Monitoring salt crusts on an AMD contaminated coastal wetland using hyperspectral Hyperion data (Estuary of the River Odiel, SW Spain)

Riaza, A.; Buzzi, J.; García‐Meléndez, Eduardo; Moral, Begoña del; Carrère, V.; Richter, Rudolf

doi:10.1080/01431161.2017.1302621

Cited by 9 publications

(6 citation statements)

References 39 publications

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“…The mineral uptake that we identified in trees is in line with results from the chemical signature of sediments analyzed with spectroscopy (using hyperspectral sensors), and both processes are causally related to mine waste deposits. Analysis of hyperspectral time series can assist in the detection of changes in precipitated crusts and efflorescence along the flow path of Odiel River, thereby evidencing that the mine at Sotiel is indeed a notable source of acid mine drainage to the Odiel River (Buzzi Marcos, 2012;Buzzi et al, 2014;Riaza et al, 2017).…”

Section: Temporal Evolution In the Uptake Of Metallic Pollutant In Treesmentioning

confidence: 99%

Trees as sentinels of metallic pollution induced by mining along the Odiel River (Southern Iberian Peninsula)

Delapierre

Cánovas²,

Marcos³

et al. 2021

CIG

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Mining activity is often responsible for the drainage of acid or metal-enriched waters to fluvial systems. The release of metals is especially disturbing due to the toxicity and persistence of these products and their accumulation in the biosphere. Hence, a systematic detection and delimitation of highly polluted floodplains and linkages between pollution and high-flow stages would likely assist the improvement of land management and ease the design of mitigation or rehabilitation measures. Here we test how trees growing in different geomorphic positions along a fluvial system uptake metal during floods and how these uptakes can be documented “a posteriori”. To this end, we apply dendrogeochemical analyses to twenty Pinus pinaster Ait. trees growing on the banks of Odiel River (south-western Spain) as well as to five reference trees growing outside the river channel. In the field, trees were sampled with a large-diameter (1 cm) increment borer. In the lab, tree-ring series were dendrochronologically cross dated and separated into 5-yr blocks, so that wood blocks contained the dates of major floods. Then, Inductively Coupled Plasma Mass Spectrometry (ICPM) was employed to evaluate toxic metal concentrations in trees. Results point to clear correlations between the accumulation of toxic metals and the geomorphic position of trees within the fluvial network. We show that morphological units along a river exert control on toxic metal concentrations in trees, with uptake being much higher in trees located on meander cut banks than in trees growing on point-bar structures. Besides, we detect chemical signatures in trees located farthest away from the main river channel after the largest floods, but not in the aftermath of smaller events. We conclude that tree position is the single-most important determinant for metallic pollution in an environment controlled by fluvial processes, but also find that more studies are still needed to determine linkages with individual floods and interactions of metal uptake in roots via the water table in the river corridor.

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Section: Temporal Evolution In the Uptake Of Metallic Pollutant In Treesmentioning

confidence: 99%

Trees as sentinels of metallic pollution induced by mining along the Odiel River (Southern Iberian Peninsula)

Delapierre

Cánovas²,

Marcos³

et al. 2021

CIG

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“…In recent years, hyperspectral image research has received extensive attention [26]- [29]. Some hyperspectral sensors have been utilized in mapping coastal wetlands, e.g., Earth Observing-1 (EO-1) Hyperion, Hyperspectral InfraRed Image (HyspIRI), Compact High Resolution Imaging Spectrometer (CHRIS) and Hyperspectral Imager for the Coastal Ocean (HICO) [10], [30]- [34]. Zomer et al [35] used prosted-1 airborne hyperspectral data (5 m resolution, 128 bands) and wetland spectrum library to classify vegetation in Pacheco Creek wetland in California, demonstrating that advanced sensors have unparalleled advantages in wetland vegetation classification and mapping.…”

Section: Introductionmentioning

confidence: 99%

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

Liu

Tao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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It is significant for restoration and protection of natural resources and ecological services in coastal wetlands to map different land cover types with satellite remote sensing data. Considering difficulties of wetland species classification, hyperspectral images (HSI) with high spectral resolution and multispectral images (MSI) with high spatial resolution are considered to achieve complementary advantages of multi-source data. An effective approach, named as multi-stream convolutional neural network (CNN), is proposed to achieve fine classification of coastal wetlands. Firstly, regression processing is adopted to make chaotically scattered coastal wetland data more compact and different. Secondly, through appropriate feature extraction and feature fusion strategies, high-level information of multi-source data in regression domain is fused to distinguish different land cover. Experiments on GF-5 hyperspectral images and Sentinel-2 multispectral images are carried out in order to validate the classification performance of the proposed approach in two coastal wetlands of research value in China, i.e., Yellow River Estuary and Yancheng coastal wetland. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods in the field, especially when the number of sample size is extremely small. Index Terms-Coastal wetlands; data fusion; hyperspectral imagery (HSI); multispectral imagery (MSI); convolutional neural network (CNN); least squares regression (LSR).

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“…Satellite remote sensing techniques provide an important and effective data source for mapping and monitoring coastal wetlands, because of its unique characteristics in an easy data acquisition, spatially continuous coverage and short revisiting periods [4,13,14]. During the last several decades, the fast development of satellite remote sensing sensors has greatly enhanced our capability to map coastal wetlands, that is, panchromatic sensors [13,15], Synthetic Aperture Radar (SAR) [16], multispectral sensors [17,18] and even hyperspectral imagers [19,20]. Conventional panchromatic sensors could not discriminate different species in coastal wetlands (e.g., vegetation) because of their single limited band information [21].…”

Section: Introductionmentioning

confidence: 99%

“…The high spectral resolution (<10 nm) of hyperspectral images renders that it owns greater potentials in identifying different ground objects of coastal wetlands with subtle spectral divergence [28,29]. Some hyperspectral sensors have been utilized in mapping coastal wetlands, for example, Earth Observing-1 (EO-1) Hyperion [20], Hyperspectral InfraRed Image (HyspIRI) [19], Compact High Resolution Imaging Spectrometer (CHRIS) [30] and Hyperspectral Imager for the Coastal Ocean (HICO) [31].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

et al. 2019

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Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

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Monitoring salt crusts on an AMD contaminated coastal wetland using hyperspectral Hyperion data (Estuary of the River Odiel, SW Spain)

Cited by 9 publications

References 39 publications

Trees as sentinels of metallic pollution induced by mining along the Odiel River (Southern Iberian Peninsula)

Trees as sentinels of metallic pollution induced by mining along the Odiel River (Southern Iberian Peninsula)

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

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