2021
DOI: 10.2478/eces-2021-0008
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Quantifying Urban Vegetation Coverage Change with a Linear Spectral Mixing Model: A Case Study in Xi’an, China

Abstract: With the rapid development of urban area of Xi’an in recent years, the contradiction between ecological environmental protection and urban development has become prominent. The traditional remote sensing classification method has been unable to meet the accuracy requirements of urban vegetation monitoring. Therefore, how to quickly and accurately conduct dynamic monitoring of urban vegetation based on the spectral component characteristics of vegetation is urgent. This study used the data of Landsat 5 TM and L… Show more

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Cited by 4 publications
(2 citation statements)
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“…With the progress of urban construction and urban greening projects, Zhao and Liu [8] used Linear Spectral Mixing Model (LSMM) and variational domain raster analysis to analyse the spatial and temporal variation patterns of vegetation composition in urban Xi'an and its influencing factors based on a linear spectral mixture model. This study not only provides rapid information on the dynamic changes of urban vegetation, but also offers suggestions and data support for urban ecological and environmental protection planning and for the construction of a green city.…”
mentioning
confidence: 99%
“…With the progress of urban construction and urban greening projects, Zhao and Liu [8] used Linear Spectral Mixing Model (LSMM) and variational domain raster analysis to analyse the spatial and temporal variation patterns of vegetation composition in urban Xi'an and its influencing factors based on a linear spectral mixture model. This study not only provides rapid information on the dynamic changes of urban vegetation, but also offers suggestions and data support for urban ecological and environmental protection planning and for the construction of a green city.…”
mentioning
confidence: 99%
“…Between 2000 and 2020, the NDVI was calculated using Landsat (5, 8) satellite data, using equation ( 1), after the Landsat data had been processed to ensure precise NDVI rates were available. Digital signal processing was used to figure out this number from reflectance data in the (NIR) and (RED) wavelength bands (Zhao and Liu, 2021). Red-light absorption by chlorophyll and near-infrared reflection by mesophyll tissue in leaves causes brightness levels acquired by Landsat sensors in these bands to be varied.…”
Section: -Vegetation Changing Trend Analysis Based On Ndvimentioning
confidence: 99%