scholarly journals ANALYZING THE EFFECT OF CLIMATE CHANGE (RAINFALL AND TEMPERATURE) ON VEGETATION COVER OF NEPAL USING TIME SERIES MODIS IMAGES

2019 ◽  
Vol IV-2/W5 ◽  
pp. 209-216
Author(s):  
N. Joshi ◽  
P. Gyawali ◽  
S. Sapkota ◽  
D. Neupane ◽  
S. Shrestha ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Correlation Coefficient ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Entire Study ◽  
Relationship Analysis ◽  
Ecological Regions ◽  
Hilly Region ◽  
The Relationship

<p><strong>Abstract.</strong> Climate change and so its effect on terrestrial ecosystem has been a focus point for a while now. Among them, rainfall and temperature changes happen to exert a strong influence on the condition of vegetation cover. So, it is imperative to analyze the variation and inter-relationship between vegetation cover and climate pattern, especially country like Nepal having a dynamic ecosystem. This paper aims to analyze the spatial-temporal distribution of vegetation cover, temperature, and rainfall, and to examine the relationship of the latter two with vegetation for entire Nepal. Primary data used were vegetation and temperature data from Moderate Resolution Imaging Spectroradiometer (MODIS) and rainfall data from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data product. The relationship analysis was carried out in three phases; first, the trend of vegetation with respect to rainfall and land surface temperature (LST) was inspected over entire study area by creating a time series of Normalized Difference Vegetation Index (NDVI) monthly means for six months, averaged over the whole study period. However, vegetation change pattern across various ecological regions of Nepal also needed to be considered, for the three different regions are profoundly different from each other in a number of factors like altitude and soil type. Finally, the variation of vegetation with climatic parameters, i.e. rainfall and temperature, along the eleven-year study period was also portrayed, to depict how the vegetation cover has been fluctuating over the years. During the study period, the correlation coefficient between vegetation index and rainfall was the highest in October in Terai while that with temperature was in July in Hilly region. Overall, vegetation was influenced greater by the temperature than rainfall in all three ecological regions with the highest correlation coefficient of vegetation with temperature and rainfall, being &amp;minus;0.937 and 0.556 respectively.</p>

A Preliminary Study on the Relationship between Vegetation Cover and Regional Climate Change in Jiangsu Area of China

2013 ◽  
Vol 448-453 ◽  
pp. 916-922
Author(s):  
Yan Rong Yang ◽  
Zhe Kong ◽  
Chun Ming Liu
Keyword(s):  
Climate Change ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Climate Model ◽  
Regional Climate ◽  
Precipitation Amount ◽  
Regional Climate Change ◽  
Research Field ◽  
Research Fields ◽  
The Relationship

The relationship between vegetation cover and climate change is one of the most important research fields in global change. Herein Jiangsu province and thereabout in China is chosen to be the research field. Under the support of observations from normalized differential vegetation index (NDVI) during years from 1998 to 2008 and corresponding benchmark weather stations, the relationship between vegetation and climate change had been analyzed combined with simulations from regional climate model RegCM3, in perspectives of point vegetation cover amount and area vegetation cover type respectively. Conclusions are: (1) Points observations showed that NDVI had positive correlation with annual total precipitation and negative correlation with annual average temperature. (2) Area simulations showed that two different vegetation types in south and north Jiangsu almost had same 8warming value, but the incremental annual precipitation amount is more significant in south Jiangsu.

scholarly journals A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
Nndanduleni Muavhi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Simple Approach ◽  
Limpopo Province ◽  
Difference Image ◽  
Normalised Difference Vegetation Index ◽  
Gain And Loss ◽  
The Difference

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

scholarly journals Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

2011 ◽  
Vol 4 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
F. Maignan ◽  
F.-M. Bréon ◽  
F. Chevallier ◽  
N. Viovy ◽  
P. Ciais ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Vegetation Dynamic ◽  
Rate Of Increase ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

Abstract. Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

scholarly journals Multifractal analysis of atmospheric carbon emissions and OECD industrial production index

2020 ◽  
Vol 12 (4) ◽  
pp. 411-430
Author(s):  
Sezer Kahyaoglu Bozkus ◽  
Hakan Kahyaoglu ◽  
Atahirou Mahamane Mahamane Lawali
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Carbon Emissions ◽  
Industrial Production ◽  
Environmental Cost ◽  
Content Type ◽  
Production Index ◽  
Atmospheric Carbon ◽  
The Relationship

Purpose The purpose of this study aims to analyze the dynamic behavior of the relationship between atmospheric carbon emissions and the Organisation for Economic Co-operation and Development (OECD) industrial production index (IPI) in the short and long term by applying multifractal techniques. Design/methodology/approach Multifractal de-trended cross-correlation technique is used for this analysis based on the relevant literature. In addition, it is the most widely used approach to estimate multifractality because it generates robust empirical results against non-stationarities in the time series. Findings It is revealed that industrial production causes long and short term environmental costs. The OECD IPI and atmospheric carbon emissions were found to have a strong correlation between the time domain. However, this relationship does not mostly take into account the frequency-based correlations with the tail effects caused by shocks that are effective on the economy. In this study, the long-term dependence of the relationship between the OECD IPI and atmospheric carbon emissions differs from the correlation obtained by linear methods, as the analysis is based on the frequency. The major finding is that the Hurst coefficient is in the range 0.40-0.75 indicating. Research limitations/implications In this study, the local singular behavior of the time-series is analyzed to test for the multifractality characteristics of the series. In this context, the scaling exponents and the singularity spectrum are obtained to determine the origins of this multifractality. The multifractal time series are defined as the set of points with a given singularity exponent a where this exponent a is illustrated as a fractal with fractal dimension f(α). Therefore, the multifractality term indicates the existence of fluctuations, which are non-uniform and more importantly, their relative frequencies are also scale-dependent. Practical implications The results provide information based on the fluctuation in IPI, which determines the main conjuncture of the economy. An optimal strategy for shaping the consequences of climate change resulting from industrial production activities will not only need to be quite comprehensive and global in scale but also policies will need to be applicable to the national and local conditions of the given nation and adaptable to the needs of the country. Social implications The results provide information for the analysis of the environmental cost of climate change depending on the magnitude of the impact on the total supply. In addition to environmental problems, climate change leads to economic problems, and hence, policy instruments are introduced to fight against the adverse effects of it. Originality/value This study may be of practical and technical importance in regional climate change forecasting, extreme carbon emission regulations and industrial production resource management in the world economy. Hence, the major contribution of this study is to introduce an approach to sustainability for the analysis of the environmental cost of growth in the supply side economy.

Projection of land and ecosystem degradation for preventing risk of natural capital decline in Tibet plateau.

2020 ◽  
Author(s):  
Lu Zhang
Keyword(s):  
Climate Change ◽  
Natural Capital ◽  
Tibet Plateau ◽  
Impact Factors ◽  
Monthly Interval ◽  
Ecosystem Degradation ◽  
Relationship Analysis ◽  
Current Trends ◽  
Risk Projection ◽  
The Relationship

&lt;p&gt;Numerous methodologies are available so far measuring trends of land (LD) and ecosystem degradation (ED) with spatially explicit manner. Yet the delineation of spatial and temporal covariance between LD and ED remains challenging which limited the effectiveness of future conservation decision making for preventing risks of LD and ED simultaneously, especially in cold and drought areas because of high cost of restoration. Here, we produced the spatial networks for managing and restoring LD and ED based on the risk projection of LD and ED in Tibet plateau under human exploitation pressure and climate change. Firstly, we simulated 10 indicators for LD and ED separately by monthly interval from 2000 to 2015 to capture the current trends of LD and ED. Secondly, resilience, resistant, and risk exposure have been assessed to connect the vegetation traits, threaten factors and their reflections. Thirdly, by the exploration of relationship between LD and ED and their impact factors, we projected risks for both of them using 12 scenarios from different climate and land use change combinations identifying the key area of preventing LD and ED spatially. Finally, an effectiveness analysis has been processed ordering results under each scenarios leaded to the decline of nature capital for providing alternative strategies of regional land and ecosystem management. By our research, we found that LD and ED in Tibetan plateau have similar pattern of dynamic, while ED shows more significant correlation with climate change due to stronger intrinsic resilience in front of stressors. In opposites, once serious land degradation occurs, it is hardly being recovered by increasing of precipitation and temperature. Based on the relationship analysis, we modeled LE and ED risks under various potential scenarios suggesting that at least 100,000km2 area needed to human intervention for restoration. These suggested sites covered the worst 60% areas of both LD and ED producing 12.5 billion USD &amp;#160;dollars revenue from the maintenance of key regulating ecosystem services.&lt;/p&gt;

scholarly journals Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

2019 ◽  
Vol 11 (12) ◽  
pp. 1398 ◽  
Author(s):  
Xuanlong Ma ◽  
Alfredo Huete ◽  
Ngoc Nguyen Tran
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Growing Season ◽  
The Sun ◽  
Space And Time ◽  
Ndvi Time Series ◽  
Angle Effect ◽  
Phenological Metrics

Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.

scholarly journals Relating historical vegetation cover to aridity index patterns in the greater desert region of northern China: Implications to planned and existing restoration projects

2016 ◽  
Author(s):  
Yanying Shao ◽  
Yuqing Zhang ◽  
Xiuqin Wu ◽  
Charles P.-A. Bourque ◽  
Jutao Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Ecological Integrity ◽  
Aridity Index ◽  
Northern China ◽  
Management Planning ◽  
Desert Region

Abstract. Desert regions of northern China have always been the most severely affected by climate change, especially in terms of their ecological integrity and social sustainable development. Assessments of dryness in both space and time are central to the development of adaptation strategies to climate change. Earlier studies have identified long-term patterns of dryness in northern China, but these studies have usually been of limited value to land-management planning as they ignore local-to-regional-scale climate features. To identify potential cause-and-effect relationship between aridity and vegetation cover, changes in aridity index (AI) and vegetation cover were tracked with the assistance of a chronological series of surfaces based on the mapping of AI and normalized difference vegetation index (NDVI) and convergent cross mapping. By tracking regional-scale variation in precipitation, air temperature, AI from 1961–2013 (53 years), and vegetation cover dynamics from 1982–2013 (32 years), we show that precipitation increased in approximately 70 % of the greater desert region, including in the Ulanbuh, Tengger, Badain Jaran, Qaidam, Kumtag, Gurbantunggut, and Taklimakan Deserts. This increase was statistically strongest for the Gurbantunggut (p 

scholarly journals Attribution of satellite observed vegetation trends in a hyper-arid region of the Heihe River Basin, Central Asia

2014 ◽  
Vol 11 (2) ◽  
pp. 1529-1554 ◽  
Author(s):  
Y. Wang ◽  
M. L. Roderick ◽  
Y. Shen ◽  
F. Sun
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Vegetation Cover ◽  
Arid Region ◽  
Vegetation Index ◽  
Northwest China ◽  
Heihe River ◽  
Terrestrial Vegetation ◽  
Fractional Vegetation Cover ◽  
Green Vegetation

Abstract. Terrestrial vegetation dynamics are closely influenced by both climate change and by direct human activities that modify land use and/or land cover (LULCC). Both can change over time in a monotonic way and it can be difficult to separate the effects of climate change from LULCC on vegetation. Here we attempt to attribute the trend of fractional green vegetation cover to climate change and to human activity in Ejina region, a hyper-arid landlocked region in northwest China. This region is dominated by extensive deserts with relatively small areas of irrigation located along the major water courses as is typical throughout much of Central Asia. Variations of fractional vegetation cover from 2000 to 2012 were determined using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data with 250 m spatial resolution over 16 day intervals. We found that the fractional vegetation cover in this hyper-arid region is very low, but that the mean growing season vegetation cover has increased from 3.4% in 2000 to 4.5% in 2012. The largest contribution to the overall greening was due to changes in green vegetation cover of the extensive desert areas with a smaller contribution due to changes in the area of irrigated land. Comprehensive analysis with different precipitation data sources found that the greening of the desert was associated with increases in regional precipitation. We found that the area of land irrigated each year was mostly dependent on the runoff gauged one year earlier. Taken together, water availability both from precipitation in the desert and runoff inflow for the irrigation agricultural lands can explain at least 52% of the total variance in regional vegetation cover from 2000 to 2010.

scholarly journals Identifying Drought Status in Duhok Governorate (Iraqi Kurdistan Region) from 1998 through 2012 using Landsat Time Series Dataset

2020 ◽  
Vol 1 (1) ◽  
pp. 17-23
Author(s):  
Heman Gaznayee ◽  
Ayad Al-Quraishi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Drought Severity ◽  
Kurdistan Region ◽  
Water Index ◽  
Iraqi Kurdistan ◽  
Study Results ◽  
Normalized Difference Water Index

Drought is a natural hazard that has a significant impact on the various aspects (i.e., economic, agricultural, environmental, and social). This study was carried out to evaluate drought severity and frequency during the growing season (April month) in Duhok Governorate (DUG), the Iraqi Kurdistan Region (IKR), for the period from 1998 through 2012 based on Landsat-based spectral indices. In this study, 15 mosaics assembled for 15 years consist of two scenes of Landsat time series, in a total of 30 TM and ETM+ images (WRS2: 170/34 & 170/35) acquired in 1998 to 2012. Annual precipitation data were collected from 18 meteorological stations distributed in the (DUG) for the study period. Drought status was investigated using the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI2), and Normalized Difference Water Index (NDWI). The study results showed an increase in drought severity and frequency in the (DUG) during the fifty years, particularly in 2000 and 2008. Whereas, the NDVI-based vegetation cover area has been reduced by 21.5% and 50.2% in 2000 and 2008, respectively. Additionally, the lowest values of the MSAVI2 (0.012 and 0.266) occurred in 2000 and 2008. As a result, the percentage of the vegetation cover reduction was 14.0% and 23.9%, respectively. Moreover, drop-in precipitation averages have occurred in those two drought years 2000 and 2008, as well as a significant reduction in the vegetation cover. On the other side, the most significant shrinkage in Duhok Dam (DUD) was by 1.13, 1.44, and 1.36 km2 in 2007, 2008, and 2009. It can be concluded that there are increasing drought episodes in the last two decades, declining in the water body surface area, and decreasing the precipitation averages in DUG from 1998 through 2012.

Effect of Maximum Average Temperature on Suicide Rates in California, 2008-2017: Public Health Implications of Climate Change

2020 ◽  
Author(s):  
Mavra Qamar ◽  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie Nanos ◽  
David N Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Time Series ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Maximum Temperature ◽  
Suicide Rates ◽  
Binomial Regression ◽  
The Relationship ◽  
Heat Events

Abstract BackgroundSuicide prevention is a salient public health responsibility, as it is one of the top ten leading causes of premature mortality in the United States. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. Previous studies have been country-based. Currently, studies focused solely on regions, provinces, or states, such as California, are limited. The present study holds two purposes: i) to assess the effect of maximum temperature on suicides, and ii) to evaluate the effect of number of monthly heat events on suicide rates, in California from 2008-2017.MethodsThe exposure was measured as the average Californian daily maximum temperature within each month, and the number of monthly heat events, which was calculated as a count of the days exhibiting a >15% increase from the historical monthly temperature. The outcome was measured as California’s monthly suicide rate. Negative binomial regression models assessed the relationship between maximum temperature and suicides, and heat events and suicide. A seasonal decomposition of a time series and auto-correlogram further analyzed the seasonality of suicide and the trend from 2008-2017. ResultsThere were 40,315 deaths by suicide in California between 2008-2017. Negative binomial regression indicated a 6.1% increase in suicide incidence rate ratio (IRR) per 10°F increase in maximum temperature (IRR=1.00590 per 1°F, 95% CI: 1.00387, 1.00793, p<0.0001) and a positive, non-significant association between suicide rates and number of heat events adjusted for month of occurrence (IRR 1.00148 per heat event, 95% CI: 0.99636, 1.00661, p=0.572). The time series analysis and auto-correlogram suggested seasonality of deaths by suicide.ConclusionThe present study provided preliminary evidence that will generate future directions for research. We must seek to further illuminate the relationship of interest and apply our findings to public health interventions that will lower the rates of death by suicide as we are confronted with the effects of climate change.

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