Estimating the height of wetland vegetation using airborne discrete-return LiDAR data

Several studies have been conducted to estimate wetland vegetation height using airborne LiDAR data. However, no studies have been conducted to explore the influence of vegetation cover on vegetation height estimation models. The objective of this research is to estimate vegetation height in wetlands with varying vegetation cover for further analyzing the effects of vegetation cover. First, we performed both linear and logarithmic regression analyses between field-measured heights and each LiDAR-derived metric in wetlands with different vegetation cover. Then LiDAR-derived metrics were combined through multiple regression analysis to estimate wetland vegetation height. The height estimates were finally validated by leave-one-out cross-validation method. The results showed that the logarithmic regression analysis performed better than the linear regression analysis in estimating wetland vegetation height for almost all LiDAR-derived metrics regardless of vegetation cover. The max height of LiDAR returns (Hmax) provided the best agreement with field-measured heights in wetlands with high vegetation cover. In contrast, the strongest correlation was observed using the standard deviation of LiDAR heights (HSD) for low vegetation cover. Results of multiple regression analysis indicated that the best model was based on Hmax and 90 percentile height of LiDAR returns (H90) in wetlands with high vegetation cover, while the best model can be expressed as a function of HSD and H90 for low vegetation cover. Therefore, our study concluded that vegetation cover is an important factor of vegetation height models. Additionally, our study will provide valuable guidance for field-measured data collection and vegetation height estimation using LiDAR data.