scholarly journals Using OCO-2 Satellite Data for Investigating the Variability of Atmospheric CO2 Concentration in Relationship with Precipitation, Relative Humidity, and Vegetation over Oman

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 101
Author(s):  
Foroogh Golkar ◽  
Malik Al-Wardy ◽  
Seyedeh Fatemeh Saffari ◽  
Kathiya Al-Aufi ◽  
Ghazi Al-Rawas
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Monsoon Region ◽  
Annual Cycles ◽  
Atmospheric Co2 Concentration ◽  
Carbon Dioxide Co2

Recognition of the carbon dioxide (CO2) concentration variations over time is critical for tracing the future changes in climate both globally and regionally. In this study, a time series analysis of atmospheric CO2 concentration and its relationship with precipitation, relative humidity (RH), and vegetation is investigated over Oman. The daily XCO2 data from OCO-2 satellite was obtained from September 2014 to March 2019. The daily RH and precipitation data were also collected from the ground weather stations, and the Normalized Difference Vegetation Index was obtained from MODIS. Oman was studied in four distinct regions where the main emphasis was on the Monsoon Region in the far south. The CO2 concentration time series indicated a significant upward trend over different regions for the study period, with annual cycles being the same for all regions except the Monsoon Region. This is indicative of RH, precipitation, and consequently vegetation cover impact on atmospheric CO2 concentration, resulting in an overall lower annual growth in the Monsoon Region. Simple and multiple correlation analyses of CO2 concentration with mentioned parameters were performed in zero to three-month lags over Oman. They showed high correlations mainly during the rainfall period in the Monsoon Region.

scholarly journals Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2497
Author(s):  
Laura Recuero ◽  
Javier Litago ◽  
Jorge E. Pinzón ◽  
Margarita Huesca ◽  
Maria C. Moyano ◽  
...  
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Land Surface ◽  
Arid Regions ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Periodic Patterns ◽  
Annual Cycles ◽  
Ndvi Time Series ◽  
Global Vegetation

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

scholarly journals Assessment of Rangeland Degradation in New Mexico Using Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND)

2021 ◽  
Vol 13 (9) ◽  
pp. 1618
Author(s):  
Melakeneh G. Gedefaw ◽  
Hatim M. E. Geli ◽  
Temesgen Alemayehu Abera
Keyword(s):  
Time Series ◽  
New Mexico ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rangeland Management ◽  
Environmental Benefits ◽  
Drought Severity ◽  
Assessment Framework ◽  
Rangeland Degradation

Rangelands provide significant socioeconomic and environmental benefits to humans. However, climate variability and anthropogenic drivers can negatively impact rangeland productivity. The main goal of this study was to investigate structural and productivity changes in rangeland ecosystems in New Mexico (NM), in the southwestern United States of America during the 1984–2015 period. This goal was achieved by applying the time series segmented residual trend analysis (TSS-RESTREND) method, using datasets of the normalized difference vegetation index (NDVI) from the Global Inventory Modeling and Mapping Studies and precipitation from Parameter elevation Regressions on Independent Slopes Model (PRISM), and developing an assessment framework. The results indicated that about 17.6% and 12.8% of NM experienced a decrease and an increase in productivity, respectively. More than half of the state (55.6%) had insignificant change productivity, 10.8% was classified as indeterminant, and 3.2% was considered as agriculture. A decrease in productivity was observed in 2.2%, 4.5%, and 1.7% of NM’s grassland, shrubland, and ever green forest land cover classes, respectively. Significant decrease in productivity was observed in the northeastern and southeastern quadrants of NM while significant increase was observed in northwestern, southwestern, and a small portion of the southeastern quadrants. The timing of detected breakpoints coincided with some of NM’s drought events as indicated by the self-calibrated Palmar Drought Severity Index as their number increased since 2000s following a similar increase in drought severity. Some breakpoints were concurrent with some fire events. The combination of these two types of disturbances can partly explain the emergence of breakpoints with degradation in productivity. Using the breakpoint assessment framework developed in this study, the observed degradation based on the TSS-RESTREND showed only 55% agreement with the Rangeland Productivity Monitoring Service (RPMS) data. There was an agreement between the TSS-RESTREND and RPMS on the occurrence of significant degradation in productivity over the grasslands and shrublands within the Arizona/NM Tablelands and in the Chihuahua Desert ecoregions, respectively. This assessment of NM’s vegetation productivity is critical to support the decision-making process for rangeland management; address challenges related to the sustainability of forage supply and livestock production; conserve the biodiversity of rangelands ecosystems; and increase their resilience. Future analysis should consider the effects of rising temperatures and drought on rangeland degradation and productivity.

scholarly journals Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models

2019 ◽  
Vol 16 (19) ◽  
pp. 3883-3910 ◽  
Author(s):  
Lina Teckentrup ◽  
Sandy P. Harrison ◽  
Stijn Hantson ◽  
Angelika Heil ◽  
Joe R. Melton ◽  
...  
Keyword(s):  
Land Use ◽  
Co2 Concentration ◽  
Fire Regimes ◽  
Atmospheric Co2 ◽  
Anthropogenic Factors ◽  
Burned Area ◽  
Earth System ◽  
Atmospheric Co2 Concentration ◽  
Fire Models ◽  
The Impact

Abstract. Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.

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