽  
International Markets ◽  
Sensitive Data ◽  
Evaluation Approach ◽  
Multi Criteria Evaluation ◽  
George Town

Although the aquaculture industry contributes less than 0.2% to the Gross Domestic Product (GDP) of Malaysia, it has slowly become an important economic activity due to the high-value species productions for domestic and international markets. In addition, aquaculture can potentially be used as a sustainable solution for food security in the future. At present, the selection of aquaculture sites has not received much attention. Thus, this study aims to integrate a Geographic Information System and multi-criteria evaluation approach in identifying the potential sites for brackish aquaculture in the George Town Conurbation, Malaysia. ArcGIS 10.4 was used to perform site selection analysis together with the essential spatial data such as current land use, environmentally sensitive data, and soil quality that influence suitable sites selection for aquaculture. The selection was undertaken in ad hoc manners based on available land identified by aquaculture operators. The results indicated that the George Town Conurbation has a minimal potential site (0.37%) for aquaculture sites. This minimal number results from the expansion of built-up areas towards urban fringe areas; hence less land becomes available for aquaculture. A reasonable buffer zone should be designated as a boundary between urban development and aquaculture to avoid land-use conflict between these two activities.

scholarly journals Impact of Flood on the Socio-Economic Conditions in the Southern Part of Kamrup District, Assam

2021 ◽  
Vol 8 (4) ◽  
pp. 106-119
Author(s):  
Indira Das ◽  
Sujit Deka
Keyword(s):  
Evaluation Method ◽  
Flood Hazard ◽  
Human Life ◽  
Drainage Density ◽  
Economic Conditions ◽  
Flood Vulnerability ◽  
Study Region ◽  
Multi Criteria Evaluation ◽  
Hazard Zoning ◽  
Moderate Resolution Imaging Spectroradiometer

Flood causes extreme loss of infrastructure and human life; besides it also propagates the condition of poverty and unceasing marginalisation of the affected region from development. This study elucidates how flood contributes to the socio-economic conditions of the rural people living in the Southern part of the Kamrup district of Assam. It focusses on flood hazard zoning and flood vulnerability analyses that are delineated based on the data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) Near Real-Time (NRT) Global Flood Mapping Product Portal. Flood hazard zoning of the study area is done using Multi-Criteria evaluation method based on rainfall distribution, slope, drainage density, population density, soil type, elevation, flow accumulation, roads, and embankment utilising Cartosat DEM and IRS P6 LISS III data. The zones are identified as actively flooded, chronically flooded, and occasionally flooded zones, which affects 39.4 per cent, 12.9 per cent and 26.1 per cent population respectively covering 1189.2 sq. km, that is, 56.5 per cent area of the study region.  The flood vulnerability assessment of the study area is done at village and ward level adapting geospatial assessment in a GIS environment. The findings of the research are generated through observations, key informant interviews with the rural population surveying 1420 number of households. It reveals that 200 villages are affected by floods every year that constitutes 76.6 per cent households and 78.4 per cent of the population of the study area.

scholarly journals Remote Sensing and Geographic Information System in Water Erosion Assessment

2020 ◽  
Author(s):  
Nirmal Kumar ◽  
S. K. Singh ◽  
G. P. Obi Reddy ◽  
V. N. Mishra ◽  
R. K. Bajpai
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Soil Loss ◽  
Water Erosion ◽  
Geographic Information ◽  
Geographical Information ◽  
Comprehensive Overview

The aim of this review paper is to provide a comprehensive overview of geographical information system and remote sensing–based water erosion assessment. With multispectral and multi-temporal low cost data at various resolutions, remote sensing plays an important role for mapping the distribution and severity of water erosion and for modeling the risk and/or potential of soil loss. The ability of geographic information system to integrate spatial data of different types and sources makes its role unavoidable in water erosion assessment. The role of satellite data in identification of eroded lands and in providing inputs for erosion modeling has been discussed. The role of GIS in mapping eroded lands based on experts’ opinion, in generating spatial data inputs from sources other than remote sensing and in integrating the inputs to model the potential soil loss has been discussed.

scholarly journals Environmental Inequalities in Flood Exposure: A Matter of Scale

2021 ◽  
Vol 3 ◽  
Author(s):  
Clémence Poussard ◽  
Benjamin Dewals ◽  
Pierre Archambeau ◽  
Jacques Teller
Keyword(s):  
Flood Risk ◽  
Flood Hazard ◽  
Economic Status ◽  
Flood Hazards ◽  
Socio Economic Status ◽  
Flood Exposure ◽  
Class 1 ◽  
Riverine Floods ◽  
Peripheral Areas ◽  
Hazard Level

Studies on inequalities in exposure to flood risk have explored whether population of a lower socio-economic status are more exposed to flood hazard. While evidence exist for coastal flooding, little is known on inequalities for riverine floods. This paper addresses two issues: (1) is the weakest population, in socio-economic terms, more exposed to flood hazard, considering different levels of exposure to hazard? (2) Is the exposure to flood risk homogeneous across the territory, considering different scales of analysis? An analysis of the exposure of inhabitants of Liège province to flood risk was conducted at different scales (province, districts, and municipalities), considering three levels of exposure to flood hazard (level 1- low hazard, level 3- high hazard), and five socio-economic classes (class 1-poorest, class 5-wealthiest households). Our analysis confirms that weaker populations (classes 2 and 3) are usually more exposed to flood hazards than the wealthiest (classes 4 and 5). Still it should be stressed that the most precarious households (class 1) are less exposed than low to medium-range ones (classes 2 and 3). Further on the relation between socio-economic status and exposure to flood hazard varies along the spatial scale considered. At the district level, it appears that classes 4 and 5 are most exposed to flood risk in some peripheral areas. In municipalities located around the center of the city, differences of exposure to risk are not significant.

scholarly journals DATA ARCHIVING AND DISTRIBUTION OF LiDAR AND DERIVED DATASETS IN THE PHILIPPINES

2016 ◽  
Vol XLI-B4 ◽  
pp. 195-199
Author(s):  
M. E. A. Tupas ◽  
S. C. Lat ◽  
R. A. Magturo
Keyword(s):  
Data Acquisition ◽  
Spatial Data ◽  
Distribution System ◽  
Data Exchange ◽  
Flood Hazard ◽  
Data Access ◽  
The Philippines ◽  
Data Sets ◽  
Data Archiving ◽  
Main Site

LiDAR programs in the Philippines have been generating valuable resource and hazard information for most of the country at a substantial rate since 2012. Significant progress have been made due to the programs design of engaging 16 Universities and research institutions spatially distributed across the country. Because of this, data has been accumulating at a brisk rate which poses significant technical and logistic issues. While a central node, the University of the Philippines, Diliman, handles data acquisition, pre-processing, and quality checking, processing and ground validation are devolved to the various nodes. For this setup to be successful, an efficient data access and distribution system should be in place. <br><br> In this paper, we discuss the spatial data infrastructure and data access protocols implemented by the program. At the center of the data access and distribution operations is LiPAD or our LiDAR portal for archiving and distribution. LiPAD is built on open source technologies, established web standards, and protocols. At its back-end a central data archive has been established using state of the art Object Storage technology to store both raw, processed Lidar and derived data sets. Catalog of available data sets ranging from data acquisition foot prints, to DEM coverages, to derived products such as flood hazard, and crop suitability are viewable and accessible on the main site based on the popular GeoNode application. Data exchange is performed using varying protocols to address various logistical problems. Given the various challenges the program is successful in distributing data sets not just to partner processing nodes but to other stakeholders where main requesters include national agencies and general research and academic institutions.

Sign in / Sign up

Export Citation Format

Share Document