- Feb 2017
Comparison of ITCD algorithms is challenging when there are differences in study focus, study area, data applied, and accuracy assessment method used. Before 2005, the few studies that compared methods generally tested approaches on a common dataset.
This difficulty in comparing algorithms (due to differences in forest type, location, and assessment strategy used for different algorithms) indicates a clear need for set of open data and centralized assessment to allow different methods to be competed against one another to determine the best routes forward.
This kind of approach has been very successful in other image analysis problems (e.g., ImageNET).
The National Ecological Observatory Network data seems ideal for doing something like this. Data is/will be available for a variety of different systems and with LiDAR, Hyperspectral, RGB, and field data for large numbers of plots.
Additionally, it is often challenging to apply an algorithm developed in one forest type to another area.
This difficulty of applying across forest types is central to the challenges of developing approaches that can be applied to continental scale data collection like that being conducted by NEON. Overcoming this challenge will likely require incorporating ecological information into models, not just the remote sensing, and determining how to choose and adjust different approaches to get the best delineations possible based on information about the forest type/location.
The most useful information that can be incorporated into ITCD studies is the expected crown size and stand density [25,67].
This kind of data is available for NEON plots and so these methods could potentially be well leveraged with NEON data. This would be particularly true if the NEON plot data could be used to develop a spatial model for these features that could be used to predict their values across space.