Spectral Characteristics of Reclaimed Vegetation in a Rare Earth Mine and Analysis of its Correlation with the Chlorophyll Content

2020 ◽  
Vol 87 (3) ◽  
pp. 553-562
Author(s):  
H. Li ◽  
Zh. Wei ◽  
X. Wang ◽  
F. Xu
Keyword(s):  
Rare Earth ◽  
Chlorophyll Content ◽  
Spectral Characteristics ◽  
Rare Earth Mine

scholarly journals Hyperspectral Characterization and Chlorophyll Content Inversion Study of Reclaimed Vegetation in Rare Earth Mines

2021 ◽  
Author(s):  
Hengkai Li ◽  
Feng Xu ◽  
Beibei Zhou ◽  
Zhian Wei
Keyword(s):  
Rare Earth ◽  
Chlorophyll Content ◽  
Spectral Characteristics ◽  
Visible Region ◽  
Small Sample ◽  
Spectral Variation ◽  
Estimation Model ◽  
Spectral Parameters ◽  
Network Algorithm ◽  
Sparse Autoencoder

Abstract Taking the Lingbei rare earth mining area in Dingnan county of Jiangxi Province as the research object of the reclaimed vegetation, the original spectrum, derivative spectrum and the continuum removed spectrum of the reclaimed vegetation were detected. The spectral characteristics and variation regularity of the typical reclaimed vegetation were analyzed, the correlation between chlorophyll content and spectral characteristic index of reclaimed vegetation was analyzed, and the sensitive spectral parameters were extracted. Partial Least Squares Algorithm, Back Propagation Neural Network Algorithm and Sparse Autoencoder Network Algorithm were selected to construct the estimation model of chlorophyll content, and compare the accuracy. The results show that; The vegetation spectrum of rare earth mine reclamation has the spectral characteristics of higher reflectance in visible region, red shift of green peak and red valley, blue shift of “red edge”, with less spectral variation in bamboo willow; Variability in the sensitive spectral parameters extracted from different vegetation; Sparse Autoencoder network algorithm is the optimal estimation model (R2 value of three vegetation is 0.9117,0.7418 and 0.9815 respectively). In the case of the small sample, it has higher estimation precision and universality for different reclaimed vegetation.

The effect of combined ecological remediation (plant microorganism modifier) on rare earth mine wasteland

2020 ◽  
Vol 27 (12) ◽  
pp. 13679-13691
Author(s):  
Qiao Yang ◽  
Zhongqiu Zhao ◽  
Hong Hou ◽  
Zhongke Bai ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Rare Earth ◽  
Ecological Remediation ◽  
Rare Earth Mine

Restoration of rare earth mine areas: organic amendments and phytoremediation

2015 ◽  
Vol 22 (21) ◽  
pp. 17151-17160 ◽  
Author(s):  
Lingyan Zhou ◽  
Zhaolong Li ◽  
Wen Liu ◽  
Shenghong Liu ◽  
Limin Zhang ◽  
...  
Keyword(s):  
Rare Earth ◽  
Organic Amendments ◽  
Rare Earth Mine

scholarly journals The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

2019 ◽  
Vol 11 (17) ◽  
pp. 2050 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Chlorophyll Content ◽  
Management Practices ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Crop Management ◽  
Area Index ◽  
Spectral Bands ◽  
The Mean ◽  
Sentinel 2

Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency’s (ESA) Sentinel-2 satellite—delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres—could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements—the mean R2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two—the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices.

Effect of defects in the rare-earth sublattice of the Kondo insulator YbB12 on its spectral characteristics and magnetic susceptibility

2017 ◽  
Vol 124 (6) ◽  
pp. 957-967 ◽  
Author(s):  
P. A. Alekseev ◽  
A. V. Kuznetsov ◽  
P. S. Savchenkov ◽  
A. P. Menushenkov ◽  
N. Yu. Shitsevalova
Keyword(s):  
Magnetic Susceptibility ◽  
Rare Earth ◽  
Spectral Characteristics ◽  
Kondo Insulator ◽  
The Rare Earth

Investigating the composition of dissolved organic matter in natural water in rare earth mine using EEM-PARAFAC analysis

2014 ◽  
Vol 16 (11) ◽  
pp. 2527-2535 ◽  
Author(s):  
Yang Hongxia ◽  
Gao Jinxu ◽  
Liu Wei ◽  
Tan Keyan
Keyword(s):  
Organic Matter ◽  
Rare Earth ◽  
Dissolved Organic Matter ◽  
Natural Water ◽  
Strong Correlation ◽  
Parafac Model ◽  
Rare Earth Mine ◽  
Ore District

Three-component DOM was identified using the EEM-PARAFAC model, showing strong correlation with the concentrations of REEs in the natural water of an ore district.

Sign in / Sign up

Export Citation Format

Share Document