Alternate spatial sampling approaches for ecosystem structure inventory using spaceborne lidar

The application of spaceborne lidar data to mapping of ecosystem structure is currently limited by the relatively small fraction of the earth's surface sampled by these sensors; this limitation is likely to remain over the next generation of lidar missions. Currently planned lidar missions will collect transects of data with contiguous observations along each transect; transects will be spread over swaths of multiple kilometers, a sampling pattern that results in high sampling density along track and low sampling density across track. In this work we demonstrate the advantages of a hybrid spatial sampling approach that combines a single conventional transect with a systematic grid of observations. We compare this hybrid approach to traditional lidar sampling that distributes the same number of observations into five transects. We demonstrate that a hybrid sampling approach achieves benchmarks for the spatial distribution of observations in approximately 1/3 of the time required for transect sampling and results in estimates of ecosystem height that have half the uncertainty as those from transect sampling. This type of approach is made possible by a suite of technologies, known together as Electronically Steerable Flash Lidar. A spaceborne sensor with the flexibility of this technology would produce estimates of ecosystem structure that are more reliable and spatially complete than a similar number of observations collected in transects and should be considered for future lidar remote sensing missions.

Research highlights► Spaceborne lidar sensors are currently designed to collect transects of contiguous observations. ► New technology makes possible systematic grids of observations, which can combine with transects. ► Benchmarks of spatial coverage are achieved three times as quickly for equal number of observations. ► For our test landscapes, estimates of average vegetation height are half as variable as with transects. ► Flexible sampling, and the technologies that support it, should be considered for future missions.