A scalable monitoring framework for leaf area index and green area index using 30°-tilted cameras

• Leaf area index (LAI) and green area index (GAI) are fundamental plant traits. However, there is a lack of ground observation network for LAI/GAI due to technical limitations. Here we present a new method to achieve continuous LAI/GAI monitoring using ordinary cameras.
• By tilting ordinary cameras by 30°, images can cover a large view zenith angle range to measure multi-angular gap fractions and thus to quantify LAI using radiative transfer theory. In addition, using cameras can separate green tissues from non-green tissues. We conducted intensive experiments to evaluate the performance of 30°-tilted cameras and built a countywide LAI/...  more

• Leaf area index (LAI) and green area index (GAI) are fundamental plant traits. However, there is a lack of ground observation network for LAI/GAI due to technical limitations. Here we present a new method to achieve continuous LAI/GAI monitoring using ordinary cameras.
• By tilting ordinary cameras by 30°, images can cover a large view zenith angle range to measure multi-angular gap fractions and thus to quantify LAI using radiative transfer theory. In addition, using cameras can separate green tissues from non-green tissues. We conducted intensive experiments to evaluate the performance of 30°-tilted cameras and built a countywide LAI/GAI ground observation network.
• LAI/GAI derived from 30°-tilted cameras are consistent with LAI-2200 and destructive samplings. The countywide LAI/GAI ground observation network can capture distinct seasonality of corn, soybean, miscanthus, switchgrass, restored prairie and deciduous forest.
• 30°-tilted cameras provide an accurate, robust, automatic, standardized and scalable method to acquire spatially-distributed and temporally-continuous LAI/GAI at low cost. It is promising for building a LAI ground observation network at regional and global scales.