Abstract
Forest topsoil supports vegetation growth and contains the majority of soil nutrients that are important indices of soil fertility and quality. Therefore, estimating forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, litter-organic (O-A) horizon depth (Depth) and available phosphorous (AvaP), is of particular importance for forest development and management. As an emerging technology, light detection and ranging (LiDAR) can capture the three-dimensional structure and intensity information of scanned objects, and can generate high resolution digital elevation models (DEM) using ground echoes. Moreover, great power for estimating forest topsoil properties is enclosed in the intensity information of ground echoes. However, the intensity has not been well explored for this purpose. In this study, we collected soil samples from 62 plots and the coincident airborne LiDAR data in a Korean pine forest in Northeast China, and assessed the effectiveness of both multi-scale intensity data and LiDAR-derived topographic factors for estimating forest topsoil properties. The results showed that LiDAR-derived variables could be robust predictors of four topsoil properties (SOM, Total N, pH, and Depth), with coefficients of determination (R2) ranging from 0.46 to 0.66. Ground-returned intensity was identified as the most effective predictor for three topsoil properties (SOM, Total N, and Depth) with R2 values of 0.17–0.64. Meanwhile, LiDAR-derived topographic factors, except elevation and sediment transport index, had weak explanatory power, with R2 no more than 0.10. These findings suggest that the LiDAR intensity of ground echoes is effective for estimating several topsoil properties in forests with complicated topography and dense canopy cover. Furthermore, combining intensity and multi-scale LiDAR-derived topographic factors, the prediction accuracies (R2) were enhanced by negligible amounts up to 0.40, relative to using intensity only for topsoil properties. Moreover, the prediction accuracy for Depth increased by 0.20, while for other topsoil properties, the prediction accuracies increased negligibly, when the scale dependency of soil–topography relationship was taken into consideration.
Highlights
Forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, O-A horizon depth (Depth) and available phosphorous (AvaP), are the most important indices for soil fertility and quality [1]
The result suggested that the relationships between intensity and three topsoil properties (SOM, Total N, and pH) were significantly positive, while intensity was negatively related to Depth (r Depth = ́0.51, p < 0.05)
We obtained several conclusions: (1) light detection and ranging (LiDAR) intensity was an effective predictor of three topsoil properties (SOM, Total N, and Depth) with R2 ranging from 0.17 to
Summary
Forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, O-A horizon depth (Depth) and available phosphorous (AvaP), are the most important indices for soil fertility and quality [1]. Estimating factors of forest topsoil properties using traditional methods can be categorized into two groups: based on topographic factors and based on soil spectral reflectance information [3]. DEM-derived topographic factors, including but not limited to elevation, slope, aspect, and topographic wetness, have been shown to be effective predictors of SOM, Total N, pH, Depth, hydraulic factors and other soil properties across different spatial scales. As for soil spectral reflection information, previous studies found that there was a strong correlation between certain soil properties (including soil total carbon, SOM, Total N, mineralized nitrogen, pH value, etc.) and soil spectral reflectance in the NIR-SWIR (near infrared to short wave infrared) range, with R2 values greater than 0.8 [9,10,11,12].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
