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Tree Height Classification
Tree Height Classification
Tree Height Classification
Tree Height Classification
Technology description
The Tree Height Classification provides detailed geospatial data on the vertical structure of vegetation, offering insights into the height of trees across various landscapes. Derived from satellite and LiDAR data, this layer delivers accurate measurements at a high spatial resolution, enabling better decision-making for urban planning, forestry management, and environmental research.
Uses scenario
When integrated with biodiversity datasets, Tree Height Classification aids in ecosystem assessments and recovery efforts. By monitoring variations in tree heights, it facilitates habitat identification, biodiversity tracking, and targeted restoration actions.
Classifying tree heights along roads helps municipalities identify tall trees near critical infrastructure like power lines and traffic systems. This supports proactive maintenance to prevent safety hazards such as falling branches.
Tree height distribution data optimizes the placement of greenery to act as buffers against noise and air pollution in urban areas. Taller trees enhance pollution capture and improve urban livability.
Understand forest recovery dynamics and ecological shifts after disturbances like wildfires or logging. This data also evaluates risk factors and supports sustainable forest management.
Access high-resolution Land Surface Temperature (LST) data at 10-meter accuracy in Celsius (°C) with our tool.
The Surface Urban Heat Island (SUHI) layer assesses the Urban Heat Island effect, highlighting urban areas that are warmer than their rural surroundings.
The Albedo layer measures surface reflectivity, indicating the percentage of sunlight that is reflected into space.
The Heatwave Risk map integrates temperature data (hazard), population demographics (exposure), and area morphology (vulnerability) to generate a risk index ranging from 0 to 100.
The Park Cool Islands (PCI) layer distinguishes urban parks based on their cooling effects, categorizing them into Major and Minor Cool Islands.
The Microclimatic Performance Index (MPI) evaluates the effectiveness of Urban Green Infrastructure (UGI) in combating the Urban Heat Island (UHI) effect.
The Carbon Storage layer quantifies CO2 absorption by vegetation, offering a detailed view of nature's impact on atmospheric carbon reduction.
The Tree Cover Density (TCD) layer accurately depicts the percentage of an area covered by tree canopy, ranging from 0 to 100%.
The Land Surface Temperature (LST) layer delivers 10-meter resolution temperature data with four daily measurements, supporting urban, agricultural, climate, disaster, and energy applications.
The Flooding Risk Layer identifies areas prone to flooding using data from Sentinel-1, Sentinel-2, DEM, and ERA5, supporting urban planning, real estate, agriculture, climate research, and insurance.
The Tree Height Classification layer provides high-resolution data on tree heights using satellite and LiDAR, supporting urban planning, forestry, and environmental research.
The Building Height Layer provides 10-meter resolution data on building heights, supporting urban planning, density analysis, disaster preparedness, and public space design.
Latitudo 40 is an innovative startup in the field of Earth Observation, born from the passion, expertise, and experience of a team united by a common goal: to improve the planet through satellite technologies. We define ourselves as a lean organization, basing our success on the principles of user-centered design.
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