Issue link: https://read.dmtmag.com/i/53167
In addition to bare-Earth ground models, airborne LIDAR provides an abundance of information about above-ground characteristics. Buildings are efficiently extracted from LIDAR point clouds and transformed into discrete 3-D elements. Buildings often have the greatest cumulative impact on local climates (hydro- logical cycles and latent heat fluxes), because they often cover the largest land area. Building roof areas have a vast rainwater-management potential through the implementation of green roofing. Green roofs can reduce annual precipitation runoff by more than 50 percent in temperate climates. The ben- efits of green roofing are evident, but some buildings are more suitable than others; the greatest peak-flow rate reduction occurs on slopes of less than 2 percent. Rooftop-slope attributes are readily available from airborne LIDAR data (see Figure 3). Green roofs can help with climate-change adaptation, and LIDAR is used to quantify the potential hydrological benefits of green roofs, which is important for improving adap- tive capacity. Optimal rollout plans can be developed based on re-zoning and slated development. Having the ability to accurately model hydrological outcomes of realistic rainwater-management strate- gies enables effective evaluation of these strategies against other adaptation measures such as increasing volume capacity in the creek. Vegetation Studies Vegetation plays a major role in providing communities with greater adaptive capacity to unpredictable future climates. Vegetation is an integral component of the hydrological, nitrogen and carbon cycles, and it's a powerful moderator of ambient temperature—through transpiration, a mature tree releases approximately 400 liters of water per day, a cooling effect similar to a 20-kilowatt air conditioner. Figure 4. A one-meter-pixel ASIA dual-channel hyperspectral false-color image mosaic was orthorectified to a LIDAR digital surface model. Having the ability to gain detailed insight into the Figure 3. A digital surface model slope map indicates "green- roof" potential. Rooftops areas with less than 2-percent slope are considered "green." 24 GEO W ORLD /JANUAR Y 2O12 3-D spatial relationship an individual tree has with its environment, anywhere at any time, is an exciting pros- pect. Understanding the complex dynamics between vegetation and climate can help guide regional climate models and provide better insight into how vegetation is being affected by climate change. These data help provide guidance for vegetation-management pro- grams, including early warning detection of pests such as the mountain pine beetle and emerald ash borer beetle, successional urban tree planning, carbon- sequestration modeling, and valuation studies. Research groups such as the Centre for Applied Remote Sensing, Modelling and Simulation at the University of Victoria are constantly advancing the integration of hyperspectral and LIDAR data. Fusing LIDAR and hyperspectral data offers an incredibly comprehensive and accurate assessment of individual tree characteristics. Standalone hyperspectral imagery lacks spatial accuracy, but hyperspectral imagery orthorectified to a LIDAR digital surface model of the vegetation canopy provides the accuracy necessary to examine spectra from individual trees at sub-meter resolutions. The fine spectral and spatial resolutions of airborne hyper- spectral sensors have the ability to parse the electro- magnetic spectrum into extremely small bands (2-10 microns) from the visible to the very-near infrared. These bands provide detailed information on the biogeochemical properties of vegetation, allowing for large-scale studies with fine-scale accuracy. Vegetation Imagery/LIDAR Special Issue Airborne Technology