Discovering forest height changes based on spaceborne lidar data of ICESat-1 in 2005 and ICESat-2 in 2019:a case study in the Beijing-Tianjin-Hebei region of China

Background:The assessment of change in forest ecosystems,especially the change of canopy heights,is essential for improving global carbon estimates and understanding effects of climate change.Spaceborne lidar systems provide a unique opportunity to monitor changes in the vertical structure of forests.NASA’s Ice,Cloud and Land Elevation Satellites,ICESat-1 for the period 2003 to 2009,and ICESat-2(available since 2018),have collected elevation data over the Earth’s surface with a time interval of 10 years.In this study,we tried to discover forest canopy changes by utilizing the global forest canopy height map of 2005(complete global coverage with 1 km resolution)derived from ICESat-1 data and the ATL08 land and vegetation products of 2019(sampling footprints with 17 m diameter)from ICESat-2.Results:Our study revealed a significant increase in forest canopy heights of China’s Beijing-Tianjin-Hebei region.Evaluations of unchanging areas for data consistency of two products show that the bias values decreased significantly from line-transect-level(−8.0 to 6.2 m)to site-level(^(−1).5 to 1.1 m),while RMSE values are still relatively high(6.1 to 15.2 m,10.2 to 12.0 m).Additionally,58%of ATL08 data are located in‘0m’pixels with an average height of 7.9 m,which are likely to reflect the ambitious tree planting programs in China.Conclusions:Our study shows that it is possible,with proper calibrations,to use ICESat-1 and-2 products to detect forest canopy height changes in a regional context.We expect that the approach presented in this study is potentially suitable to derive a fine-scale map of global forest change.

Fine scale optical remote sensing experiment of mixed stand over complex terrain(FOREST)in the Genhe Reserve Area:objective,observation and a case study

Optical remote sensing allows to efficiently monitor forest ecosystems at regional and global scales.However,most of the widely used optical forward models and backward estimation methods are only suitable for forest canopies in flat areas.To evaluate the recent progress in forest remote sensing over complex terrain,a satellite-airborne-ground synchronous Fine scale Optical Remote sensing Experiment of mixed Stand over complex Terrain(FOREST)was conducted over a 1 km×1 km key experiment area(KEA)located in the Genhe Reserve Areain 2016.Twenty 30 m×30 m elementary sampling units(ESUs)were established to represent the spatiotemporal variations of the KEA.Structural and spectral parameters were simultaneously measured for each ESU.As a case study,we first built two 3D scenes of the KEA with individual-tree and voxel-based approaches,and then simulated the canopy reflectance using the LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes(LESS).The correlation coefficient between the LESS-simulated reflectance and the airborne-measured reflectance reaches 0.68-0.73 in the red band and 0.56-0.59 in the near-infrared band,indicating a good quality of the experiment dataset.More validation studies of the related forward models and retrieval methods will be done.

Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

Both leaf inclination angle distribution(LAD)and leaf area index(LAI)dominate optical remote sensing signals.The G-function,which is a function of LAD and remote sensing geometry,is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD.Large uncertainties are thus introduced.However,because numerous tiny leaves grow on conifers,it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval.In this study,we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval.Specifically,a Multi-Directional Imager(MDI)was developed to capture stereo images of the branches,and the needles were reconstructed.The accuracy of the inclination angles calculated from the reconstructed needles was high.Moreover,we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and threedimensional(3D)tree models.Results show that the constant G assumption introduces large errors in LAI retrieval,which could be as large as 53%in the zenithal viewing direction used by spaceborne LiDAR.As a result,accurate LAD estimation is recommended.In the absence of such data,our results show that a viewing zenith angle between 45 and 65 degrees is a good choice,at which the errors of LAI retrieval caused by the spherical assumption will be less than 10%for coniferous canopies.