Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized ...Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.展开更多
The Qilian Mountains(QM)possess a delicate vegetation ecosystem,amplifying the evident response of vegetation phenology to climate change.The relationship between changes in vegetation growth and climate remains compl...The Qilian Mountains(QM)possess a delicate vegetation ecosystem,amplifying the evident response of vegetation phenology to climate change.The relationship between changes in vegetation growth and climate remains complex.To this end,we used MODIS NDVI data to extract the phenological parameters of the vegetation including meadow(MDW),grassland(GSD),and alpine vegetation(ALV))in the QM from 2002 to 2021.Then,we employed path analysis to reveal the direct and indirect impacts of seasonal climate change on vegetation phenology.Additionally,we decomposed the vegetation phenology in a time series using the trigonometric seasonality,Box-Cox transformation,ARMA errors,and Trend Seasonal components model(TBATS).The findings showed a distinct pattern in the vegetation phenology of the QM,characterized by a progressive shift towards an earlier start of the growing season(SOS),a delayed end of the growing season(EOS),and an extended length of the growing season(LOS).The growth cycle of MDW,GSD,and ALV in the QM species is clearly defined.The SOS for MDW and GSD occurred earlier,mainly between late April and August,while the SOS for ALVs occurred between mid-May and mid-August,a one-month delay compared to the other vegetation.The EOS in MDW and GSD were concentrated between late August and April and early September and early January,respectively.Vegetation phenology exhibits distinct responses to seasonal temperature and precipitation patterns.The advancement and delay of SOS were mainly influenced by the direct effect of spring temperatures and precipitation,which affected 19.59%and 22.17%of the study area,respectively.The advancement and delay of EOS were mainly influenced by the direct effect of fall temperatures and precipitation,which affected 30.18%and 21.17%of the area,respectively.On the contrary,the direct effects of temperature and precipitation in summer and winter on vegetation phenology seem less noticeable and were mainly influenced by indirect effects.The indirect effect of winter precipitation is the main factor affecting the advance or delay of SOS,and the area proportions were 16.29%and 23.42%,respectively.The indirect effects of fall temperatures and precipitation were the main factors affecting the delay and advancement of EOS,respectively,with an area share of 15.80%and 21.60%.This study provides valuable insight into the relationship between vegetation phenology and climate change,which can be of great practical value for the ecological protection of the Qinghai-Tibetan Plateau as well as for the development of GSD ecological animal husbandry in the QM alpine pastoral area.展开更多
Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate.However,the effects of different intensitie...Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate.However,the effects of different intensities of extreme climatic events on vegetation phenology remain poorly understood.Using a long-term solar-induced chlorophyll fluorescence dataset,we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau(TP)from 2000 to 2018.We found that the effect of maximum temperature exposure days(TxED)and minimum temperature exposure days(TnED)on the start of the growing season(SOS)was initially delayed and shifted to advance along the increasing temperature gradients.However,the response of the end of the growing season(EOS)to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached,above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early end.The corresponding maximum and minimum temperature thresholds were 10.12 and 2.54℃,respectively.In contrast,cumulative precipitation(CP)was more likely to advance SOS and delay EOS as the precipitation gradient increased,but the advance of SOS is gradually weakening.Four vegetation types(i.e.,forest,shrubland,meadow,and steppe)showed similar trends in response to different climates,but the optimal climatic conditions varied between the vegetation types.Generally,meadow and steppe had lower optimal temperatures and precipitation than forest and shrubland.These findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities,implying that the SOS will continue to advance with warming,whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming.展开更多
Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized ...Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized difference vegetation index)data,this study analysed the spatial and temporal patterns of snow phenology including snow onset date,snow end date,snow cover days,and vegetation phenology including the start of growing season,the end of growing season and the length of growing season in the Chinese Tianshan Mountainous Region(CTMR)from 2002 to 2018,and then investigated the snow phenological effects on the vegetation phenology among different ecogeographic zones and diverse vegetation types.The results indicated that snow onset date was earlier at higher elevations and later at lower elevations,while snow end date showed opposite spatial distribution characteristics.The end of growing season occurred later on the northwest slope of the CTMR and the Yili Valley.The earliest end of growing season was in the middle part of the CTMR.A long growing season was mainly distributed along the northern slope and the Yili Valley,while a short growing season was concentrated in the middle part of the CTMR.The response of vegetation phenology to changes in snow phenology varied among vegetation types and ecogeographic zones.The effect of snow phenology on vegetation phenology was more significant in IID5(Yili Valley)than in the other ecogeographic zones.A negative correlation was observed between the start of growing season and snow end date in nearly 54.78%of the study area,while a positive correlation was observed between the start of growing season and the snow end date in 66.85%of the study area.The sensitivity of vegetation phenology to changes in snow cover varied among different vegetation types.Snow onset date had the greatest effect on the start of growing season in the four vegetation cover types(alpine meadows,alpine steppes,shrubs,and alpine sparse vegetation),whereas the snow cover days had the least impact.Snow end date had the greatest impact on the end of growing season in the alpine steppes and shrub areas.The study results are helpful for understanding the vegetation dynamics under ongoing climate change,and can benefit vegetation management and pasture development in the CTMR.展开更多
Enhanced vegetation index(EVI)data can be used to identify and define the space in which ungulates practice parturition and encounter predation.This study explores the use of EVI data to identify landscapes linked to ...Enhanced vegetation index(EVI)data can be used to identify and define the space in which ungulates practice parturition and encounter predation.This study explores the use of EVI data to identify landscapes linked to ungulate parturition and predation events across space,time,and environmental conditions.As a case study,we used the moose population(Alces alces)of northern Minnesota in the USA.Using remotely sensed EVI data rasters and global positioning system collar data,we quantified how vegetation phenology and moose movement shaped the births and predation of 52 moose calves from 2013 to 2020 on or adjacent to the Grand Portage Indian Reservation.The known sources of predation were American black bears(Ursus americanus,n=22)and gray wolves(Canis lupus,n=28).Satellite-derived data summarizing seasonal landscape features at the local level revealed that landscape heterogeneity use by moose can help to quantitatively identify landscapes of parturition and predation in space and time across large areas.Vegetation phenology proved to be differentiable between adult moose ranges,sites of cow parturition,and sites of calf predation.Landscape characteristics of each moose group were consistent and tractable based on environment,suggesting that sites of parturition and predation of moose are predictable in space and time.It is possible that moose selected specific landscapes for parturition despite risk of increased predation of their calves,which could be an example of an"ecological trap."This analytical framework can be employed to identify areas for future ungulate research on the impacts of landscape on parturition and predation dynamics.展开更多
Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning suc...Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning such as carbon uptake.However,the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated.These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity.Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019,we found that an advancement in spring phenology increased spring gross(GPP)and net primary productivity(NPP)in subtropical vegetation of China by 2.1 gC m^(-2)yr^(-1)and 1.4 gC m^(-2)yr^(-1),respectively.A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m^(-2)yr^(-1)and 0.2 gC m^(-2)yr^(-1),respectively.Temporally,the contribution of the spring phenology to spring carbon uptake increased significantly during the study period,while this positive contribution showed a nonsignificant trend in summer.In comparison,the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake;however,this contributing effect was weakened.Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology,respectively.Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.展开更多
Knowledge of both vegetation distribution pattern and phenology changes is very important.Their complicated relationship with elevation and accessibility were explored through a geographically weighted regression(GWR)...Knowledge of both vegetation distribution pattern and phenology changes is very important.Their complicated relationship with elevation and accessibility were explored through a geographically weighted regression(GWR) framework in Fujian province,China.The 16-day time series of 250 m Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) dataset from 2000 to 2010 was applied.Wavelet transform method was adopted to decompose the original time series and construct the annual maximum EVI and amplitude of the annual phenological cycle(EVI).Candidate explaining factors included topographic conditions,accessibility variables and proportions of primary vegetation types.Results revealed very strong positive influence from parameters of elevation and accessibility to big rivers and negative effect from accessibility to resident on both maximum EVI and phenological magnitude through ordinary linear least square(OLS) regression analysis.GWR analysis revealed that spatially,the parameters of topography and accessibility had a very complex relationship with both maximum EVI and phenology magnitude,as a result of the various combinations of environmental factors,vegetation composition and also intensive anthropogenic impact.Apart from the continuously increasing trend of phenology magnitude with increasing altitude,the influence of topography and accessibility on maximum EVI and phenological magnitude generally decreased,even from strongly positive to negative,with increasing altitude or distance.Specially,the most rapid change of correlation coefficient between them was observed within a low elevation or close distance;less variation was discovered within a certain range of medium altitude or distance and their relationship might change above this range.Non-stationary approaches are needed to better characterize the complex vegetation dynamic pattern in Mountain-hill Region.展开更多
Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian...Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian Plateau features both abundant snow cover resources and typical grassland ecosystems.In recent years,with the intensification of global climate change,the snow cover on the Mongolian Plateau has changed correspondingly,with resulting effects on vegetation growth.In this study,using MOD10A1 snow cover data and MOD13A1 Normalized Difference Vegetation Index(NDVI)data combined with remote sensing(RS)and geographic information system(GIS)techniques,we analyzed the spatiotemporal changes in snow cover and grassland phenology on the Mongolian Plateau from 2001 to 2018.The correlation analysis and grey relation analysis were used to determine the influence of snow cover parameters(snow cover fraction(SCF),snow cover duration(SCD),snow cover onset date(SCOD),and snow cover end date(SCED))on different types of grassland vegetation.The results showed wide snow cover areas,an early start time,a late end time,and a long duration of snow cover over the northern Mongolian Plateau.Additionally,a late start,an early end,and a short duration were observed for grassland phenology,but the southern area showed the opposite trend.The SCF decreased at an annual rate of 0.33%.The SCD was shortened at an annual rate of 0.57 d.The SCOD and SCED in more than half of the study area advanced at annual rates of 5.33 and 5.74 DOY(day of year),respectively.For grassland phenology,the start of the growing season(SOS)advanced at an annual rate of 0.03 DOY,the end of the growing season(EOS)was delayed at an annual rate of 0.14 DOY,and the length of the growing season(LOS)was prolonged at an annual rate of 0.17 d.The SCF,SCD,and SCED in the snow season were significantly positively correlated with the SOS and negatively correlated with the EOS and LOS.The SCOD was significantly negatively correlated with the SOS and positively correlated with the EOS and LOS.The SCD and SCF can directly affect the SOS of grassland vegetation,while the EOS and LOS were obviously influenced by the SCOD and SCED.This study provides a scientific basis for exploring the response trends of alpine vegetation to global climate change.展开更多
Exploring the impact of climate factors on vegetation phenology is crucial to understanding climate–vegetation interactions as well as carbon and water cycles in ecosystems in the context of climate change.In this ar...Exploring the impact of climate factors on vegetation phenology is crucial to understanding climate–vegetation interactions as well as carbon and water cycles in ecosystems in the context of climate change.In this article,we extracted the vegetation phenology data from 2002 to 2021 based on the dynamic threshold method in the source region of the Yangtze and Yellow Rivers.Trend and correlation analyses were used to investigate the relationship between vegetation phenology and temperature,precipitation and their spatial evolution characteristics.The results showed that:(i)From 2002 to 2021,the multi-year average start of growing season(SOS),end of growing season(EOS)and length of growing season(LOS)for plants were concentrated in May,October and 4–6 months,with a trend of 4.9 days(earlier),1.5 days(later),6.3 days/10 a(longer),respectively.(ii)For every 100 m increase in elevation,SOS,EOS and LOS were correspondingly delayed by 1.8 days,advanced by 0.8 days and shortened by 2.6 days,respectively.(iii)The impacts of temperature and precipitation on vegetation phenology varied at different stages of vegetation growth.Influencing factors of spring phenology experienced a shift from temperature to precipitation,while autumn phenology experienced precipitation followed by temperature.(iv)The climate factors in the previous period significantly affected the vegetation phenology in the study area and the spatial variability was obvious.Specifically,the temperature in April significantly affected the spring phenology and precipitation in August widely affected the autumn phenology.展开更多
Aims Information about changes in the start and end of the vegetation growing season(SOS and EOS)is crucial for assessing ecosystem responses to climate change because of the high sensitivity of both to climate and th...Aims Information about changes in the start and end of the vegetation growing season(SOS and EOS)is crucial for assessing ecosystem responses to climate change because of the high sensitivity of both to climate and their extensive influence on ecological processes in temperate and cold regions.climatic warming substantially advanced SOS on the tibetan Plateau from 1982 to 2011.However,it is unclear why EOS showed little delay despite increasing tem-perature over this period.Methods We used multiple methods to determine EOS from the satellite-observed normalized-difference vegetation index and investigated the relationships between EOS and its potential drivers on the tibetan Plateau over 1982-2011.Important findings We found a slight but non-significant delay in regionally averaged EOS of 0.7 day decade−1(P=0.18)and a widespread but weak delaying trend across the Plateau over this period.the inter-annual variations in regionally averaged EOS were driven mainly by pre-season temperature(partial R=0.62,P<0.01),and precipitation and insolation showed weak impact on EOS(P>0.10).Pre-season warming delayed EOS mainly in the eastern half and north-western area of the plateau.In the south-west,EOS was significantly and positively related to SOS,suggesting potentially indirect effects of winter weather conditions on the following autumn’s phenology through regulation of spring phenology.EOS was more strongly related with pre-season temperature in colder and wetter areas,reflecting vegetation adaptation to local climate.Interestingly,pre-season temperature had weaker delaying effects on EOS for vegeta-tion with a shorter growing season,for which SOS had a stronger control on inter-annual variations in EOS than for vegetation with a longer growing season.this indicates that shorter-season tibetan Plateau vegetation may have lower plasticity in adjusting the length of its growing season,whenever it begins,and that climate change is more likely to shift the growing season than extend it for that vegetation.展开更多
Ecosystem water use efficiency(WUE)is an integrated physiological metric for the coupling cycle between terrestrial carbon,water,and energy.How WUE responds to vegetation phenology(e.g.,SOS,EOS-start,end of growing se...Ecosystem water use efficiency(WUE)is an integrated physiological metric for the coupling cycle between terrestrial carbon,water,and energy.How WUE responds to vegetation phenology(e.g.,SOS,EOS-start,end of growing season,and GSL-growing season length)shifting in temperate semi-arid regions is a hot spot in relative research fields.Based on remotesensing products and in-situ measured climate data,this study discussed how gross primary productivity(GPP),evapotranspiration(ET),and WUE(quantified by GPP/ET)would change with the altering vegetation phenology and climate in the untouched semi-arid forests and grasslands of the Chinese Loess Plateau during 2001–2020.Our results show that vegetation tended to green-up earlier and brown-down later from 2001 to 2020,causing an extended GSL.The forests had an earlier SOS,later EOS,and longer GSL than the grasslands,but the latter had a bigger variation amplitude.The WUE in the study area decreased significantly during spring and summer,while the grassland WUE increased in autumn;the annual mean reduction rate in grassland WUE was approximately twice that of woodland.Earlier SOS could increase forest WUE but reduce grassland WUE in spring,mainly because leaf unfolding has a more pronounced limitation on soil evaporation beneath the forest canopy.EOS had less impact on WUE,and no apparent difference existed between these two ecosystems.Climate change could affect WUE directly by changing GPP and ET and indirectly by regulating vegetation phenology.Warming can increase GPP and ET,causing an earlier SOS,further promoting GPP and ET(except forest ET).Precipitation significantly affected forest GPP and ET in spring,grassland GPP and ET in summer,and grassland ET in autumn;precipitation affects spring grassland WUE mainly via regulating SOS.Enhanced solar radiation could suppress grassland GPP in spring,promote forest ET in autumn,and regulate grassland WUE by affecting phenology.This study is meaningful for improving the process-based vegetation model and studying arid and semi-arid ecosystems’responses to a changing climate.展开更多
Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greennes...Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.展开更多
The black soil region of northeast China is a vital food base and is one of the most sensitive regions to climate change in China.However,the characteristics of the crop phenological response and the integrated impact...The black soil region of northeast China is a vital food base and is one of the most sensitive regions to climate change in China.However,the characteristics of the crop phenological response and the integrated impact of climate and phenological changes on agricultural productivity in the region under the background of climate change are not clear.The future agricultural risk assessment has been insufficiently quantified and the existing risk level formulation lacks a sound basis.Based on remote sensing products,climate data,and model simulations,this study integrated a logistic function fitting curvature derivation,multiple linear regression,and scenario simulation to investigate crop phenology dynamics and their climate response characteristics in the black soil region.Additionally,the compound effects of climate and phenology changes on agricultural production and possible future risks were identified.The key results were as follows:(1)From 2000 to 2017,29.76%of the black soil region of northeast China experienced a significant delay in the start of the growing season(SOS)and 16.71%of the total area displayed a trend for the end of the growing season(EOS)to arrive earlier.The time lagged effects of the SOS in terms of the crop response to climatic factors were site and climatic parameter dependent.The influence of temperature was widespread and its effect had a longer lag time in general;(2)Both climatic and phenological changes have had a significant effect on the inter-annual variability of crop production,and the predictive ability of both increased by 70.23%,while the predictive area expanded by 85.04%,as compared to that of climate change in the same period of the growing season;(3)Under the RCP8.5 scenario,there was a risk that the future crop yield would decrease in the north and increase in the south,and the risk area was constantly expanding.With a 2.0℃rise in global temperature,the crop yield of the southern Songnen black soil sub-region would reduce by almost 10%.This finding will improve our understanding of the mechanisms underlying climate change and vegetation productivity dynamics,and is also helpful in the promotion of the risk management of agrometeorological disasters.展开更多
Spatiotemporal patterns of dust storms are affected by climate change through changes in convective instability, regional meteorological characteristics, and local sediment supply. Linking dust storm dynamics to clima...Spatiotemporal patterns of dust storms are affected by climate change through changes in convective instability, regional meteorological characteristics, and local sediment supply. Linking dust storm dynamics to climate change helps the understanding of what controls the initiation of dust storms, and assists the prediction of future dust storm occurrence. This study examines the temporal dynamics of spring dust storms in Inner Mongolia, a major dust source area in East Asia. We found that severe spring dust storms have significantly declined from1954 to 2007. Four dust storm types showed similar decreasing trends from 2001 to 2012. This change in spring dust storm dynamics is attributed to the shift in vegetation green-up dates based on the analysis of a satellite derived vegetation index. Earlier vegetation green-up has a dampening effect on spring dust storms. Suitable environmental conditions for vegetation green-up hinder the emergence of dust storms. This study expands our understanding of the dynamics of spring dust storms in the changing climate through a new perspective on vegetation phenology in the spring.展开更多
The study developed a feasible method for large-area land cover mapping with combination of geographical data and phenological characteristics, taking Northeast China （NEC） as the study area. First, with the monthly...The study developed a feasible method for large-area land cover mapping with combination of geographical data and phenological characteristics, taking Northeast China （NEC） as the study area. First, with the monthly average of precipitation and temperature datasets, the spatial clustering method was used to divide the NEC into four ecoclimate regions. For each ecoclimate region, geographical variables （annual mean precipitation and temperature, elevation, slope and aspect） were combined with phenological variables derived from the moderate resolution imaging spectroradiometer （MODIS） data （enhanced vegetation index （EVI） and land surface water index （LSWI））, which were taken as input variables of land cover classification. Decision Tree （DT） classifiers were then performed to produce land cover maps for each region. Finally, four resultant land cover maps were mosaicked for the entire NEC （NEC_MODIS）, and the land use and land cover data of NEC （NEC_LULC） interpreted from Landsat-TM images was used to evaluate the NEC_MODIS and MODIS land cover product （MODIS_IGBP） in terms of areal and spatial agreement. The results showed that the phenological information derived from EVI and LSWI time series well discriminated land cover classes in NEC, and the overall accuracy was significantly improved by 5.29% with addition of geographical variables. Compared with NEC_LULC for seven aggregation classes, the area errors of NEC_MODIS were much smaller and more stable than that of MODIS_IGBP for most of classes, and the wall-to-wall spatial comparisons at pixel level indicated that NEC_MODIS agreed with NEC_LULC for 71.26% of the NEC, whereas only 62.16% for MODIS_IGBP. The good performance of NEC_MODIS demonstrates that the methodology developed in the study has great potential for timely and detailed land cover mapping in temperate and boreal regions.展开更多
基金Under the auspices of National Key Research and Development Projects(No.2018YFE0207800)National Natural Science Foundation of China(No.41871103)。
文摘Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.
基金financially supported by the National Natural Sciences Foundation of China(42261026,41971094,and 42161025)Gansu Science and Technology Research Project(22ZD6FA005)+1 种基金Higher Education Innovation Foundation of Education Department of Gansu Province(2022A-041)the open foundation of Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone(XJYS0907-2023-01).
文摘The Qilian Mountains(QM)possess a delicate vegetation ecosystem,amplifying the evident response of vegetation phenology to climate change.The relationship between changes in vegetation growth and climate remains complex.To this end,we used MODIS NDVI data to extract the phenological parameters of the vegetation including meadow(MDW),grassland(GSD),and alpine vegetation(ALV))in the QM from 2002 to 2021.Then,we employed path analysis to reveal the direct and indirect impacts of seasonal climate change on vegetation phenology.Additionally,we decomposed the vegetation phenology in a time series using the trigonometric seasonality,Box-Cox transformation,ARMA errors,and Trend Seasonal components model(TBATS).The findings showed a distinct pattern in the vegetation phenology of the QM,characterized by a progressive shift towards an earlier start of the growing season(SOS),a delayed end of the growing season(EOS),and an extended length of the growing season(LOS).The growth cycle of MDW,GSD,and ALV in the QM species is clearly defined.The SOS for MDW and GSD occurred earlier,mainly between late April and August,while the SOS for ALVs occurred between mid-May and mid-August,a one-month delay compared to the other vegetation.The EOS in MDW and GSD were concentrated between late August and April and early September and early January,respectively.Vegetation phenology exhibits distinct responses to seasonal temperature and precipitation patterns.The advancement and delay of SOS were mainly influenced by the direct effect of spring temperatures and precipitation,which affected 19.59%and 22.17%of the study area,respectively.The advancement and delay of EOS were mainly influenced by the direct effect of fall temperatures and precipitation,which affected 30.18%and 21.17%of the area,respectively.On the contrary,the direct effects of temperature and precipitation in summer and winter on vegetation phenology seem less noticeable and were mainly influenced by indirect effects.The indirect effect of winter precipitation is the main factor affecting the advance or delay of SOS,and the area proportions were 16.29%and 23.42%,respectively.The indirect effects of fall temperatures and precipitation were the main factors affecting the delay and advancement of EOS,respectively,with an area share of 15.80%and 21.60%.This study provides valuable insight into the relationship between vegetation phenology and climate change,which can be of great practical value for the ecological protection of the Qinghai-Tibetan Plateau as well as for the development of GSD ecological animal husbandry in the QM alpine pastoral area.
基金supported by the National Natural Science Foundation of China(Grant Nos.41901117,U22A20570)the Science and Technology Innovation Program of Hunan Province(Grant No.2022RC4027)。
文摘Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate.However,the effects of different intensities of extreme climatic events on vegetation phenology remain poorly understood.Using a long-term solar-induced chlorophyll fluorescence dataset,we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau(TP)from 2000 to 2018.We found that the effect of maximum temperature exposure days(TxED)and minimum temperature exposure days(TnED)on the start of the growing season(SOS)was initially delayed and shifted to advance along the increasing temperature gradients.However,the response of the end of the growing season(EOS)to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached,above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early end.The corresponding maximum and minimum temperature thresholds were 10.12 and 2.54℃,respectively.In contrast,cumulative precipitation(CP)was more likely to advance SOS and delay EOS as the precipitation gradient increased,but the advance of SOS is gradually weakening.Four vegetation types(i.e.,forest,shrubland,meadow,and steppe)showed similar trends in response to different climates,but the optimal climatic conditions varied between the vegetation types.Generally,meadow and steppe had lower optimal temperatures and precipitation than forest and shrubland.These findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities,implying that the SOS will continue to advance with warming,whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming.
基金supported by the National Natural Science Foundation of China(41761014)the“One Hundred Outstanding Young Talents Training Program”of Lanzhou Jiaotong University,the National Natural Science Foundation of China(41971094)the Youth Innovation Promotion Association CAS(2019414)。
文摘Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized difference vegetation index)data,this study analysed the spatial and temporal patterns of snow phenology including snow onset date,snow end date,snow cover days,and vegetation phenology including the start of growing season,the end of growing season and the length of growing season in the Chinese Tianshan Mountainous Region(CTMR)from 2002 to 2018,and then investigated the snow phenological effects on the vegetation phenology among different ecogeographic zones and diverse vegetation types.The results indicated that snow onset date was earlier at higher elevations and later at lower elevations,while snow end date showed opposite spatial distribution characteristics.The end of growing season occurred later on the northwest slope of the CTMR and the Yili Valley.The earliest end of growing season was in the middle part of the CTMR.A long growing season was mainly distributed along the northern slope and the Yili Valley,while a short growing season was concentrated in the middle part of the CTMR.The response of vegetation phenology to changes in snow phenology varied among vegetation types and ecogeographic zones.The effect of snow phenology on vegetation phenology was more significant in IID5(Yili Valley)than in the other ecogeographic zones.A negative correlation was observed between the start of growing season and snow end date in nearly 54.78%of the study area,while a positive correlation was observed between the start of growing season and the snow end date in 66.85%of the study area.The sensitivity of vegetation phenology to changes in snow cover varied among different vegetation types.Snow onset date had the greatest effect on the start of growing season in the four vegetation cover types(alpine meadows,alpine steppes,shrubs,and alpine sparse vegetation),whereas the snow cover days had the least impact.Snow end date had the greatest impact on the end of growing season in the alpine steppes and shrub areas.The study results are helpful for understanding the vegetation dynamics under ongoing climate change,and can benefit vegetation management and pasture development in the CTMR.
基金This project was funded by the U.S.Fish and Wildlife Service Tribal Wildlife Grant,U.S.Environmental Protection Agency Great Lakes Restoration InitiativeBureau of Indian Affairs Endangered Species Program+1 种基金Funding was also provided by the Minnesota Zoo Ulysses S.Seal Conservation GrantIndianapolis Zoo Conservation Fund.
文摘Enhanced vegetation index(EVI)data can be used to identify and define the space in which ungulates practice parturition and encounter predation.This study explores the use of EVI data to identify landscapes linked to ungulate parturition and predation events across space,time,and environmental conditions.As a case study,we used the moose population(Alces alces)of northern Minnesota in the USA.Using remotely sensed EVI data rasters and global positioning system collar data,we quantified how vegetation phenology and moose movement shaped the births and predation of 52 moose calves from 2013 to 2020 on or adjacent to the Grand Portage Indian Reservation.The known sources of predation were American black bears(Ursus americanus,n=22)and gray wolves(Canis lupus,n=28).Satellite-derived data summarizing seasonal landscape features at the local level revealed that landscape heterogeneity use by moose can help to quantitatively identify landscapes of parturition and predation in space and time across large areas.Vegetation phenology proved to be differentiable between adult moose ranges,sites of cow parturition,and sites of calf predation.Landscape characteristics of each moose group were consistent and tractable based on environment,suggesting that sites of parturition and predation of moose are predictable in space and time.It is possible that moose selected specific landscapes for parturition despite risk of increased predation of their calves,which could be an example of an"ecological trap."This analytical framework can be employed to identify areas for future ungulate research on the impacts of landscape on parturition and predation dynamics.
基金National Natural Science Foundation of China,No.42371121Joint Fund for Regional Innovation and Development of the National Natural Science Foundation of China,No.U22A20570Science and Technology Innovation Program of Hunan Province,China,No.2022RC4027。
文摘Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning such as carbon uptake.However,the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated.These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity.Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019,we found that an advancement in spring phenology increased spring gross(GPP)and net primary productivity(NPP)in subtropical vegetation of China by 2.1 gC m^(-2)yr^(-1)and 1.4 gC m^(-2)yr^(-1),respectively.A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m^(-2)yr^(-1)and 0.2 gC m^(-2)yr^(-1),respectively.Temporally,the contribution of the spring phenology to spring carbon uptake increased significantly during the study period,while this positive contribution showed a nonsignificant trend in summer.In comparison,the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake;however,this contributing effect was weakened.Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology,respectively.Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.
基金support forthis work from Chinese National Natural Science Foundation (Grant no. 41071267)Scientific Research Foundation for Returned Scholars,Ministry of Education of China ([2012]940)Science Foundation of Fujian province (Grant no.2012J01167,2012I0005)
文摘Knowledge of both vegetation distribution pattern and phenology changes is very important.Their complicated relationship with elevation and accessibility were explored through a geographically weighted regression(GWR) framework in Fujian province,China.The 16-day time series of 250 m Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) dataset from 2000 to 2010 was applied.Wavelet transform method was adopted to decompose the original time series and construct the annual maximum EVI and amplitude of the annual phenological cycle(EVI).Candidate explaining factors included topographic conditions,accessibility variables and proportions of primary vegetation types.Results revealed very strong positive influence from parameters of elevation and accessibility to big rivers and negative effect from accessibility to resident on both maximum EVI and phenological magnitude through ordinary linear least square(OLS) regression analysis.GWR analysis revealed that spatially,the parameters of topography and accessibility had a very complex relationship with both maximum EVI and phenology magnitude,as a result of the various combinations of environmental factors,vegetation composition and also intensive anthropogenic impact.Apart from the continuously increasing trend of phenology magnitude with increasing altitude,the influence of topography and accessibility on maximum EVI and phenological magnitude generally decreased,even from strongly positive to negative,with increasing altitude or distance.Specially,the most rapid change of correlation coefficient between them was observed within a low elevation or close distance;less variation was discovered within a certain range of medium altitude or distance and their relationship might change above this range.Non-stationary approaches are needed to better characterize the complex vegetation dynamic pattern in Mountain-hill Region.
基金supported by the National Natural Science Foundation of China(41861014)the Natural Science Foundation of Inner Mongolia Autonomous Region,China(2020BS03042,2020BS04009)the Scientific Research Start-up Fund Projects of Introduced Talents(5909001803,1004031904).
文摘Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian Plateau features both abundant snow cover resources and typical grassland ecosystems.In recent years,with the intensification of global climate change,the snow cover on the Mongolian Plateau has changed correspondingly,with resulting effects on vegetation growth.In this study,using MOD10A1 snow cover data and MOD13A1 Normalized Difference Vegetation Index(NDVI)data combined with remote sensing(RS)and geographic information system(GIS)techniques,we analyzed the spatiotemporal changes in snow cover and grassland phenology on the Mongolian Plateau from 2001 to 2018.The correlation analysis and grey relation analysis were used to determine the influence of snow cover parameters(snow cover fraction(SCF),snow cover duration(SCD),snow cover onset date(SCOD),and snow cover end date(SCED))on different types of grassland vegetation.The results showed wide snow cover areas,an early start time,a late end time,and a long duration of snow cover over the northern Mongolian Plateau.Additionally,a late start,an early end,and a short duration were observed for grassland phenology,but the southern area showed the opposite trend.The SCF decreased at an annual rate of 0.33%.The SCD was shortened at an annual rate of 0.57 d.The SCOD and SCED in more than half of the study area advanced at annual rates of 5.33 and 5.74 DOY(day of year),respectively.For grassland phenology,the start of the growing season(SOS)advanced at an annual rate of 0.03 DOY,the end of the growing season(EOS)was delayed at an annual rate of 0.14 DOY,and the length of the growing season(LOS)was prolonged at an annual rate of 0.17 d.The SCF,SCD,and SCED in the snow season were significantly positively correlated with the SOS and negatively correlated with the EOS and LOS.The SCOD was significantly negatively correlated with the SOS and positively correlated with the EOS and LOS.The SCD and SCF can directly affect the SOS of grassland vegetation,while the EOS and LOS were obviously influenced by the SCOD and SCED.This study provides a scientific basis for exploring the response trends of alpine vegetation to global climate change.
基金supported by the National Key Research and Development Project(2022YFC3201704)the National Natural Science Foundation of China(52079008,52009006,52109038)+2 种基金the Research Fund of Key Laboratory of Water Management and Water Security for Yellow River Basin,Ministry of Water Resources(2023-SYSJJ-10)the Natural Science Foundation of Hubei Province(2022CFB554,2022CFD037)National Public Research Institutes for Basic R&D Operating Expenses Special Project(CKSF2023311/SZ).
文摘Exploring the impact of climate factors on vegetation phenology is crucial to understanding climate–vegetation interactions as well as carbon and water cycles in ecosystems in the context of climate change.In this article,we extracted the vegetation phenology data from 2002 to 2021 based on the dynamic threshold method in the source region of the Yangtze and Yellow Rivers.Trend and correlation analyses were used to investigate the relationship between vegetation phenology and temperature,precipitation and their spatial evolution characteristics.The results showed that:(i)From 2002 to 2021,the multi-year average start of growing season(SOS),end of growing season(EOS)and length of growing season(LOS)for plants were concentrated in May,October and 4–6 months,with a trend of 4.9 days(earlier),1.5 days(later),6.3 days/10 a(longer),respectively.(ii)For every 100 m increase in elevation,SOS,EOS and LOS were correspondingly delayed by 1.8 days,advanced by 0.8 days and shortened by 2.6 days,respectively.(iii)The impacts of temperature and precipitation on vegetation phenology varied at different stages of vegetation growth.Influencing factors of spring phenology experienced a shift from temperature to precipitation,while autumn phenology experienced precipitation followed by temperature.(iv)The climate factors in the previous period significantly affected the vegetation phenology in the study area and the spatial variability was obvious.Specifically,the temperature in April significantly affected the spring phenology and precipitation in August widely affected the autumn phenology.
基金This work was funded by grants from the National Natural Science Foundation of China(41571103 and 41501103)the‘Strategic Priority Research Program(B)’of the Chinese Academy of Sciences(XDB03030404)+2 种基金the National Basic Research Program of China(2013CB956303)the China Postdoctoral Science Foundation(2015M580137)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2015055).
文摘Aims Information about changes in the start and end of the vegetation growing season(SOS and EOS)is crucial for assessing ecosystem responses to climate change because of the high sensitivity of both to climate and their extensive influence on ecological processes in temperate and cold regions.climatic warming substantially advanced SOS on the tibetan Plateau from 1982 to 2011.However,it is unclear why EOS showed little delay despite increasing tem-perature over this period.Methods We used multiple methods to determine EOS from the satellite-observed normalized-difference vegetation index and investigated the relationships between EOS and its potential drivers on the tibetan Plateau over 1982-2011.Important findings We found a slight but non-significant delay in regionally averaged EOS of 0.7 day decade−1(P=0.18)and a widespread but weak delaying trend across the Plateau over this period.the inter-annual variations in regionally averaged EOS were driven mainly by pre-season temperature(partial R=0.62,P<0.01),and precipitation and insolation showed weak impact on EOS(P>0.10).Pre-season warming delayed EOS mainly in the eastern half and north-western area of the plateau.In the south-west,EOS was significantly and positively related to SOS,suggesting potentially indirect effects of winter weather conditions on the following autumn’s phenology through regulation of spring phenology.EOS was more strongly related with pre-season temperature in colder and wetter areas,reflecting vegetation adaptation to local climate.Interestingly,pre-season temperature had weaker delaying effects on EOS for vegeta-tion with a shorter growing season,for which SOS had a stronger control on inter-annual variations in EOS than for vegetation with a longer growing season.this indicates that shorter-season tibetan Plateau vegetation may have lower plasticity in adjusting the length of its growing season,whenever it begins,and that climate change is more likely to shift the growing season than extend it for that vegetation.
基金supported by the National Natural Science Foundation of China(Grant Nos.52279030,51779272,52009140&U2243601)the Special Support Funds for National High-level Talents(Grant No.WR0166A012019)the Independent Research Project of State Key Laboratory of Simulations and Regulation of Water Cycle in River Basin(Grant No.SKL2020ZY04).
文摘Ecosystem water use efficiency(WUE)is an integrated physiological metric for the coupling cycle between terrestrial carbon,water,and energy.How WUE responds to vegetation phenology(e.g.,SOS,EOS-start,end of growing season,and GSL-growing season length)shifting in temperate semi-arid regions is a hot spot in relative research fields.Based on remotesensing products and in-situ measured climate data,this study discussed how gross primary productivity(GPP),evapotranspiration(ET),and WUE(quantified by GPP/ET)would change with the altering vegetation phenology and climate in the untouched semi-arid forests and grasslands of the Chinese Loess Plateau during 2001–2020.Our results show that vegetation tended to green-up earlier and brown-down later from 2001 to 2020,causing an extended GSL.The forests had an earlier SOS,later EOS,and longer GSL than the grasslands,but the latter had a bigger variation amplitude.The WUE in the study area decreased significantly during spring and summer,while the grassland WUE increased in autumn;the annual mean reduction rate in grassland WUE was approximately twice that of woodland.Earlier SOS could increase forest WUE but reduce grassland WUE in spring,mainly because leaf unfolding has a more pronounced limitation on soil evaporation beneath the forest canopy.EOS had less impact on WUE,and no apparent difference existed between these two ecosystems.Climate change could affect WUE directly by changing GPP and ET and indirectly by regulating vegetation phenology.Warming can increase GPP and ET,causing an earlier SOS,further promoting GPP and ET(except forest ET).Precipitation significantly affected forest GPP and ET in spring,grassland GPP and ET in summer,and grassland ET in autumn;precipitation affects spring grassland WUE mainly via regulating SOS.Enhanced solar radiation could suppress grassland GPP in spring,promote forest ET in autumn,and regulate grassland WUE by affecting phenology.This study is meaningful for improving the process-based vegetation model and studying arid and semi-arid ecosystems’responses to a changing climate.
基金National Natural Science Foundation of China(41601478)National Key Research and Development Program of China(2018YFB0505301,2016YFC0500103)
文摘Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.
基金The Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA28130104。
文摘The black soil region of northeast China is a vital food base and is one of the most sensitive regions to climate change in China.However,the characteristics of the crop phenological response and the integrated impact of climate and phenological changes on agricultural productivity in the region under the background of climate change are not clear.The future agricultural risk assessment has been insufficiently quantified and the existing risk level formulation lacks a sound basis.Based on remote sensing products,climate data,and model simulations,this study integrated a logistic function fitting curvature derivation,multiple linear regression,and scenario simulation to investigate crop phenology dynamics and their climate response characteristics in the black soil region.Additionally,the compound effects of climate and phenology changes on agricultural production and possible future risks were identified.The key results were as follows:(1)From 2000 to 2017,29.76%of the black soil region of northeast China experienced a significant delay in the start of the growing season(SOS)and 16.71%of the total area displayed a trend for the end of the growing season(EOS)to arrive earlier.The time lagged effects of the SOS in terms of the crop response to climatic factors were site and climatic parameter dependent.The influence of temperature was widespread and its effect had a longer lag time in general;(2)Both climatic and phenological changes have had a significant effect on the inter-annual variability of crop production,and the predictive ability of both increased by 70.23%,while the predictive area expanded by 85.04%,as compared to that of climate change in the same period of the growing season;(3)Under the RCP8.5 scenario,there was a risk that the future crop yield would decrease in the north and increase in the south,and the risk area was constantly expanding.With a 2.0℃rise in global temperature,the crop yield of the southern Songnen black soil sub-region would reduce by almost 10%.This finding will improve our understanding of the mechanisms underlying climate change and vegetation productivity dynamics,and is also helpful in the promotion of the risk management of agrometeorological disasters.
基金supported by the National Natural Science Foundation of China (41171401)the National Basic Research Program of China (973) (2012CB955402) Programs
文摘Spatiotemporal patterns of dust storms are affected by climate change through changes in convective instability, regional meteorological characteristics, and local sediment supply. Linking dust storm dynamics to climate change helps the understanding of what controls the initiation of dust storms, and assists the prediction of future dust storm occurrence. This study examines the temporal dynamics of spring dust storms in Inner Mongolia, a major dust source area in East Asia. We found that severe spring dust storms have significantly declined from1954 to 2007. Four dust storm types showed similar decreasing trends from 2001 to 2012. This change in spring dust storm dynamics is attributed to the shift in vegetation green-up dates based on the analysis of a satellite derived vegetation index. Earlier vegetation green-up has a dampening effect on spring dust storms. Suitable environmental conditions for vegetation green-up hinder the emergence of dust storms. This study expands our understanding of the dynamics of spring dust storms in the changing climate through a new perspective on vegetation phenology in the spring.
基金The National 973 Program, No.2010CB950901-2-1The program of Ministry of Science and Technology, No.SB2007FY110300-1-2
文摘The study developed a feasible method for large-area land cover mapping with combination of geographical data and phenological characteristics, taking Northeast China （NEC） as the study area. First, with the monthly average of precipitation and temperature datasets, the spatial clustering method was used to divide the NEC into four ecoclimate regions. For each ecoclimate region, geographical variables （annual mean precipitation and temperature, elevation, slope and aspect） were combined with phenological variables derived from the moderate resolution imaging spectroradiometer （MODIS） data （enhanced vegetation index （EVI） and land surface water index （LSWI））, which were taken as input variables of land cover classification. Decision Tree （DT） classifiers were then performed to produce land cover maps for each region. Finally, four resultant land cover maps were mosaicked for the entire NEC （NEC_MODIS）, and the land use and land cover data of NEC （NEC_LULC） interpreted from Landsat-TM images was used to evaluate the NEC_MODIS and MODIS land cover product （MODIS_IGBP） in terms of areal and spatial agreement. The results showed that the phenological information derived from EVI and LSWI time series well discriminated land cover classes in NEC, and the overall accuracy was significantly improved by 5.29% with addition of geographical variables. Compared with NEC_LULC for seven aggregation classes, the area errors of NEC_MODIS were much smaller and more stable than that of MODIS_IGBP for most of classes, and the wall-to-wall spatial comparisons at pixel level indicated that NEC_MODIS agreed with NEC_LULC for 71.26% of the NEC, whereas only 62.16% for MODIS_IGBP. The good performance of NEC_MODIS demonstrates that the methodology developed in the study has great potential for timely and detailed land cover mapping in temperate and boreal regions.
