scholarly journals Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index

2020 ◽  
Vol 12 (4) ◽  
pp. 595
Author(s):  
Jinhui Wu ◽  
Shunlin Liang
Keyword(s):  
Positive Impact ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Global Scale ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Ecosystem Resilience ◽  
Area Index

Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.

scholarly journals Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yuhao Feng ◽  
Haojie Su ◽  
Zhiyao Tang ◽  
Shaopeng Wang ◽  
Xia Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Vegetation Index ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Ecological Resilience ◽  
Terrestrial Vegetation ◽  
Ecosystem Resilience

AbstractGlobal climate change likely alters the structure and function of vegetation and the stability of terrestrial ecosystems. It is therefore important to assess the factors controlling ecosystem resilience from local to global scales. Here we assess terrestrial vegetation resilience over the past 35 years using early warning indicators calculated from normalized difference vegetation index data. On a local scale we find that climate change reduced the resilience of ecosystems in 64.5% of the global terrestrial vegetated area. Temperature had a greater influence on vegetation resilience than precipitation, while climate mean state had a greater influence than climate variability. However, there is no evidence for decreased ecological resilience on larger scales. Instead, climate warming increased spatial asynchrony of vegetation which buffered the global-scale impacts on resilience. We suggest that the response of terrestrial ecosystem resilience to global climate change is scale-dependent and influenced by spatial asynchrony on the global scale.

scholarly journals Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems

2016 ◽  
Vol 40 (2) ◽  
pp. 322-351 ◽  
Author(s):  
Jadunandan Dash ◽  
Booker O. Ogutu
Keyword(s):  
Remote Sensing ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Data Availability ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Ecosystem Monitoring ◽  
Sensing Data

Since the launch of the first Landsat satellite in the early 1970s, the field of space-borne optical remote sensing has made significant progress. Advances have been made in all aspects of optical remote sensing data, including improved spatial, temporal, spectral and radiometric resolutions, which have increased the uptake of these data by wider scientific communities. Flagship satellite missions such as NASA’s Terra and Aqua and ESA’s Envisat with their high temporal (<3days) and spectral (15–36 bands) resolutions opened new opportunities for routine monitoring of various aspects of terrestrial ecosystems at the global scale and have provided greater understanding of critical biophysical processes in the terrestrial ecosystem. The launch of new satellite sensors such as Landsat 8 and the European Space Agency’s Copernicus Sentinel missions (e.g. Sentinel 2 with improved spatial resolution (10–60 m) and potential revisit time of five days) is set to revolutionise the availability and use of remote sensing data in global terrestrial ecosystem monitoring. Furthermore, the recent move towards use of constellations of nanosatellites (e.g. the Flock missions by Planet Labs) to collect on-demand high spatial and temporal resolution optical remote sensing data would enable uptake of these data for operational monitoring. As a result of increase in data availability, optical remote sensing data are now increasingly used to support a number of operational services (e.g. land monitoring, atmosphere monitoring and climate change studies). However, many challenges still remain in exploiting the growing volume of optical remote sensing data to monitor global terrestrial ecosystems. These challenges include ensuring the highest data quality both in terms of the sensitivity of sensors and the derived biophysical products, affordability and availability of the data and continuity of data acquisition. This review provides an overview of the developments in space-borne optical remote sensing in the past decade and discusses a selection of aspects of global terrestrial ecosystems where the data are currently used. It concludes by highlighting some of the challenges and opportunities of using optical remote sensing data in monitoring global terrestrial ecosystems.

scholarly journals Upside-down fluxes Down Under: CO<sub>2</sub> net sink in winter and net source in summer in a temperate evergreen broadleaf forest

2018 ◽  
Vol 15 (12) ◽  
pp. 3703-3716 ◽  
Author(s):  
Alexandre A. Renchon ◽  
Anne Griebel ◽  
Daniel Metzen ◽  
Christopher A. Williams ◽  
Belinda Medlyn ◽  
...  
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Vapour Pressure Deficit ◽  
Summer Rainfall ◽  
Mean Annual Precipitation ◽  
Surface Conductance ◽  
Mean Annual Temperature ◽  
Eddy Covariance Method ◽  
Area Index

Abstract. Predicting the seasonal dynamics of ecosystem carbon fluxes is challenging in broadleaved evergreen forests because of their moderate climates and subtle changes in canopy phenology. We assessed the climatic and biotic drivers of the seasonality of net ecosystem–atmosphere CO2 exchange (NEE) of a eucalyptus-dominated forest near Sydney, Australia, using the eddy covariance method. The climate is characterised by a mean annual precipitation of 800 mm and a mean annual temperature of 18 ∘C, hot summers and mild winters, with highly variable precipitation. In the 4-year study, the ecosystem was a sink each year (−225 g C m−2 yr−1 on average, with a standard deviation of 108 g C m−2 yr−1); inter-annual variations were not related to meteorological conditions. Daily net C uptake was always detected during the cooler, drier winter months (June through August), while net C loss occurred during the warmer, wetter summer months (December through February). Gross primary productivity (GPP) seasonality was low, despite longer days with higher light intensity in summer, because vapour pressure deficit (D) and air temperature (Ta) restricted surface conductance during summer while winter temperatures were still high enough to support photosynthesis. Maximum GPP during ideal environmental conditions was significantly correlated with remotely sensed enhanced vegetation index (EVI; r2 = 0.46) and with canopy leaf area index (LAI; r2 = 0.29), which increased rapidly after mid-summer rainfall events. Ecosystem respiration (ER) was highest during summer in wet soils and lowest during winter months. ER had larger seasonal amplitude compared to GPP, and therefore drove the seasonal variation of NEE. Because summer carbon uptake may become increasingly limited by atmospheric demand and high temperature, and because ecosystem respiration could be enhanced by rising temperatures, our results suggest the potential for large-scale seasonal shifts in NEE in sclerophyll vegetation under climate change.

scholarly journals Spatial variations and controls of carbon use efficiency in China’s terrestrial ecosystems

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zhi Chen ◽  
Guirui Yu
Keyword(s):  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Spatial Variations ◽  
Environmental Drivers ◽  
Future Climate Change ◽  
Mean Annual Temperature ◽  
Forest Types ◽  
Carbon Use Efficiency ◽  
Use Efficiency ◽  
Strong Control

AbstractCarbon use efficiency (CUE), one of the most important eco-physiological parameters, represents the capacity of plants to transform carbon into new biomass. Understanding the variations and controls of CUE is crucial for regional carbon assessment. Here, we used 15-years of continuous remote sensing data to examine the variations of CUE across broad geographic and climatic gradients in China. The results showed that the vegetation CUE was averaged to 0.54 ± 0.11 with minor interannual variation. However, the CUE greatly varied with geographic gradients and ecosystem types. Forests have a lower CUE than grasslands and croplands. Evergreen needleleaf forests have a higher CUE than other forest types. Climate factors (mean annual temperature (MAT), precipitation (MAP) and the index of water availability (IWA)) dominantly regulated the spatial variations of CUE. The CUE exhibited a linear decrease with enhanced MAT and MAP and a parabolic response to the IWA. Furthermore, the responses of CUE to environmental change varied with individual ecosystem type. In contrast, precipitation exerted strong control on CUE in grassland, while in forest and cropland, the CUE was mainly controlled by the available water. This study identifies the variations and response of CUE to environmental drivers in China, which will be valuable for the regional assessment of carbon cycling dynamics under future climate change.

How biotic and abiotic factors affect stemflow production? Insights from both local and global scales

2020 ◽  
Author(s):  
Yafeng Zhang ◽  
Xinping Wang ◽  
Yanxia Pan ◽  
Rui Hu
Keyword(s):  
Abiotic Factors ◽  
Northern China ◽  
Boosted Regression Trees ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Abiotic Factor ◽  
Area Index ◽  
Biotic And Abiotic Factors ◽  
Vegetation Height ◽  
Caragana Korshinskii

&lt;p&gt;Stemflow production has been reported to be influenced by a suite of biotic and abiotic factors, and those factors would be quite different considering local and global scales. Although the number of published stemflow studies showed a steady increasing trend in recent years, the relative contributions of biotic and abiotic factors to stemflow production were still largely unclear due to the large number of influencing factors and the complex interactions among those factors. Here we present stemflow results conducted from both from local scale and global scale: (1) stemflow of nine xerophytic shrubs of Caragana korshinskii were measured in nearly nine growing seasons from 2010 to 2018 within a desert area of northern China, accompanying with observing on six biotic variables (shrub morphological attributes) and ten abiotic variables (meteorological conditions); (2) a global synthesis of stemflow production results (stemflow percentage was reported) derived from Web of Science for more than 200 peer-reviewed papers published in the last 50 years (1970-2019), and ten most reported biotic factors (vegetation life form, phenology, leaf form, bark form, community density, community age, vegetation height, diameter at breast height, leaf area index, stemflow measuring scale) and four abiotic factors (climate types, mean annual precipitation, elevation, mean annual temperature) were considered. We performed a machine learning method (boosted regression trees) to evaluate the relative contribution of each biotic and abiotic factor to stemflow percentage, and partial dependence plots were presented to visualize the effects of individual explanatory variables on stemflow percentage, respectively.&lt;/p&gt;

scholarly journals Spatio-Temporal Variation in AOD and Correlation Analysis with PAR and NPP in China from 2001 to 2017

2020 ◽  
Vol 12 (6) ◽  
pp. 976 ◽  
Author(s):  
Xin Li ◽  
Hongyu Liang ◽  
Weiming Cheng
Keyword(s):  
Net Primary Productivity ◽  
Negative Impact ◽  
Positive Impact ◽  
Ground Surface ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Plant Production ◽  
Positive Correlation ◽  
Increasing Trend ◽  
Spatio Temporal

Atmospheric aerosols can elicit variations in how much solar radiation reaches the ground surface due to scattering and absorption, which may affect plant photosynthesis and carbon uptake in terrestrial ecosystems. In this study, the spatio-temporal variations in aerosol optical depth (AOD) are compared with that in photosynthetically active radiation (PAR) and net primary productivity (NPP) during 2001–2017 in China using multiple remote sensing data. The correlations between them are analyzed at different scales. Overall, the AOD exhibited a northeast-to-southwest decreasing pattern in space. A national increasing trend of 0.004 year−1 and a declining trend of −0.007 year−1 of AOD are observed during 2001–2008 and 2009–2017. The direct PAR (PARdir) and diffuse PAR (PARdif) present consistent and opposite tendency with AOD during two periods, respectively. The total PAR (PARtotal) shows a similar variation pattern with PARdir. In terms of annual variation, the peaks of AOD coincide with the peaks of PARdif and the troughs of PARdir, indicating that aerosols have a significant positive impact on PARdir and a negative impact on PARdif. Furthermore, the PARdir has a stronger negative association with AOD than the positive correlation between PARdif and AOD at national and regional scales, indicating that PARdir is more sensitive to aerosol changes. The NPP has higher values in the east than in the west and exhibits a significant increasing trend of 0.035 gCm−2day−1 after 2008. The NPP has a negative correlation (−0.4–0) with AOD and PARdif and a positive correlation (0–0.4) with PARdir in most areas of China. The area covered by forests has the highest NPP-PAR correlation, indicating that NPP in forests is more sensitive to the PAR than is the NPP in grasslands and croplands. This study is beneficial for interpreting the aerosol-induced PAR impact on plant growth and for predicting plant production on haze days.

scholarly journals GRASSLAND NPP MONITORING BASED ON MULTI-SOURCE REMOTE SENSING DATA FUSION

2018 ◽  
Vol XLII-3 ◽  
pp. 113-118
Author(s):  
Y. R. Cai ◽  
J. H. Zheng ◽  
M. J. Du ◽  
C. Mu ◽  
J. Peng
Keyword(s):  
Carbon Cycle ◽  
Primary Productivity ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
The United States ◽  
Estimation Accuracy ◽  
Modis Ndvi

Vegetation is an important part of the terrestrial ecosystem. It plays an important role in the energy and material exchange of the ground-atmosphere system and is a key part of the global carbon cycle process.Climate change has an important influence on the carbon cycle of terrestrial ecosystems. Net Primary Productivity (Net Primary Productivity)is an important parameter for evaluating global terrestrial ecosystems. For the Xinjiang region, the study of grassland NPP has gradually become a hot issue in the ecological environment.Increasing the estimation accuracy of NPP is of great significance to the development of the ecosystem in Xinjiang. Based on the third-generation GIMMS AVHRR NDVI global vegetation dataset and the MODIS NDVI (MOD13A3) collected each month by the United States Atmospheric and Oceanic Administration (NOAA),combining the advantages of different remotely sensed datasets, this paper obtained the maximum synthesis fusion for New normalized vegetation index (NDVI) time series in 2006&amp;ndash;2015.Analysis of Net Primary Productivity of Grassland Vegetation in Xinjiang Using Improved CASA Model The method described in this article proves the feasibility of applying data processing, and the accuracy of the NPP calculation using the fusion processed NDVI has been greatly improved. The results show that: (1) The NPP calculated from the new normalized vegetation index (NDVI) obtained from the fusion of GIMMS AVHRR NDVI and MODIS NDVI is significantly higher than the NPP calculated from these two raw data; (2) The grassland NPP in Xinjiang Interannual changes show an overall increase trend; interannual changes in NPP have a certain relationship with precipitation.

scholarly journals Global patterns of leaf nutrient resorption in herbaceous plants

2018 ◽  
Author(s):  
Zhiqiang Wang ◽  
Zhexuan Fan ◽  
Qi Zhao ◽  
Jinzhi Ran ◽  
Karl J. Niklas
Keyword(s):  
Current Knowledge ◽  
Global Scale ◽  
Plant Ecology ◽  
Nutrient Resorption ◽  
Conservation Strategies ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Herbaceous Plant ◽  
Herbaceous Species ◽  
Resorption Efficiency

Abstract. Nutrient resorption plays an important role in plant ecology because it plays a key role in nutrient conservation strategies of plants. However, our current knowledge about the patterns of nutrient resorption among herbaceous species at a global scale is still inadequate. Here, we present a meta-analysis using a global dataset of nitrogen (N) and phosphorus (P) resorption efficiency spanning 521 observations and 248 herbaceous species. This analysis shows that the N resorption efficiency (NRE) and P resorption efficiency (PRE) across all herbaceous plant groups are 54.7 % and 64.5 %, respectively. Across all species, NRE, PRE and N : P resorption ratios (NRE : PRE) vary statistically significantly at a global scale, i.e., NRE, PRE and NRE : PRE increase with increasing latitude but decrease with increasing mean annual temperature (MAT) and mean annual precipitation (MAP). For different functional groups, similar patterns of NRE, PRE and NRE : PRE with respect to latitude, MAT and MAP are observed. Our study are very important complementary to global-scale studies of nutrient resorption and also can inform attempts to model biogeochemical cycling at a global scale.

scholarly journals Application of a new model using productivity coupled with hydrothermal factors (PCH) for evaluating net primary productivity of grassland in southern China

Solid Earth ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 545-552
Author(s):  
Zheng-Guo Sun ◽  
Jie Liu ◽  
Hai-Yang Tang
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Southern China ◽  
Hot Spot ◽  
Terrestrial Ecosystems ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
New Model ◽  
Significant Relationships ◽  
Grassland Ecosystems

Abstract. Grassland ecosystems play important roles in the global carbon cycle. The net primary productivity (NPP) of grassland ecosystems has become the hot spot of terrestrial ecosystems. To simulate grassland NPP in southern China, a new model using productivity coupled with hydrothermal factors (PCH) was built and validated based on data recorded from 2003 to 2014. The results show a logarithmic correlation between grassland NPP and mean annual temperature and a linear positive correlation between grassland NPP and mean annual precipitation in southern China, both highly significant relationships. There was a highly significant correlation between simulated and measured NPP (R2 = 0. 8027). Both RMSE and relative root mean square error (RRMSE) were relatively low, showing that the simulation results of the model were reliable. The NPP values in the study area had a decreasing trend from east to west and south to north. Mean NPP was 471.62 g C m−2 from 2003 to 2014. Additionally, the mean annual NPP of southern grassland presented a rising trend, increasing 3.49 g C m−2 yr−1 during the past 12 years. These results document performance and use of a new method to estimate the grassland NPP in southern China.

Foliar physiognomic climate estimates for the Late Cretaceous (Cenomanian–Turonian) Lark Quarry fossil flora, central-western Queensland, Australia

2013 ◽  
Vol 61 (8) ◽  
pp. 575 ◽  
Author(s):  
Tamara L. Fletcher ◽  
Patrick T. Moss ◽  
Steven W. Salisbury
Keyword(s):  
Late Cretaceous ◽  
Global Scale ◽  
Mean Annual Precipitation ◽  
Fossil Flora ◽  
Mean Annual Temperature ◽  
Leaf Margin ◽  
Museum Collections ◽  
Growth Season ◽  
Cold Temperatures ◽  
Leaf Analysis

Although there is a broad knowledge of Cretaceous climate on a global scale, quantitative climate estimates for terrestrial localities are limited. One source of terrestrial palaeoproxies is foliar physiognomy. The use of foliar physiognomy to explore Cretaceous assemblages has been limited, and some of its potential sources of error have not been fully explored. Although museum collections house a wealth of material, collection bias toward particular taxa or preservation qualities of specimens further magnifies existing taphonomic bias to cold temperatures. As a result, specific collection for foliar physiognomy can be necessary. Here, we conduct three foliar physiognomic analyses on the early Late Cretaceous Lark Quarry flora and assess the results in the context of other proxies from the same formation. Our results suggest that the climate at the Cenomanian–Turonian boundary in central western Queensland was warm and had high precipitation (leaf-area analysis: 1321 mm + 413 mm – 315 mm mean annual precipitation; leaf-margin analysis: 16.4°C mean annual temperature, 5.3°C binomial sample error; climate leaf-analysis multivariate program: 16 ± 2°C for mean annual temperature, 9-month growth season, 1073 ± 483 mm growth-season precipitation). Our analysis also gave higher mean annual temperature estimates than did a previous analysis by climate leaf-analysis multivariate program, based on museum collections for the Winton Formation.

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