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Research Article

Estimating Seasonal Green View Index via Fusion of Sentinel-2 NDVI Time Series and High-Resolution Vegetation Mask

NDVI와 고해상도 식생 마스크를 활용한 계절별 Green View Index 추정

하승주1 · 강영옥2†

Seungjoo Ha1 · Youngok Kang2†

1이화여자대학교 사회과교육학과 지리학전공 석사과정
2이화여자대학교 사회과교육과/빅데이터 분석학 협동과정 교수
1M.A. Student, Dept. of Social Studies (Geography), Ewha Womans University
2Professor, Dept. of Social Studies(Geography) / Big Data Analytics, Ewha Womans University
31 December 2025. pp. 93~106
PDF Citation
Abstract
The Green View Index (GVI) quantifies the visibility of greenery from a pedestrian perspective and is widely used to assess walking environments. As the importance of pedestrian-view visual greenery has grown, there is a need to analyze seasonal diversity beyond the greenery-rich summer months. GVI is typically derived from street-view imagery; however, such imagery is often captured intensively in specific seasons and updated at roughly annual intervals, limiting its ability to reflect seasonal variability. This study proposes a framework for estimating the seasonal Green View Index (GVI) by fusing a Sentinel-2 NDVI time series with a high-resolution vegetation mask. Using the proposed approach, which incorporates seasonal NDVI, high-resolution vegetation cover, and cyclical month encoding, we achieved strong performance (R2 = 0.819, MAE = 0.038, r = 0.906). Improvements were especially pronounced in spring and autumn, when vegetation vigor is low but visually perceived greenery is salient. These results demonstrate that augmenting traditional satellite-based greenness metrics (NDVI) with high-resolution structural vegetation information and acquisition month substantially enhances the estimation of pedestrian-view GVI and its seasonal variability.
Keywords
Green View Index (GVI)
Urban greenery
Street view imagery
Satellite imagery
Remote sensing
NDVI
Semantic segmentation
Green View Index(GVI)는 보행자 관점의 녹지 가시성을 정량화하는 지표로 보행 환경 평가에 널리 활용된다. 최근 보행자 시점의 시각적 녹지 중요성이 대두되며 녹색이 많은 여름 외에도 녹지의 계절별 다양성을 분석할 필요성이 제기되고 있다. GVI는 주로 거리영상을 통해 생성되는데, 거리영상은 특정 계절에 집중적으로 촬영되고, 영상 갱신이 약 1년 주기로 이뤄지므로 계절에 따른 변동성을 반영하는 데에 한계가 있다. 본 연구는 위성영상에 기반한 NDVI 시계열과 고해상도 식생 마스크를 결합하여 GVI의 계절별 다양성을 추정하는 프레임워크를 제안한다. 제안한 방법론으로 GVI를 추정할 때, 계절별 NDVI, 고해상도 식생 커버, 월 변수를 포함할 경우 R2 = 0.819, MAE = 0.038, Pearson’s r = 0.906의 높은 성능을 달성하였다. 특히 식생 활력이 낮으나 시각적 식생이 두드러지는 봄·가을 GVI 추정 성능이 크게 향상되었다. 이는 전통적인 위성 기반 녹지 모니터링 지표인 NDVI에 고해상도 식생 마스크와 월 정보를 추가함으로써 보행자 시각 GVI 추정 성능을 유의하게 향상시킴을 입증한 것이라 할 수 있다.
키워드
Green View Index
도시 녹지
거리영상
위성 영상
원격탐사
NDVI
시멘틱 세그먼테이션
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Information
  • Publisher :The Korean Cartographic Association
  • Publisher(Ko) :한국지도학회
  • Journal Title :Journal of the Korean Cartographic Association
  • Journal Title(Ko) :한국지도학회지
  • Volume : 25
  • No :3
  • Pages :93~106
  • DOI :https://doi.org/10.16879/jkca.2025.25.3.093
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