Quantitative assessment of forest ecosystem stress caused by cement plant pollution using in situ measurements and Sentinel-2 satellite data in a part of the UNESCO World Heritage Site

Authors

  • Ali Asharfi The PhD candidate of Remote Sensing and GIS at University of Tehran, Tehran, Iran.
  • Yousef Erfanifard Associate Professor. Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran
  • Farshad Amiraslani Associate Professor. Department of Remote Sensing and GIS. Faculty of Geography. University of Tehran, Iran. And NUIST-Reading Academy, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu, China
  • Ali Darvishi Boloorani Associate Professor. Department of Remote Sensing and GIS. Faculty of Geography. University of Tehran, Iran.
  • AliJafar Mousivand Assistant Professor, Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran

DOI:

https://doi.org/10.14712/23361964.2020.4

Keywords:

Hyrcanian forests, Industrial Dust, Leaf Area Index (LAI), BPNN Parameter Retrieval, Sentinel-2, Drought Analysis

Abstract

 

 Anthropogenic industrial dust decreases productivity and slows down the growth of plants. Quantifying the effects of industrial dust on vegetation and determining the distance over which factories scatter dust are of paramount importance for biodiversity conservation and sustaining ecosystem services. This study aims at quantifying the effect of dust emitted by the Neka cement plant (NCP), Mazandaran province, northern Iran, on the surrounding Hyrcanian forests based on an analysis of the Leaf Area Index (LAI) retrieved from Sentinel-2 imagery. An Inductively Coupled Plasma Mass Spectrometer (ICP-MS) was used to quantify the concentrations of cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), calcium (Ca), magnesium (Mg), sodium (Na), silicon (Si) and zinc (Zn) in leaves of the dominant Chestnut-leaved Oak (Quercus castaneifolia). A feed-forward neural network algorithm and field measurements were used to retrieve the leaf area index (LAI) from Sentinel-2 data with a RMSE of 0.42 (m2/m2). MODIS-NDVI and EVI time series spanning 17 years (2000 to 2017) were analysed to ensure the independence of the variation in the condition of the vegetation from drought or other environmental factors. The results indicate that Sentinel-2 data can be used to map degradation due to pollution from the cement plant and quantify the effect of the dust spatially. Dust from the cement plant (dust source) was carried approximately 4700 meters in the direction of the prevailing wind. A significant correlation of 0.849 was recorded between LAI and distance from the NCP. It is concluded that dust from the NCP had adverse ecological effects on the neighbouring forest ecosystems recently designated a UNESCO World Heritage Site.

Author Biography

Yousef Erfanifard, Associate Professor. Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran

Department of Remote Sensing and GIS, Faculty of Geography (ranked under 200; QS 2018) University of Tehran, Iran.

Published

2020-06-22

Issue

Section

Articles