Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China

Abstract

Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.

Assessing the performance of random forest regression for estimating canopy height in tropical dry forests

“…The selected image consists of a collection of images obtained through GEE with a spatial resolution of 25 meters and a temporal range from 2019 to 2020. The rh95 metric was used because, according to previous studies, it has shown better results compared to other metrics [13].…”

“…In this context, the GEDI by National Aeronautics and Space Administration (NASA) [11] has stood out for providing valuable information on forest height through medium-resolution time series [12]. Although field validation is important to correlate two types of satellite data, the lack of it does not invalidate the usefulness of GEDI as an input for canopy height estimation [13]. GEDI datasets are improving height models and offering an important tool for global and regional forest management [4].…”

“…The combination of LIDAR and SAR data offers a unique synergy for the evaluation of canopy height and texture in any type of forest [12], allowing for detailed information on structure and its vertical distribution. SAR data from the ALOS PALSAR-2 sensor [17], through the generation of various polarimetric indices, are suitable for texture detection, improving the accuracy of canopy height models [13].…”

Canopy-Height and Stand-Age Estimation in Northeast China at Sub-Compartment Level Using Multi-Resource Remote Sensing Data

“…Once the long-term Landsat time-series stacks (LTSS) were opened for free access, disturbance detection methods were able to be rapidly developed based on these data [19]. A number of studies used a change-detection algorithm to extract various parameters based on different vegetation indices, and the results indicated that age information could be estimated by quantifying the parameters related to forest disturbance [20].…”