Towards rainfall interception capacity estimation using ALS LiDAR data

Abstract

In this study we develop a spatial model for interception capacity of vegetation based on LiDAR data. The study is conducted in the natural wetland river valley dominated meadows, reeds and small bushes. The multiple regression model was chosen to relate the field measurements of interception capacity and LiDAR statistics at 2m grid. The optimal model was chosen by stepwise selection and further manual variables selection resulting in the r 2 of 0.52 and the residual standard error of 0.27 mm. The model preserved the vegetation pattern spatially and showed reasonable estimates for both vegetation covered and not covered by field sampling. The model was, however, affected by LiDAR measurements corrupted by river inundation. The results show good perspective for using LiDAR data for interception capacity estimation.

Remote sensing of river corridors: A review of current trends and future directions

“…ALS shows the greatest utility in river corridor vegetation monitoring. At reach scales, ALS has been used for riparian zone classification (Antonarakis, Richards, & Brasington, ; Gilvear, Tyler, & Davids, ; Michez et al, ), assessment of wood and debris retention (Abalharth, Hassan, Klinkenberg, Leung, & McCleary, ; Bertoldi, Gurnell, & Welber, ), upscaling from TLS models (Manners et al, ), creating rainfall interception models (Berezowski, Chormanski, Kleniewska, & Szporak‐Wasilewska, ), and for linking vegetation to morphological and anthropogenic contexts and needs (Bertoldi, Gurnell, & Drake, ; Cartisano et al, ; Picco, Comiti, Mao, Tonon, & Lenzi, ). At landform scales, ALS has been used to identify sources and volumes of woody debris (Kasprak, Magilligan, Nislow, & Snyder, ), the health of riparian ecosystems (Michez et al, ), the influence of vegetation on groundwater connectivity (Emanuel, Hazen, McGlynn, & Jencso, ), bank stability (McMahon et al, ), and water temperature through shading (Greenberg, Hestir, Riano, Scheer, & Ustin, ; Loicq, Moatar, Jullian, Dugdale, & Hannah, ; Wawrzyniak, Allemand, Bailly, Lejot, & Piegay, ).…”

Modelling Wetland Growing Season Rainfall Interception Losses Based on Maximum Canopy Storage Measurements

“…Lately, a few studies on rainfall interception have been made in a natural wetland ecosystem in Biebrza Basin (NE Poland). Those studies focused on determining the spatial distribution of interception capacity based on ground truth measurements of maximum canopy storage and ALS (airborne laser scanning) [35], also ground truth data was used with Landsat 7 ETM+ image [36].…”

“…Other values were linearly interpolated assuming that Imax is 0 at the beginning and end of the growing season. Previous research on wetland meadow canopy storage in this area[35,36,45,47] also focused only on the growing season. This is due to the negligible height of sedge communities before and after the growing season in this area.…”

Modelling Wetland Growing Season Rainfall Interception Losses Based on Maximum Canopy Storage Measurements

“…Other values were linearly interpolated assuming that I max is 0 at the beginning and end of the growing season. Previous research on wetland meadow canopy storage in this area [35,36,45,47] also focused only on the growing season. This is due to the negligible height of sedge communities before and after the growing season in this area.…”

“…Those studies focused on determining the spatial distribution of interception capacity based on ground truth measurements of maximum canopy storage and ALS (airborne laser scanning) [35], also ground truth data was used with Landsat 7 ETM+ image [36]. There have also been numerous studies conducted on estimation of interception using remote sensing techniques that have focused on forest [37,38], croplands [39] or grasslands [38].…”