The focus of this talk is to illustrate various new remote sensing technologies and how the data can be used synergistically to produce new and/or better Essential Biodiversity Variables. The National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) have embarked on a wide range of new instruments and capabilities that are available and accessible from policies of “free and open” data. These technologies include multispectral and hyperspectral imagers, multiband thermal imagers, LiDAR and RaDAR imagers on the International Space Station, and polar, and geostationary satellite orbits. These data can be used to retrieve Essential Biodiversity Variables (EBVs). Data can be temporally combined, either seasonally or inter-annually, to derive information about phenology and on longer time-scales about disturbance regimes. Multispectral and hyperspectral imagers in tandem can provide more detailed information on ecosystem functions like net primary productivity, on functional types, traits, and on species distributions. Multispectral and/or hyperspectral imagers can be combined with LiDAR or RaDAR data to improve understanding of ecosystem structure, fragmentation, habitat extent and other properties. Such data combined with multispectral and/or hyperspectral imagery improves estimates of species and/or community distributions, and can be used to estimate biodiversity of the canopy species.
Results/Conclusions
We will present previously unpublished results of studies from my lab that illustrate different ways that integration of data types can improve EBVs. I will provide an example of using high spatial resolution data such as the National Agricultural Imagery Program (NAIP) can be used to validate retrieval of subpixel heterogeneity of wetland species in the Sacramento-San Joaquin Delta, to improve species maps derived from multispectral Landsat with 30m pixels or hyperspectral imagery airborne imagery at 3m pixels. We will provide an example of using multidate (seasonal) airborne Advanced Visible Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery over the forests in the Sierra Nevada Mts., California to map tree species distributions. I will show an example of how inclusion of National Ecological Observatory Network (NEON) LiDAR data can improve tree species classification maps. I will also show how these forests responded to a multiyear drought by comparing changes in canopy water content over a three year period, and retrieval of other traits such as leaf mass area, and biochemicals like total chlorophyll and total carotenoids.