Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization

Urbanization and the resulting increase in development areas and populations cause micro-climate changes such as the urban heat island (UHI) effect. This micro-climate change can affect vegetation phenology. It can advance leaf unfolding and flowering and delay the timing of fallen leaves. This study was carried out to clarify the impact of urbanization on the leaf unfolding of Mongolian oak. The survey sites for this study were established in the urban center (Mts. Nam, Mido, and Umyeon in Seoul), suburbs (Mts. Cheonggye and Buram in Seoul), a rural area (Gwangneung, Mt. Sori in Gyeonggi-do), and a natural area (Mt. Jeombong in Gangwon-do). Green-up dates derived from the analyses of digital camera images and MODIS satellite images were the earliest in the urban center and delayed through the suburbs and rural area to the natural area. The difference in the observed green-up date compared to the expected one, which was determined by regarding the Mt. Jeombong site located in the natural area as the reference site, was the biggest in the urban center and decreased through the suburbs and rural area to the natural area. Green-up dates in the rural area, suburbs, and urban center were earlier by 11.0, 14.5, and 16.3 days than the expected ones. If these results are transformed into the air temperature based on previous research results, it could be deduced that the air temperature in the urban center, suburbs, and rural area rose by 3.8 to 4.6 °C, 3.3 to 4.1 °C, and 2.5 to 3.1 °C, respectively. Green-up dates derived based on the accumulated growing degree days (AGDD) showed the same trend as those derived from the image interpretation. Green-up dates derived from the change in sap flow as a physiological response of the plant showed a difference within one day from the green-up dates derived from digital camera and MODIS satellite image analyses. The change trajectory of the curvature K value derived from the sap flow also showed a very similar trend to that of the curvature K value derived from the vegetation phenology. From these results, we confirm the availability of AGDD and sap flow as tools predicting changes in ecosystems due to climate change including phenology. Meanwhile, the green-up dates in survey sites were advanced in proportion to the land use intensity of each survey site. Green-up dates derived based on AGDD were also negatively correlated with the land use intensity of the survey site. This result implies that differences in green-up dates among the survey sites and between the expected and observed green-up dates in the urban center, suburbs, and rural area were due to the increased temperature due to land use in the survey sites. Based on these results, we propose conservation and restoration of nature as measures to reduce the impact of climate change.

[1]  Elisabeth M. Hamin,et al.  Urban form and climate change: Balancing adaptation and mitigation in the U.S. and Australia , 2009 .

[2]  B. Stone.  Urban and rural temperature trends in proximity to large US cities: 1951–2000 , 2007 .

[3]  H. Dixon,et al.  Ascent of Sap , 1894, Nature.

[4]  J. Peñuelas,et al.  Responses to a Warming World , 2001, Science.

[5]  Annette Menzel,et al.  Can spatial data substitute temporal data in phenological modelling? A survey using birch flowering. , 2013, Tree physiology.

[6]  N. Holbrook,et al.  Dry-season leaf flushing of Enterolobium cyclocarpum (ear-pod tree): above- and below-ground phenology and water relations. , 2007, Tree physiology.

[7]  Jiaxing Huang,et al.  Self-Healing Microcapsule-Thickened Oil Barrier Coatings , 2019, Research.

[8]  Dave I. Thompson,et al.  Plant phenology and climate change , 2015 .

[9]  Mark A. Friedl,et al.  Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery , 2014 .

[10]  D. Vodnik,et al.  Chronological Sequence of Leaf Phenology, Xylem and Phloem Formation and Sap Flow of Quercus pubescens from Abandoned Karst Grasslands , 2017, Front. Plant Sci..

[11]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[12]  Lei Tian,et al.  A promising trend for field information collection: An air-ground multi-sensor monitoring system , 2018, Information Processing in Agriculture.

[13]  Mike Hutchins,et al.  The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom , 2017 .

[14]  Annette Menzel,et al.  Urban phenological studies - Past, present, future. , 2015, Environmental pollution.

[15]  A. Arnfield.  Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island , 2003 .

[16]  J. Amthor.  Terrestrial higher‐plant response to increasing atmospheric [CO2] in relation to the global carbon cycle , 1995 .

[17]  N. Grimm,et al.  Global Change and the Ecology of Cities , 2008, Science.

[18]  Nam-shin Kim,et al.  A Study on Changes of Phenology and Characteristics of Spatial Distribution Using MODIS Images , 2013 .

[19]  Otso Ovaskainen,et al.  Community-level phenological response to climate change , 2013, Proceedings of the National Academy of Sciences.

[20]  Yong-Chan Cho,et al.  Restoration planning for the Seoul Metropolitan Area, Korea. , 2008 .

[21]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[22]  Richard A. Houghton,et al.  Land‐use change and the carbon cycle , 1995 .

[23]  C. Körner,et al.  Physiological minimum temperatures for root growth in seven common European broad-leaved tree species. , 2014, Tree physiology.

[24]  Reiko Ide,et al.  Use of digital cameras for phenological observations , 2010, Ecol. Informatics.

[25]  C. Kucharik,et al.  Urban heat island impacts on plant phenology: intra-urban variability and response to land cover , 2016 .

[26]  S. Frolking,et al.  Exploring diurnal cycles of surface urban heat island intensity in Boston with land surface temperature data derived from GOES-R geostationary satellites. , 2020, The Science of the total environment.

[27]  M. Adams,et al.  An improved heat pulse method to measure low and reverse rates of sap flow in woody plants. , 2001, Tree physiology.

[28]  Ricardo da Silva Torres,et al.  Leafing Patterns and Drivers across Seasonally Dry Tropical Communities , 2019, Remote. Sens..

[29]  Qi Fu,et al.  Spatiotemporal Dynamics of Carbon Storage in Response to Urbanization: A Case Study in the Su-Xi-Chang Region, China , 2019, Processes.

[30]  Youngryel Ryu,et al.  The influence of wind flows on thermal comfort in the Daechung of a traditional Korean house , 2009 .

[31]  R. Primack,et al.  The impact of climate change on cherry trees and other species in Japan , 2009 .

[32]  Chi-Ru Chang,et al.  A preliminary study on the local cool-island intensity of Taipei city parks , 2007 .

[33]  Keith C. Clarke,et al.  The impact of urbanization and climate change on ecosystem services: A case study of the city belt along the Yellow River in Ningxia, China , 2019, Comput. Environ. Urban Syst..

[34]  Jianguo Wu,et al.  Effects of urbanization on plant flowering phenology: A review , 2006, Urban Ecosystems.

[35]  Sunyurp Park,et al.  Cloud and cloud shadow effects on the MODIS vegetation index composites of the Korean Peninsula , 2013 .

[36]  I. Urban.  Urbanization and Climate Change , 2014 .

[37]  W. Nel,et al.  Spatiotemporal monitoring of land surface temperature and estimated radiation using remote sensing: human health implications for East London, South Africa , 2018, Environmental Earth Sciences.

[38]  Hengzhou Xu,et al.  Effects of land urbanization and land finance on carbon emissions: A panel data analysis for Chinese provinces , 2017 .

[39]  C. Lim,et al.  Forest Decline Under Progress in the Urban Forest of Seoul, Central Korea , 2019, Forest Degradation Around the World.

[40]  Shuguang Liu,et al.  Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. , 2016 .

[41]  Junxiang Li,et al.  Impacts of Urbanization on Vegetation Phenology over the Past Three Decades in Shanghai, China , 2017, Remote. Sens..

[42]  Mark A. Friedl,et al.  Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology , 2012 .

[43]  Yuyu Zhou,et al.  Urban warming advances spring phenology but reduces the response of phenology to temperature in the conterminous United States , 2020, Proceedings of the National Academy of Sciences.

[44]  Ji Zhou,et al.  Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation , 2018 .

[45]  K. Beurs,et al.  Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology , 2012 .

[46]  Fossil Fuels.  Fossil Fuels , 2019, Washington Information Directory 2019–2020.

[47]  Dennis D. Baldocchi,et al.  Phenology of Vegetation Photosynthesis , 2003 .

[48]  Jin Wu,et al.  Investigating the urban-induced microclimate effects on winter wheat spring phenology using Sentinel-2 time series , 2020 .

[49]  J. Peñuelas,et al.  Changed plant and animal life cycles from 1952 to 2000 in the Mediterranean region , 2002 .

[50]  P. Campanello,et al.  Leaf phenology and water-use patterns of canopy trees in Northern Argentinean subtropical forests , 2018, Tree physiology.

[51]  Rebecca E. Irwin,et al.  Phenological change in a spring ephemeral: implications for pollination and plant reproduction , 2016, Global change biology.

[52]  Dev Niyogi,et al.  Meta-analysis of urbanization impact on rainfall modification , 2019, Scientific Reports.

[53]  N. L. Bradley,et al.  Phenological changes reflect climate change in Wisconsin. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[54]  O. Hoegh‐Guldberg,et al.  Ecological responses to recent climate change , 2002, Nature.

[55]  Chi Hong Lim,et al.  Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea , 2020, Remote. Sens..

[56]  Geoffrey M. Henebry,et al.  A Comparison of Multiple Datasets for Monitoring Thermal Time in Urban Areas over the U.S. Upper Midwest , 2016, Remote. Sens..

[57]  A. Pathirana,et al.  Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam , 2011 .

[58]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[59]  Yanga A. Willie,et al.  Monitoring spatial pattern of land surface thermal characteristics and urban growth: A case study of King Williams using remote sensing and GIS , 2019, Earth Science Informatics.

[60]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[61]  T. Oke.  The urban energy balance , 1988 .

[62]  Andrew D Richardson,et al.  Near-surface remote sensing of spatial and temporal variation in canopy phenology. , 2009, Ecological applications : a publication of the Ecological Society of America.

[63]  Tom Milliman,et al.  Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing , 2018, Scientific Reports.

[64]  Jiangong Liu,et al.  Monitoring mangrove phenology using camera images , 2020, IOP Conference Series: Earth and Environmental Science.

[65]  Yanhong Tang,et al.  Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis? , 2014 .

[66]  K. Seto,et al.  Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools , 2012, Proceedings of the National Academy of Sciences.

[67]  L. Kleerekoper,et al.  How to make a city climate-proof, addressing the urban heat island effect , 2012 .

[68]  C. Lim,et al.  Ecological consideration for several methodologies to diagnose vegetation phenology , 2018, Ecological Research.

[69]  R. Pielke.  Land Use and Climate Change , 2005, Science.

[70]  C. D. Keeling,et al.  Increased activity of northern vegetation inferred from atmospheric CO2 measurements , 1996, Nature.

[71]  T. Yue,et al.  Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China. , 2019, The Science of the total environment.

[72]  W. Kuttler,et al.  Counteracting urban climate change: adaptation measures and their effect on thermal comfort , 2013, Theoretical and Applied Climatology.

[73]  R. Pawankar,et al.  Climate Change and Air Pollution: Effects on Respiratory Allergy , 2016, Allergy, asthma & immunology research.

[74]  M. O. Obiakor,et al.  Effects of Vegetated and Synthetic (Impervious) Surfaces on the Microclimate of Urban Area , 2012 .

[75]  M. Friedl,et al.  Interactions between urban vegetation and surface urban heat islands: a case study in the Boston metropolitan region , 2016 .

[76]  D. Hollinger,et al.  Use of digital webcam images to track spring green-up in a deciduous broadleaf forest , 2007, Oecologia.

[77]  S. Brady,et al.  Plant developmental responses to climate change. , 2016, Developmental biology.

[78]  Koen Hufkens,et al.  Testing Hopkins’ Bioclimatic Law with PhenoCam data , 2019, Applications in plant sciences.

[79]  M. Rossini,et al.  Using digital camera images to analyse snowmelt and phenology of a subalpine grassland , 2014 .

[80]  T. Sparks,et al.  Plant development scores from fixed-date photographs: the influence of weather variables and recorder experience , 2006, International journal of biometeorology.

[81]  Hua Zheng,et al.  Urbanization Impacts on Natural Habitat and Ecosystem Services in the Guangdong-Hong Kong-Macao “Megacity” , 2020, Sustainability.

[82]  I. Wing,et al.  Net carbon uptake has increased through warming-induced changes in temperate forest phenology , 2014 .

[83]  Dan Zhang,et al.  Estimation of the Relationship between Urban Park Characteristics and Park Cool Island Intensity by Remote Sensing Data and Field Measurement , 2013 .

[84]  Fumiaki Fujibe,et al.  Urban warming in Japanese cities and its relation to climate change monitoring , 2011 .

[85]  Thales Sehn Korting,et al.  Environmental Drivers of Water Use for Caatinga Woody Plant Species: Combining Remote Sensing Phenology and Sap Flow Measurements , 2020, Remote. Sens..

[86]  H. Kashiwagi,et al.  Atmospheric carbon dioxide and climate change since the Late Jurassic (150 Ma) derived from a global carbon cycle model , 2016 .