Review of Conceptual Models of Estimating the Spatio-Temporal Variations of Water Depth Using Remote Sensing and GIS for the Management of Dams and Reservoirs

2022 ◽  
pp. 83-109
Author(s):  
Abdulkadir Isah Funtua
Keyword(s):  
Remote Sensing ◽  
Water Depth ◽  
Conceptual Models ◽  
Temporal Variations ◽  
Remote Sensing And Gis ◽  
Spatio Temporal

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

Spatio-temporal Analysis of Shoreline Positional Change of Ondo State Coastline Using Remote Sensing and GIS: A Case Study of Ilaje Coastline at Ondo State in Nigeria

2021 ◽  
Author(s):  
Akinola Adesuji Komolafe ◽  
Paul Ayodeji Apalara ◽  
Matthew Olomolatan Ibitoye ◽  
Abiodun Olufemi Adebola ◽  
Idowu Ezekiel Olorunfemi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Analysis ◽  
Remote Sensing And Gis ◽  
Positional Change ◽  
Ondo State ◽  
Spatio Temporal

scholarly journals Contribution à l’étude de la vulnérabilité des terres de cultures de la région de Thiès (Sénégal) à l’aide de la télédétection et des systèmes d’information géographique (SIG)

2019 ◽  
pp. 6731-6746 ◽  
Author(s):  
Amadou SALL ◽  
Assize TOURE ◽  
Alioune KANE ◽  
Awa Niang Fall
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Temporal Dynamics ◽  
Remote Sensing And Gis ◽  
Cultivated Land ◽  
Landsat Images ◽  
Systèmes D’Information Géographique ◽  
Spatio Temporal ◽  
Systèmes D’Information

L’objectif de cette étude est d’établir à partir de la télédétection et des SIG, la dynamique spatio-temporelle des terres de cultures et d’explorer les futurs possibles de l’occupation du sol dans trois communes rurales de la région de Thiès (Fandène, Notto Diobass et Taiba Ndiaye). Une classification multidate des images landsat (1988, 2002 et 2014) a permis de quantifier les changements d’occupation des terres. Les résultats montrent que les zones de culture de Fandène sont passées entre 1988 et 2014 de 62% à 52% de la superficie totale de la commune. A l’opposée la commune de Taiba Ndiaye connait une expansion des zones de culture entre ces deux dates. Les changements enregistrés à Notto sont négligeables. Les simulations, faites sur la base des probabilités pour que la valeur d’une cellule i reste inchangée ou prenne la valeur d’une autre cellule j à l’horizon 2035, révèlent que les terres de culture de Fandène ont 69% de probabilité d’évoluer vers d’autres classes d’occupation du sol. ABSTRACT The objective of this study is to quantify from remote sensing and GIS the spatio temporal dynamics of cultivated land and explore possible futures of land use in three rural municipalities of Thies (Fandene, Notto Diobass, and Taiba Ndiaye). A multidate classification Landsat images (1988, 2002 et 2014) was used to quantify change in land cover. The results show that between 1988 and 2014 Fandene cropping areas have passed from 62% to 52% of the total area. At the opposite the commune of Taiba Ndiaye has known an expansion of cropping areas between these two dates. Minor changes are noted in Notto district. Simulations carried out on the basis of probabilities for a unit i to stay in the same cell or to be converted to another unit j in 2035, reveals that the probability for a cultivated land unit to be transformed into a another land cover category is high in Fandene (69 %).

Investigating of River-Aquifer Interactions using Sub-Surface Hydrological Model and Remote Sensing Inputs in an agriculturally Dominated Kosi River Basin, India.

2021 ◽  
Author(s):  
Naga Venkata Satish Laveti ◽  
Suresh A. Kartha ◽  
Subashisa Dutta
Keyword(s):  
Remote Sensing ◽  
Groundwater Recharge ◽  
River Basin ◽  
Hydrological Model ◽  
Temporal Variations ◽  
Interaction Process ◽  
Groundwater Levels ◽  
Physically Based ◽  
Spatio Temporal ◽  
Exchange Flux

<p>River-Aquifer Interaction is a natural and complex phenomenon for understanding its physical dynamic processes. These interactions highly vary with time and space and are to be investigated at river reach scale. The present study aims to understand and quantify the spatio-temporal variations of river-aquifer interaction process in Kosi river basin, India. This basin is majorly dominated with agricultural lands and irrigation requirement of the crops are mostly met by groundwater. In order to quantify the river-aquifer exchange flux at reach scale, a physically based sub-surface hydrological model has been carried for the study area. For this purpose, high resolution remotely sensed evapotranspiration data and groundwater recharge (estimated using soil water budget method method) along with other aquifer parameters were utilized for simulating the monthly groundwater levels as well as exchange flux between river and aquifer. The model results showed that simulated groundwater levels were well calibrated and validated with measured groundwater levels. Further, this calibrated groundwater flow model has been used to quantify the river-aquifer exchange flux. Based on the obtained exchange flux values, three different interaction zones were identified from upstream (Kosi barrage) to downstream (confluence point with Ganga river) in the study reach. It is observed that the river mostly loses water to the aquifer (as influent) in Zone I (80km from upstream) and the river mostly gains water from the aquifer (as effluent) in Zone III (40 km above downstream to confluence point). Whereas, the river has a combination of both losing and gaining natures in Zone II (between Zone I and III). From this study, it can be concluded that use of satellite remote sensing inputs (groundwater recharge and evapotranspiration) in the sub-surface hydrological model, facilitated to improve the assessment and understanding river-aquifer interaction process in an alluvial River basin.</p>

Sign in / Sign up

Export Citation Format

Share Document