Forestry
Advanced Forestry Metrics
Integrating LiDAR, hyperspectral, and optical data provides a comprehensive view of forests from the micro to macro scale. When paired with machine learning, the capability to understand, predict, and respond to forestry challenges is significantly amplified. Whether it's conservation efforts, timber production, or research, this multi-layered approach is revolutionizing forestry for a sustainable future.
Forest Metrics
Tree Counts
Leveraging machine learning algorithms, the collected data is processed to identify and count individual trees within the forest landscape.
Tree Metrics
Machine learning assists in extracting specific metrics from the data, such as tree height, crown width, volume and canopy density, providing a comprehensive forest profile.
Growth Rate Monitoring
By comparing datasets taken at different intervals, machine learning algorithms can detect growth patterns within the forest.
Individual Tree Metrics
Species Identification
The nuanced data from VNIR hyperspectral sensors, when processed with machine learning, can potentially distinguish between tree species based on their unique spectral signatures.
Individual Tree Metrics
Machine learning assists in extracting specific metrics from the data, such as tree height, crown width, and volume, DBH, providing a comprehensive tree profile.
Carbon Stocks and Sequestration Modelling
Using biomass estimates, species and growth rate data, machine learning models can be used to estimate carbon storage capacities and forecast sequestration scenarios.
Wood Biomass Volumes
Machine learning algorithms analyse the structural data from LiDAR in conjunction with hyperspectral data to estimate wood volume within a defined area.
Forest Management
Continual Multispectral Monitoring for Tree Health
The frequent acquisition of satellite NDVI data allows for continuous health monitoring. Machine learning algorithms, specifically designed for change detection, analyze this data, providing insights into forest health dynamics.
Large Scale Hydrology, Surface Runoff and Topographic Wetness Index
Using DEMs, potential runoff paths and water accumulation zones can be identified, providing insights into hydrological patterns within the forest.
Efficient Tree Felling Planning in Mixed Woodlands
Machine learning, combined with species identification and structural data, can aid in strategizing logging operations, aiming for efficiency and minimal ecological impact.
Digital Elevation Models (DEMs) and Topographic surveys
LiDAR data is processed to create DEMs, offering a detailed topographical view of the forested region.