Using ICESat-2 ATL08 heights and Landsat time series to assess the age-height relationship of southern yellow pines in the US

It is critical to understand the age-height relationship in actively managed pine plantations to evaluate forest productivity and inform sustainable forest management practices. This study utilized the spaceborne lidar data from ICESat-2 ATL08 and Landsat-derived disturbance maps to assess the site index for actively managed pine plantations across the southeastern United States. Specifically, we (1) assessed the agreement between ICESat-2 ATL08 canopy heights and airborne laser scanning (ALS) data, (2) showed the distribution of canopy height by age for the actively managed pine stands, and (3) compared the site index guide curve derived using ICESat-2 and Landsat data with the FIA-derived site index guide curve. We analyzed over 137,998 ICESat-2 ATL08 segments intersecting pine plantation locations and LCMS Fast Loss. We identified plantation ages by subtracting the LCMS Fast Loss year from the ICESat-2 acquisition date. Actively managed stands were defined as those experiencing both thinning and harvesting. ICESat-2 ATL08 heights were compared to heights derived using airborne laser scanning (ALS) data from the USGS 3D Elevation Program. The heights were highly correlated in the assessed region (R 2 = 0.90), providing confidence in ICESat-2 ATL08 heights. Despite the challenges with LCMS misclassifying thinning events as clear-cut disturbances, we developed a model using quantile regression relating the logarithm of ICESat-2 ATL08 height to the reciprocal of age, resulting in a regional site index guide curve. The ICESat-2-derived site index (19.9 m) closely matched the FIA-derived site index (19.8 m) at the base age of 25 years, as supported by the Cramér-von Mises test, indicating that the site index guide curves are statistically similar. The resulting site index map provides insights into spatial variability in regional forest productivity, emphasizing the utility of remote sensing data for large-scale forest monitoring and management