How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: a review

Effort has been devoted to automatic or semi-automatic individual tree detection and crown delineation from remotely sensed data, aiming at the effective creation of forest inventory, to assist forest management and ecosystem modelling. However, there is no uniform evaluation scheme to assess the resulting products. This paper reviews the available techniques and essential considerations for evaluating remote sensing-based forest inventory products, including detected individual tree locations, crown delineation maps, forest structure parameters, and species classification results. The reference data collection, individual tree matching method, mismatched individual tree crown treatment, and stratification criteria are summarized as four key factors in the evaluation process. Additionally, a discussion of future working directions, regarding limitations of existing techniques, multi-scale evaluation, sampling schemes, uncertainty analysis, and results interpretation, is also included. A new accuracy assessment framework is proposed, and an accuracy report example is provided.