The Remote Sensing of Factors Influencing Underwater Acoustics

1981 ◽  
pp. 93-98
Author(s):  
G. Tacconi ◽  
B. Wannamaker
Keyword(s):  
Remote Sensing ◽  
Underwater Acoustics ◽  
Factors Influencing

Remote Sensing-Based Evapotranspiration Modelling for Agricultural Water Management in the Limpopo Basin

2018 ◽  
pp. 50-85
Author(s):  
Berhanu F. Alemaw ◽  
Thebeyame Ronald Chaoka ◽  
Brigton Munyai
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Data Sets ◽  
Landsat Data ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Factors Influencing ◽  
Five Factors ◽  
Semi Arid

The study was motivated by the need to determine the spatial variation of ET and to test the applicability of RS based methods in arid to semi-arid climates with limited ground-based measurements. In this paper we present results of an effort of determining spatial actual evapotranspiration in the Limpopo basin, the Notwane subcatchment in the south-eastern part of Botswana, using remote sensing data from MODIS and Landsat Data sets. The Simplified Surface Energy Balance Index (S-SEBI) was applied to determine actual evapotranspiration using the seven bands of Landsat and MODIS surface reflectance and temperature channels. Three different dates were used to estimate ET from both Landsat and MODIS scenes. The estimated ET values from the two sensors show approximately equally comparable results. An assessment was also conducted to determine the factors influencing evapotranspiration. No strong correlation was identified for ET against the five factors investigated: Net radiation, NDVI, Surface Temperature, emissivity and surface albedo.

PREHISPANIC MAYA BURNT LIME INDUSTRIES: PREVIOUS STUDIES AND FUTURE DIRECTIONS

2018 ◽  
Vol 30 (2) ◽  
pp. 199-219 ◽  
Author(s):  
Kenneth E. Seligson ◽  
Soledad Ortiz Ruiz ◽  
Luis Barba Pingarrón
Keyword(s):  
Remote Sensing ◽  
Significant Role ◽  
Natural Environment ◽  
Future Directions ◽  
Burnt Lime ◽  
The Past ◽  
Factors Influencing ◽  
Lime Consumption ◽  
Maya Area ◽  
Lime Production

AbstractBurnt lime has played a significant role in daily Maya life since at least as far back as 1100 b.c., and yet its ephemeral nature has limited archaeological studies of its production. The application of new surveying and remote sensing technologies in recent decades is now allowing for a more in-depth investigation of the burnt lime industries that existed in different subregions of the Maya area. This article provides an overview of the current understanding of pre-Hispanic Maya burnt lime production. It then presents an analysis of the factors influencing the development and identification of distinct subregional lime production industries, including: lime consumption requirements and inter-site spacing; natural environment; local social and economic trajectories; and the objectives and survey universes of archaeological investigations. In reporting the tremendous advances made over the past few decades, this paper encourages archaeologists to include a focus on identifying lime production features in their research agendas.

scholarly journals Factors influencing the estimation of aboveground biomass (AGB) in tropical forests using RADAR remote sensing.

2018 ◽  
Author(s):  
Victoria E Espinoza-Mendoza
Keyword(s):  
Remote Sensing ◽  
Tropical Forests ◽  
Aboveground Biomass ◽  
Field Data ◽  
Spatial Information ◽  
Alos Palsar ◽  
Radar Remote Sensing ◽  
Radar Sensors ◽  
Factors Influencing ◽  
Optical Images

Despite the large amount of accessible spatial information, the issue of estimating aboveground biomass through remote sensing, especially radar, remains a challenge in complex ecosystems such as tropical forests. One of the advantages of radar sensors is that of "crossing clouds" (capacity that does not have optical images like Landsat), facilitating their use in areas with permanent cloud cover. This work defines, from several studies conducted in tropical forests using ALOS PALSAR, which are the factors with the most influence on the signal of the radar. This can be useful in the development and/or improvement of methodologies to estimate aboveground biomass in tropical forests, combining field data and satellite imagery of radar.

Remote Sensing-Based Evapotranspiration Modelling for Agricultural Water Management in the Limpopo Basin

2019 ◽  
pp. 249-286
Author(s):  
Berhanu F. Alemaw ◽  
Thebeyame Ronald Chaoka ◽  
Brigton Munyai
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Data Sets ◽  
Landsat Data ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Factors Influencing ◽  
Five Factors ◽  
Semi Arid

The study was motivated by the need to determine the spatial variation of ET and to test the applicability of RS based methods in arid to semi-arid climates with limited ground-based measurements. In this paper we present results of an effort of determining spatial actual evapotranspiration in the Limpopo basin, the Notwane subcatchment in the south-eastern part of Botswana, using remote sensing data from MODIS and Landsat Data sets. The Simplified Surface Energy Balance Index (S-SEBI) was applied to determine actual evapotranspiration using the seven bands of Landsat and MODIS surface reflectance and temperature channels. Three different dates were used to estimate ET from both Landsat and MODIS scenes. The estimated ET values from the two sensors show approximately equally comparable results. An assessment was also conducted to determine the factors influencing evapotranspiration. No strong correlation was identified for ET against the five factors investigated: Net radiation, NDVI, Surface Temperature, emissivity and surface albedo.

Factors influencing the structure of macroinvertebrate communities in subarctic lakes affected by wildfires

2020 ◽  
Author(s):  
Thomas James Pretty ◽  
Charles-Matthew Chanyi ◽  
Catherine Kuhn ◽  
Derek K. Gray
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Boreal Forest ◽  
Trophic Levels ◽  
Natural Phenomenon ◽  
Field Observations ◽  
Macroinvertebrate Communities ◽  
Subarctic Lakes ◽  
Factors Influencing ◽  
Invertebrate Composition

Fires are a natural phenomenon in the boreal forest, but their frequency is expected to increase over the coming century. Fires may affect water quality and invertebrates in lakes, but there have been few studies in the northern boreal forest to describe these impacts. We collected data on water quality, macrophytes, and invertebrates from 20 lakes in the Sahtú Settlement Area of the Northwest Territories. Nine lakes were affected by fires in their catchments 4-5 years before data collection, while eleven were not. Our results showed that few water quality variables were associated with fires. However, remote sensing and field observations suggested that macrophyte biomass was higher in lakes affected by burns and this variable was a significant predictor of invertebrate composition. Burn history was an important predictor of the richness and abundance of invertebrates, but natural variability in lake properties was more important for explaining differences among lakes. Our results suggest that a better understanding of the effects of wildfires might be gained by examining how post-fire changes in macrophytes affect other trophic levels.

REMOTE SENSING OF BIOGEOPHYSICAL FACTORS INFLUENCING LOCAL WATER QUALITY AND CORAL REEF HEALTH, U.S. VIRGIN ISLANDS

2017 ◽  
Author(s):  
Hannah L. Schlaerth ◽  
◽  
Dulci Avouris ◽  
David Flanagan ◽  
Marilyn E. Brandt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Coral Reef ◽  
Virgin Islands ◽  
Factors Influencing ◽  
Reef Health ◽  
Local Water

scholarly journals Performance Evaluation of Four Ocean Reflectance Model

2021 ◽  
Vol 13 (14) ◽  
pp. 2748
Author(s):  
Jun Li ◽  
Tongji Li ◽  
Qingjun Song ◽  
Chaofei Ma
Keyword(s):  
Remote Sensing ◽  
Sea Surface ◽  
Concentration Data ◽  
In Situ Data ◽  
Factors Influencing ◽  
Remote Sensing Reflectance ◽  
Reflectance Model ◽  
Main Factors ◽  
And Performance

Phytoplankton are the main factors influencing light under the sea surface in Case Ι water. The ocean reflectance model (ORM), which takes into account the chlorophyll a concentration data, can calculate the remote sensing reflectance of Case Ι water. In this study, we examined the differences and performance of four ORMs, including Morel and Maritorena (2001, MM01), Morel and Gentili (2007, MG07), Mobley (2014, MO14), and Hydrolight Abcase1 Lookup Tables. The differences between the four ORMs in terms of their absorption and backscattering coefficients were evaluated. Preformation of the four ORMs was compared using the NASA bio-Optical Marine Algorithm Dataset and in situ data from the South China Sea. The results showed that preformation of MM01 was the best.

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