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Aerial Photo of a Field

Environmental Survey & Monitoring

Environmental Monitoring

Altitude Imaging provides the Industry with a comprehensive environmental survey service specializing in providing high-resolution surveys and spatial solutions. The team at FAA understands the need for reliable environmental data.
Our UAV systems allow you to rapidly and frequently survey inaccessible areas without exposing personnel to health and safety risks. They are a cost-effective alternative to conventional aerial surveys & mapping.
Whether it be simple oblique photography, complete GIS data sets, or more complex 3D modeling required, we will capture and process your data accurately, efficiently, and cost-effectively. Our professional UAV systems provide outstanding, repeatable positional accuracy and definition.


Flood & Erosion

Shoreline, Hillside

Modeling and Mapping the changing formation of river basins is the critical application of mapping UAS technology. Government transport departments use UAVs to map sediment jams and other potential blockage points as part of their flood prevention program.


Ground Change Monitoring

Control Over Time

Digital UAS data allows an operator to document a site or a natural object's evolution over time, down to the finest detail, whether assessing a shoreline erosion or the movement of a glacier.



Control native species

  • Mapping: First, the company uses drones to survey the area and gather information on its topography, soil type, current vegetation, and any obstructions. The company uses that data to create a pattern for the planting drones to follow.

  • Planting: Drones carrying biodegradable seedpods fly over the area, dropping seeds according to the pattern. A drone carrying 300 pods can cover one hectare in 18 minutes and have multiple species of seeds.

  • Monitoring: After the planting phase is complete, drones periodically survey the area and record data on the seeds' progress.

  • Data Analysis: The company analyzes the data collected in the monitoring phase using machine-learning algorithms to learn more about their progress and the area’s ecosystem. They use this information to improve their patterns for future rounds of planting.

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