Modeling Geospatial Distribution of Peat Layer Thickness Using Machine Learning and Aerial Laser Scanning Data

Janis Ivanovs (), Andreas Haberl and Raitis Melniks
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Janis Ivanovs: Latvian State Forest Research Institute ‘Silava’, Rigas Str. 111, LV-2169 Salaspils, Latvia
Andreas Haberl: Michael Succow Foundation, Greifswald Mire Centre, Ellernholzstr. 1/3, D-17489 Greifswald, Germany
Raitis Melniks: Latvian State Forest Research Institute ‘Silava’, Rigas Str. 111, LV-2169 Salaspils, Latvia

Land, 2024, vol. 13, issue 4, 1-14

Abstract: Organic horizons including peat deposits are important terrestrial carbon pools, and various chemical, biological, and water exchange processes take place within them. Accurate information on the spatial distribution of organic soils and their properties is important for decision-making and land management. In this study, we present a machine learning approach for mapping the distribution of organic soils and determining the thickness of the peat layer using more than 24,000 peat layer thickness measurements obtained from field data, airborne laser scanning (ALS) data and various indices obtained from therein, as well as other cartographic materials. Our objectives encompassed two primary aims. Firstly, we endeavored to develop updated cartographic materials depicting the spatial distribution of peat layers. Secondly, we aimed to predict the depth of peat layers, thereby enhancing our understanding of soil organic carbon content. Continentality, a wet area map, latitude, a depth to water map with catchment area of 10 ha, and a digital elevation model were the most important covariates for the machine learning model. As a result, we obtained a map with three peat layer thickness classes, an overall classification accuracy of 0.88, and a kappa value of 0.74. This research contributes to a better understanding of organic soil dynamics and facilitates improved assessments of soil organic carbon stocks.

Keywords: organic deposits; national forest inventories; extreme gradient boosting (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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