Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

Mueller, Thomas; Dreßler, Gunnar; Tucker, Compton J.; Pinzón, Jorge Enrique Dí­az; Leimgruber, Peter; Dubayah, Ralph; Hurtt, George C.; Böhning‐Gaese, Katrin; Fagan, William F.

doi:10.3390/rs6065717

Cited by 70 publications

(46 citation statements)

References 44 publications

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“…In cases where NDVI values were lower than the 0.05 threshold value, they were set equal to 0.05 (following [27]). The bimonthly NDVI data were aggregated to monthly values.…”

Section: Discussionmentioning

confidence: 99%

Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison

et al. 2016

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Abstract:The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model-based NPP estimates (from five models of the TRENDY project) for the period 1982-2012, to study the role of terrestrial ecosystems in carbon cycle under the prevailing climate conditions. We found that 80% and 67% of the global land area showed positive NPP and NDVI values, respectively, for this period. The global NPP was estimated to be about 63 Pg C¨y´1, with an increase of 0.214 Pg C¨y´1¨y´1. Similarly, the global mean NDVI was estimated to be 0.33, with an increasing trend of 0.00041 y´1. The spatial patterns of NPP and NDVI demonstrated substantial variability, especially at the regional level, for most part of the globe. However, on temporal scale, both global NPP and NDVI showed a corresponding pattern of increase (decrease) for the duration of this study except for few years (e.g., 1990 and 1995-1998). Generally, the Northern Hemisphere showed stronger NDVI and NPP increasing trends over time compared to the Southern Hemisphere; however, NDVI showed larger trends in Temperate regions while NPP showed larger trends in Boreal regions. Among the five models, the maximum and minimum NPP were produced by JULES (72.4 Pg C¨y´1) and LPJ (53.72 Pg C¨y´1) models, respectively. At latitudinal level, the NDVI and NPP ranges were~0.035 y´1 to~´0.016 y´1 and~0.10 Pg C¨y´1¨y´1 to~´0.047 Pg C¨y´1¨y´1, respectively. Overall, the results of this study suggest that the modeled NPP generally correspond to the NDVI trends in the temporal dimension. The significant variability in spatial patterns of NPP and NDVI trends points to a need for research to understand the causes of these discrepancies between molded and observed ecosystem dynamics, and the carbon cycle.

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“…In cases where NDVI values were lower than the 0.05 threshold value, they were set equal to 0.05 (following [27]). The bimonthly NDVI data were aggregated to monthly values.…”

Section: Discussionmentioning

confidence: 99%

Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison

et al. 2016

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“…For example, in some areas such as densely populated areas [31] and drylands [32][33][34], human-induced disturbances have been regularly cited as one of the principal causes of changes in vegetation cover. In Inner Mongolia, human factors such as population growth and city expansion were found have a greater influence on vegetation degradation than climatic changes from 2000 to 2012, and ecological restoration programs also played a large role in vegetation improvement [35].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Changes in Vegetation Activity and Its Driving Factors during the Growing Season in China from 1982 to 2011

et al. 2015

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Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic variables and human activities on vegetation variation. Growing-season (period between the onset and end of plant growth) NDVI significantly increased (p < 0.01) on a national scale and showed positive trends in 52.76% of the study area. A multiple regression model was used to investigate the response of vegetation to climatic factors during recent and previous time intervals. The interactions between growing-season NDVI and climatic variables were more complex than expected, and a lag existed between climatic factors and their effects on NDVI. The regression residuals were used to show that over 6% of the study area experienced significantly human-induced vegetation variations (p < 0.05). These regions were mostly located in densely populated, reclaimed agriculture, afforestation, and conservation areas. Similar conclusions were drawn based on land-use change over the study period. OPEN ACCESSRemote Sens. 2015, 7 13730

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“…Further, uncertainties in existing model parameterization and structure (Supplementary Section 7) and biases from the remote sensing data sets (Supplementary Section 6) can cause a mismatch between simulated and observed LAI trends. Interestingly, positive effects tentatively attributed to OF are mainly found in areas of intensive ecosystem management, such as northeast China, Europe and India 27 . Negative OF effects are mainly found in northern high latitudes, where most models lack a representation of regionally important ecosystems (peatlands, wetlands) as well as of specific disturbances 28,29 .…”

Section: ) D Probability Density Function Of Lai Trends For Gimmsmentioning

confidence: 99%

Greening of the Earth and its drivers

et al. 2016

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Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with consequences for the functioning of the Earth system and provision of ecosystem services 1,2 . Yet how global vegetation is responding to the changing environment is not well established. Here we use three long-term satellite leaf area index (LAI) records and ten global ecosystem models to investigate four key drivers of LAI trends during 1982-2009. We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area, whereas less than 4% of the globe shows decreasing LAI (browning). Factorial simulations with multiple global ecosystem models suggest that CO 2 fertilization e ects explain 70% of the observed greening trend, followed by nitrogen deposition (9%), climate change (8%) and land cover change (LCC) (4%). CO 2 fertilization e ects explain most of the greening trends in the tropics, whereas climate change resulted in greening of the high latitudes and the Tibetan Plateau. LCC contributed most to the regional greening observed in southeast China and the eastern United States. The regional e ects of unexplained factors suggest that the next generation of ecosystem models will need to explore the impacts of forest demography, di erences in regional management intensities for cropland and pastures, and other emerging productivity constraints such as phosphorus availability.Changes in vegetation greenness have been reported at regional and continental scales on the basis of forest inventory and satellite measurements 3-8 . Long-term changes in vegetation greenness are driven by multiple interacting biogeochemical drivers and land-use effects 9 . Biogeochemical drivers include the fertilization effects of elevated atmospheric CO 2 concentration (eCO 2 ), regional climate change (temperature, precipitation and radiation), and varying rates of nitrogen deposition. Land-use-related drivers involve changes in land cover and in land management intensity, including fertilization, irrigation, forestry and grazing 10 . None of these driving factors can be considered in isolation, given their strong interactions with one another. Previously, a few studies had investigated the drivers of global greenness trends 6,7,11 , with a limited number of models and satellite observations, which prevented an appropriate quantification of uncertainties 12 .Here, we investigate trends of leaf area index (LAI) and their drivers for the period 1982 to 2009 using three remotely sensed data sets (GIMMS3g, GLASS and GLOMAP) and outputs from ten ecosystem models run at global extent (see Supplementary Information). We use the growing season integrated leaf area index (hereafter, LAI; Methods) as the variable of our study. We first analyse global and regional LAI trends for the study period and differences between the three data sets. Using modelling results, we then quantify the contributions of CO 2 fertilization, climatic factors, nitrogen deposition and LCC to the observed trends...

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Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

Cited by 70 publications

References 44 publications

Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison

Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison

Spatio-Temporal Changes in Vegetation Activity and Its Driving Factors during the Growing Season in China from 1982 to 2011

Greening of the Earth and its drivers

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