Spatiotemporal Analysis of Urban Forest in Chattanooga, Tennessee from 1984 to 2021 Using Landsat Satellite Imagery

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth’s unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-07, Vol.16 (13), p.2419
Main Authors: Stuart, William, Hossain, A. K. M. Azad, Hunt, Nyssa, Mix, Charles, Qin, Hong
Format: Article
Language:English
Subjects:
Agricultural production
Algorithms
Archives & records
Biomass
Canopies
Carbon cycle
Carbon sequestration
Cities
Forests
Image acquisition
Image classification
Industrial Revolution
Land area
land use and land cover
Landsat
Landsat satellites
Metropolitan areas
multispectral
Radiation
Remote sensing
Satellite imagery
Sensors
spatiotemporal
Support vector machines
Sustainable development
Temperate forests
Urban areas
Urban development
urban forest
Urban forests
Urban sprawl
Urbanization
Vegetation
Water quality
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Summary:Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth’s unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16132419