Next Article in Journal
A Novel Approach for Automatic Urban Surface Water Mapping with Land Surface Temperature (AUSWM)
Previous Article in Journal
Terrestrial and Airborne Structure from Motion Photogrammetry Applied for Change Detection within a Sinkhole in Thuringia, Germany
Previous Article in Special Issue
Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery
 
 
Article

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
*
Author to whom correspondence should be addressed.
Academic Editors: Pablo Rodríguez-Gonzálvez and Eben Broadbent
Remote Sens. 2022, 14(13), 3054; https://doi.org/10.3390/rs14133054
Received: 12 May 2022 / Revised: 20 June 2022 / Accepted: 24 June 2022 / Published: 25 June 2022
In this study, we propose integrating unmanned aerial systems (UASs) and machine learning classification for suitability prediction of expanding habitats for endangered flora species to prevent further extinction. Remote sensing imaging of the protected steppe-like grassland in Bilje using the DJI P4 Multispectral UAS ensured non-invasive data collection. A total of 129 individual flora units of five endangered flora species, including small pasque flower (Pulsatilla pratensis (L.) Miller ssp. nigricans (Störck) Zämelis), green-winged orchid (Orchis morio (L.)), Hungarian false leopardbane (Doronicum hungaricum Rchb.f.), bloody cranesbill (Geranium sanguineum (L.)) and Hungarian iris (Iris variegate (L.)) were detected and georeferenced. Habitat suitability in the projected area, designated for the expansion of the current area of steppe-like grassland in Bilje, was predicted using the binomial machine learning classification algorithm based on three groups of environmental abiotic criteria: vegetation, soil, and topography. Four machine learning classification methods were evaluated: random forest, XGBoost, neural network, and generalized linear model. The random forest method outperformed the other classification methods for all five flora species and achieved the highest receiver operating characteristic (ROC) values, ranging from 0.809 to 0.999. Soil compaction was the least favorable criterion for the habitat suitability of all five flora species, indicating the need to perform soil tillage operations to potentially enable the expansion of their coverage in the projected area. However, potential habitat suitability was detected for the critically endangered flora species of Hungarian false leopardbane, indicating its habitat-related potential for expanding and preventing further extinction. In addition to the current methods of predicting current coverage and population count of endangered species using UASs, the proposed method could serve as a basis for decision making in nature conservation and land management. View Full-Text
Keywords: nature conservation; random forest; environmental criteria; classification; multispectral imaging nature conservation; random forest; environmental criteria; classification; multispectral imaging
Show Figures

Figure 1

MDPI and ACS Style

Jurišić, M.; Radočaj, D.; Plaščak, I.; Rapčan, I. A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species. Remote Sens. 2022, 14, 3054. https://doi.org/10.3390/rs14133054

AMA Style

Jurišić M, Radočaj D, Plaščak I, Rapčan I. A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species. Remote Sensing. 2022; 14(13):3054. https://doi.org/10.3390/rs14133054

Chicago/Turabian Style

Jurišić, Mladen, Dorijan Radočaj, Ivan Plaščak, and Irena Rapčan. 2022. "A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species" Remote Sensing 14, no. 13: 3054. https://doi.org/10.3390/rs14133054

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop