MODIS-based estimates of global terrestrial ecosystem respirationAi, J., G. Jia, H. Epstein, H. Wang, A. Zhang, Y. Hu.Journal of Geophysical Research – Biogeosciences:2018, doi: 10.1002/2017JG004107,123,326-352AbstractTerrestrial ecosystem respiration (Reco) represents a large carbon source from land to atmosphere and is highly spatiotemporally heterogeneous across scales. Upscaling of field‐measured respiration data using remote sensing information is urgently needed for understanding regional and global patterns of ecosystem respiration. Using Moderate Resolution Imaging Spectroradiometer (MODIS) data with resolutions of 1 km and 8 days and flux measurements from 171 sites (total of 812 site years) across the world from 2000 to 2014, we developed a semiempirical, yet physiologically based, remote sensing model, which can simulate Reco observed across most biomes with a small margin of error (R2 = 0.55, root‐mean‐square error = 1.67 gCm−2d−1, efficiency = 0.46, and mean bias error = 0.18 gCm−2d−1). The reference respiration at the annual mean nighttime land surface temperature (LST) can be well represented by MODIS enhanced vegetation index and LST. A comprehensive comparison of six respiration‐temperature (R‐T) models shows that the more physiologically based R‐T model (extended Arrhenius model) may be most suitable for estimating the respiration rate at higher latitudes. Integrating an effect of vegetation change on Reco in different biomes effectively improves estimates of Reco in almost all of the biomes.
Observed and simulated sensitivities of spring greenup to preseason climateXu, X., W.J. Riley, C.D. Koven, G. Jia.Journal of Geophysical Research - Biogeosciences:2018, doi: 10.1002/2017JG004117,123,60-78AbstractVegetation phenology plays an important role in regulating land‐atmosphere energy, water, and trace‐gas exchanges. Changes in spring greenup (SG) have been documented in the past half‐century in response to ongoing climate change. We use normalized difference vegetation index generated from NOAAs advanced very high resolution radiometer data in the Global Inventory Modeling and Monitoring Study project over the 1982–2005 period, coupled with climate reanalysis (Climate Research Unit‐National Centers for Environmental Prediction) to investigate the SG responses to preseason climate change in northern temperate and boreal regions. We compared these observed responses to the simulated SG responses to preseason climate inferred from the Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) over 1982–2005. The observationally inferred SG suggests that there has been an advance of about 1 days per decade between 1982 and 2005 in the northern midlatitude to high latitude, with significant spatial heterogeneity. The spatial heterogeneity of the SG advance results from heterogeneity in the change of the preseason climate as well as varied vegetation responses to the preseason climate across biomes. The SG to preseason temperature sensitivity is highest in forests other than deciduous needleleaf forests, followed by temperate grasslands and woody savannas. The SG in deciduous needleleaf forests, open shrublands, and tundra is relatively insensitive to preseason temperature. Although the extent of regions where the SG is sensitive to preseason precipitation is smaller than the extent of regions where the SG is sensitive to preseason temperature, the biomes that are more sensitive to temperature are also more sensitive to precipitation, suggesting the interactive control of temperature and precipitation. In the mean, the CMIP5 ESMs reproduced the dominant latitudinal preseason climate trends and SG advances. However, large biases in individual ESMs for the preseason period, climate, and SG sensitivity imply needed model improvements to climate prediction and phenological process parameterizations.
the Delhi ‘gas chamber’: smog, air pollution and the health emergency of November 2017Terry, J.P., G. Jia, R. Boldi, S. Khan.Weather:2018, doi:10.1002/wea.3242AbstractThe thick smog that blanketed Indias capital, New Delhi, in early November 2017 saw air quality index values peak above 1000 – a figure in excess of three times the threshold value for ‘hazardous’ conditions. A public health emergency was declared. Delhis smog was the result of an existing ambient urban air‐pollution problem, significantly worsened by smoke blowing in from numerous agricultural fires burning across neighbouring Punjab and Haryana states. Post‐summer monsoon regional air‐flow patterns, decreasing autumn temperatures, high‐pressure stability, temperature inversion and light local winds helped to produce climatic conditions that were conducive to smog build‐up and subsequently prevented it from readily dispersing. Well‐intentioned measures introduced by the authorities saw only partial improvement in city air quality after three weeks. To reduce the severity of future smog hazards, a region‐wide agreement to restrict stubble burning during late autumn across northwest India will be needed.
Satellite view of seasonal greenness trends and controls in South AsiaSarmah, S., G. Jia, A. Zhang.Environmental Research Letters:2018, doi: 10.1088/1748-9326/aaa866,13,034026AbstractSouth Asia (SA) has been considered one of the most remarkable regions for changing vegetation greenness, accompanying its major expansion of agricultural activities, especially irrigated farming. The influence of the monsoon climate on the seasonal trends and anomalies of vegetation greenness is poorly understood in this area. Herein, we used the satellite-based Normalized Difference Vegetation Index (NDVI) to investigate various spatiotemporal patterns in vegetation activity during summer and winter monsoon (SM and WM) seasons and among irrigated croplands (IC), rainfed croplands (RC), and natural vegetation (NV) areas during 1982–2013. Seasonal NDVI variations with climatic factors (precipitation and temperature) and land use and cover changes (LUCC) have also been investigated. This study demonstrates that the seasonal dynamics of vegetation could improve the detailed understanding of vegetation productivity over the region. We found distinct greenness trends between two monsoon seasons and among the major land use/cover classes. Winter monsoons contributed greater variability to the overall vegetation dynamics of SA. Major greening occurred due to the increased productivity over irrigated croplands during the winter monsoon season; meanwhile, browning trends were prominent over NV areas during the same season. Maximum temperatures had been increasing tremendously during the WM season; however, the precipitation trend was not significant over SA. Both the climate variability and LUCC revealed coupled effects on the long term NDVI trends in NV areas, especially in the hilly regions, whereas anthropogenic activities (agricultural advancements) played a pivotal role in the rest of the area. Until now, advanced cultivation techniques have proven to be beneficial for the region in terms of the productivity of croplands. However, the crop productivity is at risk under climate change.
Contrasting responses of grassland water and carbon exchanges to climate change between Tibetan Plateau and Inner MongoliaLiu, D., Y. Li, T. Wang, P. Peylin, N. MacBean, P. Ciais, G. Jia, M. Ma, Y. Ma, M. Shen, X. Zhang, S. Piao.Agricultural and Forest Meteorology:2018, doi: 10.1016/j.agrformet.2017.11.034,249,163-175AbstractInner Mongolia (IM) of China play important roles in climate change mitigation and food and livestock production. These two regions have increasingly experienced higher temperatures and changing precipitation regimes over the past three decades. However, it remains uncertain to what extent rising temperature and varying precipitation regulate the water and carbon fluxes across alpine (TP) and temperate (IM) grasslands. Here, we first optimize a process-based model of carbon and water fluxes using eddy-covariance data (three sites in TP and six sites in IM), and analyze the simulated carbon and water fluxes based upon the optimized model exposed to a range of annual temperature and precipitation anomalies. We found that the changes in net ecosystem-atmosphere carbon exchange (NEE) of TP grassland are relatively small because the ecosystem respiration (Re) and the gross primary productivity (GPP) increase at comparable rate with warming across multiple sites (Re: 22.1 ± 21.4 g C m−2 year−1 °C−1, GPP: 22.43 ± 36.41 g C m−2 year−1 °C−1), which is due to the possibility that grasslands cannot respire more than the available supply of photosynthesis. The NEE of IM grassland increases (more carbon loss from ecosystem) with warming, which is mainly because GPP decreases faster than Re under warm-induced reduction in moisture availability, and the sensitivity of Re to warming (1.17 ± 3.56 g C m−2 year−1 °C−1) is much smaller than that of GPP (15.53 ± 15.91 g C m−2 year−1 °C−1). These results indicate that water is the major limiting factor in IM grasslands, but not in TP grasslands. In contrast to warming, we found an asymmetric response of water and carbon fluxes to drying and wetting in TP grasslands (i.e. a large decrease under the drying condition and a small increase under the wetting condition) but almost a linear response in IM grasslands. We therefore highlight that the underlying processes regulating the responses of water and carbon cycles to warming are fundamentally different between TP and IM grasslands, with the moisture being the major limiting factor in IM while grasslands in TP are much more limited by thermal conditions. Our results also imply that warming would significantly stimulate the net ecosystem carbon loss to atmosphere but not significantly enhance ET in IM grasslands, which may provide a positive feedback to accelerate climate change. Inversely, warming could not significantly affect the ecosystem carbon exchange but significantly enhance ET in TP grasslands, which may provide a negative feedback to mitigate climate change in alpine grasslands.
Functional group, biomass, and climate change effects on drought in semiarid grasslandsWilson, S.D., D.R. Schlaepfer, J.B. Bradford, W.K. Lauenroth, M.C. Duniway, S.A. Hall, K. Jamiyansharav, G. Jia, A. Lkhagva, S.M. Munson, D.A. Pyke and B. Tietjen.Journal of Geophysical Research – Biogeosciences:2018, doi: 10.1002/2017JG004173,123,1072-1085AbstractWater relations in plant communities are influenced both by contrasting functional groups (grasses and shrubs) and by climate change via complex effects on interception, uptake, and transpiration. We modeled the effects of functional group replacement and biomass increase, both of which can be outcomes of invasion and vegetation management, and climate change on ecological drought (soil water potential below which photosynthesis stops) in 340 semiarid grassland sites over 30 year periods. Relative to control vegetation (climate and site‐determined mixes of functional groups), the frequency and duration of drought were increased by shrubs and decreased by annual grasses. The rankings of shrubs, control vegetation, and annual grasses in terms of drought effects were generally consistent in current and future climates, suggesting that current differences among functional groups on drought effects predict future differences. Climate change accompanied by experimentally increased biomass (i.e., the effects of invasions that increase community biomass or management that increases productivity through fertilization or respite from grazing) increased drought frequency and duration and advanced drought onset. Our results suggest that the replacement of perennial temperate semiarid grasslands by shrubs, or increased biomass, can increase ecological drought in both current and future climates.
Spatiotemporal variability of precipitation during 1961-2014 across the Mongolian Plateau
Qin, F., G. Jia, J. Yang, M. Hou.Journal of Mountain Sciences:2018, doi: 10.1007/s11629-018-4837-1,15(5),992-1005
The underestimated magnitude and decline trend in near-surface wind over ChinaJiang, Y., X. Xu, H. Liu, X. Dong, W. Wang, G. Jia.Atmos. Sci. Lett:2017, doi: 10.1002/asl.791,18,475–483AbstractThis study reports the magnitude, spatial pattern and temporal trend of near‐surface wind speed (NWS) by comparing 20th century simulations in Coupled Model Intercomparison Project phase 5 (CMIP5) and 3 (CMIP3) and the climate reanalyses with measurements at 563 weather stations in China over 1961–2005. Both CMIP5 and CMIP3 agree quite well with observations in reproducing the spatial pattern of annual mean NWS. CMIP5 models are superior to CMIP3 models in hindcasting the magnitude and spatial pattern of seasonal mean NWS, the temporal trend in annual and seasonal mean NWS. Although both CMIP3 and CMIP5 reproduced the decline trend in the annual and seasonal mean NWS, the hindcasted decline rate is smaller than observed decline trend by the magnitude of one order. The ensemble of optimal models that are better correlated to observations in both NWS and temporal trend possesses advantages over individual model hindcast and reanalyses. The reanalyzed data are not able to represent the observed either the spatial pattern or the decline trend of NWS. The analyses presented here reveal the uncertainties in the current wind field products including reanalyses and model outputs and highlight the benefits of parameterization development and increased horizontal resolution in the new‐generation CMIP models.
ENSO elicits opposing responses of semi-arid vegetation between HemispheresZhang, A., G. Jia, H. Epstein, J. Xia.Scientific Reports:2017,7,42281, doi:10.1038/srep42281AbstractSemi-arid ecosystems are key contributors to the global carbon cycle and may even dominate the inter-annual variability (IAV) and trends of the land carbon sink, driven largely by the El Niño–Southern Oscillation (ENSO). The linkages between dynamics of semi-arid ecosystems and climate at the hemispheric scale however are not well known. Here, we use satellite data and climate observations from 2000 to 2014 to explore the impacts of ENSO on variability of semi-arid ecosystems, using the Ensemble Empirical Mode Decomposition method. We show that the responses of semi-arid vegetation to ENSO occur in opposite directions, resulting from opposing controls of ENSO on precipitation between the Northern Hemisphere (positively correlated to ENSO) and the Southern Hemisphere (negatively correlated to ENSO). Also, the Southern Hemisphere, with a robust negative coupling of temperature and precipitation anomalies, exhibits stronger and faster responses of semi-arid ecosystems to ENSO than the Northern Hemisphere. Our findings suggest that natural coherent variability in semi-arid ecosystem productivity responded to ENSO in opposite ways between two hemispheres, which may imply potential prediction of global semi-arid ecosystem variability, particularly based on variability in tropical Pacific Sea Surface Temperatures.
Multiple satellite-based analysis reveals complex climate effects of temperate forests and related energy budgetMa, W., G. Jia, A. Zhang.Journal of Geophysical Research – Atmospheres:2017,122(7),3806-3820, doi: 10.1002/2016/JD026278AbstractForest conversion-driven biophysical processes have been examined in various case studies that largely depend on sensitivity analysis of climate modeling. However, much remains unknown in the real world due to the complicated process and uncertainty in magnitude, especially in the temperate bioclimate regions. This study applied satellite-based observation to investigate the biophysical climate response to potential forest conversion in China, especially on the spatial and temporal patterns and underlying mechanisms. We evaluated the differences of land surface temperature (ΔLST) between adjacent forest and cropland, in terms of the latitudinal and seasonal patterns. Compared to cropland, the temperate forest to the south of 40°N showed the cooling effect of −0.61 ± 0.02°C (95% confidence interval, and hereafter), and it presented the warming effect of 0.48 ± 0.06°C to the north of 48°N (the transition zone was between 40°N and 48°N). Seasonal analysis further demonstrated that the cooling effects of temperate forest in China in spring (March, April, May), summer (June, July, August), and autumn (September, October, November) were −0.53 ± 0.02°C, −0.55 ± 0.02°C, and −0.30 ± 0.02°C, respectively, while the forest caused the warming effect of 0.10 ± 0.04°C in winter (December, January, February). However, the biophysical climate response to forest conversion in temperate regions was complex and showed highly spatial and temporal heterogeneity. We further assessed the role of two major biophysical processes, i.e., albedo and evapotranspiration (ET), in shaping land surface temperature from surface energy budget perspective. Results showed that the latitudinal, seasonal, and spatiotemporal patterns of ΔLST was determined by the net effect of ET-induced latent heat changes and albedo-induced solar radiation absorption changes.
Climate change reduces extent of temperate drylands and intensifies drought in deep soilsSchlaepfer, D.R., J.B. Bradford, W.K. Lauenroth, S.M. Munson, B. Tietjen, S.A. Hall, S.D.Wilson, M.C. Duniway, G. Jia, D.A. Pyke, A. Lkhagva, K. Jamiyansharav.Nature Communications:2017,8,14196, doi:10.1038/ncomms14196AbstractDrylands cover 40% of the global terrestrial surface and provide important ecosystem services. While drylands as a whole are expected to increase in extent and aridity in coming decades, temperature and precipitation forecasts vary by latitude and geographic region suggesting different trajectories for tropical, subtropical, and temperate drylands. Uncertainty in the future of tropical and subtropical drylands is well constrained, whereas soil moisture and ecological droughts, which drive vegetation productivity and composition, remain poorly understood in temperate drylands. Here we show that, over the twenty first century, temperate drylands may contract by a third, primarily converting to subtropical drylands, and that deep soil layers could be increasingly dry during the growing season. These changes imply major shifts in vegetation and ecosystem service delivery. Our results illustrate the importance of appropriate drought measures and, as a global study that focuses on temperate drylands, highlight a distinct fate for these highly populated areas.
Land surface temperature shaped by urban fractions in megacity regionZhang, X., Y. Hu , G. Jia, M. Hou, Y. Fan, Z. Sun, Y. Zhu.Theoretical and Applied Climatology:2017,127(3),965-975, doi: 10.1007/s00704-015-1683-8AbstractLarge areas of cropland and natural vegetation have been replaced by impervious surfaces during the recent rapid urbanization in China, which has resulted in intensified urban heat island effects and modified local or regional warming trends. However, it is unclear how urban expansion contributes to local temperature change. In this study, we investigated the relationship between land surface temperature (LST) change and the increase of urban land signals. The megacity of Tianjin was chosen for the case study because it is representative of the urbanization process in northern China. A combined analysis of LST and urban land information was conducted based on an urban–rural transect derived from Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectrometer (MODIS), and QuickBird images. The results indicated that the density of urban land signals has intensified within a 1-km2 grid in the urban center with an impervious land fraction ＞60 %. However, the construction on urban land is quite different with low-/mid-rise buildings outnumbering high-rise buildings in the urban–rural transect. Based on a statistical moving window analysis, positive correlation (R 2 ＞ 0.9) is found between LST and urban land signals. Surface temperature change (ΔLST) increases by 0.062 °C, which was probably caused by the 1 % increase of urbanized land (ΔIF) in this case region.
Climate change-induced vegetation shifts lead to more ecological droughts despite projected rainfall increases in many global temperate drylandsTietjen, B., D.R. Schlaepfer, J.B. Bradford, W.K. Lauenroth, S.A. Hall, M.C. Duniway, T. Hochstrasser, G. Jia, S.M. Munson, D.A. Pyke, S.D. Wilson.Global Change Biology:2017,doi: 10.1111/gcb.13598AbstractDrylands occur world-wide and are particularly vulnerable to climate change since dryland ecosystems depend directly on soil water availability that may become increasingly limited as temperatures rise. Climate change will both directly impact soil water availability, and also change plant biomass, with resulting indirect feedbacks on soil moisture. Thus, the net impact of direct and indirect climate change effects on soil moisture requires better understanding. We used the ecohydrological simulation model SOILWAT at sites from temperate dryland ecosystems around the globe to disentangle the contributions of direct climate change effects and of additional indirect, climate change-induced changes in vegetation on soil water availability. We simulated current and future climate conditions projected by 16 GCMs under RCP 4.5 and RCP 8.5 for the end of the century. We determined shifts in water availability due to climate change alone and due to combined changes of climate and the growth form and biomass of vegetation. Vegetation change will mostly exacerbate low soil water availability in regions already expected to suffer from negative direct impacts of climate change (with the two RCP scenarios giving us qualitatively similar effects). By contrast, in regions that will likely experience increased water availability due to climate change alone, vegetation changes will counteract these increases due to increased water losses by interception. In only a small minority of locations, climate change induced vegetation changes may lead to a net increase in water availability. These results suggest that changes in vegetation in response to climate change may exacerbate drought conditions and may dampen the effects of increased precipitation, i.e. leading to more ecological droughts despite higher precipitation in some regions. Our results underscore the value of considering indirect effects of climate change on vegetation when assessing future soil moisture conditions in water-limited ecosystems.
Assessment of spatial representativeness of eddy covariance flux data from flux tower to regional gridWang H., G. Jia, A. Zhang, C. Miao.Remote Sensing:2016,8(9),742AbstractCombining flux tower measurements with remote sensing or land surface models is generally regarded as an efficient method to scale up flux data from site to region. However, due to the heterogeneous nature of the vegetated land surface, the changing flux source areas and the mismatching between ground source areas and remote sensing grids, direct use of in-situ flux measurements can lead to major scaling bias if their spatial representativeness is unknown. Here, we calculate and assess the spatial representativeness of 15 flux sites across northern China in two aspects: first, examine how well a tower represents fluxes from the specific targeted vegetation type, which is called vegetation-type level; and, second, examine how representative is the flux tower footprint of the broader landscape or regional extents, which is called spatial-scale level. We select fraction of target vegetation type (FTVT) and Normalized Difference Vegetation Index (NDVI) as key indicators to calculate the spatial representativeness of 15 EC sites. Then, these sites were ranked into four grades based on FTVT or cluster analysis from high to low in order: (1) homogeneous; (2) representative; (3) acceptable; and (4) disturbed measurements. The results indicate that: (1) Footprint climatology for each site was mainly distributed in an irregular shape, had similar spatial pattern as spatial distribution of prevailing wind direction; (2) At vegetation-type level, the number of homogeneous, representative, acceptable and disturbed measurements is 8, 4, 1 and 2, respectively. The average FTVT was 0.83, grass and crop sites had greater representativeness than forest sites; (3) At spatial-scale level, flux sites with zonal vegetation had greater representativeness than non-zonal vegetation sites, and the scales were further divided into three sub-scales: (a) in flux site scale, the average of absolute NDVI bias was 4.34%, the number of the above four grades is 9, 4, 1 and 1, respectively; (b) in remote sensing pixel scale, the average of absolute NDVI bias was 8.27%, the number is 7, 2, 2 and 4, respectively; (c) in land model grid scale, the average of absolute NDVI bias was 12.13%, the number is 5, 4, 3 and 3. These results demonstrate the variation of spatial representativeness of flux measurements among different application levels and scales and highlighted the importance of proper interpretation of EC flux measurements. These results also suggest that source area of EC flux should be involved in model validation and/or calibration with EC flux measurements.
Multi-scale remote sensing estimates of urban fractions and road widths for regional modelsJia, G., R. Xu, Y. Hu, Y. He.Climatic Change:2015,129,543-554AbstractLanduse in East Asia has changed substantially during the last three decades, featured with expansion of urban built-up at unprecedented scale and speed. The fast expansion of urban areas could contribute to local and even regional climate change. However, current spatial datasets of urban fractions do not well represent the extent and expansion of urban areas in the regions, and that best available satellite data and remote sensing techniques have not been well applied to serve regional modeling of urbanization impacts on near surface temperature and other climate variables. Better estimates of localized urban fractions are badly needed. Here we use high and mid resolution satellite data to estimate urban fractions and road width at local and regional scales. With our fractional cover, data fusion, and differentiated threshold approaches, more spatial details of urban cover are demonstrated than previously reported in many global datasets. Many city clusters were merging into each other, with gradual blurring of boundaries and disappearance of gaps among member cities. Cities and towns were more connected with roads and commercial corridors, while wildland and urban green areas have become more isolated as patches among built-up areas. Average road width in commercial areas was 37.2 m in Beijing (north, temperate) and 24.2 m in Guangzhou (south, tropical), which are greater than these listed in model default values. Those new estimates could effectively improve climate simulation at local and regional scales in East Asia.
The cumulative effects of urban expansion on land surface temperatures in metropolitan Jing-Jin-Tang, ChinaHu, Y., G. Jia, M. Hou, X. Zhang, F. Zheng, Y. Liu.Journal of Geophysical Research: Atmospheres:2015,120 (19),9932-9943AbstractRapid urbanization has resulted in the permanent conversion of large areas of cropland and natural vegetation to impervious surfaces and therefore greatly modified land surface properties and land-atmosphere interactions. This study sought to examine the urbanization process using Landsat images from 2001 to 2010 in metropolitan JingjinTang (JJT), a rapidly expanding urban cluster in northern China. We aggregated the original results of land use data as fractional cover information in 1 km and 10 km grids. Annual and seasonal land surface temperatures (LSTs) were processed from Moderate Resolution Imaging Spectroradiometer products. We used moving window and gradient analysis methods to examine the differences in LST between urban and other land types, further identifying LST increases in gradients of urbanization levels. Urban extent increased by 1.6 times, and approximately 45% newly developed areas were converted from croplands during this process. Emerging urban land in JJT has caused approximately 0.85 ± 0.68°C warming in terms of annual mean LST, and the greatest warming occurred in the summer. An increase in urban land of 10% in a 1 km grid in JJT would cause approximately a 0.21°C increase in annual LST. Urbanization also led to increases in daytime LSTs and nighttime LSTs by approximately 1.03 ± 1.38°C and 0.78 ± 1.02°C, respectively. The warming trend induced by urbanization exhibits clear seasonal and diurnal differences, and this warming trend is most likely caused by the cumulative effects of changes in land properties, radiation storage, and anthropogenic heat release by urbanization.
Age-dependent forest carbon sink: Estimation via inverse modelingZhou, T., P. Shi, G. Jia, Y. Dai, X. Zhao, W. Shangguan, L. Du, H. Wu, Y. Luo.Journal of Geophysical Research: Biogeosciences:2015,120 (12),2473-2492, doi: 10.1002/2015JG002943AbstractForests have been recognized to sequester a substantial amount of carbon (C) from the atmosphere. However, considerable uncertainty remains regarding the magnitude and time course of the C sink. Revealing the intrinsic relationship between forest age and C sink is crucial for reducing uncertainties in prediction of forest C sink potential. In this study, we developed a step-wise data assimilation approach to combine a process-based TECO-R model, observations from multiple sources, and stochastic sampling to inversely estimate carbon cycle parameters including carbon sink at different forest ages for evergreen needle-leaved forests in China. The new approach is effective to estimate age-dependent parameter of maximal light-use efficiency (R2 = 0.99) and, accordingly, can quantify a relationship between forest age and the vegetation and soil C sinks. The estimated ecosystem C sink increases rapidly with age, peaks at 0.451 kg C m−2 yr−1 at age 22 years (ranging from 0.421 to 0.465 kg C m−2 yr−1), and gradually decreases thereafter. The dynamic patterns of C sinks in vegetation and soil are significantly different. C sink in vegetation first increases rapidly with age and then decreases. C sink in soil, however, increases continuously with age; it acts as a C source when the age is less than 20 years, after which it acts as a sink. For the evergreen needle-leaved forest, the highest C sink efficiency (i.e., C sink per unit NPP) is approximately 60%, with age between 11 and 43 years. Overall, the inverse estimation of carbon cycle parameters can make reasonable estimates of age-dependent C sequestration in forests.
Comparison of three different methods to identify fractional urban signals for improving climate modelingHu, Y., M. Hou, G. Jia, X. Zhang, R. Xu, Y. He.International Journal of Remote Sensing:2015,36 (12),3274-3292, doi: 10.1080/01431161.2015.1042593AbstractUrbanization has changed the properties of the Earth’s surface and resulted in modification of the biogeochemical cycle and possible climate feedback at global and regional scales. Such climate effects are especially evident locally over short periods in megacity areas. Climate model simulation and urbanization process analysis are often limited by poor accuracy of land-cover products that largely neglect mixed urban-surface information below certain thresholds. The present study compares three urban land identification methods (fractional cover, overlapping parabolic interpolation, and threshold) used in remote sensing and climate model parameterization with Landsat Thematic Mapper images and Moderate Resolution Imaging Spectroradiometer land-cover data sets in a systematic evaluation. We also analyse deviation induced by scaling effects and its influence on the urban radiation budget to better understand the implications of land-surface parameter deviation on regional climate analysis. A positive linear relationship is found between the spatial scale and urban-area deviation based on combined analysis of the three land identification methods, and deviation trends levelled off with an increase in the spatial scale. Coarse-resolution land-cover products could not capture well the urbanization process indicated by reference data from Beijing between 2000 and 2009, especially in urban fringe areas where major urban expansion was detected. Detailed sub-pixel information was possibly neglected by threshold methods, which resulted in strong deviation between land-cover products and actual conditions. The overlapping parabolic interpolation method used in climate models also produced deviation in surface parameter derivation during nested simulation work. This might further affect model performance at the regional scale and should be considered in climate model simulation.
Projections of the Advance in the Start of the Growing Season During the 21st Century Based on CMIP5 SimulationsXia JJ, Yan ZW, Jia GS, Zeng HQ, Jones PD, Zhou W, Zhang AZ.Advances in Atmospheric Sciences:2015,32 (6),831-838AbstractIt is well-known that global warming due to anthropogenic atmospheric greenhouse effects advanced the start of the vegetation growing season (SOS) across the globe during the 20th century. Projections of further changes in the SOS for the 21st century under certain emissions scenarios (Representative Concentration Pathways, RCPs) are useful for improving understanding of the consequences of global warming. In this study, we first evaluate a linear relationship between the SOS (defined using the normalized difference vegetation index) and the April temperature for most land areas of the Northern Hemisphere for 1982--2008. Based on this relationship and the ensemble projection of April temperature under RCPs from the latest state-of-the-art global coupled climate models, we show the possible changes in the SOS for most of the land areas of the Northern Hemisphere during the 21st century. By around 2040--59, the SOS will have advanced by −4.7 days under RCP2.6, −8.4 days under RCP4.5, and −10.1 days under RCP8.5, relative to 1985--2004. By 2080--99, it will have advanced by −4.3 days under RCP2.6, −11.3 days under RCP4.5, and −21.6 days under RCP8.5. The geographic pattern of SOS advance is considerably dependent on that of the temperature sensitivity of the SOS. The larger the temperature sensitivity, the larger the date-shift-rate of the SOS.
Satellite based estimation of daily average net radiation under clear sky conditionsHou, J. G. Jia, T. Zhao, H. Wang, B. Tang.Advances in Atmospheric Sciences:2014,31(3),705-720AbstractDownloadDaily average net radiation (DANR) is an important variable for estimating evapotranspiration from satellite data at regional scales, and is used for atmospheric and hydrologic modeling, as well as ecosystem management. A scheme is proposed to estimate the DANR over large heterogeneous areas under clear-sky conditions using only remotely sensed data. The method was designed to overcome the dependence of DANR estimates on ground data, and to map spatially consistent and reasonably distributed DANR, by using various land and atmospheric data products retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) data. An improved sinusoidal model was used to retrieve the diurnal variations of downward shortwave radiation using a single instantaneous value from satellites. The downward shortwave component of DANR was directly obtained from this instantaneous value, and the upward shortwave component was estimated using satellite-derived albedo products. Four observations of air temperature from MOD07 L2 and MYD07 L2 data products were used to derive the downward longwave component of DANR, while the upward longwave component was estimated using the land surface temperature (LST) and the surface emissivity from MOD11 L2. Compared to in situ observations at the cropland and grassland sites located in Tongyu, northern China, the root mean square error (RMSE) of DANR estimated for both sites under clear-sky conditions was 37 W m−2 and 40 W m−2, respectively. The errors in estimation of DANR were comparable to those from previous satellite-based methods. Our estimates can be used for studying the surface radiation balance and evapotranspiration. doi: 10.1007/s00376-013-3047-6
Net ecosystem productivity of temperate grasslands in northern China: an upscaling studyZhang, L., H. Guo, G. Jia, B. Wylie, T. Gilmanov, et al..Agricultural and Forest Meteorology:2014,184,71-81AbstractGrassland is one of the widespread biome types globally, and plays an important role in the terrestrial car- bon cycle. We examined net ecosystem production (NEP) for the temperate grasslands in northern China from 2000 to 2010. We combined flux observations, satellite data, and climate data to develop a piece- wise regression model for NEP, and then used the model to map NEP for grasslands in northern China. Over the growing season, the northern China’s grassland had a net carbon uptake of 158±25gCm−2 during 2000–2010 with the mean regional NEP estimate of 126 Tg C. Our results showed generally higher grassland NEP at high latitudes (northeast) than at low latitudes (central and west) because of different grassland types and environmental conditions. In the northeast, which is dominated by meadow steppes, the growing season NEP generally reached 200–300 g C m−2 . In the southwest corner of the region, which is partially occupied by alpine meadow systems, the growing season NEP also reached 200–300 g C m−2 . In the central part, which is dominated by typical steppe systems, the growing season NEP generally varied in the range of 100–200 g C m−2 . The NEP of the northern China’s grasslands was highly variable through years, ranging from 129 (2001) to 217 g C m−2 growing season−1 (2010). The large interannual variations of NEP could be attributed to the sensitivity of temperate grasslands to climate changes and extreme cli- matic events. The droughts in 2000, 2001, and 2006 reduced the carbon uptake over the growing season by 11%, 29%, and 16% relative to the long-term (2000–2010) mean. Over the study period (2000–2010), precipitation was significantly correlated with NEP for the growing season (R2 = 0.35, p-value ＜ 0.1), indi- cating that water availability is an important stressor for the productivity of the temperate grasslands in semi-arid and arid regions in northern China. We conclude that northern temperate grasslands have the potential to sequester carbon, but the capacity of carbon sequestration depends on grassland types and environmental conditions. Extreme climate events like drought can significantly reduce the net carbon uptake of grasslands. doi: 10.1016/j.agrformet.2013.09.004
Monitoring meteorological drought in semiarid region using multi-sensor microwave remote sensing dataZhang, A., G. Jia.Remote Sensing of Environment:2013,134,12-23AbstractThe existing remote sensing drought indices were mainly derived from optical and infrared bands, and have been widely used in monitoring agricultural drought; however, their application in monitoring meteorolog- ical drought was limited. This study proposes a new multi-sensor microwave remote sensing drought index, the Microwave Integrated Drought Index (MIDI), for monitoring short-term drought, especially the meteoro- logical drought over semi-arid regions, by integrating three variables: Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Advanced Microwave Scanning Radiometer for EOS (AMSR-E) derived soil moisture, and AMSR-E derived land surface temperature. Each variable was linearly scaled from 0 to 1 for each pixel based on absolute minimum and maximum values over time to relatively monitor drought. Pearson correlation analyses were performed between remote sensing drought indices and scale- dependent Standardized Precipitation Index (SPI) during the growing season (April to October) from 2003 to 2010 to assess the capability of remotely sensed drought indices over three bioclimate regions in northern China. The results showed that MIDI with proper weights of three components outperformed individual re- mote sensing drought indices and other combined microwave drought indices in monitoring drought. It nearly possessed the best correlations with different time scale SPI; meanwhile it showed the highest corre- lation with 1-month SPI, and then decreased as SPI time scale increased, suggesting that the MIDI was a very reliable index in monitoring meteorological drought. Furthermore, similar spatial patterns and temporal changes were found between MIDI and 1- or 3-month SPI in monitoring drought. Therefore, the MIDI was recommended to be the optimum drought index, in monitoring short-term drought, especially for meteoro- logical drought over cropland and grassland across northern China or similar regions globally with the ability to work in all weather conditions.
Fluctuation of farming-pastoral ecotone in association with changing East Asia monsoon climateLu, W., G. Jia.Climatic Change:2013,119 (3),747-760AbstractAs a monsoon climate dominated region, East Asia has a high rate of climate variation. Previous studies demonstrated that the East Asian monsoon had weakened since the end of 1970’s; however, contrary to the climatic trend, a common scenario of advancing farming-pastoral ecotone (FPE) has been proposed. The objective of this study is to analyze land surface changes in association with monsoon climate variability over past 25 years in East Asia. A combination of intensive ground survey of vegetation and land use, meteoro- logical data, and remote sensing are used to quantify the relationship between vegetation and climate and to analyze the FPE fluctuations associated with changing climate. Field precip- itation data from 1981 to 2005, are used to represent climate variations and to delineate the FPE boundary. NDVI data are used to evaluate greenness-precipitation linkages by vegeta- tion type and to create land cover maps depicting spatial pattern fluctuations of the FPE. This study demonstrates that: (1) There was no persistent northwest shifting trend of either the FPE boundary or vegetation cover during last 25 years. (2) Time integrated NDVI (TI- NDVI) varies with precipitation, and the maximum or minimum NDVI may be only sensitive to precipitation for areas with mean annual precipitation lower than approximately 200 mm. (3) A significant relationship exists between NDVI and precipitation variations for areas with mean annual precipitation greater than approximately 300 mm, especially the ecotone with a ΔNDVI of 0.122±0.032. (4) The “advances” of FPE closely mimic fluctu- ations of precipitation in East Asia.
Response of Arctic phenological shifts to climate and anthropogenic factors as detected from multi-satellite dataZeng, H., G. Jia, B.C. Forbes.Environmental Research Letters:2013,8(3),035036AbstractThere is an urgent need to reduce the uncertainties in remotely sensed detection of phenological shifts of high latitude ecosystems in response to climate changes in past decades. In this study, vegetation phenology in western Arctic Russia (the Yamal Peninsula) was investigated by analyzing and comparing Normalized Difference Vegetation Index (NDVI) time series derived from the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and SPOT-Vegetation (VGT) during the decade 2000–2010. The spatial patterns of key phenological parameters were highly heterogeneous along the latitudinal gradients based on multi-satellite data. There was earlier SOS (start of the growing season), later EOS (end of the growing season), longer LOS (length of the growing season), and greater MaxNDVI from north to south in the region. The results based on MODIS and VGT data showed similar trends in phenological changes from 2000 to 2010, while quite a different trend was found based on AVHRR data from 2000 to 2008. A significantly delayed EOS (p ＜ 0.01), thus increasing the LOS, was found from AVHRR data, while no similar trends were detected from MODIS and VGT data. There were no obvious shifts in MaxNDVI during the last decade. MODIS and VGT data were considered to be preferred data for monitoring vegetation phenology in northern high latitudes. Temperature is still a key factor controlling spatial phenological gradients and variability, while anthropogenic factors (reindeer husbandry and resource exploitation) might explain the delayed SOS in southern Yamal. Continuous environmental damage could trigger a positive feedback to the delayed SOS.
Detecting urban warming signals in climate recordsHe, Y., G. Jia, Y. Hu, Z. Zhou.Advances in Atmospheric Sciences:2013,30(4),1143-1153AbstractDetermining whether air temperatures recorded at meteorological stations have been contaminated by the urbanization process is still a controversial issue at the global scale. With support of historical remote sensing data, this study examined the impacts of urban expansion on the trends of air temperature at 69 meteorological stations in Beijing, Tianjin, and Hebei Province over the last three decades. There were significant positive relations between the two factors at all stations. Stronger warming was detected at the meteorological stations that experienced greater urbanization, i.e., those with a higher urbanization rate. While the total urban area affects the absolute temperature values, the change of the urban area (urbanization rate) likely affects the temperature trend. Increases of approximately 10% in urban area around the meteorological stations likely contributed to the 0.13◦C rise in air temperature records in addition to regional climate warming. This study also provides a new approach to selecting reference stations based on remotely sensed urban fractions. Generally, the urbanization-induced warming contributed to approximately 44.1% of the overall warming trends in the plain region of study area during the past 30 years, and the regional climate warming was 0.30◦C (10 yr)−1 in the last three decades.
Assessing disagreement and tolerance of misclassification of satellite- derived land cover products used in WRF model applicationsGao, H., G. Jia.Advances in Atmospheric Sciences:2013,30(1),125-141AbstractAs more satellite-derived land cover products used in the study of global change, especially climate modeling, assessing their quality has become vitally important. In this study, we developed a distance metric based on the parameters used in weather research and forecasting (WRF) to characterize the degree of disagreement among land cover products and to identify the tolerance for misclassification within the International Geosphere Biosphere Programme (IGBP) classification scheme. We determined the spatial degree of disagreement and then created maps of misclassification of Moderate Resolution Imaging Spec- toradiometer (MODIS) products, and we calculated overall and class-specific accuracy and fuzzy agreement in a WRF model. Our results show a high level of agreement and high tolerance of misclassification in the WRF model between large-scale homogeneous landscapes, while a low level of agreement and tolerance of misclassification appeared in heterogeneous landscapes. The degree of disagreement varied significantly among seven regions of China. The class-specific accuracy and fuzzy agreement in MODIS Collection 4 and 5 products varied significantly. High accuracy and fuzzy agreement occurred in the following classes: water, grassland, cropland, and barren or sparsely vegetated. Misclassification mainly occurred among specific classes with similar plant functional types and low discriminative spectro-temporal signals. Some classes need to be improved further; the quality of MODIS land cover products across China still does not meet the common requirements of climate modeling. Our findings may have important implications for improving land surface parameterization for simulating climate and for better understanding the influence of the land cover change on climate.
Nonsteady-State Carbon Sequestration in Forest Ecosystems of China Estimated by Data AssimilationZhou, T., P. Shi, G. Jia, Y. Luo.Journal of Geophysical Research:2013,118,1369–1384AbstractCarbon sequestration occurs only when terrestrial ecosystems are at nonsteady states. Despite of their ubiquity in the real world, the nonsteady states of ecosystems have not been well quantified, especially at regional and global scales. In this study, we developed a two-step data assimilation scheme to estimate carbon sink strength in China’s forest ecosystems. Specifically, the two-step scheme consists of a steady state step and a nonsteady state step. In the steady state step, we constrained a process-based model (Terrestrial Ecosystem Regional (TECO-R) model) against biometric data (net primary production NPP, biomass, litter, and soil organic carbon) in mature forests. With a subset of the parameter values estimated from the steady state data assimilation being fixed, the nonsteady state data assimilation was performed to estimate carbon sequestration in China’s forest ecosystems. Our results indicated that 17 out of the 22 total parameters in the TECO-R model were well constrained by the biometric data with the steady state data assimilation. When observations from both mature and developing forests were used, all the 10 parameters related to carbon sequestration in vegetation and soil carbon pools were well constrained at the nonsteady state step. The estimated mean vegetation carbon sink in China’s forests is 89.7 ± 16.8 gC m2 yr1, comparable with the values estimated from the forest inventory and other process-based regional models. The estimated mean soil and litter carbon sinks in China’s forests are 14.1 ± 20.7 and 4.7 ± 6.5 gC m2 yr1. This study demonstrated that a two-step data assimilation scheme can be a potent tool to estimate regional carbon sequestration in nonsteady state ecosystems. DOI: 10.1002/jgrg.20114
Regional estimates of evapotranspiration over northern China with a remotely sensed triangle interpolation methodWang, H., G. Jia.Advances in Atmospheric Sciences:2013,30(5),1479-1490AbstractRegional estimates of evapotranspiration (ET) are critical for a wide range of applications. Satellite remote sensing is a promising tool for obtaining reasonable ET spatial distribution data. However, there are at least two major problems that exist in the regional estimation of ET from remote sensing data. One is the conflicting requirements of simple data over a wide region, and accuracy of those data. The second is the lack of regional ET products that cover the entire region of northern China. In this study, we first retrieved the evaporative fraction (EF) by interpolating from the difference of day/night land surface temperature (ΔT) and the normalized difference vegetation index (NDVI) triangular-shaped scatter space. Then, ET was generated from EF and land surface meteorological data. The estimated eight-day EF and ET results were validated with 14 eddy covariance (EC) flux measurements in the growing season (July–September) for the year 2008 over the study area. The estimated values agreed well with flux tower measurements, and this agreement was highly statistically significant for both EF and ET (p ＜0.01), with the correlation coefficient for EF (R2=0.64) being relatively higher than for ET (R2=0.57). Validation with EC-measured ET showed the mean RMSE and bias were 0.78 mm d−1 (22.03 W m−2) and 0.31 mm d−1 (8.86 W m−2), respectively. The ET over the study area increased along a clear longitudinal gradient, which was probably controlled by the gradient of precipitation, green vegetation fractions, and the intensity of human activities. The satellite-based estimates adequately captured the spatial and seasonal structure of ET. Overall, our results demonstrate the potential of this simple but practical method for monitoring ET over regions with heterogeneous surface areas.
Nested high-resolution modeling of the impact of urbanization on regional climate in three vast urban agglomerations in ChinaWang, J., J. Feng, Z. Yan, Y. Hu, G. Jia.Journal of Geophysical Research:2012,117,D21103, doi:10.1029/2012JD018226AbstractIn this paper, the Weather Research and Forecasting Model, coupled to the Urban Canopy Model, is employed to simulate the impact of urbanization on the regional climate over three vast city agglomerations in China. Based on high-resolution land use and land cover data, two scenarios are designed to represent the nonurban and current urban land use distributions. By comparing the results of two nested, high-resolution numerical experiments, the spatial and temporal changes on surface air temperature, heat stress index, surface energy budget, and precipitation due to urbanization are analyzed and quantified. Urban expansion increases the surface air temperature in urban areas by about 1°C, and this climatic forcing of urbanization on temperature is more pronounced in summer and nighttime than other seasons and daytime. The heat stress intensity, which reflects the combined effects of temperature and humidity, is enhanced by about 0.5 units in urban areas. The regional incoming solar radiation increases after urban expansion, which may be caused by the reduction of cloud fraction. The increased temperature and roughness of the urban surface lead to enhanced convergence. Meanwhile, the planetary boundary layer is deepened, and water vapor is mixed more evenly in the lower atmosphere. The deficit of water vapor leads to less convective available potential energy and more convective inhibition energy. Finally, these combined effects may reduce the rainfall amount over urban areas, mainly in summer, and change the regional precipitation pattern to a certain extent.
Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modelingWang, H., G. Jia, C. Fu, et al.Remote Sensing of Environment:2010,114,2248-2258AbstractRemote sensing models based on light use efficiency (LUE) provide promising tools for monitoring spatial and temporal variation of gross primary production (GPP) at regional scale. In most of current LUE-based models, maximal LUE (εmax) heavily relies on land cover types and is considered as a constant, rather than a variable for a certain vegetation type or even entire eco-region. However, species composition and plant functional types are often highly heterogeneous in a given land cover class; therefore, spatial heterogeneity of εmax must be fully considered in GPP modeling, so that a single cover type does not equate to a single εmax value. A spatial dataset of εmax accurately represents the spatial heterogeneity of maximal light use would be of significant beneficial to regional GPP models. Here, we developed a spatial dataset of εmax by integrating eddy covariance flux measurements from 14 field sites in a network of coordinated observation across northern China and satellite derived indices such as enhanced vegetation index (EVI) and visible albedo to simulate regional distribution of GPP. This dynamic modeling method recognizes the spatial heterogeneity of εmax and reduces the uncertainties in mixed pixels. Further, we simulated GPP with the spatial dataset of εmax generated above. Both εmax and growing season GPP show complex patterns over northern China that reflect influences of humidity, green vegetation fractions, and land use intensity. “Green spots” such as oasis meadow and alpine forests in dryland and “brown spots” such as build-up and heavily degraded vegetation in the east are clearly captured by the simulation. The correlation between simulated GPP and EC measured GPP indicate that the simulated GPP from this new approach is well matched with flux-measured GPP. Those results have demonstrated the importance of considering εmax as both a spatially and temporally variable values in GPP modeling.
Circumpolar Arctic tundra vegetation change is linked to sea-ice declineBhatt, U.S., D.A. Walker, M.K. Raynolds, J.C. Comiso, H.E. Epstein, G.Jia, et al.Earth Interactions:2010,14(8),1-20AbstractLinkages between diminishing Arctic sea ice and changes in Arctic terrestrial ecosystems have not been previously demonstrated. Here, the authors use a newly available Arctic Normalized Difference Vegetation Index (NDVI) dataset (a measure of vegetation photosynthetic capacity) to document coherent temporal relationships between near-coastal sea ice, summer tundra land surface temperatures, and vegetation productivity. The authors find that, during the period of satellite observations (1982–2008), sea ice within 50 km of the coast during the period of early summer ice breakup declined an average of 25% for the Arctic as a whole, with much larger changes in the East Siberian Sea to Chukchi Sea sectors (＞44% decline). The changes in sea ice conditions are most directly relevant and have the strongest effect on the villages and ecosystems immediately adjacent to the coast, but the terrestrial effects of sea ice changes also extend far inland. Low-elevation (＜300 m) tundra summer land temperatures, as indicated by the summer warmth index (SWI; sum of the monthly-mean temperatures above freezing, expressed as °C month−1), have increased an average of 5°C month−1 (24% increase) for the Arctic as a whole; the largest changes (+10° to 12°C month−1) have been over land along the Chukchi and Bering Seas. The land warming has been more pronounced in North America (+30%) than in Eurasia (16%). When expressed as percentage change, land areas in the High Arctic in the vicinity of the Greenland Sea, Baffin Bay, and Davis Strait have experienced the largest changes (＞70%). The NDVI has increased across most of the Arctic, with some exceptions over land regions along the Bering and west Chukchi Seas. The greatest change in absolute maximum NDVI occurred over tundra in northern Alaska on the Beaufort Sea coast [+0.08 Advanced Very High Resolution Radiometer (AVHRR) NDVI units]. When expressed as percentage change, large NDVI changes (10%–15%) occurred over land in the North America High Arctic and along the Beaufort Sea. Ground observations along an 1800-km climate transect in North America support the strong correlations between satellite NDVI observations and summer land temperatures. Other new observations from near the Lewis Glacier, Baffin Island, Canada, document rapid vegetation changes along the margins of large retreating glaciers and may be partly responsible for the large NDVI changes observed in northern Canada and Greenland. The ongoing changes to plant productivity will affect many aspects of Arctic systems, including changes to active-layer depths, permafrost, biodiversity, wildlife, and human use of these regions. Ecosystems that are presently adjacent to year-round (perennial) sea ice are likely to experience the greatest changes.
Estimates of forest canopy fuel attributes using hyperspectral dataJia, G.J., I.C. Burke, M.R. Kaufmann, A.F.H. Goetz, et al..Forest Ecology and Management:2006,229(1),27-38AbstractIncreasingly severe forest fires in the west have triggered a high demand for accurate and timely information on forest fuel attributes. There is great interest in the potential for using recent advances in high spectral resolution remotely sensed imagery to estimate fuel characteristics. We combined field forest inventory and field spectroscopy in the Colorado Front Range with airborne imaging spectrometer measurements of the region to test their capacity to estimate fire related forest attributes including canopy cover, forest type, and burn severity in ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii var. glauca) dominated forests. Spectral angle mapper and mixture-tuned matched filtering techniques were tested for mapping fuel attributes. Estimates of canopy cover using spectral angle mapper techniques found 61% agreement with observed values, while mixture-tuned matched filtering estimates of forest canopy cover matched 78% with field observations. The distinction of ponderosa pine versus Douglas-fir is crucial for predicting fire spread in the Rocky Mountains; we found that spectral discrimination of these species was also promising, with an accuracy of 53–57%. The average canopy cover of mixed conifer forest in the area is 38.6%, 24.7% contributed by ponderosa pine and 13.9% by Douglas-fir. The values of canopy cover ranged from 53% to 56% in US Forest Service planned fuel treatment areas, among the highest in the region. Recent forest fires have created approximately 684 km2 of burned area, with very low canopy cover (13–22%).
The relative aboundance of plant functional types in temperate grasslands and shrublands of North and South America: Effects of projected climate changeEpstein, H.E., R.A. Gill, J.M. Paruelo, G.J. Jia, W.K. Lauenroth and I.C. Burke.Journal of Biogeography:2002,29(7),875-888AbstractKeywords: C3 grasses; C4 grasses; climate change; North America; plant functional types; precipitation; shrubs; South America; temperature; vegetation dynamics Aim Use a regression model that relates climatic variables to the relative abundances of shrubs, C4 and C3 grasses to project the plant functional type composition of temperate grasslands and shrublands within North and South America in response to climate change. Location The temperate zone grassland and shrubland regions of North and South America. Methods We used a regression model to project changes in the relative abundances of shrubs, C4 and C3 grasses under three general circulation model (GFDL, GISS, UKMO) climate change scenarios. The three climate change scenarios were applied to a global data set of mean monthly temperatures and precipitation. The regression model, which incorporates mean annual temperature, mean annual precipitation and seasonality of precipitation as input variables, was used to project plant functional type changes. Spatial patterns of change were analysed using a geographical information system. Results Relative abundance of C4 grasses were projected to increase ＞10% throughout most of the study region at the expense of C3 grasses. There were essentially no areas where C4 grasses decreased in abundance, and the areas with no change were largely the southern Great Plains and the Intermountain Basin and Range of North America. C3 grasses declined throughout with the exception of the north-western Great Plains of the US and Canada, and north central Argentina. Changes in shrub abundance were mixed with some increases in Patagonia and the desert regions of the south-western US; there were also some projected decreases, however, the locations varied across models. Main conclusions The projections made by our regression model were consistent with those of other more complex vegetation dynamics models. Changes in plant community composition in response to climate change may be substantial in certain areas and will probably lead to changes in water and nutrient cycling.