Indirect Prediction of Salt Affected Soil Indicator Properties through Habitat Types of a Natural Saline Grassland Using Unmanned Aerial Vehicle Imagery

László Pásztor, Katalin Takács (), János Mészáros, Gábor Szatmári, Mátyás Árvai, Tibor Tóth, Gyöngyi Barna, Sándor Koós, Zsófia Adrienn Kovács, Péter László and Kitti Balog
Additional contact information
László Pásztor: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Katalin Takács: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
János Mészáros: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Gábor Szatmári: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Mátyás Árvai: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Tibor Tóth: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Gyöngyi Barna: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Sándor Koós: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Zsófia Adrienn Kovács: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Péter László: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary
Kitti Balog: Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó Rd. 15., H-1022 Budapest, Hungary

Land, 2023, vol. 12, issue 8, 1-23

Abstract: Salt meadows, protected within National Parks, cannot be directly surveyed, yet understanding their soil condition is crucial. Our study indirectly estimates soil parameters (Total Salt Content (TSC), Na, and pH) related to salinization/sodification/alkalinization using spectral indices and UAV survey-derived elevation model, focusing on continental lowland salt meadows. A vegetation map was created using 16 spectral indices and a Digital Elevation Model calculated from RGB orthophotos using photogrammetry. Field observations helped define habitat types based on the General National Habitat Classification System (Hungary), and quadrats with complete coverage of specific plant species were identified. Machine learning was employed on 84 training quadrats to develop a prediction algorithm for vegetation patterns. Five saline habitat types, representing variations in soil properties and topography, were identified. Spectral and topomorphometric indices derived from UAV were key to the spatial prediction of soil properties, employing random forest and co-kriging methods. TSC, Na, and pH data served as indicators of salt-affected soils (SAS), and thematic maps were generated for each indicator (57 samples). Overlapping with the vegetation map, the probability range of estimated SAS indicator values was determined. Consequently, a model-based estimation of soil pH, TSC, and Na conditions is provided for habitat types without disturbing protected areas.

Keywords: protected salt meadows; vegetation map; machine learning methods; UAV; spectral indices; SAS indicator prediction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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