scholarly journals Evaluation of Anzali Wetland Depth Changes Using Satellite Images and Meteorological Data over Thirty Years

2020 ◽  
Vol 12 (2) ◽  
pp. 73-82
Author(s):  
Saba Kharyaband ◽  
S Attarchi
Keyword(s):  
Satellite Images ◽  
Meteorological Data ◽  
Anzali Wetland

Quantifying recent lake level changes in Patagonia (Argentina and Chile): Back to the future?

2021 ◽  
Author(s):  
Daniel Ariztegui ◽  
Clément Pollier ◽  
Andrés Bilmes
Keyword(s):  
Water Level ◽  
Lake Level ◽  
Satellite Images ◽  
Meteorological Data ◽  
Tierra Del Fuego ◽  
Water Level Fluctuations ◽  
Lake Levels ◽  
The Past ◽  
Rainfall Variations ◽  
The Impact

<p>Lake levels in hydrologically closed-basins are very sensitive to climatically and/or anthropogenically triggered environmental changes. Their record through time can provide valuable information to forecast changes that can have substantial economical and societal impact.</p><p>Increasing precipitation in eastern Patagonia (Argentina) have been documented following years with strong El Niño (cold) events using historical and meteorological data. Quantifying changes in modern lake levels allow determining the impact of rainfall variations while contributing to anticipate the evolution of lacustrine systems over the next decades with expected fluctuations in ENSO frequencies. Laguna Carrilaufquen Grande is located in the intermontane Maquinchao Basin, Argentina. Its dimension fluctuates greatly, from 20 to 55 km<sup>2</sup> water surface area and an average water depth of 3 m. Several well-preserved gravelly beach ridges witness rainfall variations that can be compared to meteorological data and satellite images covering the last ~50 years. Our results show that in 2016 lake level was the lowest of the past 44 years whereas the maximum lake level was recorded in 1985 (+11.8 m above the current lake level) in a position 1.6 km to the east of the present shoreline. A five-years moving average rainfall record of the area was calculated smoothing the extreme annual events and correlated to the determined lake level fluctuations. The annual variation of lake levels was up to 1.2 m (e.g. 2014) whereas decadal variations related to humid-arid periods for the interval 2002 to 2016 were up to 9.4 m. These data are consistent with those from other monitored lakes and, thus, our approach opens up new perspectives to understand the historical water level fluctuations of lakes with non-available monitoring data.</p><p> </p><p>Laguna de los Cisnes in the Chilean section of the island of Tierra del Fuego, is a closed-lake presently divided into two sections of 2.2 and 11.9 km<sup>2</sup>, respectively. These two water bodies were united in the past forming a single larger lake. The lake level was  ca. 4 m higher than today as shown by clear shorelines and the outcropping of large Ca-rich microbialites. Historical data, aerial photographs and satellite images indicate that the most recent changes in lake level are the result of a massive decrease of water input during the last half of the 20<sup>th</sup> century triggered by an indiscriminate use of the incoming water for agricultural purposes. The spectacular outcropping of living and fossil microbialites is not only interesting from a scientific point of view but has also initiated the development of the site as a local touristic attraction. However, if the use of the incoming water for agriculture in the catchment remains unregulated the lake water level might drop dangerously and eventually the lake might fully desiccate.</p><p>These two examples illustrate how recent changes in lake level can be used to anticipate the near future of lakes. They show that ongoing climate changes along with the growing demand of natural resources have already started to impact lacustrine systems and this is likely to increase in the decades to come.</p>

scholarly journals Monitoring Oats and Winter Wheat Within-Field Spatial Variability by Satellite Images

2018 ◽  
Vol 49 (2) ◽  
pp. 127-135
Author(s):  
J. Kumhálová ◽  
P. Novák ◽  
M. Madaras
Keyword(s):  
Winter Wheat ◽  
Spatial Variability ◽  
Satellite Images ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Growing Season ◽  
Yield Component ◽  
Small Field ◽  
Agronomic Management ◽  
Time Series Images

Abstract Remote sensing is a methodology using different tools to monitor and predict yields. Spatial variability of crops can be monitored through sampling of vegetation indices derived from the entire crop growth; spatial variability can be used to plan further agronomic management. This paper evaluates the suitability of vegetation indices derived from satellite Landsat and EO-1 data that compare yield, topography wetness index, solar radiation, and meteorological data over a relatively small field (11.5 ha). Time series images were selected from 2006, 2010, and 2014, when oat was grown, and from 2005, 2011 and 2013, when winter wheat was grown. The images were selected from the entire growing season of the crops. An advantage of this method is the availability of these images and their easy application in deriving vegetation indices. It was confirmed that Landsat and EO-1 images in combination with meteorological data are useful for yield component prediction. Spatial resolution of 30 m was sufficient to evaluate a field of 11.5 ha.

scholarly journals High-resolution urban air quality monitoring using sentinel satellite images and low-cost ground-based sensor networks

2020 ◽  
Vol 171 ◽  
pp. 02002
Author(s):  
Joseph Gitahi ◽  
Michael Hahn
Keyword(s):  
Satellite Images ◽  
Meteorological Data ◽  
Low Cost ◽  
Correlation Coefficients ◽  
Urban Environments ◽  
Average Correlation ◽  
Second Stage ◽  
Aerosol Distribution ◽  
Level Monitoring ◽  
Sentinel 2

Satellite remote sensing aerosol monitoring products are readily available but limited to regional and global scales due to low spatial resolutions making them unsuitable for city-level monitoring. Freely available satellite images such as Sentinel -2 at relatively high spatial (10m) and temporal (5 days) resolutions offer the chance to map aerosol distribution at local scales. In the first stage of this study, we retrieve Aerosol Optical Depth (AOD) from Sentinel -2 imagery for the Munich region and assess the accuracy against ground AOD measurements obtained from two Aerosol Robotic Network (AERONET) stations. Sen2Cor, iCOR and MAJA algorithms which retrieve AOD using Look-up-Tables (LUT) pre-calculated using radiative transfer (RT) equations and SARA algorithm that applies RT equations directly to satellite images were used in the study. Sen2Cor, iCOR and MAJA retrieved AOD at 550nm show strong consistency with AERONET measurements with average correlation coefficients of 0.91, 0.89 and 0.73 respectively. However, MAJA algorithm gives better and detailed variations of AOD at 10m spatial resolution which is suitable for identifying varying aerosol conditions over urban environments at a local scale. In the second stage, we performed multiple linear regression to estimate surface Particulate Matter (PM2.5) concentrations using the satellite retrieved AOD and meteorological data as independent variables and ground-measured PM2.5 data as the dependent variable. The predicted PM2.5 concentrations exhibited agreement with ground measurements, with an overall coefficient (R2) of 0.59.

scholarly journals Soil moisture and evapotranspiration of wetlands vegetation habitats retrieved from satellite images

2010 ◽  
Vol 7 (4) ◽  
pp. 5929-5955 ◽  
Author(s):  
K. Dabrowska-Zielinska ◽  
M. Budzynska ◽  
W. Kowalik ◽  
K. Turlej
Keyword(s):  
Soil Moisture ◽  
Satellite Images ◽  
Water Cycle ◽  
Incidence Angle ◽  
Meteorological Data ◽  
Wave Length ◽  
Ground Truth ◽  
Test Site ◽  
Heat Fluxes ◽  
Ramsar Convention

Abstract. The research has been carried out in Biebrza Ramsar Convention test site situated in the N-E part of Poland. Data from optical and microwave satellite images have been analysed and compared to the detailed soil-vegetation ground truth measurements conducted during the satellite overpasses. Satellite data applied for the study include: ENVISAT.ASAR, ENVISAT.MERIS, ALOS.PALSAR, ALOS.AVNIR-2, ALOS.PRISM, TERRA.ASTER, and NOAA.AVHRR. Optical images have been used for classification of wetlands vegetation habitats and vegetation surface roughness expressed by LAI. Also, heat fluxes have been calculated using NOAA.AVHRR data and meteorological data. Microwave images have been used for the assessment of soil moisture. For each of the classified wetlands vegetation habitats the relationship between soil moisture and backscattering coefficient has been examined, and the best combination of microwave variables (wave length, incidence angle, polarization) has been used for mapping and monitoring of soil moisture. The results of this study give possibility to improve models of water cycle over wetlands ecosystems by adding information about soil moisture and surface heat fluxes derived from satellite images. Such information is very essential for better protection of the European sensitive wetland ecosystems. ENVISAT and ALOS images have been obtained from ESA for AO ID 122 and AOALO.3742 projects.

scholarly journals 25 years (1981–2005) of equilibrium-line altitude and mass-balance reconstruction on Glacier Blanc, French Alps, using remote-sensing methods and meteorological data

2008 ◽  
Vol 54 (185) ◽  
pp. 307-314 ◽  
Author(s):  
Antoine Rabatel ◽  
Jean-Pierre Dedieu ◽  
Emmanuel Thibert ◽  
Anne Letréguilly ◽  
Christian Vincent
Keyword(s):  
Remote Sensing ◽  
Mass Balance ◽  
Satellite Images ◽  
Meteorological Data ◽  
Equilibrium Line ◽  
Surface Mass Balance ◽  
Mass Balances ◽  
Equilibrium Line Altitude ◽  
Surface Mass ◽  
French Alps

AbstractAnnual equilibrium-line altitude (ELA) and surface mass balance of Glacier Blanc, Ecrins region, French Alps, were reconstructed from a 25 year time series of satellite images (1981–2005). The remote-sensing method used was based on identification of the snowline, which is easy to discern on optical satellite images taken at the end of the ablation season. In addition, surface mass balances at the ELA were reconstructed for the same period using meteorological data from three nearby weather stations. A comparison of the two types of series reveals a correlation of r > 0.67 at the 0.01 level of significance. Furthermore, the surface mass balances obtained from remote-sensing data are consistent with those obtained from field measurements on five other French glaciers (r = 0.76, p < 0.01). Also consistent for Glacier Blanc is the total mass loss (10.8 m w.e.) over the studied period. However, the surface mass balances obtained with the remote-sensing method show lower interannual variability. Given that the remote-sensing method is based on changes in the ELA, this difference probably results from the lower sensitivity of the surface mass balance to climate parameters at the ELA.

scholarly journals <i>Brief communication</i> "The integration of remote sensing and meteorological data for monitoring irrigation demand in Cyprus"

2009 ◽  
Vol 9 (6) ◽  
pp. 2009-2014 ◽  
Author(s):  
G. C. Papadavid ◽  
A. Agapiou ◽  
S. Michaelides ◽  
D. G. Hadjimitsis
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Satellite Images ◽  
Meteorological Data ◽  
Crop Water ◽  
Irrigation Demand ◽  
In Situ Techniques ◽  
Modeling Techniques ◽  
Agricultural Areas

Abstract. This paper examines and evaluates the integrated use of satellite remote sensing and meteorological data for estimating crop water requirements over agricultural areas of Cyprus. Intended purpose of this project is to estimate evapotranspiration using modeling techniques, satellite and meteorological data for monitoring irrigation demand. ETc was calculated with the FAO Penman-Monteith method by using satellite images acquired from July to December 2008. ETc estimates obtained in this project were compared to previous empirical data found by using in-situ techniques. ETc values have been correlated with the meteorological data to crosscheck the significance of the meteorological inputs.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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