IFI_PG58_61_ForestMngt_Innovation ESRI_02 20/02/2013 16:08 Page 61
FOREST MANAGEMENT – INNOVATION
Figure 2:FUSION’s Lidar Data Viewer (LDV) allows users to visualise and explore point clouds in a 3D environment with a variety of display options
lidar canopy metrics generated by FUSION Category Descriptive
Output variable Total number of returns Count of returns by return number Minimum Maximum Mean Median (output as 50th percentile) Mode Standard deviation Variance Coefficient of variation Interquartile distance Skewness Kurtosis AAD (Average Absolute Deviation) L‐moments (L1, L2, L3, L4) L‐moment skewness L‐moment kurtosis Height percentile values (1st, 5th, 10th , 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th, 95th, 99th percentiles) Canopy related metrics Percentage of first returns above a specified (calculated when the height (canopy cover estimate) “/above:#” switch is used Percentage of first returns above the mean height/elevation Percentage of first returns above the mode height/elevation Percentage of all returns above a specified height Percentage of all returns above the mean height/elevation Percentage of all returns above the mode height/elevation Number of returns above a specified height / total first returns * 100 Number of returns above the mean height / total first returns * 100 Number of returns above the mode height / total first returns * 100
be purchased to obtain the benefits of adding FUSION to the workflow. The USDA Forest Service Remote Sensing Applications Center (RSAC) provides training and helpdesk support for Forest Service personnel interested in using FUSION software for forest management and provides an online tutorial for public reference (http://www.fs.fed.us/eng/rsac/f usion/).
Case study Due in part to high severity fires and insect infestations, a workflow incorporating ArcGIS and FUSION, as highlighted above, was implemented for a forest restoration effort in the Pinaleño Mountains on the Coronado National Forest in southeastern Arizona. In order to identify habitat and catalogue forest inventory variables at a landscape level, forest inventory parameters were modelled by creating regression models between forest inventory parameters measured on field plots and the correlating lidar canopy metrics, which were subset for each plot and summarised using FUSION (Figure 4). Before applying the resulting statistical models to the landscape, a series of GIS
procedures were conducted to ensure the models were applied appropriately and successfully across the landscape. First, a forest-non forest mask was created to ensure the models were only applied in forested areas. This was accomplished using the spatial analyst conditional tool in ArcGIS and the canopy height and canopy cover structure grid layers output from FUSION. Each pixel had to meet a minimum vegetation height of 3 m and 2% canopy cover. All pixels that did not meet the criteria were masked out when models were applied across the study area. The final step was to apply the regression models created in the initial modelling steps to the appropriate ASCII grid layers, which created continuous inventory parameter GIS layers covering the entire study area. Each calculation produced a new grid in which each 25 m cell spatially represents the estimated forest inventory parameter of interest such as biomass, basal area, Lorey’s mean height, timber volume, etc (Figure 5). The resulting GIS inventory layers were qualitatively validated with local experts and conformed well to trends known to occur on the landscape. The forest canopy
FEBRUARY/MARCH 2013 | International Forest Industries 61