The Dynamic of Vegetation Growth with Regular Climate and Climatic Fluctuations in a Subtropical Mountainous Island, Taiwan

Abstract

Vegetation phenology is an integrative indicator of environmental change, and remotely–sensed data provide a powerful way to monitor land surface vegetation responses to climatic fluctuations across various spatiotemporal scales. In this study, we synthesize the local climate, mainly temperature and precipitation, and large-scale atmospheric anomalies, El Niño-Southern Oscillation (ENSO)-connected dynamics, on a vegetative surface in a subtropical mountainous island, the northwest Pacific of Taiwan. We used two decadal photosynthetically active vegetation cover (PV) data (2001–2020) from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data to portray vegetation dynamics at monthly, seasonal, and annual scales. Results show that PV is positively related to both temperature and precipitation at a monthly timescale across various land cover types, and the log-linear with one-month lagged of precipitation reveals the accumulation of seasonal rainfall having a significant effect on vegetation growth. Using TIMESAT, three annual phenological metrics, SOS (start of growing season), EOS (end of growing season), and LOS (length of growing season), have been derived from PV time series and been related to seasonal rainfall. The delayed SOS was manifestly influenced by a spring drought, <40 mm during February–March. The later SOS led to a ramification on following late EOS, shorter LOS, and reduction of annual NPP. Nevertheless, the summer rainfall (August–October) and EOS had no significant effects on vegetation growth owing to abundant rainfall. Therefore, the SOS associated with spring rainfall, instead of EOS, played an advantageous role in regulating vegetation development in this subtropical island. The PCA (principal component analysis) was applied for PV time series and explored the spatiotemporal patterns connected to local climate and climatic fluctuations for entire Taiwan, North Taiwan, and South Taiwan. The first two components, PC1 and PC2, explained most of data variance (94–95%) linked to temporal dynamics of land cover (r > 0.90) which was also regulated by local climate. While the subtle signals of PC3 and PC4 explained 0.1–0.4% of the data variance, related to regional drought (r = 0.35–0.40) especially in central and southwest Taiwan and ENSO-associated rainfall variation (r = −0.40–−0.37). Through synthesizing the relationships between vegetation dynamics and climate based on multiple timescales, there will be a comprehensive picture of vegetation growth and its cascading effects on ecosystem productivity.

Cross‐scale assessments of the impacts and resilience of subtropical montane cloud forests to chronic seasonal droughts and episodic typhoons

“…The cross‐scale monitoring of temporal dynamics in vegetation growth and litterfall is crucial for understanding the impact of climatic fluctuations on terrestrial ecosystems (Lin et al., 2017; Wang et al., 2016; Wang & Chang, 2021). In this study, we found that the time‐lag effect plays a crucial role in coordinating the temporal dynamics of litterfall and the EVI.…”

“…In addition, it is imperative not only to investigate the impacts of drought or typhoons on MCFs but also to discern how MCFs respond to these disruptions. Understanding how disturbances specifically alter vegetation phenology remains a relatively unexplored area, even though changes in phenology provide valuable insights into the adaptive strategies of vegetation in response to climatic fluctuations (Wang & Chang, 2021). Moreover, delving into the responses of carbon dynamics within the ecosystems to these perturbations is pivotal for assessing how MCFs manage their carbon equilibrium in the face of such disruptions.…”

“…Eller et al (2016) found that cloud forest trees with higher foliar water uptake capacity and anisohydric behavior were vulnerable to the impacts of drought and climate change. Drought stress may exacerbate forest dieback (Allen et al, 2015;Anderegg et al, 2015) and suppress vegetation growth across different bioclimatic regions (Chang et al, 2018;Rimkus et al, 2017;Wang & Chang, 2021). However, the long-term impacts and resilience of MCFs to drought conditions remain unclear.…”

Visual Digital Forest Model Based on a Remote Sensing Data and Forest Inventory Data

“…To answer this question, specific goals and objectives for various types of research must be implemented. At the same time, a digital forest model based on remote sensing data and obtained inventory data will allow us to determine the resources that need to be carefully managed, realizing the vital ecosystem value of forests [52][53][54][55]. Despite the initial difficulties in building a forest model, the peculiarity of organizing and preserving digital data allows An essential task in developing the model was creating an environment for interaction with the model and its functional features; to implement this task, intelligent geoinformation system technologies were used [42][43][44].…”

Vertical Differences in the Long-Term Trends and Breakpoints of NDVI and Climate Factors in Taiwan

“…The temperature has long been the dominant control factor in global vegetation trends, especially in the higher latitude and altitude areas [6,7,12]. A recent study conducted by Wang and Chang (2021) [80] performed a detailed assessment of the relationships between vegetation, climatic factors, and non-climatic factors at multiple timescales across various land cover types in Taiwan; based on their findings, temperature and precipitation play important roles in vegetation growth; however, the effects vary across seasons. The conclusions of Wang and Chang (2021) [80] correspond to the statement presented by Piao et al (2014) [81], who recognized that the relationship between vegetation and climate factors might change over time due to the changes in climate and other environmental factors [81].…”