Figure 1 - uploaded by Marco Gianinetto
Content may be subject to copyright.
Flood map derived with STMNF transform and DTM filtering, showing the accordance with ground truth data (both omission and commission errors).
Source publication
In this paper are described some enhancements for a straightforward method recently developed by the authors for evaluating post flood damages using Landsat TM/ETM+ data integrated with digital terrain models (DTMs) and based on the principal components transform (PCT). In particular, the main updates refer to the computational scheme in deriving t...
Contexts in source publication
Context 1
... the Tanaro river between the cities of Asti and Alessandria. Both the conditions on the MNF threshold and the terrain slope threshold have to be satisfied at the same time (logical AND). Eventually, the flood map was refined with classical segmentation and clumping techniques to boost the spatial coherency and homogeneity of the final mapping. In Fig. 1 is shown the flood map derived from the processing of the STMNF band 2 compared with ground truth data. Both the errors in omission and commission are ...
Context 2
... the flood extension map produced (Fig. 1), the maximum heights reached by the water during the event were then derived by means of interpolating the water level gathered at the border of the flooded area (spatial density of 0.7 points/km 2 ). Along the borderline separating the flooded and non flooded areas a set of terrain heights have been collected through the ...
Similar publications
In this work, the construction of a statistical shape deformable model of a lumbar vertebra is presented. Statistical deformable models describe the variability of the structure under investigation basing on the training set characteristics. Their application is closely linked with automatic image segmentation. The procedure is successfully applied...
To detect lung cancer at an earlier stage, a promising method is to apply perfusion magnetic resonance imaging (pMRI) modified to assess tumor angiogenesis. One key issue is to effectively characterize angiogenic patterns of pulmonary nodules. Based on our previous study addressing this issue, in this work, we develop STAT, a Spatio-Temporal Analys...
Citations
... Satellite-derived flood maps also represent support for emergency responses and the evaluation of flood severity and costs. Several studies assessed post-flood damages to infrastructures, built-up, and agricultural areas [63][64][65][66][67][68], as well as the estimation of flood impacts on natural environments and floodplain ecosystems (e.g., [61,62]). ...
The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium-high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites .
... Interpolation technique is classified as an enhancement technique of image quality in spatial domain [3]. This interpolation technique has been proven to be used in various purposes, including analysis of forensic evidence [4], scientific analysis either space condition or space objects [5], analysis of low resolution video object [3], and the most popular lately is object identification in medical [6] and in geography mapping [7,8,9]. ...
A good quality image is required in various applications such as object identification and authentication. This research presents the performance of image resolution enhancement method, in which the low-resolution image originated from low-resolution CCTV video. The enhancement method is initialized by averaging video frames and continued by interpolating the resulted images using the existing interpolation techniques, namely bilinear, bi-cubic, nearest neighbor and spleen. Frame rate of 15 and 25 frames per second (fps) has been applied to the testing video. The result shows that the differences of frame rate and number of the averaged frame would affect image quality. Subjective assessment of respondents of MOS above 3 has been obtained by increasing the frame rate and the number of averaging frames.
... Our analysis was able to simultaneously delineate flood-related surface water and soil in very narrow channels-some only two or three Landsat pixels in width-across multiple drainages and surrounding land cover types. Moreover, this was accomplished without the use of clumping procedures and moving windows that are often applied to smooth flood width maps [17,47,48]. While such post-processing techniques can be appropriate for large river systems with deep, homogenous channels, they yield layers that homogenize the channel variability observed in more shallow, braided rivers-one of the most important yet difficult aspects of a flood to capture with stream gages [3]. ...
Maximum flood extent--a key data need for disaster response and mitigation--is rarely quantified due to storm-related cloud cover and the low temporal resolution of optical sensors. While change detection approaches can circumvent these issues through the identification of inundated land and soil from post-flood imagery, their accuracy can suffer in the narrow and complex channels of increasingly developed and heterogeneous floodplains. This study explored the utility of the Operational Land Imager (OLI) and Independent Component Analysis (ICA) for addressing these challenges in the unprecedented 2013 Flood along the Colorado Front Range, USA. Pre-and post-flood images were composited and transformed with an ICA to identify change classes. Flooded pixels were extracted using image segmentation, and the resulting flood layer was refined with cloud and irrigated agricultural masks derived from the ICA. Visual assessment against aerial orthophotography showed close agreement with high water marks and scoured riverbanks, and a pixel-to-pixel validation with WorldView-2 imagery captured near peak flow yielded an overall accuracy of 87% and Kappa of 0.73. Additional tests showed a twofold increase in flood class accuracy over the commonly used modified normalized water index. The approach was able to simultaneously distinguish flood-related water and soil moisture from pre-existing water bodies and other spectrally similar classes within the narrow and braided channels of the study site. This was accomplished without the use of post-processing smoothing operations, enabling the important preservation of nuanced inundation patterns. Although flooding beneath moderate and sparse riparian vegetation canopy was captured, dense vegetation cover and paved regions of the floodplain were main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the flood edge. Nevertheless, the unsupervised nature of ICA, in conjunction with the global availability of Landsat imagery, offers a straightforward, robust, and flexible approach to flood mapping that requires no ancillary data for rapid implementation. Finally, the spatial layer of flood extent and a summary of impacts were provided for use in the region's ongoing hydrologic research and mitigation planning.
... For example, in [1], Landsat measurements of an area before and after flooding were used to estimate the extent of flood waters. In [2], a wireless sensor network was deployed to measure water quality, and in [3], a wired sensor network. ...
... First, clouds tend to obscure direct observation. For example, in [1] the authors were forced to use satellite measurements made before and well after a flooding event because of cloud cover. The ironic issue is that flooding tends to happen under cover of clouds. ...
... , A l R ρ = (1) where ρ is the bulk resistivity of the material, l is the length of the material, and A is the area of the material. l/A, then, is the cell constant. ...
Demand for embedded environmental sensors is increasing. One of the key sensor capabilities is the ability to measure salinity. In addition to understanding the environment, a salinity measurement is needed to estimate water density and to convert pressure to estimates of water depth. This outlines a low-cost sensor that combines an H-bridge and digital potentiometer to make an AC resistance measurement suitable for measuring salinity.
... Recent works of the authors have focused on the development of a straightforward and efficient processing algorithm for analyzing and mapping flood damages using optical remotely sensed satellite data, both at regional and local scale [9], [10], [20], [21]. The basic idea is to highlight the environment's changes by processing, through the use of a change detection technique, imagery collected before and after the flood, together with a digital terrain model (DTM). ...
Every year, floods cause enormous damage and loss of human life all over the world. Regarding the European Union, extreme floods are the most common types of natural disasters (44% of the total in the last decade), and in the future, the number of flash floods is expected to rise. Recent works of the authors have focused on the development of a straightforward and efficient processing algorithm for analyzing and mapping flood damages using optical remotely sensed satellite data and digital terrain models. In this paper, some improvements of the processing technique, both regarding the flood mapping and the water depth estimation, are presented. With respect to the first issue, a new data transformation is introduced, replacing the spectral-temporal principal component analysis (STPCA) with the spectral-temporal minimum noise fraction (STMNF) transformation, while the peak water depth is obtained through more sophisticated interpolation methods. The STMNF-based technique was applied to the data collected for the worst flood of the 20th Century that struck Piemonte Region, Italy, in 1994. Regarding the flood mapping, the STMNF method allowed an overall accuracy of 97.09% with a kappa coefficient of 0.889 to be established, obtaining a user accuracy of 85.76%, and a producer accuracy of 95.96%, with a lower commission error if compared to the previous STPCA method. Regarding the water depth computation, the best results were obtained using the second-order composed splines interpolator, obtaining an overall agreement with ground reference data of about 83%.
Inadequate infrastructural networks can be detrimental to society if transport between locations becomes hindered or delayed, especially due to natural hazards which are difficult to control. Thus determining natural hazard susceptible areas and incorporating them in the initial planning process, may reduce infrastructural damages in the long run. The objective of this study was to evaluate the usefulness of expert judgments for assessing natural hazard susceptibility through a spatial multi-criteria analysis approach using hydrological, geological, and land use factors. To utilize spatial multi-criteria analysis for decision support, an analytic hierarchy process was adopted where expert judgments were evaluated individually and in an aggregated manner. The estimates of susceptible areas were then compared with the methods weighted linear combination using equal weights and factor interaction method. Results showed that inundation received the highest susceptibility. Using expert judgment showed to perform almost the same as equal weighting where the difference in susceptibility between the two for inundation was around 4%. The results also showed that downscaling could negatively affect the susceptibility assessment and be highly misleading. Susceptibility assessment through spatial multi-criteria analysis is useful for decision support in early road planning despite its limitation to the selection and use of decision rules and criteria. A natural hazard spatial multi-criteria analysis could be used to indicate areas where more investigations need to be undertaken from a natural hazard point of view, and to identify areas thought to have higher susceptibility along existing roads where mitigation measures could be targeted after in-situ investigations.
This paper introduces a new and general method for change detection based on the normalized difference change detection (NDCD) technique. A case study shows the use of the NDCD technique for flood mapping. Flood maps for the city of New Orleans (Louisiana, USA) resulting from the passage of Hurricane Katrina in 2005 were produced from the data processing of Satellite Pour l‘Observation de la Terre (SPOT)-4 High-Resolution Visible Infra-Red (HRVIR) and Landsat-5 Thematic Mapper (TM) images and the accuracies of the maps were verified using as ground truth the flood extension map of the city of New Orleans produced at the Dartmouth Flood Observatory (Dartmouth College, USA). The potentialities and performances of the NDCD technique in flood mapping were also compared to other standard change detection methods such as: (i) the near-infrared (NIR) normalized difference, (ii) unsupervised post-classification comparison, (iii) change vector analysis (CVA) and (iv) spectral–temporal minimum noise fraction (STMNF) transformation. When using the SPOT-4 HRVIR data, the NDCD technique led to better results than the other change detection methods considered here, while for the Landsat-5 TM data processing the closeness of the post-flood image to the Katrina landfall influenced the overall performances negatively. However, with respect to flood mapping in the urban area alone, which may be of major interest in most cases, the NDCD technique also performed better than all the other change detection methods considered here when using the Landsat-5 TM data.
