Estimating characteristics of planted forests’ relative yield index using low pulse density LiDAR and satellite remote sensing

• Established method for estimating forest density over a wide area. • Surface temperature and leaf area are sensitive indicators of forest density. • LiDAR and satellite imaging combined to improve statistical accuracy and costs. The increased demand for forest management, particularly thinning, in Japan is a direct consequence of aging planted forests. However, forest inventories (FI) in Japan lack crucial details regarding the developmental stage or ecological status of forests, often only providing tree species, age, and owner information. The relative yield index ( R y ) is a forest density index widely used in the forestry industry in Japan. It can be combined with tree height data to calculate tree density, diameter at breast height, timber volume, and basal area at breast height as the stand scale. Although R y is a valuable indicator for forest management, no studies have been reported on its estimation over a large spatial scale. Therefore, in this study, we aimed to estimate the R y of planted Japanese cedar and cypress forests at the stand scale over a large area by combining satellite imagery and airborne light detection and ranging (LiDAR) data, which offer excellent vertical resolution. Data on surface temperature, which is sensitive to differences in forest density, was obtained from Landsat8 satellite imagery. Considering that surface temperature is highly dependent on topography, we developed a topography-aware normalized surface temperature index ( Ω ST ) using surface temperature data and a digital elevation model. The leaf area index (LAI), which was positively correlated with R y , was estimated from the enhanced vegetation index obtained from Landsat. A normalized LAI ( Ω LAI ) was developed to address differences in LAI attributable to tree height. The R y estimation index ( R y_estimated ) calculated using Ω ST and Ω LAI was correlated with the R y estimated from LiDAR data (correlation coefficient; r = 0.61–0.65), confirming its high accuracy (root mean square error; RMSE = 0.07–0.11). By applying this method to a 3,650 km 2 area of planted Japanese cedar and cypress forests in the Kanto region of Japan, large-scale and detailed information on various forest characteristics was obtained. This method derives tree height data from LiDAR and extracts forest density information from satellite imagery. The combination of LiDAR data and satellite imagery potentially enhances the accuracy of forest-based estimates, reduces data acquisition costs, and improves the efficiency of creating and updating FIs.