scholarly journals The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties

2019 ◽  
Vol 16 (18) ◽  
pp. 3434 ◽  
Author(s):  
Junyu He ◽  
Yong Wang ◽  
Di Mu ◽  
Zhiwei Xu ◽  
Quan Qian ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Meta Analysis ◽  
National Level ◽  
Hemorrhagic Fever ◽  
Early Warning Systems ◽  
Mean Temperature ◽  
Non Linear

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses. About 90% of global cases were reported in China. We collected monthly data on counts of HFRS cases, climatic factors (mean temperature, rainfall, and relative humidity), and vegetation (normalized difference vegetation index (NDVI)) in 109 Chinese counties from January 2002 to December 2013. First, we used a quasi-Poisson regression with a distributed lag non-linear model to assess the impacts of these four factors on HFRS in 109 counties, separately. Then we conducted a multivariate meta-analysis to pool the results at the national level. The results of our study showed that there were non-linear associations between the four factors and HFRS. Specifically, the highest risks of HFRS occurred at the 45th, 30th, 20th, and 80th percentiles (with mean and standard deviations of 10.58 ± 4.52 °C, 18.81 ± 17.82 mm, 58.61 ± 6.33%, 198.20 ± 22.23 at the 109 counties, respectively) of mean temperature, rainfall, relative humidity, and NDVI, respectively. HFRS case estimates were most sensitive to mean temperature amongst the four factors, and the lag patterns of the impacts of these factors on HFRS were heterogeneous. Our findings provide rigorous scientific support to current HFRS monitoring and the development of early warning systems.

scholarly journals Short-term effects of ambient temperature and relative humidity on the risk of COVID-19 and SARS in Guangzhou, China: A time-series analysis

2020 ◽  
Author(s):  
Hui Wang ◽  
Chun Chen ◽  
Qiaoxuan Lin ◽  
Tiegang Li
Keyword(s):  
Relative Humidity ◽  
Ambient Temperature ◽  
Meteorological Factors ◽  
Severe Disease ◽  
Early Warning Systems ◽  
Optimal Range ◽  
Distributed Lag ◽  
Mean Temperature ◽  
Non Linear ◽  
Coronavirus Infection

Abstract Coronavirus infection has exerted a severe disease burden on the world, especially the newly emerged SARS-CoV-2 that has caused worldwide pandemic. It is possible meteorological factors can influence the transmission of coronavirus. The aim of this study was to evaluate the effect of meteorological factors on COVID-19 and SARS, and to provide evidence for disease control and prevention. Data of COVID-19 and SARS cases and daily mean temperature, relative humidity and other meteorological factors in Guangzhou in 2003 and 2020 were collected. Using a distributed lag non-linear model approach, we assessed the relationship between ambient temperature, relative humidity and the risks of COVID-19 and SARS. The numbers of cases for COVID-19 and SARS during the study period were 347 and 1072, respectively. There was a dome-shaped relation between mean temperature and COVID-19, with a threshold of 14.50°C (RR=1.48, 95%CI: 1.01, 2.16) and the optimal range was 12.40-16.40°C. A similar association was found between mean temperature and SARS occurrence, with a threshold of 18.40°C (RR=1.02, 95%CI: 1.00, 1.04), and the optimal range was 15.30-19.30°C. Besides, there were non-linear negative relationships between both RH and COVID-19, SARS. In addition, the largest overall effect of RH on COVID-19 and SARS were obtained at 52% and 45%, yielding relative risk of 7.47 (95%CI: 1.66, 33.55) and 47.56 (95%CI: 11.49, 196.95), respectively. The optimal ranges were below 77.00% and below 82.70%, respectively. Meteorological parameters should be taken into consideration while developing early warning systems and risk strategies for controlling and preventing coronavirus infection.

scholarly journals Temporal and Spatial Variations in NDVI and Analysis of the Driving Factors in the Desertified Areas of Northern China From 1998 to 2015

2021 ◽  
Vol 9 ◽  
Author(s):  
Xuyang Wang ◽  
Yuqiang Li ◽  
XinYuan Wang ◽  
Yulin Li ◽  
Jie Lian ◽  
...  
Keyword(s):  
Ecological Restoration ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Northern China ◽  
Gobi Desert ◽  
Desert Grassland ◽  
Dune Stability ◽  
Mobile Dunes

China faces some of the most serious desertification in the world, leading to many problems. To solve them, large-scale ecological restoration projects were implemented. To assess their effectiveness, we analyzed normalized-difference vegetation index (NDVI) data derived from SPOT VEGETATION and gridded climate datasets from 1998 to 2015 to detect the degrees of desertification and the effects of human and climate drivers on vegetation dynamics. We found that NDVI of desertified areas generally decreased before 2000, then increased. The annual increase in NDVI was fixed dunes (0.0013) = semi-fixed dunes (0.0013) > semi-mobile dunes (0.0012) > gobi (gravel) desert (0.0011) > mobile dunes (0.0003) > saline–alkali land (0.0000). The proportions of the area of each desert type in which NDVI increased were fixed dunes (43.4%) > semi-mobile dunes (39.7%) > semi-fixed dunes (26.7%) > saline–alkali land (23.1%) > gobi desert (14.4%) > mobile dunes (12.5%). Thus, the vegetation response to the restoration efforts increased as the initial dune stability increased. The proportion of the area where desertification was dominated by temperature (1.8%) was far less than the area dominated by precipitation (14.1%). However, 67.6% of the change was driven by non-climatic factors. The effectiveness of the ecological restoration projects was significant in the Loess Plateau and in the Mu Us, Horqin, and Hulunbuir sandy lands. In contrast, there was little effect in the Badain Jaran, Ulan Buh, and Tengger deserts; in particular, vegetation cover has declined seriously in the Hunshandake Sandy Land and Alkin Desert Grassland. Thus, more or different ecological restoration must be implemented in these areas.

scholarly journals Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China

2022 ◽  
Vol 14 (2) ◽  
pp. 262
Author(s):  
Hui Guo ◽  
Xiaoyan Wang ◽  
Zecheng Guo ◽  
Siyong Chen
Keyword(s):  
Snow Cover ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Cycle ◽  
Water Source ◽  
Driving Factors ◽  
Change Rate ◽  
Mean Temperature ◽  
Forested Areas

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.

THE NORMALIZED DIFFERENCE VEGETATION INDEX AS AN INDICATOR OF DYNAMICS

2021 ◽  
Author(s):  
Haddad Amar ◽  
Beldjazia Amina ◽  
Kadi Zahia ◽  
Redjaimia Lilia ◽  
Rached-Kanouni Malika
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Mediterranean Ecosystems ◽  
Point Of View ◽  
Aleppo Pine ◽  
Landsat 8 ◽  
North East ◽  
The North ◽  
Pine Trees

Mediterranean ecosystems are considered particularly sensitive to climate change. Any change in climatic factors affects the structure and functioning of these ecosystems and has an influence on plant productivity. The main objective of this work is to characterize one of the Mediterranean ecosystems; the Chettaba forest massif (located in the North-East of Algeria) from a vegetation point of view and their link with monthly variations using Landsat 8 satellite images from five different dates (June 25, 2017, July 27, 2017, August 28, 2017, October 15, 2017). The comparison of NDVI values in Aleppo pine trees was performed using analysis of variance and the use of Friedman's non-parametric test. The Mann-Kendall statistical method was applied to the monthly distribution of NDVI values to detect any trends in the data over the study period. The statistical results of NDVI of Aleppo pine trees indicate that the maximum value is recorded in the month of June, while the lowest values are observed in the month of August where the species studied is exposed to periods of thermal stress.

scholarly journals Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models

2019 ◽  
Vol 16 (23) ◽  
pp. 4845
Author(s):  
Bipin Acharya ◽  
Wei Chen ◽  
Zengliang Ruan ◽  
Gobind Pant ◽  
Yin Yang ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Scrub Typhus ◽  
Control Strategies ◽  
National Level ◽  
Spatial Prediction ◽  
Health Concern ◽  
Environmental Suitability ◽  
And Control

Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.

scholarly journals Assessing the Effectiveness for Achieving Policy Objectives of Land Consolidation in China: Evidence from Project Practices in Jiangsu Province from 2001 to 2017

2021 ◽  
Vol 13 (24) ◽  
pp. 13891
Author(s):  
Yan Sun ◽  
Xiaojun Song ◽  
Jing Ma ◽  
Haochen Yu ◽  
Xiaoping Ge ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
National Level ◽  
Jiangsu Province ◽  
Land Consolidation ◽  
Investment Funds ◽  
Strategic Factors ◽  
Implementation Scheme ◽  
Policy Objectives

Land consolidation (LC) is an important measure taken to increase the quantity and productivity of farmland while reducing land fragmentation and ensuring food security. However, long-term land consolidation project (LCP) practices are rarely analyzed to assess the effectiveness for achieving current policy objectives of LC in China. Taking the practices of LCPs in Jiangsu Province from 2001 to 2017 as a case study, we used the spatial self-related analysis, the consistency analysis, and the redundant analysis (RDA), and found that the construction scale and the investment amount of LC in Jiangsu Province displayed varying trends, and that the newly increased farmland rate is clearly divided into three stages and gradually decreases. The newly increased farmland area, the investment funds, and reserved land resources for farmlands are not spatially synchronized in Jiangsu Province. Only the positive relationship between the LC rate and the Normalized Difference Vegetation Index (NDVI) growth rate continue to rise. The earlier stage of land consolidation projects (LCPs)’s practices is mainly affected by natural and social factors, and the late stage is mainly affected by economic and strategic factors. Finally, a new implementation scheme framework of LC planning has been proposed. This framework provides reference for top-level design, planning, and management of LC policies at the national level in China and other developing countries. Check meaning retained.

scholarly journals Agrometeorological conditions associated with phenological responses of tucumã-do-pará (Astrocaryum vulgare Mart.)

2018 ◽  
Vol 42 (2) ◽  
pp. 127-137
Author(s):  
Lucieta Guerreiro Martorano ◽  
Maria do Socorro Padilha de Oliveira ◽  
Gleidson Guilherme Caldas Mendes ◽  
José Reinaldo da Silva Cabral de Moraes ◽  
Daniel Pereira Pinheiro ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Amazon Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Positive Association ◽  
High Rate ◽  
Maximum Temperature ◽  
Palm Species ◽  
Thermal Amplitude

ABSTRACT Palms are among the class of hyperdominant species in the Amazon region, and for the tucumã palm (Astrocaryum vulgare Mart.) demand of climatic and phenological information in order to support strategic planning and sustainable management of this palm species native to the Amazon basin. The objective of this work was to evaluate agrometeorological conditions associated to phenological responses of tucumã as a species that has high economic potential for fruit pulp production. Meteorological data were collected during the period in which data were also collected for the phenology of the germplasm bank. Sensors were installed to monitor temperature and air relative humidity to where they are observed as phenophases. Analyses were conducted to identify the responses of the tucumã stems in function of agrometeorological conditions of the study area. Precipitation, thermal amplitude, and insolation showed positive correlations principally with respect to the percent of stems with bracts, inflorescences, or with fertilized inflorescences. In the fruiting phenological phase precipitation and air relative humidity influenced the percentage of stems with fruit clusters that were immature and also ones with mature clusters. High maximum temperatures compromise the expression of the percentage of stems with green fruit clusters. The tucumã stems were photosynthesizing and carrying out metabolic processes at a very high rate during the study period based on the high Normalized Difference Vegetation Index which was superior to 0.41 during the three years of this study. The tucumã phenological phases, demonstrating a strong positive association with insolation, maximum temperature and thermal amplitude.

scholarly journals Land Use Changes and Cluster Identification of Dengue Hemorrhagic Fever Cases in Bandung, Indonesia

2020 ◽  
Vol 5 (2) ◽  
pp. 70
Author(s):  
Sri Yusnita Irda Sari ◽  
Yessika Adelwin ◽  
Fedri Ruluwedrata Rinawan
Keyword(s):  
Land Use ◽  
Dengue Hemorrhagic Fever ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
Control Program ◽  
Hemorrhagic Fever ◽  
Cluster Identification ◽  
Vector Control Program ◽  
High Precipitation

Dengue Hemorrhagic Fever (DHF) in Indonesia has increased steadily with Bandung as a hyper-endemic area holding a high number of cases for years. This study aimed to identify cluster areas and their correlation with land use changes which was indicated by changes of Normalized Difference Vegetation Index (NDVI). Hospital surveillance of 28,327 cases during 2008–2013 was geo-coded into sub-district levels and analyzed to find cluster areas over time and space using SaTScan and ArcGIS. Spearman correlation was used to analyze NDVI with Incidence Rate (IR) in each area. IR of DHF cases tended to increase over 6 years during high precipitation period. Cases were concentrated in several cluster areas in 2009 then moved to eastern part of the city in 2013. NDVI had negative correlation with IR in 2008 (r = −0.258; p = 0.001) and positive correlation in 2012 (r = 0.193; p = 0.017). Clear geographical pattern by cluster identification overtime is beneficial for targeting appropriate vector-control program.

scholarly journals An Integrated Approach for Detection and Prediction of Greening Situation in a Typical Desert Area in China and Its Human and Climatic Factors Analysis

2020 ◽  
Vol 9 (6) ◽  
pp. 364
Author(s):  
Lei Zhou ◽  
Siyu Wang ◽  
Mingyi Du ◽  
Jianhua Yang ◽  
Yinuo Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Ecological Engineering ◽  
Integrated Approach ◽  
Vegetation Coverage ◽  
Desert Area ◽  
Unused Land

The combined study of vegetation coverage (VC) and land use change provides important scientific guidance for the restoration and protection of arid regions. Taking Hongjian Nur (HJN) Lake in the desert region as a case study, the VC of this area was calculated using a normalized difference vegetation index (NDVI), which is based on a mixed pixel decomposition method. A grey forecasting model (GM) (1, 1) was used to predict future VC. The driving factors of VC and land use change were analyzed. The results indicate that the average VC of the whole watershed showed a gradual increase from 0.29 to 0.49 during 2000–2017. The prediction results of the GM VC showed that the greening trend is projected to continue until 2027. The area of farmland in the watershed increased significantly and its area was mainly converted from unused land, grassland, and forest. The reason for increased VC may be that the combination of the exploitation of unused land and climate change, which is contrary to the country’s sustainable development goals (SDG; goal 15). Therefore, the particularities of the local ecological environment in China’s desert area needs to be considered in the development of ecological engineering projects.

scholarly journals Simplified Priestley–Taylor Model to Estimate Land-Surface Latent Heat of Evapotranspiration from Incident Shortwave Radiation, Satellite Vegetation Index, and Air Relative Humidity

2021 ◽  
Vol 13 (5) ◽  
pp. 902
Author(s):  
Yunjun Yao ◽  
Zhenhua Di ◽  
Zijing Xie ◽  
Zhiqiang Xiao ◽  
Kun Jia ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Latent Heat ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mainland China ◽  
Shortwave Radiation ◽  
Land Cover Types

An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation (Rs), satellite vegetation index, and air relative humidity (RH). Ground-measured ET for 2000–2009 collected by 100 global FLUXNET eddy covariance (EC) sites was used to calibrate and evaluate the SPT model. A series of cross-validations demonstrated the reasonable performance of the SPT model to estimate seasonal and spatial ET variability. The coefficients of determination (R2) of the estimated versus observed daily (monthly) ET ranged from 0.42 (0.58) (p < 0.01) at shrubland (SHR) flux sites to 0.81 (0.86) (p < 0.01) at evergreen broadleaf forest (EBF) flux sites. The SPT model was applied to estimate agricultural ET at high spatial resolution (16 m) from Chinese Gaofen (GF)-1 data and monitor long-term (1982–2018) ET variations in the Three-River Headwaters Region (TRHR) of mainland China using the Global LAnd-Surface Satellite (GLASS) normalized difference vegetation index (NDVI) product. The proposed SPT model without Rn provides an alternative model for estimating regional terrestrial ET across different land-cover types.

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