NDLI: Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

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Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Content Provider	MDPI
Author	Park, Sang-Jin Jeong, Seung-Gyu Park, Yong Kim, Sang-Hyuk Lee, Dong-Kun Mo, Yong-Won Jang, Dong-Seok Park, Kyung-Min
Copyright Year	2021
Description	Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R 2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.
Starting Page	286
e-ISSN	19994907
DOI	10.3390/f12030286
Journal	Forests
Issue Number	3
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2021-03-02
Access Restriction	Open
Subject Keyword	Forests Remote Sensing Phenological Analysis Spatiotemporal Data Fusion Landsat Modis Jeju Island Ndvi Climate Change
Content Type	Text
Resource Type	Article

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