Premium
Use of remote sensing to track postindustrial vegetation development
Author(s) -
Woźniak Gabriela,
Dyderski Marcin K.,
KompałaBąba Agnieszka,
Jagodziński Andrzej M.,
Pasierbiński Andrzej,
Błońska Agnieszka,
Bierza Wojciech,
Magurno Franco,
Sierka Edyta
Publication year - 2020
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.3789
Subject(s) - vegetation (pathology) , environmental science , remote sensing , detrended correspondence analysis , multispectral image , ordination , hydrology (agriculture) , physical geography , geology , geography , ecology , medicine , pathology , geotechnical engineering , biology
The effects of natural processes on deposited mineral material of postindustrial sites is underestimated. Natural vegetation development on mineral material substratum is an unappreciated way of site management. Due to the classification‐based approach to assembly of plant community diversity, remote sensing methods have limited application. We aimed to assess whether remotely sensed data allow for building predictive models, able to recognise vegetation variability along the main gradients of species composition. We assessed vegetation in 321 study plots on four coal‐mine spoil heaps in Silesia (S Poland). We determined the main gradients of species composition using detrended correspondence analysis (DCA), and we identified how DCA scores describe vegetation variability. DCA axes explained 38.5%, 35.4%, 31.4%, and 20.1% of species composition variability. We built machine learning models of DCA scores using multispectral satellite images and airborne laser scanning data as predictors. We obtained good predictive power of models for the first two DCA axes ( R 2 = 0.393 and 0.443, root mean square errors, RMSE = 0.571 and 0.526) and low power for the third and fourth DCA axes ( R 2 = 0.216 and 0.064, RMSE = 0.513 and 0.361). These scores allowed us to prepare a vegetation map based on DCA scores, and distinguish meadow‐like from forest‐edge‐like vegetation, and to identify thermophilous and highly productive vegetation patches. Our approach allowed us to account for species composition gradients, which improved remote sensing‐based vegetation surveys. This method may be used for planning future management.