Exploring Long Term Spatial Vegetation Trends in Taiwan from AVHRR NDVI3g Dataset Using RDA and HCA Analyses

Abstract

Due to 4000 m elevation variation with temperature differences equivalent to 50 degrees of latitudinal gradient, exploring Taiwan's spatial vegetation trends is valuable in terms of diverse ecosystems and climatic types covering a relatively small island with an area of 36,000 km 2 . This study analyzed Taiwan's spatial vegetation trends with controlling environmental variables through redundancy (RDA) and hierarchical cluster (HCA) analyses over three decades of monthly normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) NDVI3g data for 19 selected weather stations over the island. Results showed two spatially distinct vegetation response groups. Group 1 comprises weather stations which remained relatively natural showing a slight increasing NDVI tendency accompanied with rising temperature, whereas Group 2 comprises stations with high level of human development showing a slight decreasing NDVI tendency associated with increasing temperature-induced moisture stress. Statistically significant controlling variables include climatic factors (temperature and precipitation), orographic factors (mean slope and aspects), and anthropogenic factor (population density). Given the potential trajectories for future warming, variable precipitation, and population pressure, challenges, such as land-cover and water-induced vegetation stress, need to be considered simultaneously for establishing adequate adaptation strategies to combat climate change challenges in Taiwan.

Screening of Tree Species for Improving Outdoor Human Thermal Comfort in a Taiwanese City

“…Therefore, SVF may be an integrated indicator to describe the crown characteristics. Hierarchical cluster analysis (HCA) is suitable for clustering targets with an unknown number of groups [29], and has been applied to identify pollution sources and assess rice production risks on the basis of the similarity of observations [30][31][32]. In addition, previous studies on the planting design of green spaces have investigated the visual effects, but not the functions, of plants.…”

Penentuan Model Matematis Yang Optimal Suhu Permukaan Laut Di Pantai Utara Gresik Berbasis Nilai Reflektan Citra Satelit Aqua Modis

“…Pengukuran SPL sudah sangat banyak dilakukan dimana untuk kurun waktu beberapa tahun terakhir pemetaan SPL sudah dilakukan dengan pemanfaatan citra satelit AVHRR dari NOAA (Kusuma, 2008;Pareeth et al, 2016;Tsai, Lin & Yang, 2016). Adapun pemetaan SPL juga dilakukan dengan menggunakan citra satelit Aqua dan Terra Modis (Hosoda, Murakami, Sakaida, & Kawamura, 2007;Koner & Harris, 2016a;Liang et al, 2017).…”

Cluster and Redundancy Analyses of Taiwan Upstream Watersheds Based on Monthly 30 Years AVHRR NDVI3g Data

“…Numerous studies utilize continuous NDVI time-series data to analyze the relationship between vegetation and climatic factors such as rainfall, temperature, sunshine duration, and cloud amount [37][38][39][40][41]. Tsai et al [42] analyze Taiwan's spatial vegetation trends with controlling environmental variables, including temperature, precipitation, slope, aspects, and population density, based on the three-decades-long Advanced Very-High-Resolution Radiometer (AVHRR) NDVI3g data from 1982 to 2012 derived from 19 selected weather stations. Additionally, many scholars use NDVI to investigate natural disaster responses such as landslides in Taiwan [43][44][45].…”