Variation in the projected surface area of suspended particles: Implications for remote sensing assessment of TSM

2002 ◽  
Vol 79 (1) ◽  
pp. 23-29 ◽  
Author(s):  
O Mikkelsen
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Suspended Particles

An assessment of the Dead Sea level change using remotely sensed data

2021 ◽  
Author(s):  
Musab Mbideen ◽  
Balázs Székely
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Surface Area ◽  
Dead Sea ◽  
Spectral Radiance ◽  
Water Volume ◽  
Surface Model ◽  
Atmospheric Effects ◽  
Level 1 ◽  
The Dead

<p>Remote Sensing (RS) and Geographic Information System (GIS) instruments have spread rapidly in recent years to manage natural resources and monitor environmental changes. Remote sensing has a vast range of applications; one of them is lakes monitoring. The Dead Sea (DS) is subjected to very strong evaporation processes, leading to a remarkable shrinkage of its water level. The DS is being dried out due to a negative balance in its hydrological cycle during the last five decades. This research aims to study the spatial changes in the DS throughout the previous 48 years. Change detection technique has been performed to detect this change over the research period (1972-2020). 73 Landsat imageries have been used from four digital sensors; Landsat 1-5 MSS C1 Level-1, Landsat 4-5 TM C1 Level-1, Land sat 7 ETM+ C1  Level-1, and Landsat 8 OLI-TIRS C1 Level. After following certain selection criteria , the number of studied images decreased. Furthermore, the Digital Surface Model of the Space Shuttle Radar Topography Mission and a bathymetric map of the Dead Sea were used. The collected satellite imageries were pre-processed and normalized using ENVI 5.3 software by converting the Digital Number (DN) to spectral radiance, the spectral radiance was converted to apparent reflectance, atmospheric effects were removed, and finally, the black gaps were removed. It was important to distinguish between the DS lake and the surrounding area in order to have accurate results, this was done by performing classification techniques. The digital terrain model of the DS was used in ArcGIS (3D) to reconstruct the elevation of the shore lines. This model generated equations to detect the water level, surface area, and water volume of the DS. The results were compared to the bathymetric data as well. The research shows that the DS water level declined 65 m (1.35 m/a) in the studied period. The surface area and the water volume declined by 363.56 km<sup>2 </sup>(7.57 km<sup>2</sup>/a) and 53.56 km<sup>3</sup> (1.11 km<sup>3</sup>/a), respectively. The research also concluded that due to the bathymetry of the DS, the direction of this shrinkage is from the south to the north. We hypothesize that anthropogenic effects have contributed in the shrinkage of the DS more than the climate. The use of the DS water by both Israel and Jordan for industrial purposes is the main factor impacting the DS, another factor is the diversion of the Jordan and Yarmouk rivers. Our results also allow to give a prediction for the near future of the DS: the water level is expected to reach –445 m in 2050, while the surface area and the water volume is expected to be 455 km<sup>2</sup> and 142 km<sup>3</sup>, respectively. </p>

Forecasting future changes in Manzala Lake surface area by considering variations in land use and land cover using remote sensing approach

2018 ◽  
Vol 11 (5) ◽  
Author(s):  
Hickmat Hossen ◽  
Mona G. Ibrahim ◽  
Wael Elham Mahmod ◽  
Abdelazim Negm ◽  
Kazuo Nadaoka ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Surface Area ◽  
Lake Surface ◽  
Lake Surface Area

scholarly journals Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

2019 ◽  
Vol 12 (1) ◽  
pp. 50 ◽  
Author(s):  
Mahyar Aboutalebi ◽  
Alfonso F. Torres-Rua ◽  
Mac McKee ◽  
William P. Kustas ◽  
Hector Nieto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Point Cloud ◽  
Spatial Information ◽  
Point Cloud Data ◽  
Area Index ◽  
Cloud Data ◽  
Fractional Cover ◽  
Spatially Distributed

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

scholarly journals Investigation of surface area of lakes and marshes from satellite images by using remote sensing and geographic information system integration in Iraq

2018 ◽  
Vol 162 ◽  
pp. 03016
Author(s):  
Alaa Dawood ◽  
Yousif Kalaf ◽  
Nagham Abdulateef ◽  
Mohammed Falih
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
System Integration ◽  
Hydrological Regime ◽  
Remotely Sensed ◽  
Total Surface Area ◽  
Water Levels ◽  
Remotely Sensed Data ◽  
Surface Areas ◽  
Water Volumes

Water level and distribution is very essential in almost all life aspects. Natural and artificial lakes represent a large percentage of these water bodies in Iraq. In this research the changes in water levels are observed by calculating the areas of five different lakes in five different regions and two different marshes in two different regions of the country, in a period of 12 years (2001 - 2012), archived remotely sensed images were used to determine surface areas around lakes and marshes in Iraq for the chosen years . Level of the lakes corresponding to satellite determined surface areas were retrieved from remotely sensed data .These data were collected to give explanations on lake level and surface area fluctuations. It is important to determine these areas at different water levels to know areas which are being flooded in addition to the total area inundated .The behavior of hydrological regime of these lakes during the period was assessed using an integration of remote sensing and GIS techniques which found that the total surface area of the lakes had diminished and their water volumes reduced. The study further revealed that the levels of the lakes surfaces had lowered through these years.

Monitoring and Analysis of Changes in the Depth and Surface Area Snow of the Mountains in Iran Using Remote Sensing Data

2020 ◽  
Vol 48 (11) ◽  
pp. 1479-1494
Author(s):  
Vahid Safarianzengir ◽  
Leila Mahmoudi ◽  
Roghayeh Maleki Meresht ◽  
Behrooz Abad ◽  
Kazem Rajabi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Remote Sensing Data ◽  
Sensing Data

Contrasting changes in surface waters and barrens over the past 60 years for a subarctic forest–tundra site in northern Manitoba based on remote sensing imagery

2013 ◽  
Vol 50 (9) ◽  
pp. 967-977 ◽  
Author(s):  
Charles Umbanhowar ◽  
Philip Camill ◽  
Mark Edlund ◽  
Christoph Geiss ◽  
Wesley Durham ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Terrestrial Ecosystems ◽  
Water Bodies ◽  
Aerial Photographs ◽  
The Arctic ◽  
Remote Sensing Imagery ◽  
The Past ◽  
High Resolution Satellite Images ◽  
Wide Range

Intensified warming in the Arctic and Subarctic is resulting in a wide range of changes in the extent, productivity, and composition of aquatic and terrestrial ecosystems. Analysis of remote sensing imagery has documented regional changes in the number and area of ponds and lakes as well as expanding cover of shrubs and small trees in uplands. To better understand long-term changes across the edaphic gradient, we compared the number and area of water bodies and dry barrens (>100 m2) between 1956 (aerial photographs) and 2008–2011 (high-resolution satellite images) for eight ∼25 km2 sites near Nejanilini Lake, Manitoba (59.559°N, 97.715°W). In the modern landscape, the number of water bodies and barrens were similar (1162 versus 1297, respectively), but water bodies were larger (mean 3.1 × 104 versus 681 m2, respectively) and represented 17% of surface area compared with 0.4% for barrens. Over the past 60 years, total surface area of water did not change significantly (16.7%–17.1%) despite a ∼30% decrease in numbers of small (<1000 m2) water bodies. However, the number and area of barrens decreased (55% and 67%, respectively) across all size classes. These changes are consistent with Arctic greening in response to increasing temperature and precipitation. Loss of small water bodies suggests that wet tundra areas may be drying, which, if true, may have important implications for carbon balance. Our observations may be the result of changes in winter conditions in combination with low permafrost ice content in the region, in part explaining regional variations in responses to climate change.

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