scholarly journals Investigating Controls on Barrier Island Overwash and Evolution during Extreme Storms

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2829
Author(s):  
Jesse N. Beckman ◽  
Joseph W. Long ◽  
Andrea D. Hawkes ◽  
Lynn A. Leonard ◽  
Eman Ghoneim
Keyword(s):  
Satellite Imagery ◽  
Pressure Sensors ◽  
Barrier Island ◽  
Water Levels ◽  
Sediment Grain Size ◽  
Grain Sizes ◽  
Elevation Data ◽  
Directional Wave ◽  
Spatially Varying ◽  
Sentinel 2

Over short periods of time, extreme storms can significantly alter barrier island morphology, increasing the vulnerability of coastal habitats and communities relative to future storms. These impacts are complex and the result of interactions between oceanographic conditions and the geomorphic, geological, and ecological characteristics of the island. A 2D XBeach model was developed and compared to observations in order to study these interactions along an undeveloped barrier island near the landfall of Hurricane Florence in 2018. Beachface water levels during the storm were obtained from two cross-shore arrays of pressure sensors for comparison to model hydrodynamics. Aerial drone imagery was used to derive pre-storm and post-storm elevation data in order to quantify spatially varying erosion and overwash. Sediment grain size was measured in multiple locations, and we estimated spatially varying friction by using Sentinel-2 satellite imagery. The high spatial and temporal resolution of satellite imagery provided an efficient method for incorporating pre-storm spatially varying land cover. While previous studies have focused on the use of spatially varying friction, we found that the utilization of local median grain sizes and full directional wave spectra was critical to reproducing observed overwash extent.

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

Sentinel-2 mission Contribution for Supporting Bathymetric layers of SAFAGA coastal zone, Egypt

2021 ◽  
Author(s):  
Ramez Saeed ◽  
Saad Abdelrahman ◽  
Andrea Scozari ◽  
Abdelazim Negm
Keyword(s):  
Red Sea ◽  
Satellite Imagery ◽  
Data Science ◽  
New Technologies ◽  
Coefficient Of Determination ◽  
Change Environment ◽  
Bathymetric Data ◽  
Work Supports ◽  
Sentinel 2

<p><strong>ABSTRACT</strong></p><p>With the fast and highly growing demand for all possible ways of remote work as a result of COVID19 pandemic, new technologies using Satellite data were highly encouraged for multidisciplinary applications in different fields such as; agriculture, climate change, environment, coastal management, maritime, security and Blue Economy.</p><p>This work supports applying Satellite Derived Bathymetry (SDB) with the available low-cost multispectral satellite imagery applications, instruments and readily accessible data for different areas with only their benthic parameters, water characteristics and atmospheric conditions.  The main goal of this work is to derive bathymetric data needed for different hydrographic applications, such as: nautical charting, coastal engineering, water quality monitoring, sediment movement monitoring and supporting both green carbon and marine data science.  Also, this work proposes and assesses a SDB procedure that makes use of publicly-available multispectral satellite images (Sentinel2 MSI) and applies algorithms available in the SNAP software package for extracting bathymetry and supporting bathymetric layers against highly expensive traditional in-situ hydrographic surveys. The procedure was applied at SAFAGA harbor area, located south of Hurghada at (26°44′N, 33°56′E), on the Egyptian Red Sea coast.  SAFAGA controls important maritime traffic line in Red Sea such as (Safaga – Deba, Saudi Arabia) maritime cruises.  SAFAGA depths change between 6 m to 22m surrounded by many shoal batches and confined waters that largely affect maritime safety of navigation.  Therefore, there is always a high demand for updated nautical charts which this work supports.  The outcome of this work provides and fulfils those demands with bathymetric layers data for the approach channel and harbour usage bands electronic nautical chart of SAFAGA with reasonable accuracies.  The coefficient of determination (R<sup>2</sup>) differs between 0.42 to 0.71 after applying water column correction by Lyzenga algorithm and deriving bathymetric data depending on reflectance /radiance of optical imagery collected by sentinel2 missions with in-situ depth data values relationship by Stumpf equation.  The adopted approach proved to give  highly reasonable results that could be used in nautical charts compilation. Similar methodologies could be applied to inland water bodies.  This study is part of the MSc Thesis of the first author and is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy which is still running.</p><p><strong>Keywords: Algorithm, Bathymetry, Sentinel 2, nautical charting, Safaga port, satellite imagery, water depth, Egypt.</strong></p>

Effects of vegetation as an ecosystem service on the changes in runoff and sediment yield in a Mediterranean semi-arid basin

2021 ◽  
Author(s):  
María Rosario Vidal-Abarca Gutiérrez ◽  
Alberto Martínez-Salvador ◽  
Carmelo Conesa-García ◽  
María Luisa Suárez-Alonso ◽  
Francisco Alonso-Sarria ◽  
...  
Keyword(s):  
Sediment Yield ◽  
Ecosystem Service ◽  
Vegetation Cover ◽  
Pressure Sensors ◽  
Erosion Control ◽  
Water Levels ◽  
Event Scale ◽  
Dynamic Component ◽  
Sediment Loads ◽  
Runoff And Sediment Yield

<p>Semiarid basins contribute significantly to sediment loads, as they are often characterized by torrential flows, source areas with high sediment-producing rates, great availability of erodible material subjected to intense weathering processes, and poor vegetation cover. Vegetation, despite its scarce presence, is a dynamic component of this environment, which provides a range of important ecosystem services such as biodiversity, flood retention, nutrient sink, erosion control and groundwater recharge. This study examines the vegetation responses to the magnitude of peak flows and its contribution to the changes in runoff and sediment yield during the period 1997-2020 in a catchment Mediterranean semiarid basin: The Rambla de la Azohía (southeastern Spain).Vegetation type, density, preferred location and degree of permanence in each sub-basin were analyzed in order to determine their degree of influence on surface runoff and erosion control. Changes in riparian vegetation cover was quantified at large scale for the analysis period (1997-2020), using remotely sensed spatial information, such as satellite images and aerial photographs separated by two years on average (at scales from 1:15000 to 1:30000, and resolution between 0.22 and 0.50 m/pixel). A geo-spatial erosion prediction model was applied to estimate the runoff and sediment load generated at the event scale, taking into account the variability of the vegetation cover in each sub-basin. The simulated outputs of this model were previously calibrated with water levels measured by pressure sensors and suspended sediment records.The results showed both a poor response of vegetation (low incidence in the runoff coefficient) in steep metamorphic watersheds, capable of supplying large sediment loads, and functioned as an efficient ecosystem service (stabilization of slopes and decrease in peak flow) in less steep sub-basins with slopes in the shadow, composed of limestone formations and alluvial fans. This suggests important spatial differences in the vegetation impact, according to other environmental conditions intrinsic to each sub-basin, but also a low overall influence on the temporal variability of sediment fluxes at the event scale. This research was funded by FEDER/Spanish Ministry of Science, Innovation and Universities—State Research Agency (AEI)/Projects CGL2017-84625-C2-1-R and CGL2017-84625-C2-2-R; State Program for Research, Development and Innovation Focused on the Challenges of Society.</p>

scholarly journals Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020 ◽  
Vol 12 (20) ◽  
pp. 3376 ◽  
Author(s):  
Giovanni Romano ◽  
Giovanni Francesco Ricci ◽  
Francesco Gentile
Keyword(s):  
Winter Wheat ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Riparian Vegetation ◽  
Deciduous Forests ◽  
Sensor Data ◽  
Mountain Stream ◽  
Landsat 8 ◽  
Area Index ◽  
Sentinel 2

In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.

scholarly journals High accuracy coastal flood mapping for Norway using LiDAR data

2019 ◽  
Author(s):  
Kristian Breili ◽  
Matthew James Ross Simpson ◽  
Erlend Klokkervold ◽  
Oda Roaldsdotter Ravndal
Keyword(s):  
At Risk ◽  
Sea Level Rise ◽  
Sea Level ◽  
Storm Surge ◽  
Coastal Flooding ◽  
High Accuracy ◽  
Mapping Method ◽  
Water Levels ◽  
Climate Services ◽  
Elevation Data

Abstract. Using new high accuracy Light Detection and Ranging elevation data we generate coastal flooding maps for Norway. Thus far, we have mapped ~ 80 % of the coast, for which we currently have data of sufficient accuracy to perform our analysis. Although Norway is generally at low risk from sea-level rise largely owing to its steep topography, the maps presented here show that on local scales, many parts of the coast are potentially vulnerable to flooding. There is a considerable amount of infrastructure at risk along the relatively long and complicated coastline. Nationwide we identify a total area of 400 km2, 105,000 buildings, and 510 km of roads that are at risk of flooding from a 200 year storm-surge event at present. These numbers will increase to 610 km2, 137,000, and 1340 km with projected sea-level rise to 2090 (95th percentile of RCP8.5 as recommended in planning). We find that some of our results are likely biased high owing to erroneous mapping (at least for lower water levels close to the tidal datum which delineates the coastline). A comparison of control points from different terrain types indicates that the elevation model has a root mean square error of 0.26 m and is the largest source of uncertainty in our mapping method. The coastal flooding maps and associated statistics are freely available, and alongside the development of coastal climate services, will help communicate the risks of sea-level rise and storm surge to stakeholders. This will in turn aid coastal management and climate adaption work in Norway.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

Sign in / Sign up

Export Citation Format

Share Document