Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites

Zhengyong Zhao, Qi Yang, Xiaogang Ding and Zisheng Xing
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Zhengyong Zhao: Guangxi Key Laboratory of Forest Ecology and Conservation, College of Forestry, Guangxi University, Nanning 530004, China
Qi Yang: Guangxi Key Laboratory of Forest Ecology and Conservation, College of Forestry, Guangxi University, Nanning 530004, China
Xiaogang Ding: Guangdong Academy of Forestry, Guangzhou 510520, China
Zisheng Xing: Brandon Research and Development Centre, Portage, MB R1N 3V6, Canada

Land, 2021, vol. 10, issue 5, 1-12

Abstract: Ecosites are required for stand-level forest management and can be determined within a two-dimensional edatopic grid with soil nutrient regimes (SNRs) and soil moisture regimes (SMRs) as coordinates. A new modeling method is introduced in this study to map high-resolution SNR and SMR and then to design ecosites in Nova Scotia, Canada. Using coarse-resolution soil maps and nine topo-hydrologic variables derived from high-resolution digital elevation model (DEM) data as model inputs, 511 artificial neural network (ANN) models were developed by a 10-fold cross-validation with 1507 field samples to estimate 10 m resolution SNR and SMR maps. The results showed that the optimal models for mapping SNR and SMR engaged eight and seven topo-hydrologic variables, together with three coarse-resolution soil maps, as model inputs, respectively; 82% of model-estimated SNRs were identical to field assessments, while this value was 61% for SMRs, and the produced ecosite maps had 67–68% correctness. According to the error matrix, the predicted SNR and SMR maps greatly alleviated poor prediction in the areas of extreme nutrient or moisture conditions (e.g., very poor or very rich, wet, or very dry). Thus, the new method for modeling high-resolution SNR and SMR could be used to produce ecosite maps in sites where accessibility is hard.

Keywords: soil moisture regime; ecosite; edatopic grid; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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