Detection of Ore‐Forming Elements Migrated From Rock and Soil to Vegetation Leaves Using Hyperspectral Data

Author:

Chao Zhou1ORCID,Xianchuang Fan2ORCID,Tong Wu1,Yuanzhi Zhang3,Shengbo Chen4

Affiliation:

1. National Marine Environmental Monitoring Center Dalian China

2. College of Artificial Intelligence North China University of Science and Technology Tangshan China

3. School of Marine Sciences Nanjing University of Information Science and Technology Nanjing China

4. College of Geo‐exploration Science and Technology Jilin University Changchun China

Abstract

AbstractIn this paper, we present the detection of ore‐forming elements migrated from soil and rock to vegetation leaves using hyperspectral data. The rock, soil (C, B, and A layer), and vegetation (root, stem, and leaf) samples with the measurements of spectra and element concentrations in the vertical section of the abandoned pits were collected. The wavelet approach is applied to analyze the correlations between spectral features and element concentrations. The results show that the significant correlations are found at a lower order mother wavelets such as Haar. Moreover, the significant correlations are negative between Cu and Mo elements and the wavelet energy vector of the soil and rock to vegetation leaves spectrum. The results also show that ore‐forming elements migrated from rock and soil to vegetation leaves can be detected using hyperspectral data in the study area.

Publisher

American Geophysical Union (AGU)

Subject

Electrical and Electronic Engineering,General Earth and Planetary Sciences,Condensed Matter Physics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Wavelet-Based Hyper spectral Image Analysis to Characterize Soil Types;2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA);2024-03-15

2. A Multihop Graph Rectify Attention and Spectral Overlap Grouping Convolutional Fusion Network for Hyperspectral Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024

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