scholarly journals Potential and Limitations of Open Satellite Data for Flood Mapping

2018 ◽  
Vol 10 (11) ◽  
pp. 1673 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Caló ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Flood Hazard ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Po River ◽  
Ground Truth Data ◽  
Hazard Management ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps.

scholarly journals Potentiality and Limitations of Open Satellite Data for Flood Mapping

2018 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Calò ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Satellite Data ◽  
Low Cost ◽  
Ground Truth ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Ground Truth Data ◽  
Flood Peak ◽  
Sar Data ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

Examples of DAFNE application to multi-temporal and multi-frequency remote sensed images and geomorphic data for accurate flood mapping.

2020 ◽  
Author(s):  
Annarita D'Addabbo ◽  
Alberto Refice ◽  
Francesco Lovergine ◽  
Guido Pasquariello
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Bayesian Network ◽  
Optical Data ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Multi Temporal ◽  
Intensity Images ◽  
Ancillary Information ◽  
Flood Maps

<p>DAFNE(Data Fusion by Bayesian Network) is a Matlab-based open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach [1]. It is based on Bayesian Networks and it is composed of five modules, which can be easily modified or upgraded to meet different user needs. DAFNE provides, as output products, probabilistic flood maps, i.e., for each pixel in a given output map, the probability value that the corresponding area has been reached from the inundation is reported. Moreover, if remote sensed images have been acquired in different days during a flood event, DAFNE allows to follow the inundation temporal evolution.</p><p>It is well known that flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground [2]. In particular, the combined analysis of multi-temporal and multi-frequency SAR intensity and coherence trends, together with optical data and other ancillary information, can be particularly useful to map flooded area, characterized by different land cover and land use [3]. Here a recent upgrade is presented that allows to consider as input data multi-frequency SAR intensity images, such as X-band, C-band and L-band images.</p><p>Three different inundation events have been considered as applicative examples: for each one, multi-temporal probabilistic flood maps have been produced by combining multi-temporal and multi-frequency SAR intensity images images (such as COSMO-SkyMed , Sentinel-1 images and ALOS 2 images), InSAR coherence and optical data (such as Landsat 5 images or High Resolution images), together with geomorphic and other ground information. Experimental results show good capabilities of producing accurate flood maps with computational times compatible with a near real time application.</p><p> </p><p>[1] A. D’Addabbo, A. Refice, F. Lovergine, G. Pasquariello, DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping. Computer and Geoscience 112 (2018), 64-75.</p><p>[2] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711–2722, 2014.</p><p>[3] A. D’Addabbo et al., “A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data,” IEEE Transactions on Geoscience and Remote Sensing, vol.54, n.6, pp.3612-3625, 2016.</p><p> </p>

Application of Active Space-Borne Microwave Remote Sensing in Flood Hazard Management

2021 ◽  
pp. 457-482
Author(s):  
C. M. Bhatt ◽  
Praveen K. Thakur ◽  
Dharmendra Singh ◽  
Prakash Chauhan ◽  
Ashish Pandey ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Flood Hazard ◽  
Microwave Remote Sensing ◽  
Hazard Management ◽  
Active Space ◽  
Flood Hazard Management

scholarly journals Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

scholarly journals MULTI-SOURCED, REMOTE SENSING DATA IN LEVEES MONITORING: CASE STUDY OF SAFEDAM PROJECT

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 101-108
Author(s):  
K. Bakuła ◽  
D. Zelaya Wziątek ◽  
B. Weintrit ◽  
M. Jędryka ◽  
T. Ryfa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Flood Hazard ◽  
Multicriteria Analysis ◽  
Remote Sensing Data ◽  
Monitoring And Evaluation ◽  
Source Information ◽  
Sensing Data ◽  
Data Usage ◽  
Levee Failure

<p><strong>Abstract.</strong> In the following study, the authors present the development of a created levee monitoring system &amp;ndash; a supplement to the existing programs of flood protection providing flood hazard and risk maps in Poland. The system integrates multi-source information about levees, acquiring and analysing various types of remote sensing data, such as the photogrammetric and LiDAR data obtained from Unmanned Aerial Vehicles, optical and radar satellite data. These datasets are used in order to assess the levee failure risk resulting from their condition starting from a general inspection using satellite data and concluding with UAV data usage in a detailed semiautomatic inventory. Finally, the weakest parts of a levee can be defined to create reliable flood hazard maps in case of levee failure, thus facilitating the constant monitoring of the water level between water gauges. The presented system is an example of a multisource data integration, which by the complementation of each system, provides a powerful tool for levee monitoring and evaluation. In this paper, the authors present a scope of the preventative configuration of the SAFEDAM system and the possible products of remote sensing data processing as the result of a hierarchical methodology of remote sensing data usage, thus leading to a multicriteria analysis defining the danger associated with the risk of levee failure.</p>

scholarly journals Using volunteered geographical information to map the November 2012 floods in Slovenia

2013 ◽  
Vol 13 (11) ◽  
pp. 2753-2762 ◽  
Author(s):  
M. Triglav-Čekada ◽  
D. Radovan
Keyword(s):  
Remote Sensing ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Web Search ◽  
Aerial Images ◽  
Geographical Information ◽  
Flood Mapping ◽  
Remote Sensing Methods ◽  
Flooded Areas ◽  
Complete Account

Abstract. Volunteered geographical information represents a promising field in the monitoring and mapping of natural disasters. The contributors of volunteered geographical information have the advantage that they are at the location of the natural disaster at exactly the time when the disaster happened. Therefore, they can provide the most complete account of the extent of the damage. This is not always possible when applying photogrammetric or remote-sensing methods, as prior to the data acquisition an order to carry out the measurements has to be made. On 5 and 6 November 2012 almost half of Slovenia was badly affected by floods. The gathering of volunteered geographical information in the form of images and videos of these floods is presented. Two strategies were used: (1) a public call for volunteered contributions and (2) a web search for useful images and their authors. The authorship of these images was verified with every contributor. In total, 15 contributors provided 102 terrestrial and aerial images and one aerial video, with 45% classified as potentially useful. For actual flood mapping 22 images and 12 sequences from video were used. With the help of the volunteered images 12% of the most severely affected river sections were mapped. Altogether, 1195.3 ha of flooded areas outside of the usual river beds along a total river length of 48 km were mapped. The results are compared with those from satellite mapping of the same floods, which successfully covered 18% of the most affected river sections.

Characterization of Alpine peatlands based on remote sensing of vegetation and water content

2021 ◽  
Author(s):  
Sonia Silvestri ◽  
Alessandra Borgia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Regional Scale ◽  
Ground Truth ◽  
Organic Carbon Content ◽  
Ground Truth Data ◽  
The World ◽  
The Eu

&lt;p&gt;Storing up to 70 kg of carbon per cubic meter, peatlands are among the most carbon-dense environments in the world. If in pristine conditions, peatlands support a number of ecosystem services as for example water retention and mitigation of droughts and floods, water purification, water availability to wildlife. Their preservation is one of the main goals of the EU policy and of other initiatives around the world.&lt;/p&gt;&lt;p&gt;Despite their importance, Alpine peatlands have been rarely studied and their presence is not even included in the EU maps, as for example the JRC Relative Cover of Peat Soils map, and only some sites are included in the Corine Land Cover map. The precise localization of peatland sites and the assessment of their extent is the first fundamental step for the implementation of adequate conservation policies. To this end, satellite remote sensing is the ideal instrument to provide adequate spatial resolution to detect and characterize Alpine peatlands at the regional scale. In this study, we use Sentinal-2 satellite data combined with 2m spatial resolution digital elevation model (from LiDAR data) to detect and quantify the extent of peatlands in the Trentino - Alto Adige region, an area of about 12,000 sq km located in the heart of the Italian Alpine region. Ground truth data include 71 peatlands that cover a total surface of more than 2,000 sq m. Field campaigns and lab analyses on some selected sites show that, on average, the sampled peatlands have depth of about 1m, Bulk Density of 0.128 g cm&lt;sup&gt;-3&lt;/sup&gt; and LOI of 63%, hence indicating that the organic carbon content by soil volume is high, being on average 0.04 g cm&lt;sup&gt;-3&lt;/sup&gt;. Satellite data analysis allowed us to detect a large number of peatland sites with high accuracy, thus confirming the importance of Alpine peatlands as carbon stock sites for the region. Moreover, thanks to the correlation between two indices (NDVI and NDWI) we could characterize the water content of these sites, hence analyzing its seasonal variation and inferring possible future scenarios linked to climate change effects.&lt;/p&gt;

Satellite remote sensing of natural Mediterranean vegetation: a review within an ecological context

2000 ◽  
Vol 24 (2) ◽  
pp. 153-178 ◽  
Author(s):  
Maxim Shoshany
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Satellite Remote Sensing ◽  
Life Forms ◽  
Mediterranean Vegetation ◽  
Fire Disturbance ◽  
Current Status ◽  
Ancillary Data ◽  
Noaa Avhrr ◽  
Mediterranean Regions

Mediterrranean regions are characterized by high spatiotemporal heterogeneity of vegetation patterns. Understanding the dynamic nature of these environments requires detailed data for wide regions regarding changes in their phyto-ecology, biomass and productivity. This article assesses the current status of satellite remote sensing in this field of application. Mapping the five main life-forms (physiognomic classes) in Mediterranean regions (forests, woodlands, scrub, dwarf shrubs and herbaceous growth) has attracted major attention in recent years. Methodologies developed for this purpose are based on the spectral, temporal and spatial (textural) information domains provided by satellite data. Wide regional vegetation mapping was achieved using phenological classification of vegetation indices derived mainly from NOAA AVHRR images. More detailed mapping was conducted with multispectral techniques in local areas using mainly Landsat TM images. Assessments of multispectral and multi-temporal categories have shown limitations in their applicability over wide regions due mainly to the heterogeneity of Mediterranean regions. This heterogeneity cannot be regarded as a simple mixing of life-forms over large areas but, rather, the formation of transitional zones of varying mixtures resulting from disturbance and recovery cycles. Productivity and biomass monitoring has been found to be an active methodological development due to the introduction of new off-nadir viewing sensors in the visible and infrared spectral bands, and because of the development of methodologies for the retrieval of biophysical information from Synthetic Aperature Radar (SAR) data. Studies of ecosystem evolution using satellite data were conducted mainly in the fields of fire disturbance and desertification. Further progress in the remote sensing of Mediterranean vegetation ecology requires a better synergy of sensors, methods and ancillary data.

scholarly journals A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

2015 ◽  
Vol 12 (5) ◽  
pp. 4857-4878 ◽  
Author(s):  
Z. N. Musa ◽  
I. Popescu ◽  
A. Mynett
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Satellite Data ◽  
Satellite Remote Sensing ◽  
Flood Mapping ◽  
Research Topics ◽  
Hydrological Data ◽  
Rain Gauges ◽  
Physical Infrastructure ◽  
The World

Abstract. Hydrological data collection requires deployment of physical infrastructure like rain gauges, water level gauges, as well as use of expensive equipment like echo sounders. Many countries around the world have recorded a decrease in deployment of physical infrastructure for hydrological measurements; developing countries especially have less of this infrastructure and where they exist, they are poorly maintained. Satellite remote sensing can bridge this gap, and has been applied by hydrologists over the years, with the earliest applications in water body and flood mapping. With the availability of more optical satellites with relatively low temporal resolutions globally, satellite data is commonly used for: mapping of water bodies, testing of inundation models, precipitation monitoring, and mapping of flood extent. Use of satellite data to estimate hydrological parameters continues to increase due to use of better sensors, improvement in knowledge of/and utilization of satellite data, and expansion of research topics. A review of applications of satellite remote sensing in surface water modelling, mapping and estimation is presented, and its limitations for surface water applications are also discussed.

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