A Method of Inverting Forest Aboveground Biomass by Combining Three Types of Data Sources

Abstract

The invention discloses a method for combining high-resolution CCD data, hyperspectral image data and laser radar point cloud data to jointly invert forest aboveground biomass. Geometric correction, splicing preprocessing, geometric correction and atmospheric correction preprocessing of hyperspectral images, filtering of lidar point cloud data, interpolation to generate digital terrain models, and normalization of point cloud data; and then based on the preprocessed The texture features, spectral features and point cloud structure features are extracted from the three data sources; finally, a method for predicting forest aboveground biomass is constructed by combining the ground measured data and the extracted feature variables to construct models respectively. The invention extracts the aboveground biomass of the subtropical natural secondary forest, and compared with the aboveground biomass estimation results using other similar remote sensing methods, the relative root mean square error is reduced by more than 10%.

Description

technical field [0001] The invention relates to the fields of forest resource monitoring, environmental factor investigation and the like, and specifically relates to a method for jointly retrieving forest aboveground biomass by integrating three types of data sources. Background technique [0002] Accurate forest aboveground biomass extraction is of great significance for forest resources monitoring and environmental factor investigation. At the same time, this information can also be used to grasp the relationship between forest plants and the environment, and the laws of forest growth, development, renewal, and succession, which are of great significance for sustainable forest management, ecosystem carbon cycle research, and understanding of global climate change. Conventional aboveground biomass extraction in forests mainly relies on field measurements or statistical analysis methods based on field measurements, and its accuracy is often low, and it is difficult to be pr...

AI Technical Summary

Problems solved by technology

However, the above methods are based on two types of data sources, and do not integrate high-resolution CCD, hyperspectral and lidar data acquired simultaneously to improve the accuracy of forest aboveground biomass estimation

At the same time, there is no comprehensive and in-depth calculation of high-precision spatial detail features, spectral features, and lidar feature extraction methods for forest canopies.

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