Evaluation And Application Of Inversion Accuracy Of Understory Terrain And Forest Canopy Height Inversion Of Spaceborne Lidar GEDI Data

Forest is the largest part of the terrestrial ecosystem,with very important ecological,social and economic benefits,forest management and management is an important link to effectively protect forests and maintain ecological stability,and stand structure is its foundation.Therefore,it is extremely important to obtain data such as canopy height and understory topography efficiently and accurately.NASA launched a new generation of space-based altimetry systems on December 5,2018,called Global Ecosystem Dynamics Investigation,whose purpose is to describe the structure and dynamics of ecosystems in order to fundamentally improve the quantification and understanding of the Earth’s carbon cycle and biodiversity.GEDI lasers are attached to the International Space Station(ISS)and collect data between 51.6°north and 51.6°south latitudes on a global scale,providing the highest resolution and densest sampling of Earth’s three-dimensional structure.Since GEDI is widely used in forest observation in temperate and tropical regions,different forest types and climatic conditions will affect its detection effect,and GEDI data has strong and weak beams,and the observation system is affected differently at different times,which will affect the performance of GEDI inversion of understory terrain and canopy.In this study,the Cibola Forest in the United States and the Maoer Mountain Forest in China were mainly studied in the study areas,taking GEDI large spot data as the research object,the G-liht data and Maoershan high-precision airborne radar data were used to verify the performance of GEDI V2 data in coniferous forest and mixed coniferous and broad-leaved forest dominated by broad-leaved tree species,the effects of different beam intensity,spot time,slope and vegetation cover on understory terrain and canopy height inversion accuracy were analyzed,and the influencing factors were used to screen and process the data,after processing the data,the forest canopy height model was constructed by combining the Landsat8 data to invert the forest canopy height in the Maoer Mountain area.The results show that the root mean square error(RMSE)of topographic inversion in the Cibola area of the United States is 2.33 m,and the mean absolute error(MAE)is 1.48 m.The root mean square error(RMSE)of forest canopy inversion was 5.50 m,and the mean absolute error(MAE)was 4.92 m.The RMSE value of topographic inversion accuracy in the area of Maoer Mountain was 4.49 m,and the MAE value was 3.33 m.The forest canopy inversion accuracy is 4.40 m and the mean absolute error(MAE)is 3.66 m.GEDI data inverted understory terrain effect coniferous forest was better than coniferous and broad-leaved mixed forest dominated by broad-leaved tree species,inverted canopy height effect coniferous and broad-leaved mixed forest was better than coniferous forest,strong beam was better than cover beam,day effect was better in humid areas,night effect was better in arid area,and GEDI data was significantly better than the determination accuracy of canopy height in understory terrain.The increase in slope significantly leads to a decrease in GEDI data performance,and high vegetation coverage also leads to greater measurement errors.The increase in slope significantly leads to a decrease in GEDI data performance,and high vegetation coverage also leads to greater measurement errors.The random forest algorithm was used to construct a forest canopy height model based on GEDI and Landsat8 data,inverted the forest canopy height in Maoershan area,and verified that the model accuracy was R~2=0.57 and RMSE=3.40m using airborne radar data.GEDI data can better reflect a wide range of forest structure characteristics,but through experiments,it can be seen that the data in the terrain and canopy height inversion have a certain error,which is affected by the beam effect and time,the external environment and the overall situation of the forest stand,and the data itself also has more unusable damaged data,therefore,in the subsequent application and update of the data,attention should be paid to the selection and correction,and combined with different types of remote sensing data to promote to a larger coverage of forest structure observation,the use of it for more accurate,Efficient ecological monitoring activities.