scholarly journals Satellite-Observed Effects from Ozone Pollution and Climate Change on Growing-Season Vegetation Activity over China during 1982–2020

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1390
Author(s):  
Zhaosheng Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Growing Season ◽  
Ozone Pollution ◽  
Vegetation Growth ◽  
Vegetation Activity ◽  
The Impact

Remote sensing vegetation index data contain important information about the effects of ozone pollution, climate change and other factors on vegetation growth. However, the absence of long-term observational data on surface ozone pollution and neglected air pollution-induced effects on vegetation growth have made it difficult to conduct in-depth studies on the long-term, large-scale ozone pollution effects on vegetation health. In this study, a multiple linear regression model was developed, based on normalized difference vegetation index (NDVI) data, ozone mass mixing ratio (OMR) data at 1000 hPa, and temperature (T), precipitation (P) and surface net radiation (SSR) data during 1982–2020 to quantitatively assess the impact of ozone pollution and climate change on vegetation growth in China on growing season. The OMR data showed an increasing trend in 99.9% of regions in China over the last 39 years, and both NDVI values showed increasing trends on a spatial basis with different ozone pollution levels. Additionally, the significant correlations between NDVI and OMR, temperature and SSR indicate that vegetation activity is closely related to ozone pollution and climate change. Ozone pollution affected 12.5% of NDVI, and climate change affected 26.7% of NDVI. Furthermore, the effects from ozone pollution and climate change on forest, shrub, grass and crop vegetation were evaluated. Notably, the impact of ozone pollution on vegetation growth was 0.47 times that of climate change, indicating that the impact of ozone pollution on vegetation growth cannot be ignored. This study not only deepens the understanding of the effects of ozone pollution and climate change on vegetation growth but also provides a research framework for the large-scale monitoring of air pollution on vegetation health using remote sensing vegetation data.

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 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.

scholarly journals Climatology and Forest Phenology during 1984-2013 around Western Lake Superior, USA.

2020 ◽  
Author(s):  
Matthew Garcia
Keyword(s):  
Remote Sensing ◽  
Interannual Variability ◽  
Land Surface ◽  
Vegetation Index ◽  
Lake Superior ◽  
Growing Season ◽  
Western Lake ◽  
Phenological Cycle ◽  
The Mean

Landsat has a history of use in the diagnosis of land surface phenology, vegetation disturbance, and their impacts on numerous forest biological processes. Studies have connected remote sensing-based phenology to surface climatological patterns, often using average temperatures and derived growing degree day accumulations. I present a detailed examination of remotely sensed forest phenology in the region of western Lake Superior, USA, based on a comprehensive climatological assessment and 1984-2013 Landsat imagery. I use this climatology to explain both the mean annual land surface phenological cycle and its interannual variability in temperate mixed forests. I assess long-term climatological means, trends, and interannual variability for the study period using available weather station data, focusing on numerous basic and derived climate indicators: seasonal and annual temperature and precipitation, the traditionally defined frost-free growing season, and a newly defined metric of the climatological growing season: the warm-season plateau in accumulated chilling days. Results indicate +0.56°C regional warming during the 30-year study period, with cooler springs (–1.26°C) and significant autumn warming (+1.54°C). The duration of the climatological growing season has increased +0.27 days/y, extending primarily into autumn. Summer precipitation in my study area declined by an average –0.34 cm/y, potentially leading to moisture stress that can impair vegetation carbon uptake rates and can render the forest more vulnerable to disturbance. Many changes in temperature, precipitation, and climatological growing season are most prominent in locations where Lake Superior exerts a strong hydroclimatological influence, especially the Minnesota shoreline and in forest areas downwind (southeast) of the lake. I then develop and demonstrate a novel phenoclimatological modeling method, applied over five Landsat footprints across my study area, that combines (1) diagnosis of the mean phenological cycle from remote sensing observations with (2) a partial-least-squares regression (PLSR) approach to modeling vegetation index residuals using these numerous meteorological and climatological observations. While the mean phenology often used to inform land surface models in meteorological and climate modeling systems may explain 50-70% of the observed phenological variability, this mixed modeling approach can explain more than 90% of the variability in phenological observations based on long-term Landsat records for this region.

scholarly journals Vegetation Dynamic Changes and Their Response to Ecological Engineering in the Sanjiangyuan Region of China

2020 ◽  
Vol 12 (24) ◽  
pp. 4035
Author(s):  
Xiaohui Zhai ◽  
Xiaolei Liang ◽  
Changzhen Yan ◽  
Xuegang Xing ◽  
Haowei Jia ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Positive Impact ◽  
Ecological Engineering ◽  
Scientific Basis ◽  
Vegetation Dynamic ◽  
Vegetation Growth ◽  
The Impact

In recent decades, the vegetation of the Sanjiangyuan region has undergone a series of changes under the influence of climate change, and ecological restoration projects have been implemented. In this paper, we analyze the spatiotemporal dynamics of vegetation in this region using the satellite-retrieved normalized difference vegetation index (NDVI) from the global inventory modeling and mapping studies (GIMMS) and moderate resolution imaging and spectroradiometer (MODIS) datasets during the past 34 years. Specifically, the characteristics of vegetation changes were analyzed according to the stage of implementation of different ecological engineering programs. The results are as follows. (1) The vegetation in 65.6% of the study area exhibited an upward trend, and in 53.0% of the area, it displayed a large increase, which was mainly distributed in the eastern part of the study area. (2) The vegetation NDVI increased to differing degrees during stages of ecological engineering. (3) The NDVI in the western part of the Sanjiangyuan region is mainly affected by temperature, while in the northeastern part, the NDVI is affected more by precipitation. In the southern part, however, vegetation growth is affected neither by temperature nor by precipitation. On the whole region, vegetation growing is more affected by temperature than by precipitation. (4) The impacts of human activities on vegetation change are both positive and negative. In recent years, ecological engineering projects have had a positive impact on vegetation growth. This study can help us to correctly understand the impact of climate change on vegetation growth, so as to provide a scientific basis for the evaluation of regional ecological engineering effectiveness and the formulation of ecological protection policies.

scholarly journals The Italian TREETALKER NETWORK (ITT-Net): continuous large scale monitoring of tree functional traits and vulnerabilities to climate change

2020 ◽  
Author(s):  
Simona Castaldi ◽  
Serena Antonucci ◽  
Shahla Asgharina ◽  
Giovanna Battipaglia ◽  
Luca Belelli Marchesini ◽  
...  
Keyword(s):  
Climate Change ◽  
Real Time ◽  
High Frequency ◽  
Large Scale ◽  
Operation Mode ◽  
Cloud Services ◽  
Long Term Monitoring ◽  
The Impact ◽  
Term Monitoring

<p>The  <strong>Italian TREETALKER NETWORK (ITT-Net) </strong>aims to respond to one of the grand societal challenges: the impact of climate changes on forests ecosystem services and forest dieback. The comprehension of the link between these phenomena requires to complement the most classical approaches with a new monitoring paradigm based on large scale, single tree, high frequency and long-term monitoring tree physiology, which, at present, is limited by the still elevated costs of multi-sensor devices, their energy demand and maintenance not always suitable for monitoring in remote areas. The ITT-Net network will be a unique and unprecedented worldwide example of real time, large scale, high frequency and long-term monitoring of tree physiological parameters. By spring 2020, as part of a national funded project (PRIN) the network will have set 37 sites from the north-east Alps to Sicily where a new low cost, multisensor technology “the TreeTalker®” equipped to measure tree radial growth, sap flow, transmitted light spectral components related to foliage dieback and physiology and plant stability (developed by Nature 4.0), will monitor over 600 individual trees. A radio LoRa protocol for data transmission and access to cloud services will allow to transmit in real time high frequency data on the WEB cloud with a unique IoT identifier to a common database where big data analysis will be performed to explore the causal dependency of climate events and environmental disturbances with tree functionality and resilience.</p><p>With this new network, we aim to create a new knowledge, introducing a massive data observation and analysis, about the frequency, intensity and dynamical patterns of climate anomalies perturbation on plant physiological response dynamics in order to: 1) characterize the space of “normal or safe tree operation mode” during average climatic conditions; 2) identify the non-linear tree responses beyond the safe operation mode, induced by extreme events, and the tipping points; 3) test the possibility to use a high frequency continuous monitoring system to identify early warning signals of tree stress which might allow to follow tree dynamics under climate change in real time at a resolution and accuracy that cannot always be provided through forest inventories or remote sensing technologies.</p><p>To have an overview of the ITT Network you can visit www.globaltreetalker.org</p><p> </p>

scholarly journals Insurance as a risk management system in green construction

2022 ◽  
Vol 42 ◽  
pp. 06003
Author(s):  
Kheda Murtazova ◽  
Salambek Aliyev
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Global Climate ◽  
Green Building ◽  
Economic Research ◽  
Corporate Strategies ◽  
Climate Risks ◽  
Risk Management System ◽  
The Impact

The study of the problems of the impact of climate change on economic development has become in recent years one of the main directions of economic research. At the same time, along with the development of a global macroeconomic policy in the field of climate and green building, more and more attention is paid to the analysis of corporate strategies to reduce risks and adapt to the consequences of climate change. Without large-scale business investments in green innovative technologies and the introduction of corporate standards for reducing greenhouse gas emissions, it is impossible to achieve long-term targets for reducing global climate risks.

scholarly journals Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

2021 ◽  
Vol 13 (1) ◽  
pp. 416-430
Author(s):  
Zhengdong Deng ◽  
Zhao Lu ◽  
Guangyuan Wang ◽  
Daqing Wang ◽  
Zhibin Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Gansu Province ◽  
Vegetation Coverage ◽  
Wide Field ◽  
Red Edge ◽  
The Impact ◽  
Edge Band

Abstract The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.

scholarly journals Study on soil moisture by thermal infrared data

2013 ◽  
Vol 17 (5) ◽  
pp. 1375-1381 ◽  
Author(s):  
Jun He ◽  
Xiao-Hua Yang ◽  
Shi-Feng Huang ◽  
Chong-Li Di ◽  
Ying Mei
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Layer ◽  
Growth Period ◽  
Vegetation Growth ◽  
Term Monitoring ◽  
Infrared Data

Information on soil moisture is important for environment management. This study bases on the daily observation to study the normalized difference vegetation index and to classify the index data. The results indicate that: (1) the index is able to adequately reflect the changes of soil moisture content in 10 cm and 20 cm thickness of soil layer during the vegetation growth period and (2) Information on soil moisture can be used for regional drought monitoring. The method can be extended for long-term monitoring of droughts over large-scale regions.

scholarly journals Vegetation Change and Its Response to Climate Change in Yunnan Province, China

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Huaizhang Sun ◽  
Jiyan Wang ◽  
Junnan Xiong ◽  
Jinhu Bian ◽  
Huaan Jin ◽  
...  
Keyword(s):  
Climate Change ◽  
Yunnan Province ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Growing Season ◽  
Effect Of Temperature ◽  
Lag Effect ◽  
Different Seasons ◽  
The Impact

The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province.

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