Forest Dynamics and Their Phenological Response to Climate Warming in the Khingan Mountains, Northeastern China

Cai, Hongyan; Zhang, Shuwen; Yang, Xiaohuan

doi:10.3390/ijerph9113943

Cited by 13 publications

(12 citation statements)

References 30 publications

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“…In the GKM, an advance in SOG and delay in EOG leaded to an obvious extension of GSL over the last 34 years. This result was consistent with previous findings, demonstrating that climate warming causes an extension of GSL [46,61].…”

Section: Effects Of Phenological Parameters and Fpar On Gppsupporting

confidence: 94%

“…Located in northeastern China, the Greater Khingan Mountains (GKM) is an important state-owned forestry base; also, it is a unique bright coniferous forest of the cool temperate zone in China, making up the southern boundary of the boreal forests in Eurasia. The GKM is said to be especially sensitive to climate change [45,46], and usually precedes low elevation regions in climate change. Over the past 70 years, this area has been increasingly affected by humans; many of these forests have been subjected to fire disasters and excessive deforestation.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Fan

Ren

et al. 2018

Remote Sensing

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Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982-2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.

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Section: Effects Of Phenological Parameters and Fpar On Gppsupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Fan

Ren

et al. 2018

Remote Sensing

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“…The MOD13Q1 is widely used for assessing vegetation phenology e.g. [28,32,40,62,71,78,81,100,102]. In order to calculate vegetation index from the phenocam imagery, the Normalized Green-Red Difference Index (NGRDI = (DN G − DN R )/(DN G + DN R ), [103]) was computed from the split Red, Green, and Blue phenocam images.…”

Section: Figurementioning

confidence: 99%

“…The EVI from the resulting smoothed data was used to calculate the phenological parameters. Four parameters were retrieved: (1) Start of the season (SOS) defined as the time at which the EVI value was 20% [29,32,62,81] of the distance between the prior minimum EVI and peak EVI level, (2) End of the season (EOS) defined as the time at which the EVI was 20% of the EVI value between the post season minimum and peak EVI level (same criterion as was used for SOS above), (3) Peak of the season (POS) is the time at which maximum EVI value is reached, between SOS and EOS, i.e., the vegetation has maximum productivity [113], (4) Length of the season (LOS) is the difference, in months, between SOS and EOS [29,77]. A 3-year EVI time-series was used to calculate the phenological characteristics, e.g., to extract the phenology metrics for 2007, the images in years 2006, 2007, and 2008 were needed.…”

Section: Phenology Parameters Extractionmentioning

confidence: 99%

Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand

et al. 2019

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Vegetation phenology is the annual cycle timing of vegetation growth. Mangrove phenology is a vital component to assess mangrove viability and includes start of season (SOS), end of season (EOS), peak of season (POS), and length of season (LOS). Potential environmental drivers include air temperature (Ta), surface temperature (Ts), sea surface temperature (SST), rainfall, sea surface salinity (SSS), and radiation flux (Ra). The Enhanced vegetation index (EVI) was calculated from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD13Q1) data over five study sites between 2003 and 2012. Four of the mangrove study sites were located on the Malay Peninsula on the Andaman Sea and one site located on the Gulf of Thailand. The goals of this study were to characterize phenology patterns across equatorial Thailand Indo-Malay mangrove forests, identify climatic and aquatic drivers of mangrove seasonality, and compare mangrove phenologies with surrounding upland tropical forests. Our results show the seasonality of mangrove growth was distinctly different from the surrounding land-based tropical forests. The mangrove growth season was approximately 8–9 months duration, starting in April to June, peaking in August to October and ending in January to February of the following year. The 10-year trend analysis revealed significant delaying trends in SOS, POS, and EOS for the Andaman Sea sites but only for EOS at the Gulf of Thailand site. The cumulative rainfall is likely to be the main factor driving later mangrove phenologies.

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“…13,14 Others have used Earth system observations to link increasing temperature with earlier onset of start of spring (SOS) in large geographic areas covered with temperate deciduous forest. [15][16][17] While the methods of studies that used historical records of in situ observations made at the individual plant-species level are distinctly different from those based on remote sensing observations of ecosystems or regions, both sets of studies clearly point to a direct association between increasing temperature and earlier SOS in the northern hemisphere. Others have suggested such phenological changes to be the most sensitive indicator of ecological response to climate change.…”

Section: Introductionmentioning

confidence: 99%

Association Between Changes in Timing of Spring Onset and Asthma Hospitalization in Maryland

Sapkota

Dong

et al. 2020

JAMA Netw Open

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IMPORTANCE Ongoing climate change is affecting the health of communities across the globe. While direct consequences, including morbidity and mortality tied to increases in the frequency of extreme weather events, have received significant attention, indirect health effects, particularly those associated with climate change-driven disruptions in ecosystems, are less understood. OBJECTIVE To investigate how ongoing changes in the timing of spring onset related to climate change are associated with rates of asthma hospitalization in Maryland. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study of 29 257 patients with asthma used general additive (quasi Poisson) and mixed-effect (negative binomial) models to investigate the association between changes in the timing of spring onset, detected using satellite observations, and the risk of asthma hospitalization in Maryland from 2001 to 2012. Data analysis was conducted from January 2016 to March 2019. EXPOSURES Phenology data, derived from the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer, were used to calculate location-specific median dates for start of season from 2001 to 2012. How the start of season for a given year and location deviated from the long-term average was calculated and categorized as very early, early, normal, or late. MAIN OUTCOMES AND MEASURES Daily asthma hospitalization in Maryland during the spring season (ie, March to May). RESULTS There were 108 358 total asthma hospitalizations during the study period, of which 29 257 (27.0%; 14 379 [49.1%] non-Hispanic black patients; 17 877 [61.1%] women) took place duringspringtime. In the unadjusted model, very early (incident rate ratio [IRR], 1.17; 95% CI, 1.07-1.28) and late (IRR, 1.07; 95% CI, 1.00-1.15) onset of spring were associated with increased risk of asthma hospitalization. When the analysis was adjusted for extreme heat events and concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm, the risk remained significant for very early spring onset (IRR, 1.10; 95% CI, 1.02-1.20) but not for late spring onset (IRR, 1.03; 95% CI, 0.97-1.11). CONCLUSIONS AND RELEVANCEThese results suggest that ongoing changes in the timing of spring onset, which are related to climate variability and change, are associated with asthma hospitalization. Given the high burden of allergic diseases and the number of individuals sensitized to tree pollen, these findings serve as a wake-up call to public health and medical communities (continued) Key Points Question Are changes in timing of spring onset related to ongoing climate variability and change associated with rates of asthma hospitalization during the spring season? Findings In this cross-sectional study of 29 257 patients with asthma, very early onset of spring was associated with a 17% increase in hospital admissions for asthma and late onset of spring was associated with a 7% increase. Meaning In this study, the observed associations between changes in timing of spring onset and ...

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Forest Dynamics and Their Phenological Response to Climate Warming in the Khingan Mountains, Northeastern China

Cited by 13 publications

References 30 publications

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand

Association Between Changes in Timing of Spring Onset and Asthma Hospitalization in Maryland

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