Predicting the toxicity of post-mining substrates, a case study based on laboratory tests, substrate chemistry, geographic information systems and remote sensing

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• 作者：Cecilie Tesnerová ; ^a ; Radka Zadinová ; ^a ; Miroslav Pikl^b ; Franti&scaron ; ek Zemek^b ; &Scaron ; těpá ; nka Kadochová ; ^c ; ^d ; Lubo&scaron ; Matějí ; ček^a ; Martin Mihaljevič^e ; Jan Frouz^a ; ^d ; ^f ; ^{frouz@natur.cuni.cz} ; ^{frouz@upb.cas.cz}
• 关键词：Mining ; Toxicity ; GIS ; Hyperspectral data ; Sinapis alba
• 刊名：Ecological Engineering
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：100
• 期：Complete
• 页码：56-62
• 全文大小：446 K
• 卷排序：100

文摘

Approaches were evaluated for predicting the spatial distribution of phytotoxicity of post-mining substrates. Predictions were compared with empirical data measured in the field (a heap at a post-mining site) and laboratory. The study was performed in a highly variable 1-ha plot that was overlain with a regular grid of sampling points (with 5 m between adjacent grid points). At each of 21 points, soil pH, conductivity, and arsenic content were measured, and soil was sampled and used in a laboratory germination test with Sinapsis alba. At each grid point, a field germination test with S. alba was also conducted, and spontaneous vegetation was removed and weighed. At the same time, air-borne hyperspectral imagery data of the site were acquired, and field spectral characteristics of dominant substrates were measured. This enabled automatic substrate classification, which was used to map the spatial distribution of the substrates.S. alba germination in the laboratory was closely correlated with S. alba germination in the field (r = 0.918), and both were correlated with the biomass of spontaneously established vegetation in the field. Substrate pH and substrate type were the best predictors of S. alba germination at points between the grid points. S. alba germination was well predicted (P = 0.001) by (1) direct interpolation of toxicity between grid points (R2 = 0.51) and by (2) substrate classification based on hyperspectral images (R2 = 0.56).