Ecological Zoning Forest Height Retrieval Model
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Solution Overview
Problem
Current large-scale forest height estimation algorithms are inaccurate and lack zonal representativeness due to the neglect of ecological factors such as climate, topography, and vegetation variations across different forest zones, which limits the precision of forest height mapping and carbon sink analysis.
Innovation Solution
A multi-source remote sensing data non-parametric tree height model is developed, incorporating ecological zoning data with satellite-borne photon counting LiDAR, optical images, terrain data, meteorological data, and latitude and longitude information to estimate spatially continuous forest heights, considering various ecological zones and forest types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single forest height model is constructed for the entire large-scale forest region, then the modeling process is simple, but the accuracy of forest height estimation is limited due to spatial variability in forest growth
Solution Approach 1:
The forest region is divided into multiple ecological zones based on climate, topography, and vegetation characteristics. Separate forest height models are constructed for each ecological zone, allowing the model to capture spatial variability in forest growth while maintaining manageable complexity through systematic segmentation of the study area
2Device complexity
If fixed-size forest zones are used for modeling, then the zoning process is simple, but the accuracy of forest height estimates is affected by neglecting local climate and topography influences
Solution Approach 1:
The forest zones are defined based on local ecological characteristics including climate, topography, and vegetation types rather than using uniform fixed-size zones. This allows each zone to reflect its specific local conditions and environmental influences, thereby improving the accuracy of forest height estimates while the zoning process remains systematic through the use of ecological zoning frameworks
3Area of stationary object
If satellite LiDAR is used for large-scale forest height mapping, then continuous spatial coverage is achieved, but the data collection cost increases and data collection parameters vary across large scales
Solution Approach 1:
The method combines satellite LiDAR data with auxiliary variables from multiple sources including optical images, terrain data, meteorological data, and ecological zoning data. This merging of multiple data sources allows for comprehensive large-scale forest height mapping while distributing the data collection complexity across multiple standardized data types and sources
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of forest height estimation by accounting for ecological factors, enabling rapid and high-precision mapping of forest heights across large areas, improving the understanding of carbon sinks and forest dynamics.
Implementation Method 1
A satellite LiDAR system uses a laser ranging technology to directly determine a three-dimensional vertical structure of a large-scale forest
Data Source
AI summary
A large-scale forest height remote sensing retrieval method includes: acquiring Ice, Cloud and land Elevation Satellite (ICESAT-2) tree height data, Landsat data, Shuttle Radar Topography Mission (SRTM) data, Worldclim data, forest type data and ecological zoning data within a target zone, and preprocessing the data; carrying out georeferencing on the processed data to generate a first data set; calculating spectral features, terrain features and climatic factor features of an image, and combining the calculated features with the ecological zoning data and the forest type data to obtain a second data set; extracting eigenvalues of a same geographical location from the second data set, and combining the extracted eigenvalues with the tree height data to generate training data; constructing a random forest model covering a large zone as an ecological zoning tree height retrieval model, and dividing the obtained training data into a training sample and a verification sample.