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[ABSTRACT] => Forest ecosystems are the largest terrestrial ecosystems type and have been identified as a major carbon sink; therefore, their monitoring is critical. Forests cover large areas making it difficult to monitor them and maintain up to date information. Advances in remote sensing technologies provide opportunity for detailed small-scale monitoring to global monitoring of forest resources. Small-scale forest monitoring operations carried out using Airborne Laser Scanning (ALS) data, are expensive, not always open access, are limited in terms of spatial and temporal coverage, and therefore are unsuited for routine applications at very large scales. Spaceborne lidar (Light Detection and Ranging) measurements, on the other hand, can cover very large spatial scales but the extent to which they are suitable replacements for ALS measurement is not clear. There are still relatively few studies on the performance of such products to estimate forest attributes with sufficient accuracy and precision. Therefore, this study aimed at evaluating the agreement of spaceborne lidar ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) canopy height with ALS derived canopy heights to assess the performance of ICESat-2 canopy height metrics and understand its uncertainties and utilities. We assessed the agreement for different forest types, physiographic regions, a range of percent canopy cover, and diverse disturbance histories using equivalence tests. Results suggest that best agreements are found using strong beam data collected at night for canopy height retrieval using ICESat-2. The ICESat-2 showed great potential for estimating canopy heights, particularly in evergreen forests with high canopy cover. Overall, ICESat-2 showed good agreement with ALS canopy height and showed its predictive ability to characterize canopy height. The outcome of this study will help the scientific community understand the capabilities and limitations of ICESat-2 canopy heights as well as provides a new approach to obtain wall-to-wall canopy height maps at landscape level.
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