Digital soil mapping using drone images and machine learning at the sloping vegetable fields in cool highland in the Northern Kanto region, Japan

Takata, Yusuke; Yamada, Hitoshi; Kanuma, Nobuyuki; Ise, Yuta; Kanda, Takashi

doi:10.1080/00380768.2023.2197453

Cited by 1 publication

(1 citation statement)

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“…Remote sensing is also a valuable, fast, and non-destructive tool to overcome the limitations of direct surveys for monitoring and assessing inaccessible areas such as wetlands [22,23] or protected areas [24]. In recent years, the application of aerial surveys using Unmanned Aerial Vehicles (UAVs) has shown great potential in the spatial assessment of a wide range of features in agriculture and soil science such as vegetation patterns [25,26], monitoring invasive plants [27], peat soil properties [28], soil erosion mapping [29], soil productivity [30], or soil water content mapping [31]. UAV-based data applications have shown their utility also in improving accuracy and providing more insights into soil salinity mapping [19,20,32,33].…”

Section: Introductionmentioning

confidence: 99%

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

Pásztor,

Takács,

Mészáros

et al. 2023

Land

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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.

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