Special Issue "Assessing Forest Landscape-Level Responses to Climate Change by Remote Sensing Analysis and Ecological Modelling"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editor

Prof. Dr. Teng-Chiu Lin
E-Mail Website
Guest Editor
Department of Life Science, National Taiwan Normal University, Taipei 11677, Taiwan
Interests: disturbance ecology; forest ecology; ecosystem ecology; environmental science
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Exploration of the ecological effects of climate change is among the most urgent challenges in global change ecology. Ecosystems are interconnected in multiple ways at the landscape level such that a landscape-level approach is necessary to assess the ecological responses to climate change. Due to the broad spatial coverage, frequent lack of replicates, and uncertainties and complexity of climate effects, remote sensing analysis and ecological modeling are of increasing importance in the assessment of landscape-level responses to climate change.

This Special Issue welcome research using broadly defined remote sensing techniques and/or the application of ecological modeling to assess and/or predict climate effects on structure and function of all types of landscapes. We welcome a wide range of submissions including but not limited to the application of rapidly evolving remotely sensed data and diverse ecological modeling (e.g., niche modeling, modeling involving graph theory, Bayesian belief networks, Markov chain, etc.) to assess broad-scale responses to climate change. Submissions focusing on methodological development in remote sensing techniques and/or ecological modeling approaches for assessing landscape–climate interactions are also encouraged. In addition, we welcome both original studies and reviews and syntheses so long as they fit the main theme of this Special Issue.

Prof. Dr. Teng-Chiu Lin
Guest Editor

Manuscript Submission Information

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Keywords

  • climate change
  • ecological modeling
  • landscape
  • resilience
  • Markov chain
  • graph theory
  • Bayesian belief network
  • niche modeling
  • vegetation index

Published Papers (3 papers)

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Research

Article
The Dynamic of Vegetation Growth with Regular Climate and Climatic Fluctuations in a Subtropical Mountainous Island, Taiwan
Remote Sens. 2021, 13(16), 3298; https://doi.org/10.3390/rs13163298 - 20 Aug 2021
Viewed by 552
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 [...] Read more.
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. Full article
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Article
Cold Wave-Induced Reductions in NDII and ChlRE for North-Western Pacific Mangroves Varies with Latitude and Climate History
Remote Sens. 2021, 13(14), 2732; https://doi.org/10.3390/rs13142732 - 12 Jul 2021
Viewed by 657
Abstract
Mangrove forests growing at the poleward edges of their geographic distribution are occasionally subject to freezing (<0 °C) and cold wave (>0 °C) events. Cold wave effects on mangrove trees are well documented and adaptation to cold stress has been reported for local [...] Read more.
Mangrove forests growing at the poleward edges of their geographic distribution are occasionally subject to freezing (<0 °C) and cold wave (>0 °C) events. Cold wave effects on mangrove trees are well documented and adaptation to cold stress has been reported for local mangrove populations in the North Atlantic. However, there is less understanding of effects of cold waves on mangroves in the northern Pacific, especially at the regional scale. Moreover, it is unclear if cold tolerant mangrove species of North Asia display variation in resistance to cold temperatures across their geographic distribution. Using a cold wave event that occurred in January 2021, we evaluated the effects of low temperatures on vegetation index (VI) change (relative to a recent five-year baseline) for mangrove forests dominated by Kandelia obovata (Rhizophoraceae) and Avicennia marina (Acanthaceaee) at the northern edge of their geographical range. We used two VIs derived from Sentinel-2 imagery as indicators for canopy health: the normalized difference infrared index (NDII) and the chlorophyll red-edge index (ChlRE), which reflect forest canopy water content and chlorophyll concentration, respectively. We isolated the cold wave effects on the forest canopy from phenology (i.e., cold wave induced deviation from a five-year baseline) and used multiple linear regression to identify significant climatic predictors for the response of mangrove forest canopy VI change to low temperatures. For areas where the cold wave resulted in temperatures <10 °C, immediate decreases in both VIs were observed, and the VI difference relative to the baseline was generally greater at 30-days after the cold wave than when temperatures initially recovered to baseline values, showing a slight delay in VI response to cold wave-induced canopy damage. Furthermore, the two VIs did not respond consistently suggesting that cold-temperature induced changes in mangrove canopy chlorophyll and water content are affected independently or subject to differing physiological controls. Our results confirm that local baseline (i.e., recent past) climate predicts canopy resistance to cold wave damage across K. obovata stands in the northern Pacific, and in congruence with findings from New World mangroves, they imply geographic variation in mangrove leaf physiological resistance to cold for Northern Pacific mangroves. Full article
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Article
Climatic Regulation of Vegetation Phenology in Protected Areas along Western South America
Remote Sens. 2021, 13(13), 2590; https://doi.org/10.3390/rs13132590 - 02 Jul 2021
Viewed by 621
Abstract
Using 19 years of remotely sensed Enhanced Vegetation Index (EVI), we examined the effects of climatic variability on terrestrial vegetation of six protected areas along southwestern South America, from the semiarid edge of the Atacama desert to southern Patagonia (30°S– [...] Read more.
Using 19 years of remotely sensed Enhanced Vegetation Index (EVI), we examined the effects of climatic variability on terrestrial vegetation of six protected areas along southwestern South America, from the semiarid edge of the Atacama desert to southern Patagonia (30°S–51°S). The relationship between satellite phenology and climate indices, namely MEI (Multivariate ENSO Index), PDO (Pacific Decadal Oscillation) and SAM (Southern Annular Mode) were established using statistical analyses for non-stationary patterns. The annual mode of phenological activity fluctuated in strength through time from the semiarid region to the border of southern Patagonia. Concomitantly, enhanced synchrony between EVI and climatic oscillations appeared over interannual cycles. Cross correlations revealed that variability in MEI was the lead predictor of EVI fluctuations over scales shorter than 4 months at lower latitudes and for the most poleward study site. The PDO was correlated with EVI over lags longer than 4 months at low latitude sites, while the SAM showed relationships with EVI only for sites located around 40°S. Our results indicate that the long-term phenological variability of the vegetation within protected areas along southwestern South America is controlled by processes linked to climate indices and that their influence varies latitudinally. Further studies over longer time scales will be needed to improve our understanding the impacts of climate change on vegetation condition and its effect over phenological variability. Full article
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