scholarly journals Mapping Substrate Types and Compositions in Shallow Streams

2019 ◽  
Vol 11 (3) ◽  
pp. 262 ◽  
Author(s):  
Milad Niroumand-Jadidi ◽  
Nima Pahlevan ◽  
Alfonso Vitti
Keyword(s):  
Remote Sensing ◽  
Water Column ◽  
Aquatic Vegetation ◽  
Image Data ◽  
Correction Method ◽  
Habitat Modeling ◽  
Fluvial Systems ◽  
Reflectance Data ◽  
Edge Band

Remote sensing of riverbed compositions could enable advances in hydro-morphological and habitat modeling. Substrate mapping in fluvial systems has not received as much attention as in nearshore, optically shallow inland, and coastal waters. As finer spatial-resolution image data become more available, a need emerges to expand research on the remote sensing of riverbed composition. For instance, research to date has primarily been based on spectral reflectance data from above the water surface without accounting for attenuation by the water-column. This study analyzes the impacts of water-column correction for substrate mapping in shallow fluvial systems (depth < 1 m). To do so, we performed three different experiments: (a) analyzing spectroscopic measurements in a hydraulic laboratory setting, (b) simulating water-leaving radiances under various optical scenarios, and (c) evaluating the potential to map bottom composition from a WorldView-3 (WV3) image of a river in Northern Italy. Following the retrieval of depth and diffuse attenuation coefficient ( K d ), bottom reflectances were estimated using a water-column correction method. The results indicated significant enhancements in streambed maps based on bottom reflectances relative to maps produced from above-water spectra. Accounting for deep-water reflectance, embedded in the water-column correction, was demonstrated to have the greatest impact on the retrieval of bottom reflectance in NIR bands, when the water column is relatively thick (>0.5 m) and/or when the water is turbid. We also found that the WV3’s red-edge band (i.e., 724 nm) considerably improved the characterization of submerged aquatic vegetation (SAV) densities from either above-water or retrieved bottom spectra. This study further demonstrated the feasibility of mapping SAV density classes from a WV3 image of the Sarca River in Italy by retrieving the bottom reflectances.

Characterization of complex fluvial systems using remote sensing of spatial and temporal water level variations in the Amazon, Congo, and Brahmaputra Rivers

2010 ◽  
Vol 35 (3) ◽  
pp. 294-304 ◽  
Author(s):  
Hahn Chul Jung ◽  
James Hamski ◽  
Michael Durand ◽  
Doug Alsdorf ◽  
Faisal Hossain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Fluvial Systems ◽  
Water Level Variations

scholarly journals A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

2021 ◽  
Vol 13 (4) ◽  
pp. 623
Author(s):  
Gillian S. L. Rowan ◽  
Margaret Kalacska
Keyword(s):  
Remote Sensing ◽  
Water Column ◽  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Global Climate ◽  
Training Data ◽  
Suspended Particulate ◽  
The Subject

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

scholarly journals Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

2021 ◽  
Vol 13 (4) ◽  
pp. 1917
Author(s):  
Alma Elizabeth Thuestad ◽  
Ole Risbøl ◽  
Jan Ingolf Kleppe ◽  
Stine Barlindhaug ◽  
Elin Rose Myrvoll
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Image Data ◽  
Remote Sensing Image ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Detection Rates ◽  
Airborne Laser ◽  
Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

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