Abstract
Evergreen forest provides essential ecosystem services such as maintaining the balance in carbon and oxygen cycles and air purification. However, under cloudy and rainy weather conditions, it is difficult to obtain optical remote sensing images with high spatial resolution and complete time series. In addition, surfaces underlying the forest canopy can be complex and fragmented. To solve this problem, we developed a new approach (NDVI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CV</sub> -LS) for mapping urban evergreen forest at the subpixel scale. In order to capture more accurate growth characteristics of evergreen forest, we harmonized Landsat-8 and Sentinel-2 images with cloud cover less than 10% acquired within 1 year to denser the time-series dataset. In view of the time series fluctuation stability of evergreen forest, the coefficient of variation (CV) of the normalized difference vegetation index (NDVI) was used to distinguish evergreen forest from other vegetation. Meanwhile, the annual minimum NDVI (NDVI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ann-min</sub> ) was used as the parameter in a dimidiate pixel model for estimating fractional evergreen forest cover (FVC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ever</sub> ). Hefei, a cloudy and rainy subtropical city in China, was selected as a case study to evaluate the validity of the model. The verification results revealed that harmonizing Landsat-8 and Sentinel-2 time-series images to extract evergreen forest improved the overall accuracy by 8% compared with using Landsat-8 images alone, indicating that the NDVI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CV</sub> -LS model can improve the accuracy of FCV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ever</sub> estimation, especially for areas with complex underlying surfaces under cloudy and rainy conditions.
Highlights
V ARIATION in forest types shape the structure of the forest landscape [1] which largely determines forest ecosystemManuscript received December 11, 2020; revised February 1, 2021 and February 25, 2021; accepted March 4, 2021
From the results of simulation analysis, it can be checked that the coefficient of variation (CV) decreases with increasing evergreen forest proportion, whereas the NDVIann-min increases with increasing evergreen forest proportion
With an evergreen forest proportion of 1, the NDVIann-min associated with impervious areas was 0.51, whereas that associated with nonimpervious areas was 0.54
Summary
V ARIATION in forest types shape the structure of the forest landscape [1] which largely determines forest ecosystemManuscript received December 11, 2020; revised February 1, 2021 and February 25, 2021; accepted March 4, 2021. Date of publication March 9, 2021; date of current version April 1, 2021. A good understanding of the spatial distribution of different forest types contributes to better forest ecosystem and land space management [2], which facilitates the protection of wildlife habitats and biodiversity [3], [4]. Evergreen forest is an important part of the subtropical urban ecosystem. Evergreen trees contribute to air purification, cooling, humidification, water and soil conservation, and other ecological services [6]–[8]. They play an irreplaceable role in maintaining the carbon balance of urban ecosystems and protecting the urban ecological environment [9]. It is vital to have a comprehensive understanding of evergreen forest distribution
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
