DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2

This paper describes the detection of the surface water area in Cirata dam,  upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir

Measurement of extravascular lung water to diagnose severe reperfusion lung injury following pulmonary endarterectomy: a prospective cohort clinical validation study

The measurement of extravascular lung water is a relatively new technology which has not yet been well validated as a clinically useful tool. We studied its utility in patients undergoing pulmonary endarterectomy as they frequently suffer reperfusion lung injury and associated oedematous lungs. Such patients are therefore ideal for evaluating this new monitor. We performed a prospective observational cohort study during which extravascular lung water index measurements were taken before and immediately after surgery and postoperatively in intensive care. Data were analysed for 57 patients; 21 patients (37%) experienced severe reperfusion lung injury. The first extravascular lung water index measurement after cardiopulmonary bypass failed to predict severe reperfusion lung injury, area under the receiver operating characteristic curve 0.59 (95%CI 0.44–0.74). On intensive care, extravascular lung water index correlated most strongly at 36 h, area under the receiver operating characteristic curve 0.90 (95%CI 0.80–1.00). Peri‐operative extravascular lung water index is not a useful measure to predict severe reperfusion lung injury after pulmonary endarterectomy, however, it does allow monitoring and measurement during the postoperative period. This study implies that extravascular lung water index can be used to directly assess pulmonary fluid overload and that monitoring patients by measuring extravascular lung water index during their intensive care stay is useful and correlates with their clinical course. This may allow directed, pre‐empted therapy to attenuate the effects and improve patient outcomes and should prompt further studies

Automatic Flood Detection in SentineI-2 Images Using Deep Convolutional Neural Networks

The early and accurate detection of floods from satellite imagery can aid rescue planning and assessment of geophysical damage. Automatic identification of water from satellite images has historically relied on hand-crafted functions, but these often do not provide the accuracy and robustness needed for accurate and early flood detection. To try to overcome these limitations we investigate a tiered methodology combining water index like features with a deep convolutional neural network based solution to flood identification against the MediaEval 2019 flood dataset. Our method builds on existing deep neural network methods, and in particular the VGG16 network. Specifically, we explored different water indexing techniques and proposed a water index function with the use of Green/SWIR and Blue/NIR bands with VGG16. Our experiment shows that our approach outperformed all other water index technique when combined with VGG16 network in order to detect flood in images

Optimization of Remote Sensing Data In Monitoring Morphology Change of Siak River in Pekanbaru City

River condition that experiences change is not apart from the utilization of river boarder for human life needs. Beside the effect of built land development, river morphology changes because of erotion and deposition. Therefore, it needs detection of river morphology change, one of which is by utilizing remote sensing data with Modification Normalized Difference Water Index. Modification Normalized Difference Water Index can separate between water and land sharply until river morphology analysis can be clearer. This research aims to detect the change of Siak River morphology in Pekanbaru City caused by abrasion and deposition by using Modification of Normalized Difference Water Index on landsat temporal data of 2008 and 2018. Data used in this research were Landsat 7 image of 2008 and Landsat 8 year of 2018. The research results show that morphology changes of Siak River in Pekanbaru City is caused by erotion and deposition. The erotion area reaches 12.133 ha and deposition 4.488 ha in the period of 10 year

Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images

A method for segmenting water bodies in optical and synthetic aperture radar (SAR) satellite images is proposed. It makes use of the textural features of the different regions in the image for segmentation. The method consists in a multiscale analysis of the images, which allows us to study the images regularity both, locally and globally. As results of the analysis, coarse multifractal spectra of studied images and a group of images that associates each position (pixel) with its corresponding value of local regularity (or singularity) spectrum are obtained. Thresholds are then applied to the multifractal spectra of the images for the classification. These thresholds are selected after studying the characteristics of the spectra under the assumption that water bodies have larger local regularity than other soil types. Classifications obtained by the multifractal method are compared quantitatively with those obtained by neural networks trained to classify the pixels of the images in covered against uncovered by water. In optical images, the classifications are also compared with those derived using the so-called Normalized Differential Water Index (NDWI)

Assessing spectral similarities between rainfed and irrigated croplands in a humid environment for irrigated land mapping

Deriving accurate spatial assessments of the distribution of irrigated crops has become more important in recent years for water resource planning, particularly where irrigation water resources are constrained. However, this is easier in arid climates than in humid areas such as eastern England. The challenges in using alternative vegetation indices derived from remote sensing to discriminate between irrigated and rainfed crops in a humid climate are described, focusing on potato (Solanum tuberosum L.), the most important irrigated crop in England. Three techniques were evaluated: (a) temporal profile comparisons using the Normalized Difference Vegetation Index (NDVI); (b) cluster analysis combining the NDVI and the Normalized Difference Water Index (NDWI); and (c) identifying differences in chlorophyll content using green and near infrared bands. However, the study confirmed that the spectral signatures of irrigated and rainfed potato in England during a typical summer are very similar, presumably due to frequent rainfall events which reduce differences in water stress and chlorophyll content. The implications for using remote sensing to estimate irrigated areas in humid climates are discussed

Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data

The objectives of this work were to examine the applicability of the Dark-Object Subtraction (DOS) atmospheric correction method and water-based index techniques to map wetlands in Dhaka megacity using Landsat 8 data. With the use of both raw data and DOS- corrected imagery, the analysis revealed that DOS- corrected images performed better in discriminating wetland areas. Furthermore, the Modified Normalised Water Index (MNDWI) was the most superior technique whilst the Normalised Difference Water Index (NDWI) was the least suitable in identifying the spatial locations of wetlands in a rapidly urbanising environment such as Dhaka

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

Performances Evaluation of Surface Water Areas Extraction Techniques Using Landsat ETM+ Data: Case Study Aswan High Dam Lake (AHDL)

AbstractAswan High Dam Lake (AHDL) is the major freshwater body supplying Egypt with water used for various purposes. This paper aims to detect the better technique for extraction of the water surface of AHDL. Eight techniques are tested using Landsat ETM+ image and their performances in extracting the surface water area are evaluated. The eight techniques include Supervised and Unsupervised image classification techniques, Water Ratio Index [WRI], Normalized Difference Vegetation Index [NDVI], Normalized Difference Water Index [NDWI], Modified Normalized Difference Water Index [MNDWI], Automated Water Extraction Index [AWEI], and Normalized Difference Moisture Index [NDMI]. The results illustrate the effectiveness of the unsupervised technique, as it gave an overall accuracy about 99.91%. It is recommended to apply this technique in areas with similar conditions to efficiently extract the surface water areas from Landsat ETM+ data