scholarly journals Assessment of reclaimed soils by unsupervised clustering of proximal sensor data

2018 ◽  
Vol 98 (4) ◽  
pp. 688-695
Author(s):  
P.T. Sorenson ◽  
C. Small ◽  
S.A. Quideau ◽  
A. Underwood ◽  
A. Janz
Keyword(s):  
Organic Matter ◽  
Water Content ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Exchange Capacity ◽  
Sensor Data ◽  
Soil Parameters ◽  
Proximal Sensing ◽  
Self Organizing Maps ◽  
Soil Variation

The application of soil proximal sensors on reclaimed sites presents a novel method for assessing the quality of reclaimed landscapes. This method improves assessment reliability, information management, and environmental assurance. One proximal sensing system that could be used to provide high spatial resolution measurements of soil parameters is an on-the-go optical sensor that collects data at two wavelengths: 660 and 940 nm. Proximal soil sensing data were collected at 27 sites, where organic matter, cation exchange capacity (CEC), and soil water content were collected from 221 soil samples from 0 to 15 cm. The proximal soil sensor data were then automatically clustered using a combination of self-organizing maps and random uniform forests. Overall, the proximal sensor data combined with this data analysis approach created maps with either three or four soil zones. On average, soil zones had statistically significant differences in organic matter, CEC, and water content. This system could be used to map out zones with significant soil variation as part of reclamation monitoring and then used to guide laboratory analytical sampling. Future work should focus on development of on-the-go reflectance spectroscopy systems to provide quantitative soil data with high spatial resolution.

scholarly journals High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China

2021 ◽  
Vol 9 ◽  
Author(s):  
Yang Junting ◽  
Li Xiaosong ◽  
Wu Bo ◽  
Wu Junjun ◽  
Sun Bin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Organic Matter Content ◽  
Multiple Linear Regression Model ◽  
Northern China ◽  
Google Earth ◽  
Support Vector ◽  
Combating Desertification

Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (<1%) were identified; finally, a timely (2019) and high spatial resolution (30 m) SOM content map for the desertified land in northern China was drawn which shows obvious advantages over existing SOM products, thus providing key data support for monitoring and combating desertification.

Characteristics of organic matter on Ceres from VIR/Dawn high spatial resolution spectra

2018 ◽  
Vol 482 (2) ◽  
pp. 2407-2421 ◽  
Author(s):  
M C De Sanctis ◽  
V Vinogradoff ◽  
A Raponi ◽  
E Ammannito ◽  
M Ciarniello ◽  
...  
Keyword(s):  
Organic Matter ◽  
Spatial Resolution ◽  
High Spatial Resolution

scholarly journals Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters Retrieved Using 30-Year Landsat Archive Imagery

2019 ◽  
Vol 11 (23) ◽  
pp. 2791 ◽  
Author(s):  
Konstantin P. Klein ◽  
Hugues Lantuit ◽  
Birgit Heim ◽  
Frank Fell ◽  
David Doxaran ◽  
...  
Keyword(s):  
Organic Matter ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Spatial Resolution ◽  
Spatial Patterns ◽  
Coastal Erosion ◽  
High Spatial Resolution ◽  
Sea Surface ◽  
Nearshore Zone ◽  
Sediment Dispersal

The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in the nearshore zone are not well known, because ships do not often reach shallow waters and satellite remote sensing is traditionally focused on less dynamic environments. The goal of this study is to use the extensive Landsat archive to investigate sediment dispersal patterns specifically on an exemplary Arctic nearshore environment, where field measurements are often scarce. Multiple Landsat scenes were combined to calculate means of sediment dispersal and sea surface temperature under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use observations in the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to turbidity and sea surface temperature. We map the spatial patterns of turbidity and water temperature at high spatial resolution in order to resolve transport pathways of water and sediment at the water surface. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, both turbidity and water temperature are significantly higher in the nearshore area. The extent of the Mackenzie River plume and coastal erosion are the main explanatory variables for sediment dispersal and sea surface temperature distributions in the study area. During northwesterly wind conditions, the influence of the Mackenzie River plume is negligible. Our results highlight the potential of high spatial resolution Landsat imagery to detect small-scale hydrodynamic processes, but also show the need to specifically tune optical models for Arctic nearshore environments.

High spatial resolution AEM observations of yttrium segregation in chromia scales grown on Co-45%Cr

1986 ◽  
Vol 44 ◽  
pp. 518-519
Author(s):  
K. Przybylski ◽  
A. J. Garratt-Reed ◽  
G. J. Yurek
Keyword(s):  
Spatial Resolution ◽  
Outer Surface ◽  
Maximum Concentration ◽  
High Spatial Resolution ◽  
Reactive Elements ◽  
Chromia Scales

The addition of so-called “reactive” elements such as yttrium to alloys is known to enhance the protective nature of Cr2O3 or Al2O3 scales. However, the mechanism by which this enhancement is achieved remains unclear. An A.E.M. study has been performed of scales grown at 1000°C for 25 hr. in pure O2 on Co-45%Cr implanted at 70 keV with 2x1016 atoms/cm2 of yttrium. In the unoxidized alloys it was calculated that the maximum concentration of Y was 13.9 wt% at a depth of about 17 nm. SIMS results showed that in the scale the yttrium remained near the outer surface.

Integration of Core-Edge Spectroscopy Methods for the Study of Polymers

1996 ◽  
Vol 54 ◽  
pp. 154-155
Author(s):  
E. G. Rightor
Keyword(s):  
Excited State ◽  
Polymer Blends ◽  
Gas Phase ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Electronic States ◽  
Core Electron ◽  
Bulk Solids ◽  
Development Application

Core edge spectroscopy methods are versatile tools for investigating a wide variety of materials. They can be used to probe the electronic states of materials in bulk solids, on surfaces, or in the gas phase. This family of methods involves promoting an inner shell (core) electron to an excited state and recording either the primary excitation or secondary decay of the excited state. The techniques are complimentary and have different strengths and limitations for studying challenging aspects of materials. The need to identify components in polymers or polymer blends at high spatial resolution has driven development, application, and integration of results from several of these methods.

Color Hologram Generation Using High Spatial Resolution Camera

2013 ◽  
Vol 67 (3) ◽  
pp. J108-J115
Author(s):  
Kosuke Nomura ◽  
Ryutaro Oi ◽  
Takanori Senoh ◽  
Taiichiro Kurita ◽  
Takayuki Hamamoto
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution
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