10:45 AM - 12:15 PM
[HTT17-P10] Wide-area Estimation of Forest Density using Low Irradiation Density LiDAR and Satellite Remote Sensing
Keywords:Satellite remote sensing, Forest density, LiDAR
Japanese planted forests that are not properly managed have a very high density of tree growth. This causes an increase in interception evaporation, which evaporates directly from leaves and trunks, and also increases transpiration compared to healthy forests. These cause a decrease in groundwater recharge, reducing the amount of water available. Also, the closed canopy makes it difficult for solar radiation to reach the forest floor, making forest floor vegetation less able to grow. This reduces the infiltration capacity of rainwater in the soil and increases the likelihood of surface runoff, resulting in runoff of fertile soil. Proper management, such as thinning, is necessary to maintain an appropriate water cycle and fertile soils, based on an understanding of forest crowding.
Since appropriate forest density depends on tree species and height, the relative yield index (Ry) is used in Japan to evaluate forest crowding. This index is determined by tree species, tree height, and tree density, and is expressed between 0 and 1. Approximately, a Ry of 0.6 or less is considered a sparsely forested area, and a Ry of 0.8 or more is considered an overcrowded area. The LiDAR data is widely used for understanding forest structure because of its excellent vertical resolution. In Japan, low irradiation density LiDAR data (1 point/m2) is widely available, but it is difficult to determine forest density from this data alone. Satellite remote sensing has been used to develop vegetation indices sensitive to vegetation activity and canopy density, and to attempt to discriminate degraded planted forests by using differences in surface temperature due to differences in canopy density. Using satellite remote sensing in combination with LiDAR data has the potential to estimate forest density with a higher degree of accuracy.
In this study, we developed a method to estimate Ry, information necessary for forest management, using LiDAR data and satellite remote sensing for Japanese cedar and cypress planted forests.
Based on previous studies showing that dense forests have relatively high canopy temperature and thin canopy compared to healthy forests, we used Landsat8 to calculate surface temperature and LAI. Considering the differences in surface temperature due to topography and the tree height dependence of LAI, we created a new Ry estimation index that is highly robust.
The estimated Ry was highly correlated (r = 0.62, n = 20001) with Ry obtained from 4-point/m2 LiDAR data. The results of this study can provide important information for estimating the water cycle in Japanese planted forests and for soil conservation. The method can be applied to various coniferous forests around the world and is expected to contribute to the advancement of global-scale water cycle models.
Since appropriate forest density depends on tree species and height, the relative yield index (Ry) is used in Japan to evaluate forest crowding. This index is determined by tree species, tree height, and tree density, and is expressed between 0 and 1. Approximately, a Ry of 0.6 or less is considered a sparsely forested area, and a Ry of 0.8 or more is considered an overcrowded area. The LiDAR data is widely used for understanding forest structure because of its excellent vertical resolution. In Japan, low irradiation density LiDAR data (1 point/m2) is widely available, but it is difficult to determine forest density from this data alone. Satellite remote sensing has been used to develop vegetation indices sensitive to vegetation activity and canopy density, and to attempt to discriminate degraded planted forests by using differences in surface temperature due to differences in canopy density. Using satellite remote sensing in combination with LiDAR data has the potential to estimate forest density with a higher degree of accuracy.
In this study, we developed a method to estimate Ry, information necessary for forest management, using LiDAR data and satellite remote sensing for Japanese cedar and cypress planted forests.
Based on previous studies showing that dense forests have relatively high canopy temperature and thin canopy compared to healthy forests, we used Landsat8 to calculate surface temperature and LAI. Considering the differences in surface temperature due to topography and the tree height dependence of LAI, we created a new Ry estimation index that is highly robust.
The estimated Ry was highly correlated (r = 0.62, n = 20001) with Ry obtained from 4-point/m2 LiDAR data. The results of this study can provide important information for estimating the water cycle in Japanese planted forests and for soil conservation. The method can be applied to various coniferous forests around the world and is expected to contribute to the advancement of global-scale water cycle models.