Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance

Abstract

Introduction: High mercury (Hg) concentrations affect the chlorophyll in leaves, thereby modifying leaf spectra. Hyperspectra is a promising technique for the rapid, nondestructive evaluation of leaf Hg content. In this study, we investigated Hg contents and reflective hyperspectra of reed leaves (Phragmites communis) in a gold mining (Jilin province, China). Spectral parameters sensitive to Hg content were identified through basic spectral transformations, continuous wavelet transformation (CWT), and spectral indices techniques. Leaf Hg inversion models were developed using stepwise multiple linear regression, partial least squares regression, and random forest algorithms. Outcomes: The results indicated that: 1) leaf Hg content decreased with increasing distance from the mine: Jiapigou (JPG) > Erdaocha (EDC) > Laojingchang (LJC) > Erdaogou (EDG) > Lingqian (LQ) > Weishahe (WSH). 2) Hg-sensitive wavelengths were primarily in the visible region; CWT increased the correlation between hyperspectral data and leaf Hg content, and improved the regression and accuracy of inversion; 3) the continuum removal-CWT-stepwise multiple linear regression was better for estimating low leaf Hg content; while the differential spectral index-partial least squares regression was better for estimating high leaf Hg content. Conclusion: These hyperspectral inversion methods could be used for rapid, nondestructive monitoring of wetland plants.

Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models

“…In particular, the wetland environment poses some challenges to sample collection, making it difficult to achieve long-term dynamic monitoring [20]. Remote sensing has become an alternative monitoring method in recent years by providing an important means of monitoring plant growth and quantifying plant biophysical and biochemical characteristics [21][22][23]. Specifically, because the structure of plant components are directly affected by the C, N, and P, the content of these elements directly affects the spectral reflectance of individual leaves, as well as reflectance at the canopy level [24].…”

Effective band selection of hyperspectral image by an attention mechanism-based convolutional network

“…The total bands were labelled from 0 to 139. For basil leaf dataset, the selected band subset was [5,6,8,25,30,45,58,73,78,82,89,111,116], corresponding to the wavelengths of 486 nm, 489 nm, 496 nm, 553 nm, 569 nm, 625 nm, 665 nm, 713 nm, 728 nm, 741 nm, 764 nm, 828 nm and 841 nm respectively. These wavelengths were consistent with the strong reection of green light (553 nm, 569 nm), absorption of red light (625 nm, 665 nm), abrupt reection increment at the red edge region (728 nm, 741 nm, 764 nm) of a typical leaf's spectral prole, due to the porphyrin ring in chlorophyll molecules in basil leaves (Walsh 2020).…”

“…1,2 Compared with RGB images, contiguous spectral images provide high-resolution spatial information as well as rich descriptions about biochemical components of the studied objects. 3 As of today, hyperspectral imaging has been widely used in various fields, such as soil environmental monitoring, 4 plant elements analysis, 5,6 plant species classification, 7,8 medical disease recognition, 9,10 etc. However, a typical hyperspectral image usually contains hundreds or even thousands of spectral bands, and the excessive number of bands may give rise to the Hughes phenomenon.…”

Spatial-Temporal Variations for Pollution Assessment of Heavy Metals in Hengshui Lake of China

“…Heavy-metals pollution in wetland ecosystems is a worldwide environmental issue that has attracted increased attention because of the ecological and human health risks it poses [1-3], particularly in sediment, which more easily accumulates heavy metals and facilitates their biomagnification via food chains [1, [4][5][6]. Therefore, wetland sediments act as sinks of heavy metals, and may in turn act as sources [1].…”