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Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape

Ågren, Anneli and Larson, Johannes and Paul, Siddhartho and Laudon, Hjalmar and Lidberg, William (2021). Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape. Geoderma. 404 , 115280
[Research article]

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Abstract

Spatially extensive high-resolution soil moisture mapping is valuable in practical forestry and land management, but challenging. Here we present a novel technique involving use of LIDAR-derived terrain indices and machine learning (ML) algorithms capable of accurately modeling soil moisture at 2 m spatial resolution across the entire Swedish forest landscape. We used field data from about 20,000 sites across Sweden to train and evaluate multiple ML models. The predictor features (variables) included a suite of terrain indices generated from a national LIDAR digital elevation model and ancillary environmental features, including surficial geology, climate and land use, enabling adjustment of soil moisture class maps to regional or local conditions. Extreme gradient boosting (XGBoost) provided better performance for a 2-class model, manifested by Cohen's Kappa and Matthews Correlation Coefficient (MCC) values of 0.69 and 0.68, respectively, than the other tested ML methods: Artificial Neural Network, Random Forest, Support Vector Machine, and Naive Bayes classification. The depth to water index, topographic wetness index, and `wetland' categorization derived from Swedish property maps were the most important predictors for all models. The presented technique enabled generation of a 3-class model with Cohen's Kappa and MCC values of 0.58. In addition to the classified moisture maps, we investigated the technique's potential for producing continuous soil moisture maps. We argue that the probability of a pixel being classified as wet from a 2-class model can be used as a 0-100% index (dry to wet) of soil moisture, and the resulting maps could provide more valuable information for practical forest management than classified maps.

Authors/Creators:Ågren, Anneli and Larson, Johannes and Paul, Siddhartho and Laudon, Hjalmar and Lidberg, William
Title:Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape
Series Name/Journal:Geoderma
Year of publishing :2021
Volume:404
Article number:115280
Number of Pages:16
Publisher:ELSEVIER
ISSN:0016-7061
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Soil Science
Keywords:LIDAR, Soil moisture, Machine learning, Extreme gradient boosting, Land-use management
URN:NBN:urn:nbn:se:slu:epsilon-p-113863
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-113863
Additional ID:
Type of IDID
DOI10.1016/j.geoderma.2021.115280
Web of Science (WoS)000700886000010
ID Code:25581
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Ecology and Management
Deposited By: SLUpub Connector
Deposited On:07 Oct 2021 15:27
Metadata Last Modified:07 Oct 2021 15:31

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