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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F17%3A00477397" target="_blank" >RIV/60077344:_____/17:00477397 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/86652079:_____/17:00477397 RIV/00216208:11690/17:10337138 RIV/00216208:11310/17:10337138

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ecoleng.2016.12.014" target="_blank" >http://dx.doi.org/10.1016/j.ecoleng.2016.12.014</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecoleng.2016.12.014" target="_blank" >10.1016/j.ecoleng.2016.12.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

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

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Ecological Engineering

ISSN

0925-8574

e-ISSN

—

Svazek periodika

100

Číslo periodika v rámci svazku

Mar

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

56-62

Kód UT WoS článku

000394062600006

EID výsledku v databázi Scopus

2-s2.0-85007207171