scholarly journals Vegetation Dynamics and Their Response to Climatic Variability in China

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Bowen Zhang ◽  
Linli Cui ◽  
Jun Shi ◽  
Peipei Wei
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Future Research ◽  
Regression Methods ◽  
Temperature And Precipitation ◽  
Vegetation Activity ◽  
Increasing Trend ◽  
Response To Temperature

Based on SPOT VEGETATION data and meteorological data, NDVI (Normalized Difference Vegetation Index) and its response to temperature and precipitation in China and its different regions were investigated over the period 1998–2013 by using the maximum value composite and linear regression methods. The results showed that NDVI presented significant increase (0.0046/a) for all of China and all the regions over the last 16 years. Meanwhile, annual mean temperature of China presented a slightly increasing trend, while the annual precipitation showed a slightly decreasing trend over the last 16 years. Nevertheless, there were differences between temperature and precipitation in the subregions of China. The Annual NDVI had better relationships with precipitation (r=0.126) compared to temperature (r=-0.094), and NDVI also had a good correlation with precipitation rather than temperature in different subregions of China. Additionally, human activities also made a difference to the trends of NDVI in some regions. This study is conductive to the effects of climate change on vegetation activity in future research.

scholarly journals Impacts of Climate on Spatiotemporal Variations in Vegetation NDVI from 1982–2015 in Inner Mongolia, China

2019 ◽  
Vol 11 (3) ◽  
pp. 768 ◽  
Author(s):  
Xinxia Liu ◽  
Zhixiu Tian ◽  
Anbing Zhang ◽  
Anzhou Zhao ◽  
Haixin Liu
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Grassland Management ◽  
Spatiotemporal Variations ◽  
Study Region ◽  
Temperature And Precipitation ◽  
Vegetation Activity ◽  
One Year ◽  
Scale Data

By using the Global Inventory Modeling and Mapping Studies (GIMMS) third-generation normalized difference vegetation index (NDVI3g) data, this paper explores the spatiotemporal variations in vegetation and their relationship with temperature and precipitation between 1982 and 2015 in the Inner Mongolia region of China. Based on yearly scale data, the vegetation changes in Inner Mongolia have experienced three stages from 1982 to 2015: the vegetation activity kept a continuous improvement from 1982–1999, then downward between 1999–2009, and upward from 2009 to 2015. On the whole, the general trend is increasing. Several areas even witnessed significant vegetation increases: in the east and south of Tongliao and Chifeng, north of Xing’anmeng, north and west of Hulunbir, and in the west of Inner Mongolia. Based on monthly scale data, one-year and half-year cycles exist in normalized difference vegetation index (NDVI) and temperature but only a one-year cycle in precipitation. Finally, based on the one-year cycle, the relationship between NDVI and climatic were studied; NDVI has a significant positive correlation with temperature and precipitation, and temperature has a greater effect in promoting vegetation growth than precipitation. Moreover, based on a half-year changing period, NDVI is only affected by temperature in the study region. Those findings can serve as a critical reference for grassland managers or policy makers to make informed decisions on grassland management.

Air quality at open-cast coal mining regions at Hunter Valley, Australia and Ramagundam, India

2021 ◽  
Author(s):  
Harsh Kamath ◽  
Chanchal Chauhan ◽  
Sameer Mishra ◽  
Aariz Ahmed ◽  
Raman Srikanth
Keyword(s):  
Power Plants ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Thermal Power ◽  
Coal Mines ◽  
Export Market ◽  
P Value ◽  
Open Cast ◽  
Monitoring Stations

<p>The upper Hunter Valley region in New South Wales (NSW), Australia has several open-cast coal mines, which supply coal to two large thermal power plants (TPPs) in the area, beside the export market. Long-term Particulate Matter (PM) pollutants and meteorological measurements are recorded by a network of 13 NSW government-owned continuous monitoring stations in the upper Hunter Valley region. The Ramagundam area in the state of Telangana, India has similar pollution source characteristics (coal mines and TPPs), but PM pollutant measurements are largely carried out with manual monitoring stations at 24-hour intervals, not more than twice a week. As the coal and overburden excavation from open-cast coal mines and stack emissions from TPPs lead to local PM pollution, we have used MODIS-MAIAC Aerosol Optical Depth (AOD) at 550 nm and Normalized Difference Vegetation Index (NDVI) along with the local meteorological data such as ambient temperature, relative humidity, wind speed and direction to model PM10 and PM2.5 at the upper Hunter Valley and Ramagundam regions. Our model can explain about 60% of variation in PM10 (p-value < 0.0001), while a similar model is able to explain about 75% of the variation in the PM2.5 (p-value < 0.0001). We will extend our model results from Hunter Valley to Ramagundam area and comment on the potential of using geospatial products such as AOD as a proxy to ground-based pollution measurements in developing countries such as India, where pollution data is scarce.</p>

scholarly journals Relation of leaf water content with real evapotranspiration and biomass in Caatinga biome, using remote sensing data

2017 ◽  
Vol 10 (5) ◽  
pp. 1545
Author(s):  
Josiclêda Domiciano Galvíncio
Keyword(s):  
Energy Balance ◽  
Water Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Energy Balance ◽  
Climatic Variability ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Content Index ◽  
Caatinga Biome

R E S U M OA Caatinga é um biome que sofre com grande variabilidade climática anual e intraanual. Essa variabilidade climática faz com que o bioma em grande parte do ano sofra com grande estresse hídrico. Estudar as relações existentes entre o conteúdo de água na planta e outras variáveis do ecossistemas, tais como: biomassa e evapotranspiração pode auxiliar e prever impactos da escassez hídrica e seca climatológica sobre a produção de biomassa do bioma Caatinga. Assim, este estudo pretende analisar as relações existentes entre o conteúdo de água na folha com a biomassa e evapotranspiração em área do bioma caatinga localizado em São José do Sabugi, Paraiba, Brasil. Foi utilizado o algoritmo SEBAL-Surface Energy Balance para estimar a evapotranspiração e o foram calculados os índices de vegetação NDVI- Normalized Difference Vegetation Index, SAVI- Soil Adjusted Vegetation Index e o índice de conteúdo de água na folha LWCI- Leaf Water Content Index. Os resultados mostraram uma boa relação existente entre os índices de vegetação e o conteúdo de água na folha, sendo r=0.76 para o SAVI e 0.64 para o NDVI. Para a evapotranspiração a correlação foi de r =0.386. Conclui-se que a quantidade de água na folha está altamente correlacionada com a biomassa.Palavra chave: bioma, sazonalidade, seca, semiárido. A B S T R A C TThe Caatinga is a biome that suffers from high annual and intra-annual climatic variability. This climatic variability makes the biome in great part of the year suffer with high great water stress. To study the relationships between water content in the plant and other ecosystem variables, such as: biomass and evapotranspiration can help and predict impacts of water scarcity and climatological drought on the biomass production of the Caatinga biome. Thus, this study intends to analyze the relationship between water content in the leaf with biomass and evapotranspiration in the area of the caatinga biome located in São José do Sabugi, Paraiba, Brazil. The SEBAL-Surface Energy Balance algorithm was used to estimate the evapotranspiration and NDVI-Normalized Difference Vegetation Index, SAVI-Soil Adjusted Vegetation Index and the water content index in the LWCI- Leaf Water Content Index. were calculated. The results showed a good relationship between vegetation index and leaf water content, with r = 0.76 for SAVI and 0.64 for NDVI. For evapotranspiration the correlation was r = 0.386. It is concluded that the amount of water in the leaf is highly correlated with the biomass.Keywords: biome, seasonality, dry, semiarid

scholarly journals Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

2020 ◽  
Vol 12 (8) ◽  
pp. 1332 ◽  
Author(s):  
Linghui Guo ◽  
Liyuan Zuo ◽  
Jiangbo Gao ◽  
Yuan Jiang ◽  
Yongling Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Inner Mongolia ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lag ◽  
Growing Season ◽  
Climate Fluctuations ◽  
Temperature And Precipitation ◽  
Lag Effect

An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982–2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change.

scholarly journals Temporal Patterns of Precipitation and Vegetation Variability over Zimbabwe during Extreme Dry and Wet Rainfall Seasons

2014 ◽  
Vol 53 (12) ◽  
pp. 2790-2804 ◽  
Author(s):  
Seth Mberego ◽  
Juliet Gwenzi
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Variability ◽  
Temporal Distribution ◽  
Climate Extremes ◽  
Temporal Patterns ◽  
Distribution Patterns ◽  
Dry Spells ◽  
Dry Conditions ◽  
Dry Seasons

AbstractClimatic variability over southern Africa is a well-recognized phenomenon, yet knowledge about the temporal variability of extreme seasons is lacking. This study investigates the intraseasonal progression of extreme seasons over Zimbabwe using precipitation and normalized difference vegetation index (NDVI) data covering the 1981–2005 period. Results show that the greatest deficits/surpluses of precipitation occur during the middle of the rainfall season (January and February), and the temporal distribution of precipitation during extreme dry seasons seems to shift earlier than that of extreme wet seasons. Furthermore, anomalous wet (dry) conditions were observed prior to the development of extreme dry (wet) seasons. Impacts of precipitation variations on vegetation lag by approximately 1–2 months. The semiarid southern region experiences more variability of vegetation cover than do the northern and eastern regions. Three distinct temporal patterns of dry years were noted by considering the maximum NDVI level, the mid-postseason NDVI condition, and nested dry spells. The findings of this study emphasize that climate extremes ought not to be simply understood in terms of total seasonal precipitation, because they may have within them some nested distribution patterns that may have a strong influence on primary production.

scholarly journals Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1364 ◽  
Author(s):  
Spiliotopoulos ◽  
Loukas
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Linear Regression Analysis ◽  
Atmospheric Correction ◽  
Crop Coefficient ◽  
Experimental Period ◽  
Spectral Measurements ◽  
Image File ◽  
Crop Coefficients

The objective of the current study was the investigation of specific relationships between crop coefficients and vegetation indices (VI) computed at the water-limited environment of Lake Karla Watershed, Thessaly, in central Greece. A Mapping ET (evapotranspiration) at high Resolution and with Internalized Calibration (METRIC) model was used to derive crop coefficient values during the growing season of 2012. The proposed methodology was developed using medium resolution Landsat 7 ETM+ images and meteorological data from a local weather station. Cotton, sugar beets, and corn fields were utilized. During the same period, spectral signatures were obtained for each crop using the field spectroradiometer GER1500 (Spectra Vista Corporation, NY, U.S.A.). Relative spectral responses (RSR) were used for the filtering of the specific reflectance values giving the opportunity to match the spectral measurements with Landsat ETM+ bands. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were then computed, and empirical relationships were derived using linear regression analysis. NDVI, SAVI, and EVI2 were tested separately for each crop. The resulting equations explained those relationships with a very high R2 value (>0.86). These relationships have been validated against independent data. Validation using a new image file after the experimental period gives promising results, since the modeled image file is similar in appearance to the initial one, especially when a crop mask is applied. The CROPWAT model supports those results when using the new crop coefficients to estimate the related crop water requirements. The main benefit of the new approach is that the derived relationships are better adjusted to the crops. The described approach is also less time-consuming because there is no need for atmospheric correction when working with ground spectral measurements.

scholarly journals Monitoring Phenology in the Temperate Grasslands of China from 1982 to 2015 and Its Relation to Net Primary Productivity

2019 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Chaobin Zhang ◽  
Ying Zhang ◽  
Zhaoqi Wang ◽  
Jianlong Li ◽  
Inakwu Odeh
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Meteorological Data ◽  
Temperate Grasslands ◽  
The North ◽  
Phenological Changes ◽  
Grass Growth ◽  
Global Inventory

Both vegetation phenology and net primary productivity (NPP) are crucial topics under the background of global change, but the relationships between them are far from clear. In this study, we quantified the spatial-temporal vegetation start (SOS), end (EOS), and length (LOS) of the growing season and NPP for the temperate grasslands of China based on a 34-year time-series (1982–2015) normalized difference vegetation index (NDVI) derived from global inventory modeling and mapping studies (GIMMS) and meteorological data. Then, we demonstrated the relationships between NPP and phenology dynamics. The results showed that more than half of the grasslands experienced significant changes in their phenology and NPP. The rates of their changes exhibited spatial heterogeneity, but their phenological changes could be roughly divided into three different clustered trend regions, while NPP presented a polarized pattern that increased in the south and decreased in the north. Different trend zones’ analyses revealed that phenology trends accelerated after 1997, which was a turning point. Prolonged LOS did not necessarily increase the current year’s NPP. SOS correlated with the NPP most closely during the same year compared to EOS and LOS. Delayed SOS contributed to increasing the summer NPP, and vice versa. Thus, SOS could be a predictor for current year grass growth. In view of this result, we suggest that future studies should further explore the mechanisms of SOS and plant growth.

scholarly journals A high-resolution spatial assessment of the impacts of drought variability on vegetation activity in Spain from 1981 to 2015

2019 ◽  
Vol 19 (6) ◽  
pp. 1189-1213 ◽  
Author(s):  
Sergio M. Vicente-Serrano ◽  
Cesar Azorin-Molina ◽  
Marina Peña-Gallardo ◽  
Miquel Tomas-Burguera ◽  
Fernando Domínguez-Castro ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Variations ◽  
Forest Growth ◽  
Dominant Factor ◽  
Drought Severity ◽  
Vegetation Types ◽  
Vegetation Activity

Abstract. Drought is a major driver of vegetation activity in Spain, with significant impacts on crop yield, forest growth, and the occurrence of forest fires. Nonetheless, the sensitivity of vegetation to drought conditions differs largely amongst vegetation types and climates. We used a high-resolution (1.1 km) spatial dataset of the normalized difference vegetation index (NDVI) for the whole of Spain spanning the period from 1981 to 2015, combined with a dataset of the standardized precipitation evapotranspiration index (SPEI) to assess the sensitivity of vegetation types to drought across Spain. Specifically, this study explores the drought timescales at which vegetation activity shows its highest response to drought severity at different moments of the year. Results demonstrate that – over large areas of Spain – vegetation activity is controlled largely by the interannual variability of drought. More than 90 % of the land areas exhibited statistically significant positive correlations between the NDVI and the SPEI during dry summers (JJA). Nevertheless, there are some considerable spatio-temporal variations, which can be linked to differences in land cover and aridity conditions. In comparison to other climatic regions across Spain, results indicate that vegetation types located in arid regions showed the strongest response to drought. Importantly, this study stresses that the timescale at which drought is assessed is a dominant factor in understanding the different responses of vegetation activity to drought.

scholarly journals Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

2019 ◽  
Vol 11 (21) ◽  
pp. 2534 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
World Population ◽  
Lake Chad ◽  
In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

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