scholarly journals Open-Source Analysis of Submerged Aquatic Vegetation Cover in Complex Waters Using High-Resolution Satellite Remote Sensing: An Adaptable Framework

2022 ◽  
Vol 14 (2) ◽  
pp. 267
Author(s):  
Arthur de Grandpré ◽  
Christophe Kinnard ◽  
Andrea Bertolo
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
High Resolution ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Spatial Scales ◽  
Atmospheric Correction ◽  
Source Analysis ◽  
Radiometric Calibration ◽  
Ecological Data

Despite being recognized as a key component of shallow-water ecosystems, submerged aquatic vegetation (SAV) remains difficult to monitor over large spatial scales. Because of SAV’s structuring capabilities, high-resolution monitoring of submerged landscapes could generate highly valuable ecological data. Until now, high-resolution remote sensing of SAV has been largely limited to applications within costly image analysis software. In this paper, we propose an example of an adaptable open-sourced object-based image analysis (OBIA) workflow to generate SAV cover maps in complex aquatic environments. Using the R software, QGIS and Orfeo Toolbox, we apply radiometric calibration, atmospheric correction, a de-striping correction, and a hierarchical iterative OBIA random forest classification to generate SAV cover maps based on raw DigitalGlobe multispectral imagery. The workflow is applied to images taken over two spatially complex fluvial lakes in Quebec, Canada, using Quickbird-02 and Worldview-03 satellites. Classification performance based on training sets reveals conservative SAV cover estimates with less than 10% error across all classes except for lower SAV growth forms in the most turbid waters. In light of these results, we conclude that it is possible to monitor SAV distribution using high-resolution remote sensing within an open-sourced environment with a flexible and functional workflow.

scholarly journals A 40-year global dataset of visible channel remote sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Author(s):  
Benjamin R. Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Climate Data Record ◽  
Very High

Abstract. A consistently calibrated 40-year length dataset of visible channel remote sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time-series. The dataset uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record (CDR) for top-of-atmosphere (TOA) visible channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote sensing reflectance. The resulting dataset is produced at daily, and archived at monthly, resolution, on a 0.1° × 0.1° grid at https://doi.pangaea.de/10.1594/PANGAEA.892175. The primary aim of deriving this dataset is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania Huxleyi over the past 40 years.

Variability in nitrogen content of submerged aquatic vegetation: utility as an indicator of N dynamics within and among lakes

2012 ◽  
Vol 65 (7) ◽  
pp. 1151-1157 ◽  
Author(s):  
Catherine Blanchet ◽  
Gabriel Maltais-Landry ◽  
Roxane Maranger
Keyword(s):  
Nitrogen Content ◽  
Plant Species ◽  
Aquatic Ecosystems ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Spatial Scales ◽  
Single Site ◽  
N Availability ◽  
N Dynamics ◽  
A Site

Submerged aquatic vegetation (SAV) may serve as an integrative proxy of spatial and temporal nitrogen (N) availability in aquatic ecosystems as plants are physiologically capable of storing variable amounts of N. However, it is important to understand whether plant species behave similarly or differently within and among systems. We sampled different SAV species along a nutrient gradient at multiple sites within several lakes to determine variability in C:N ratios and % N content among species, among plants of the same species at a single site, among sites and among lakes. Species respond differently suggesting that not all plant types can be used universally as nutrient proxies. The greatest variability in % N and C:N ratios for Valliseneria americana was observed among lakes whereas for Elodea canadensis it was among sites within a lake and among plants within a site. This suggests that V. americana could be a particularly useful indicator of N availability at larger spatial scales (regional and within a large fluvial lake) but that E. canadensis was not a particularly useful proxy.

scholarly journals Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

2020 ◽  
Vol 12 (16) ◽  
pp. 2626 ◽  
Author(s):  
Qingting Li ◽  
Zhengchao Chen ◽  
Bing Zhang ◽  
Baipeng Li ◽  
Kaixuan Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Spatial Scales ◽  
Detection Accuracy ◽  
Single Shot ◽  
Tailings Dams ◽  
High Resolution Satellite Imagery ◽  
Tailings Ponds

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

Spatial structuring of submerged aquatic vegetation in an estuarine habitat of the Gulf of Mexico

2012 ◽  
Vol 93 (4) ◽  
pp. 855-866 ◽  
Author(s):  
Aretha Moriana Burgos-León ◽  
David Valdés ◽  
Ma. Eugenia Vega ◽  
Omar Defeo
Keyword(s):  
Spatial Structure ◽  
Environmental Variables ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Spatial Scales ◽  
Ruppia Maritima ◽  
Spatial Partitioning ◽  
Estuarine Habitat ◽  
Relative Contribution ◽  
Geostatistical Techniques

Seasonal changes in spatial structure of biomass of submerged aquatic vegetation (SAV) and environmental variables were evaluated in Celestun Lagoon, an estuarine habitat in Mexico. Geostatistical techniques were used to evaluate spatial autocorrelation and to predict the spatial distribution by kriging. The relative contribution of 11 environmental variables in explaining the spatial structure of biomass of SAV was evaluated by canonical correspondence analysis. Spatial partitioning between species of SAV was evident: the seagrasses Halodule wrightii and Ruppia maritima dominated the seaward and central zones of the lagoon, respectively, whereas the green alga Chara fibrosa was constrained to the inner zone. The spatial structure and seasonal variability of SAV biomass were best explained by organic carbon in the sediments, salinity and total suspended solids in the water column. Analysis at different spatial scales allowed identifying the importance of spatial structure in biotic and abiotic variables of this estuarine habitat.

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