Spatio-temporal variability in rangeland conditions associated with climate change in the Altun Mountain National Nature Reserve on the Qinghai-Tibet Plateau over the past 15 years

2015 ◽  
Vol 37 (1) ◽  
pp. 67 ◽  
Author(s):  
S. L. Liu ◽  
H. D. Zhao ◽  
X. K. Su ◽  
L. Deng ◽  
S. K. Dong ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Nature Reserve ◽  
Driving Forces ◽  
Linear Regression Analysis ◽  
Tibet Plateau ◽  
National Nature Reserve ◽  
Rangeland Degradation ◽  
Normalised Difference Vegetation Index ◽  
The Impact

One of the focuses of global change research is on the impact of climate change on alpine vegetation. The Altun Mountain National Nature Reserve is the largest alpine desert rangeland reserve in China to protect wild endangered ungulate species. This paper aims to detect changing trends in rangeland conditions in this region. Temporal changes in the Normalised Difference Vegetation Index (NDVI) for the rangelands in the Altun Nature Reserve and its correlation with climatic variables were studied over the period from 1998 to 2012. Based on the NDVI index and using ArcGIS spatial analyst, the areas of likely rangeland degradation and areas of improved in rangeland condition were identified using linear regression analysis. The results showed that NDVI values were relatively low, varying from 0.04 to 0.1, and there existed distinct monthly changes. The highest NDVI values were exhibited in August. Generally, the NDVI showed an increasing trend over time with several annual fluctuations. High values were distributed mainly in the core area of the nature reserve. Trend analysis showed that vegetation near rivers and lakes was most likely to be degraded but, overall, the vegetation conditions improved over the 15 years of the study, which meant an improvement in the habitats of key wild ungulate species. Precipitation and temperature had a significant linear positive correlation with NDVI, which suggested that they were the main driving forces for rangeland improvement. The vegetation at the edge of the protected areas appeared degraded due to human activities.

scholarly journals Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Rafia Mumtaz ◽  
Shahbaz Baig ◽  
Iram Fatima
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Crop Yield ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Least Square ◽  
Wheat Crop ◽  
Main Challenge ◽  
Normalised Difference Vegetation Index ◽  
The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (<em>R</em><sup>2</sup>) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

scholarly journals Quantifying Changes in Plant Species Diversity in a Savanna Ecosystem Through Observed and Remotely Sensed Data

2020 ◽  
Vol 12 (6) ◽  
pp. 2345
Author(s):  
Lazarus Chapungu ◽  
Luxon Nhamo ◽  
Roberto Cazzolla Gatti ◽  
Munyaradzi Chitakira
Keyword(s):  
Climate Change ◽  
Species Diversity ◽  
Plant Species ◽  
Vegetation Index ◽  
Shannon Index ◽  
Plant Species Diversity ◽  
Impact Of Climate Change ◽  
Normalised Difference Vegetation Index ◽  
The Impact ◽  
Index Of Diversity

This study examined the impact of climate change on plant species diversity of a savanna ecosystem, through an assessment of climatic trends over a period of forty years (1974–2014) using Masvingo Province, Zimbabwe, as a case study. The normalised difference vegetation index (NDVI) was used as a proxy for plant species diversity to cover for the absence of long-term historical plant diversity data. Observed precipitation and temperature data collected over the review period were compared with the trends in NDVI to understand the impact of climate change on plant species diversity over time. The nonaligned block sampling design was used as the sampling framework, from which 198 sampling plots were identified. Data sources included satellite images, field measurements, and direct observations. Temperature and precipitation had significant (p < 0.05) trends over the period under study. However, the trend for seasonal total precipitation was not significant but declining. Significant correlations (p < 0.001) were identified between various climate variables and the Shannon index of diversity. NDVI was also significantly correlated to the Shannon index of diversity. The declining trend of plant species in savanna ecosystems is directly linked to the decreasing precipitation and increasing temperatures.

A fusion of UAVs, digital cameras and micrometeorology to quantify the link between phenology and the carbon balance of a temperate peatland

2021 ◽  
Author(s):  
Gillian Simpson ◽  
Carole Helfter ◽  
Caroline Nichol ◽  
Tom Wade
Keyword(s):  
Climate Change ◽  
Carbon Balance ◽  
Vegetation Index ◽  
Natural Capital ◽  
Carbon Sink ◽  
Sink Strength ◽  
Environmental Controls ◽  
Spatial Coverage ◽  
Normalised Difference Vegetation Index ◽  
The Impact

&lt;p&gt;Peatland ecosystems are historical carbon sinks of global importance, whose management and restoration are becoming an increasingly popular approach to reach climate change targets via natural capital. However, the Net Ecosystem Exchange (NEE) of carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) can exhibit substantial variability on seasonal and inter-annual timescales, with some peatlands shifting from being a sink to a source of CO&lt;sub&gt;2&amp;#160;&lt;/sub&gt;between years. This variability is due to the complex interaction between factors such as meteorology and phenology, which are both known to control a peatland&amp;#8217;s net carbon sink strength. An improved understanding of these two drivers of peatland carbon cycling is needed to allow for better prediction of the impact of climate change on these ecosystems. This task requires us to study these environmental controls at multiple spatial and temporal scales. The role of vegetation in regulating NEE however, can be difficult to determine over shorter timescales (e.g. seasonal) and especially in peatland landscapes, which typically display strong spatial heterogeneity at the microsite scale (&lt; 0.5 m). Digital phenology cameras (PhenoCams) and Unmanned Aerial Vehicles (UAVs), offer novel opportunities to improve the temporal resolution and spatial coverage of traditional vegetation survey approaches. UAVs in particular are a more flexible, often cheaper alternative to satellite products, and can be used to collect data at the sub-centimetre scale. We employ PhenoCam imagery and UAV surveys with a Parrot Sequoia multispectral camera to map vegetation and track its phenology using vegetation indices such as the Normalised Difference Vegetation Index (NDVI) over the course of two growing seasons at Auchencorth Moss, a Scottish temperate peatland. By combining this digital camera imagery with in-situ NEE measurements (closed chambers and eddy-covariance) and meteorological data, we seek to quantify the impact of weather and phenology on carbon balance at the site.&lt;/p&gt;

Climate-forced changes of bio-productivity of Belorussian terrestrial ecosystems

2019 ◽  
pp. 77-88
Author(s):  
S. A. Lysenko
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Terrestrial Ecosystems ◽  
Vegetation Period ◽  
Climatic Trend ◽  
Vegetation Growth ◽  
Current Century ◽  
The Impact

The spatial and temporal particularities of Normalized Differential Vegetation Index (NDVI) changes over territory of Belarus in the current century and their relationship with climate change were investigated. The rise of NDVI is observed at approximately 84% of the Belarus area. The statistically significant growth of NDVI has exhibited at nearly 35% of the studied area (t-test at 95% confidence interval), which are mainly forests and undeveloped areas. Croplands vegetation index is largely descending. The main factor of croplands bio-productivity interannual variability is precipitation amount in vegetation period. This factor determines more than 60% of the croplands NDVI dispersion. The long-term changes of NDVI could be explained by combination of two factors: photosynthesis intensifying action of carbon dioxide and vegetation growth suppressing action of air warming with almost unchanged precipitation amount. If the observed climatic trend continues the croplands bio-productivity in many Belarus regions could be decreased at more than 20% in comparison with 2000 year. The impact of climate change on the bio-productivity of undeveloped lands is only slightly noticed on the background of its growth in conditions of rising level of carbon dioxide in the atmosphere.

Assessing the impact of seasonal precipitation and temperature on vegetation in a grass-dominated rangeland

2014 ◽  
Vol 36 (2) ◽  
pp. 185 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber
Keyword(s):  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Growth ◽  
Factors Affecting ◽  
Growing Seasons ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact ◽  
Precipitation And Temperature

Changes in vegetation are affected by many climatic factors and have been successfully monitored through satellite remote sensing over the past 20 years. In this study, the Normalised Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, was selected as an indicator of change in vegetation. Monthly MODIS composite NDVI at a 1-km resolution was acquired throughout the 2004–09 growing seasons (i.e. April–September). Data describing daily precipitation and temperature, primary factors affecting vegetation growth in the semiarid rangelands of Idaho, were derived from the Surface Observation Gridding System and local weather station datasets. Inter-annual and seasonal fluctuations of precipitation and temperature were analysed and temporal relationships between monthly NDVI, precipitation and temperature were examined. Results indicated NDVI values observed in June and July were strongly correlated with accumulated precipitation (R2 >0.75), while NDVI values observed early in the growing season (May) as well as late in the growing season (August and September) were only moderately related with accumulated precipitation (R2 ≥0.45). The role of ambient temperature was also apparent, especially early in the growing season. Specifically, early growing-season temperatures appeared to significantly affect plant phenology and, consequently, correlations between NDVI and accumulated precipitation. It is concluded that precipitation during the growing season is a better predictor of NDVI than temperature but is interrelated with influences of temperature in parts of the growing season.

scholarly journals Variation of Runoff and Runoff Components of the Upper Shule River in the Northeastern Qinghai–Tibet Plateau under Climate Change

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3357
Author(s):  
Jinkui Wu ◽  
Hongyuan Li ◽  
Jiaxin Zhou ◽  
Shuya Tai ◽  
Xueliang Wang
Keyword(s):  
Climate Change ◽  
Sustainable Use ◽  
Reduction Rate ◽  
Northwest China ◽  
Tibet Plateau ◽  
Glacier Area ◽  
Rainfall Runoff ◽  
Total Runoff ◽  
Increasing Temperature ◽  
The Impact

Quantifying the impact of climate change on hydrologic features is essential for the scientific planning, management and sustainable use of water resources in Northwest China. Based on hydrometeorological data and glacier inventory data, the Spatial Processes in Hydrology (SPHY) model was used to simulate the changes of hydrologic processes in the Upper Shule River (USR) from 1971 to 2020, and variations of runoff and runoff components were quantitatively analyzed using the simulations and observations. The results showed that the glacier area has decreased by 21.8% with a reduction rate of 2.06 km2/a. Significant increasing trends in rainfall runoff, glacier runoff (GR) and baseflow indicate there has been a consistent increase in total runoff due to increasing rainfall and glacier melting. The baseflow has made the largest contribution to total runoff, followed by GR, rainfall runoff and snow runoff, with mean annual contributions of 38%, 28%, 18% and 16%, respectively. The annual contribution of glacier and snow runoff to the total runoff shows a decreasing trend with decreasing glacier area and increasing temperature. Any increase of total runoff in the future will depend on an increase of rainfall, which will exacerbate the impact of drought and flood disasters.

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 Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices

2018 ◽  
Vol 23 ◽  
pp. 00030 ◽  
Author(s):  
Anshu Rastogi ◽  
Subhajit Bandopadhyay ◽  
Marcin Stróżecki ◽  
Radosław Juszczak
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Environmental Manipulation ◽  
Chlorophyll Index ◽  
The Impact ◽  
Remote Sensing Indices

The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

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