A ground-based scanning lidar (light detection and ranging) system was evaluated to assess its potential utility for tree-level forest mensuration data extraction. Ground-based-lidar and field-mensuration data were collected for two forest plots: one located within a red pine (Pinus resinosa Ait.) plantation and another in a mixed deciduous stand dominated by sugar maple (Acer saccharum Marsh.). Five lidar point cloud scans were collected from different vantage points for each plot over a 6-h period on 5 July 2002 using an Optech Inc. ILRIS-3D laser imager. Field- validation data were collected manually over several days during the same time period. Parameters that were measured in the field or derived from manual field measures included (i) stem location, (ii) tree height, (iii) stem diameter at breast height (DBH), (iv) stem density, and (v) timber volume. These measures were then compared with those derived from the ILRIS-3D data (i.e., the lidar point cloud data). It was found that all parameters could be measured or derived from the data collected by the ground-based lidar system. There was a slight systematic underestimation of mean tree height resulting from canopy shadow effects and suboptimal scan sampling distribution. Timber volume estimates for both plots were within 7% of manually derived estimates. Tree height and DBH parameters have the potential for objective measurement or derivation with little manual intervention. However, locating and counting trees within the lidar point cloud, particularly in the multitiered deciduous plot, required the assistance of field-validation data and some subjective interpretation. Overall, ground-based lidar demonstrates promise for objective and consistent forest metric assessment, but work is needed to refine and develop automatic feature identification and data extraction techniques.
Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91 % of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23 % of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.
We compared median runoff (R) and precipitation (P) relationships over 25 years from 20 mesoscale (50 to 5,000 km2) catchments on the Boreal Plains, Alberta, Canada, to understand controls on water sink and source dynamics in water‐limited, low‐relief northern environments. Long‐term catchment R and runoff efficiency (RP−1) were low and varied spatially by over an order of magnitude (3 to 119 mm/year, 1 to 27%). Intercatchment differences were not associated with small variations in climate. The partitioning of P into evapotranspiration (ET) and R instead reflected the interplay between underlying glacial deposit texture, overlying soil‐vegetation land cover, and regional slope. Correlation and principal component analyses results show that peatland‐swamp wetlands were the major source areas of water. The lowest estimates of median annual catchment ET (321 to 395 mm) and greatest R (60 to 119 mm, 13 to 27% of P) were observed in low‐relief, peatland‐swamp dominated catchments, within both fine‐textured clay‐plain and coarse‐textured glacial deposits. In contrast, open‐water wetlands and deciduous‐mixedwood forest land covers acted as water sinks, and less catchment R was observed with increases in proportional coverage of these land covers. In catchments dominated by hummocky moraines, long‐term runoff was restricted to 10 mm/year, or 2% of P. This reflects the poor surface‐drainage networks and slightly greater regional slope of the fine‐textured glacial deposit, coupled with the large soil‐water and depression storage and higher actual ET of associated shallow open‐water marsh wetland and deciduous‐forest land covers. This intercatchment study enhances current conceptual frameworks for predicting water yield in the Boreal Plains based on the sink and source functions of glacial landforms and soil‐vegetation land covers. It offers the capability within this hydro‐geoclimatic region to design reclaimed catchments with desired hydrological functionality and associated tolerances to climate or land‐use changes and inform land management decisions based on effective catchment‐scale conceptual understanding.
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