Improving Forest Canopy Height Mapping in Wuyishan National Park Through Calibration of ZiYuan-3 Stereo Imagery Using Limited Unmanned Aerial Vehicle LiDAR Data

Forests, Journal Year: 2025, Volume and Issue: 16(1), P. 125 - 125

Published: Jan. 11, 2025

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there lack of accurate FCH distribution in large areas. To address this issue, study selected Wuyishan National Park China as case to explore the calibration method mapping complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance causal effect analysis were used examine major factors causing extraction errors digital surface model (DSM) data from ZY3 imagery. Different machine learning algorithms compared calibrate DSM results. The results indicate that accuracy primarily influenced by slope aspect, elevation, vegetation characteristics. These influences particularly notable areas with topography dense coverage. A Bayesian-optimized CatBoost directly calibrating original (the difference between high-precision elevation (DEM) data) demonstrated best prediction performance. This produced map at 4 m spatial resolution, root mean square error (RMSE) was reduced 6.47 initial 3.99 after calibration, relative RMSE (rRMSE) 36.52% 22.53%. demonstrates feasibility using regional confirms superior performance algorithm enhancing accuracy. findings provide valuable insights into multidimensional impacts key environmental extraction, supporting precise monitoring carbon stock assessment terrains regions.

Language: Английский

Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 2024 - 2024

Published: Nov. 17, 2024

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense can significantly reduce the accuracy of GEDI estimations. We selected South Taihang region Henan Province, China, our study area proposed an optimization framework to improve estimation accuracy. This includes correcting geolocation errors in footprints, screening analyzing features that affect errors, combining two regression models with feature selection methods. Our findings reveal error 4 6 m footprints at orbital scale, along overestimation region. Relative (RH), waveform characteristics, topographic features, cover influenced error. Some studies have suggested estimates areas high lead underestimation, found increased higher terrain vegetation. model’s performance improved after incorporating parameter into model. Overall, R2 best-optimized model was from 0.06 0.61, RMSE decreased 8.73 2.23 m, rRMSE 65% 17%, resulting improvement 74.45%. In general, this reveals affecting vegetation cover, premise minimizing errors. Employing enhanced estimates. also highlighted crucial role improving precision estimation, providing effective approach monitoring regions conditions. Future should further classification tree species expand diversity sample test estimated by different structures, consider distortion optical remote sensing images caused rugged terrain, mine information waveforms so enhance applicability more diverse environments.

Language: Английский