A multi-scale based method for estimating coniferous forest aboveground biomass using low density airborne LiDAR data

Forest biomass acts as an important indicator of carbon resources in terrestrial system. Estimation of forest biomass enables a straightforward measurement of carbon storage and provides initial values for process-based carbon cycle models to simulate carbon dynamics. LiDAR (Light Detection and Ranging) remote sensing is increasingly used to estimate forest biomass because of its ability to detect the structure of forest. However, it is still not adequately studied from a viewpoint of multi-scale. This study is a new attempt for the application of multi-scale theory in forest aboveground biomass (AGB) estimation based on low density LiDAR data (less than 1 point/m2). The study area is located in Krycklan catchment which is approximately 50 km northwest of Umeå, Sweden. A method based on local maximum height point identification and downscaling calibration is provided. By implementing local maximum elevation extraction and visualization of aerial images, an algorithm directly based on point cloud data is designed. This algorithm retains more details of the LiDAR data and therefore provides better results. Two calibration look-up tables are provided to approximate the forest AGB derived from low density LiDAR data to the forest AGB derived from high density LiDAR data (more than 1 point/m2). The error of downscaling calibration in the test sample plot is of 0.28%, which proved validity of the method applied.Forest biomass acts as an important indicator of carbon resources in terrestrial system. Estimation of forest biomass enables a straightforward measurement of carbon storage and provides initial values for process-based carbon cycle models to simulate carbon dynamics. Recently, LiDAR (Light Detection and Ranging) remote sensing, as a surveying technology measuring distance by illuminating a target with laser light, is increasingly used to estimate forest biomass because of its ability to detect the structure of forest. However, it is still not adequately studied from a viewpoint of multi-scale. Briefly, scale describes the resolution and extent in which the data is shown. Multi-scale studies aim at analyzing the relationship between data in different resolution and/or different extent. Multi-scale transform includes downscaling and upscaling. Downscaling is to push down the scale from coarser spatial and temporal resolution into more detailed information with finer spatial and temporal resolution while upscaling is just the opposite. By implementing downscaling and upscaling, multi-scale studies provide possibilities to understand the behavior of variables while changing scale. For forest biomass estimation based on LiDAR data, most of the researches from a viewpoint of multi-scale are about extent. However, the multi-scale based research about resolution is rarely attempted. This study is an attempt for the application of multi-scale theory in forest aboveground biomass (AGB) estimation based on low density LiDAR data (less than 1 point/m2). The study area is located in Krycklan catchment which is approximately 50 km northwest of Umeå, Sweden. A method based on local maximum height point identification and downscaling calibration is provided. By implementing local maximum elevation extraction and visualization of aerial images, an algorithm directly based on point cloud data is designed. This algorithm retains more details of the LiDAR data and therefore provides better results. Two calibration look-up tables are founded from a viewpoint of downscaling which is widely applied in geomorphology study. By inferring the result extracted from high density LiDAR data (more than 1 point/m2) with low density LiDAR data, the forest parameter estimation accuracy based on low density LiDAR data is improved. The error of downscaling calibration in the test sample plot is of 0.28%, which proved validity of the method applied. Furthermore, the calibration look-up tables can be used directly in the further researches of the study area in the same situation

Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

Personal laser scanning (PLS) has significant potential for estimating the in-situ diameter of breast height (DBH) with high efficiency and precision, which improves the understanding of forest structure and aids in building carbon cycle models in the big data era. PLS collects more complete stem point cloud data compared with the present laser scanning technology. However, there is still no significant advantage of DBH estimation accuracy. Because the error caused by merging different point cloud fragments has not yet been eliminated, overlapping and inaccurate co-registered point cloud fragments are often inevitable, which are usually the leading error sources of PLS-based DBH estimation. In this study, a novel pre-processing algorithm named annular neighboring points distribution analysis (ANPDA) was developed to improve PLS-based DBH estimation accuracy. To reduce the impact of inaccurately co-registered point cloud fragments, ANPDA identified outliers through iterative removal of outermost points and analyzing the distribution of annular neighboring points. Six plots containing 247 trees under different forest conditions were selected to evaluate the ANPDA. Results showed that in the six plots, error reductions of 53.80–87.13% for bias, 38.82–57.30% for mean absolute error (MAE), and 27.17–56.02% for root mean squared error (RMSE) were achieved after applying ANPDA. These results confirmed that ANPDA was generally effective for improving PLS-based DBH estimation accuracy. It appeared that ANPDA could be conveniently fused with an automatic PLS-based DBH estimation process as a preprocessing algorithm. Furthermore, it has the potential to predict and warn operators of potential large errors during hierarchical semi-automatic DBH estimation

Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning

The accurate measurement of diameter at breast height (DBH) is essential to forest operational management, forest inventory, and carbon cycle modeling. Terrestrial laser scanning (TLS) is a measurement technique that allows rapid, automatic, and periodical estimates of DBH information. With the multitude of DBH estimation approaches available, a systematic study is needed to compare different algorithms and evaluate the ideal situations to use a specific algorithm. To contribute to such an approach, this study evaluated three commonly used DBH estimation algorithms: Hough-transform, linear least square circle fitting, and nonlinear least square circle fitting. They were each evaluated on their performance using two forest types of TLS data under numerous preprocessing conditions. The two forest types were natural secondary forest and plantation. The influences of preprocessing conditions on the performance of the algorithms were also investigated. Results showed that among the three algorithms, the linear least square circle fitting algorithm was the most appropriate for the natural secondary forest, and the nonlinear least square circle fitting algorithm was the most appropriate for the plantation. In the natural secondary forest, a moderate gray scale threshold of three and a slightly large height bin of 0.24 m were the optimal parameters for the appropriate algorithm of the multi-scan scanning method, and a moderate gray scale threshold of three and a large height bin of 1.34 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method. A small gray scale threshold of one and a small height bin of 0.1 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method in the plantation