Počet záznamů: 1
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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SYSNO ASEP 0477397 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Predicting the toxicity of post-mining substrates, a case study based on laboratory tests, substrate chemistry, geographic information systems and remote sensing Tvůrce(i) Tesnerová, C. (CZ)
Zadinová, R. (CZ)
Pikl, Miroslav (UEK-B) RID, SAI
Zemek, František (UEK-B) RID, SAI, ORCID
Kadochová, Štěpánka (BC-A)
Matějíček, L. (CZ)
Mihaljevič, M. (CZ)
Frouz, Jan (BC-A) RID, ORCIDZdroj.dok. Ecological Engineering. - : Elsevier - ISSN 0925-8574
Roč. 100, Mar (2017), s. 56-62Poč.str. 7 s. Jazyk dok. eng - angličtina Země vyd. NL - Nizozemsko Klíč. slova Mining ; Toxicity ; GIS ; Hyperspectral data ; Sinapis alba Vědní obor RIV EH - Ekologie - společenstva Obor OECD Environmental sciences (social aspects to be 5.7) Vědní obor RIV – spolupráce Biologické centrum (od r. 2006) - Ekologie - společenstva CEP LO1415 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy LM2015075 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy Výzkumná infrastruktura CzeCOS II - 90061 - Ústav výzkumu globální změny AV ČR, v. v. i. Institucionální podpora RVO:67179843 - RVO:67179843 ; BC-A - RVO:60077344 UT WOS 000394062600006 EID SCOPUS 85007207171 DOI 10.1016/j.ecoleng.2016.12.014 Anotace 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 (R-2 =0.51) and by (2) substrate classification based on hyperspectral images (R-2 = 0.56). Pracoviště Ústav výzkumu globální změny Kontakt Nikola Šviková, svikova.n@czechglobe.cz, Tel.: 511 192 268 Rok sběru 2018
Počet záznamů: 1