Using Remote Sensing to Count Trees
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Tree count management is important. A systematic tree inventory assist in decision making. Customary methods for counting trees are labor-intensive catalogue in the field or on an elucidation of large scale aerial photographs. Nevertheless these methods are pricey, time consuming and not pertinent to large, sequestered areas. Remote sensing technology know-how is the operational method for management and monitoring of green resources.
Methods of Extracting Remotely Sensed Data
- LiDAR
- Satellite Images
- UAV/Drone Images
- Terrestrial Photogrammetry
LiDAR
LiDAR methods of data collection is progressively used in forestry applications but also employed in urban environments for green cover calculations, tree canopy mapping and tree counting. Vast point clouds are usually converted software specific readable formats and are used to do the mapping for the tree counting and urban forestry mapping.
Satellite Images
Remotely sensed high-resolution or very high resolution satellite image data are crucial in this management, since it provides detailed information to administrators and planners for better decision making.
UAV/Drone Images
Hyperspectral remote sensing, which uses the modern satellite sensors ability to capture the data in multiple-bands, in amalgamation with a properly updated land information system is understood to be a worthy technique to assist in making fast decisions. The practice of using Unmanned Aerial Vehicle (UAV) platform for many remote sensing applications is done to combine the advantages of traditional remote sensing techniques and the inexpensiveness of operating such techniques. UAV drones can fly at varying altitudes subject to the objective of the mission and end-result type. This tractability allows for optimization of the procedures according the meteorological conditions over a given area and the user requirements.
Terrestrial Photogrammetry
About the Author
Anil Narendran Pillai – (Vice President – Geomatics @ SBL) Mr. Pillai heads the GSS (Geospatial Services) domain at SBL. He has worked in the digital mapping, remote sensing, and GIS industries for over 23 years. He has 23+ years experience managing and coordinating GIS projects and 12 years senior management experience. He has extensive experience in all aspects of aerial and satellite imaging technology and applications. He has utilized remotely sensed satellite and airborne imagery for a variety of environmental applications including site location analysis, forestry, telecommunications and utility corridor mapping. He has a strong background in management of GIS and Photogrammetry imaging projects to support Government and private industry needs. His Passion lies in Need Analysis and Documentation, Topographical Mapping (ArcGIS), Spatial Data Management, Integrity and Security, GIS Data transformations and projections from multiple sources, Image Processing Software user testing and documentation, Project Coordination and Tech. Support, Inter-agency communication and support, 3D Data Generation and Management, Project Management, Digital Photogrammetry, Satellite Image Processing, Pre-Sales Presentations.
See more about SBL Geospatial services http://www.sblcorp.com/geospatial-services
Using Near-Infrared Aerial Imagery to Map Oak Trees
A pilot project was developed that involved the identification of oak trees by the City’s arborist for a single aerial tile. Staff at Engineering Systems then used those marked locations to create a polygon layer in AutoCAD of all oak trees present. Engineering Systems then developed an application using Microsoft .NET that scanned the TIFF image (a 000 x 8000 4-inch pixel grid comprising 64 million pixels). Those pixel within the polygons were extract and were analyzed to prepare histograms to represent the frequency of individual Red, Green and Blue (RGB) values to determine the peak values representing the spectral signature of the oak trees.
The spectral signature was then used as input parameters for a second application that scanned the TIFF image tile and extracted pixels that matched the signature. The process went through several iterations matched against field surveys to verify that the correct species of trees were being selected. The overall analysis found that the spectral signature was accurate in identifying more mature oak trees but younger trees with smaller canopies were not being identified. Engineering Systems is working on refining the process to be able to identify those younger trees. Over 166,000 trees were located using this automated process.
The resulting geographic layer also identifies the diameter of the oak tree canopy. Since the oak tree locations are now georeferenced, the oak trees were spatially identified with the parcel number. This now allows the staff within the various city departments to know when a property has an oak tree and is subject to the constraints of the Oak Tree Ordinance when plans are submitted by developers and property owners.
