Average Normalized Difference Vegetation Index Research Articles

Perceptions of green space usage, abundance, and quality of green space were associated with better mental health during the COVID-19 pandemic among residents of Denver.

The COVID-19 pandemic has impacted both physical and mental health. This study aimed to understand whether exposure to green space buffered against stress and distress during the COVID-19 pandemic while taking into account significant stressors of the pandemic. We leveraged a cross-sectional survey on green space exposure and mental health among residents of Denver, CO that ran from November 2019 through January 2021. We measured objective green space as the average NDVI (normalized difference vegetation index) from aerial imagery within 300m and 500m of the participant's residence. Perceived green space was measured through Likert scores on five questions about vegetation near the home that captured perceived abundance, visibility, access, usage, and quality of green space. We used generalized linear models to assess the relationship between each green space exposure variable and perceived stress (PSS-4), depression (CES-D-10), or anxiety (MMPI-2) adjusted for sociodemographic and COVID-19 impact variables. We found significantly higher depression scores for all covid periods compared to the "before covid" period, and significantly higher anxiety scores during the "fall wave" compared to earlier periods. Adjusted for sociodemographic and pandemic stressors, we found that spending a lot of time in green space (usage) was significantly associated with lower anxiety and depression. We also observed significantly lower depression scores associated with NDVI in both buffers (objective abundance) and significantly lower anxiety scores with perceived abundance of green space. There was some evidence of lower anxiety scores for people reporting having high quality green spaces near the home (quality). We did not observe significant associations for any green space metric and perceived stress after adjustment for confounding variables. Our work provides further evidence of mental health benefits associated with green space exposure during the COVID-19 pandemic even after adjustment for sociodemographic variables and significant pandemic-related stressors.

Effects of residential greenness on attention in a longitudinal study at 8 and 11–13 years

In an urbanizing world, with 55% of the population living in cities, it is essential to design friendly and healthy ones. An emerging body of evidence has associated greenspace exposure with improved cognitive development, including attentional function; however, the longitudinal studies looking at the association with attentional function are still scarce. Therefore, the objective of this study was to analyze the association of the exposure to greenspace and attention in school children. This study was based on 751 participants at 8 years and 598 at 11–13 years of two sub-cohorts of the INMA cohort study in Gipuzkoa and Asturias, Spain. Greenspace exposure at home was characterized using four indicators: (i) average of Normalized Difference Vegetation Index (NDVI) and (ii) Vegetation Continuous Field (VCF) in buffers of 100 m, 300 m, and 500 m around the residential address, (ii) availability of a green space within 300 m from the residential address, and (iv) residential distance to green spaces. Participants’ attention was characterized twice at ages of 8 and 11 years, using the computerized Attentional Network Test (ANT). General linear models were used for the cross-sectional analyses and linear mixed effects model for the longitudinal analyses. Our cross-sectional analyses showed a statistical significant protective association between average NDVI at 300 m and inattentiveness (−7.20, CI 95%: 13.74; −0.67). In our longitudinal analyses, although we generally observed beneficial associations between greenspace exposure and attention, none attained statistical significance. No statistically significant indirect effect were seen for NO2. Our findings add to the emerging body of evidence on the role of green spaces in neurodevelopment, which can provide the evidence base for implementing intervention aimed at promoting neurodevelopment in urban children.

Can we detect the damage of a heatwave on vineyards using Sentinel-2 optical remote sensing data?

Climate change will exacerbate environmental threats, which will, in turn, affect agricultural production patterns. This study addresses the feasibility of using Sentinel-2 (S2) optical remote sensing data to map the consequences of heatwaves on vineyard plots. The proposed method to map damaged and undamaged vineyards is based on the use of an inter-annual (C1) and an intra-annual (C2) criterion derived from the Normalized Difference Vegetation Index (NDVI) calculated on S2 data. While the inter-annual criterion compares the NDVI of the heatwave year to the average NDVI of the previous years with no heatwave, the intra-annual criterion compares the NDVI values before and after the heatwave in the same year. Predictions from either criteria or both combined were tested against two datasets collected during two field surveys performed in 2020 and 2021, with different ways of recording the damage caused on the grapevine by the 2019 heatwave in southeastern France. Results showed that within the reference vineyard plots with heat damage, 46 %, 62 % and 40 % of the S2 pixels were correctly predicted as damaged using C1, C2 and their combination, respectively. Within undamaged plots, 91 %, 88 % and 99 % of the S2 pixels were correctly predicted as undamaged using C1, C2 and the combination, respectively. Results also showed that only severe leaf damage was detected using the S2 NDVI. The combination of C1 and C2 provides the most accurate detection of heatwave consequences on vineyards.

Driving Mechanism of the Spatiotemporal Evolution of Vegetation in the Yellow River Basin from 2000 to 2020

The NDVI (normalized difference vegetation index) was used as the vegetation coverage index. Based on the NDVI and weather data from 2000 to 2020, the characteristics of the spatiotemporal evolution and the driving mechanism of vegetation were investigated by using correlation analysis, the Theil-Sen estimator, the Mann-Kendall method, and multivariate residual trend analysis. The results showed that the growing season average NDVI in the Yellow River basin was a fluctuating upward trend of 0.005 a-1 from 2000 to 2020. Areas with significantly improved vegetation in the basin were mainly distributed in the Qinling Mountains, the Northern Shaanxi Plateau, and the Lvliang Mountains in the midstream. The average value of the partial correlation coefficient between the growing season average NDVI and rainfall in the Yellow River basin was 0.57, and the average value of the partial correlation coefficient between the growing season average NDVI and temperature was 0.49. The impact of rainfall on vegetation was higher than that of temperature. The areas where human activities significantly improved vegetation growth were mainly distributed in the northern Shaanxi Plateau, the Lvliang Mountains, and southern Ningxia. The areas where human activities inhibited vegetation growth were mainly distributed in cities with strong human activities such as Yinchuan, Baotou, Xi'an, Luoyang, Zhengzhou, and Taiyuan. Human activities and climate change contributed to 72% and 28% of the vegetation change in the Yellow River basin. Driven by human activities and climate change, the area where vegetation growth has improved in the Yellow River basin accounted for 96.4% of the basin area, of which the contribution rate of human activities greater than 80% of the area accounted for 34.3%, which was mainly distributed in the middle and southeast of the basin. The area with a contribution rate of climate change greater than 80% accounted for 4.2%, which was mainly distributed in the Sichuan-Tibet Plateau and Longzhong Loess Plateau in the basin. The results of this research can provide scientific support for the ecological protection and high-quality development of the Yellow River basin.

City-level greenness exposure is associated with COVID-19 incidence in China

Accumulating studies have suggested an important role of environmental factors (e.g. air pollutants) on the occurrence and development of coronavirus disease 2019 (COVID-19). Evidence concerning the relationship of greenness on COVID-19 is still limited. This study aimed to assess the association between greenness and COVID-19 incidence in 266 Chinese cities. A total of 12,377 confirmed COVID-19 cases were identified through February 29th, 2020. We used the average normalized difference vegetation index (NDVI) during January and February 2020 from MOD13A2 product, to represent the city-level greenness exposure. A generalized linear mixed-effects model was used to estimate the association between NDVI exposure and COVID-19 incidence using COVID-19 cases as the outcome. We evaluated whether the association was modified by population density, GDP per capita, and urbanization rate, and was mediated by air pollutants. We also performed a series of sensitivity analyses to discuss the robustness of our results. Per 0.1 unit increment in NDVI was negatively associated with COVID-19 incidence (IRR: 0.921, 95% CI: 0.898, 0.944) after adjustment for confounders. Associations with COVID-19 incidence were stronger in cities with lower population density, lower GDP per capita, and lower urbanization rate. We failed to detect any mediation effect of air pollutants on the association between NDVI and COVID-19 incidence. Sensitivity analyses also indicated consistent estimates. In conclusion, our study suggested a beneficial association between city-level greenness and COVID-19 incidence. We could not establish which mechanisms may explain this relationship.

Land Cover and Vegetation Coverage Changes in the Mining Area—A Case Study from Slovakia

Dealing with landscape changes in space and time is an important activity in terms of the process of future development of the selected area. In particular, it is necessary to focus on territories that are exposed to the effects of extraction activities. The main objective of the paper was the mapping of spatio-temporal changes in the landscape in connection with the extraction of minerals due to mining activities on the landscape using satellite images and data from the Corine land cover (CLC) database in the environment of geographic information systems. The selected study area is specific to the presence of four mineral deposits (three of which are under active mining). The Rohožník-Konopiská deposit was abandoned and the area was subsequently reclaimed. The study used Corine land cover (CLC) data and Landsat 5, 7, 8 satellite images for selected years in the period 1990–2021. The Normalized Difference Vegetation Index (NDVI) was calculated for vegetation cover analysis, which was further combined with the forest spatial division units (FSDU) layer. Areas in the immediate vicinity of the open-pit mine were selected for detailed analysis of vegetation changes. Using the FSDU data, an average NDVI index value was calculated using the Zonal statistics function for each plot. The results showed that over the selected period there have been changes indicating an improvement in the landscape condition by reclamation operations at two deposits, Rohožník-Konopiská (inactive) and Sološnica-Hrabník (active). The analyzed CLC data detected the change at the Rohožník-Konopiská deposit, but the active deposit Sološnica-Hrabník was not detected in these data. The loss of vegetation on the other two deposits is mainly due to pre-mining preparatory work, which causes the removal of soil and vegetation layers.

Linking Spatial–Temporal Changes of Vegetation Cover with Hydroclimatological Variables in Terrestrial Environments with a Focus on the Lake Urmia Basin

Investigation of vegetation cover is crucial to the study of terrestrial ecological environments as it has a close relationship with hydroclimatological variables and plays a dominant role in preserving the characteristics of a region. In Iran, the current study selected the watersheds of two rivers, Nazloo-Chay and Aji-Chay, to systematically investigate the implications and causes of vegetation cover variations under changing environments. These two rivers are among the essential inflows to Lake Urmia, the second largest saline lake on Earth, and are located on the west and east sides of the lake, respectively. There has been a debate between the people living in the rivers’ watersheds about who is responsible for the decline in the level of Lake Urmia—does responsibility fall with those on the east side or with those on the west side? In this study, the normalized difference vegetation index (NDVI) was used as a remotely sensed index to study spatial–temporal pattern changes in vegetation. Moreover, the temperature, precipitation, and streamflow time series were gathered using ground measurements to explore the causes and implications of changing vegetation cover. Discrete wavelet transform was applied to separate the different components of the time series. The Mann–Kendall (MK) test was applied to the time series on monthly, seasonal, and annual time scales. The connections and relationship between the NDVI time series and temperature, precipitation, and streamflow time series and any underlying causes were investigated using wavelet transform coherence (WTC). Land use maps were generated for different years using a support vector machine (SVM) in the final stage. The results indicated that the most dominant monthly, seasonal, and annual hydrological periodicities across the watersheds are 8 months, 6 months, and 2 years, respectively. The increasing vegetation cover during stable hydro-environmental periods revealed unusual conditions in the Aji-Chay watershed and reflected agricultural expansion. The WTC graphs indicated sudden changes in mutual periodicities and time-lags with different patterns between variables, which indicates the increasing anthropogenic activities in both watersheds. However, this was more dominant in the Aji-Chay watershed. The land use maps and investigation of the averaged NDVI maps also denoted that the areas of cultivated land have increased by 30% in the Aji-Chay watershed, and crop types have been changed to the crops with a higher demand for water in both watersheds.

内蒙古大青山国家级自然保护区植被归一化指数时空变化及其与环境因子的关系

PDF HTML阅读 XML下载 导出引用 引用提醒 内蒙古大青山国家级自然保护区植被归一化指数时空变化及其与环境因子的关系 DOI: 10.5846/stxb202108072161 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 内蒙古自治区科技重大专项项目（zdzx2018060，2019ZD007）；内蒙古自治区退耕还林生态效益监测项目 Spatio-temporal variation of NDVI and influencing factors in the Daqing Mountain National Nature Reserve, Inner Mongolia Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:选取2000-2020年MODIS影像的归一化指数（NDVI）数据及同期的气象数据，采用趋势分析、变异系数、相关分析等方法，从海拔、坡度和坡向的角度分析了内蒙古大青山国家级自然保护区生长季NDVI的时空变化特征，并进一步探讨了NDVI时空变化与气候因子的相关性。结果表明：（1）2000-2020年，内蒙古大青山国家级自然保护区植被总体覆盖状况良好且呈增长趋势，增长速率为0.058/10a；（2）保护区NDVI空间分布呈现出西部低、东部高的从西到东逐渐增加的特征。保护区生长季NDVI均值随着海拔的升高而明显增加，随着坡度的增加呈缓慢增加趋势，在不同坡向上，生长季NDVI均值大小依次为阴坡 > 半阴坡 > 半阳坡 > 阳坡；（3）从NDVI变化趋势分析来看，2000-2020年大青山自然保护区植被覆盖改善的面积占总面积的61.75%，其中，显著改善、极显著改善面积分别占总面积的41.13%、20.62%，基本保持不变的面积占总面积的38.17%，退化面积占总面积的0.07%。在空间分布上，NDVI显著、极显著增加的区域主要分布在保护区西部和东部，中部以变化不显著为主。保护区各功能区NDVI变化趋势看，NDVI极显著增加、显著增加面积百分比大小顺序为实验区 > 核心区 > 缓冲区；（4）2000-2020年，稳定性整体表现为"不同波动变化并存，较低、中等和较高波动变化居多，低波动和高波动变化面积占比相对较小"的特点，高波动变化区域主要分布在保护区西部、中部和实验区，人为影响和降水量是影响稳定性的主要驱动因素；（5）大青山自然保护区生长季NDVI与年均降水量以正相关为主，正相关面积比例达到99.79%，与温度相关性不显著为主，不显著面积占总面积的97.91%，降水量是影响大青山自然保护区植被覆盖的最主要气象因子。 Abstract:Based on the Normalized Difference Vegetation Index (NDVI) data of Moderate Resolution Imaging Spectroradiometer (MODIS) image and the relative meteorological data from 2000 to 2020, the temporal and spatial variation characteristics of NDVI in the growing season of Daqing Mountain National Nature Reserve in Inner Mongolia are analyzed from the perspective of elevation, slope and slope aspect by using the methods of trend analysis, coefficient of variation and correlation analysis. The correlation between temporal and spatial variation of NDVI and climate factors was further explored. The results show that:(1) The overall vegetation coverage of Daqing Mountain National Nature Reserve was good and growing from 2000 to 2020, with a growth rate of 0.058/10a. (2) The spatial distribution of the NDVI shows the characteristics of low in the west and high in the east, and gradually increasing from west to east of the reserve. With the increase of elevation, the mean value of NDVI in the growing season of the reserve increased significantly, but increased slowly with the increase of the slope. In different slope aspects, the mean value of NDVI in growing season was shady slope > semi-shady slope > semi-sunny slope > sunny slope. (3) From the analysis of NDVI change trend in 2000-2020, the area of vegetation coverage improvement in Daqing Mountain National Nature Reserve accounted for 61.75% of the total area, of which the areas of significant improvement and extremely significant improvement accounted for 41.13% and 20.62% of the total area, respectively, the area basically unchanged accounted for 38.17% of the total area, and the degraded area accounted for 0.07% of the total area. In terms of spatial distribution, the areas with the significant and extremely significant increase in NDVI were mainly distributed in the west and east of the reserve, and the change was not significant in the middle. According to the changing trend of NDVI in each functional area of the reserve, the NDVI increased significantly, and the area percentage increased significantly in the order of experimental zone > core zone > buffer zone. (4) From 2000 to 2020, the overall performance of stability is characterized by the coexistence of different fluctuations, the majority of low, medium and high fluctuations, and the proportion of low and high fluctuations being relatively small. The high fluctuations are mainly distributed in the western and central areas of the reserve and the experimental zone, and human disturbance and precipitation are the main driving factors. (5) The average annual maximum NDVI in the growing season of Daqing Mountain National Nature Reserve is mainly positively correlated with the average annual precipitation, and the proportion of the positive correlation area reaches 99.79%. It is mainly insignificant with temperature, and the insignificant area accounts for 97.91% of the total area. Precipitation is the main meteorological factor affecting the vegetation coverage of Daqing Mountain National Nature Reserve. 参考文献 相似文献 引证文献

近60年来印江河流域极端气候演变及其对净初级生产力和归一化植被指数的影响

极端气候事件的发生改变了区域水热条件，并影响着生态环境变化。然而，目前长时间尺度上极端气候的演变规律及其对生态环境的影响尚不明晰。采用Mann-Kendall趋势及突变检验法、连续小波变换和Hurst指数法揭示了喀斯特槽谷印江河流域极端气候的变化趋势、突变时间、周期性特征和未来演变规律，并利用Lindeman-Merenda-Gold模型定量评估了极端气候溶变对生态环境变化的影响。结果表明：（1）印江河流域极端气温显著上升，降雨量增多，呈现湿热多雨的气候特征。未来极端气温事件持续等级将更高，持续强度也更强。（2）同类型极端气候具有潜在的关联性，但不同类型极端气候间的影响较小，且多呈负相关。（3）印江河流域平均净初级生产力（NPP）和归一化植被指数（NDVI）在2000-2015年间呈现相反的变化趋势，NPP平均值为598.53 g C m^-2 a^-1，平均减少速率为-3.32 g C m^-2 a^-1。NDVI平均值为0.59，平均增长速率为0.0013/a。（4）冷持续指数（CSDI）、平均温差（DTR）、系统阈值结冰日数（ID6.4）和生长期长度（GSL）对NPP的贡献较大，贡献率分别12.64%、11.50%、11.05%和7.4%。其中，CSDI、DTR、GSL对NPP变化表现为负贡献，而ID6.4则表现为正贡献。大部分极端气候指数对NPP变化的贡献都不超过5%。ID6.4对NDVI变化的影响最大，暖夜日数（TN90p）、热持续指数（WSDI）和暖昼日数（TX90p）相对次之，贡献率分别为13.67%、13.54%、12.95%和10.02%。总体上看，在2000-2015年间，印江河流域湿热多雨的气候对NDVI增长有促进作用，但是由于气温升高和降雨量增加的原因，对NPP累积产生了较大的负面影响。研究结果可为喀斯特槽谷流域气候预测、水资源管理及生态环境恢复提供参考依据。;The occurrence of extreme climate events has changed the regional hydrothermal conditions and affected the changes of ecological environment. However, at present, the evolution of extreme climate and its impact on ecological environment are unclear. Therefore, the methods of Mann-Kendall test, continuous wavelet transform, and Hurst exponent were applied to analyze the change trend, abrupt time, periodicity and future evolution of extreme climate events in Yinjiang River basin. Then, the Lindeman-Merenda-Gold model was used to quantitatively evaluate the impacts of extreme climate events to ecological environment change. The results were obtained:(1) The extreme temperature events in the Yinjiang River basin increased significantly, and the rainfall also increased, showing a hot and rainy climate. In the future, the level of extreme temperature events will be higher and the intensity will be stronger; (2) The same type of extreme climate had a potential correlation, but the influence of different types of extreme climate was small, and most of them showed negative correlation; (3) The average net primary production (NPP) and normalized difference vegetation index (NDVI) of the Yinjiang River basin showed an opposite trend from 2000 to 2015. The average NPP was 598.53 g C m^-2 a^-1 with the average reduction rate of -3.32 g C m^-2 a^-1 while the average NDVI value was 0.59 with an average growth rate of 0.0013/a; (4) The cold spell duration indicator(CSDI), diurnal temperature range (DTR), icing days defined by system threshold (ID6.4), and growing season length (GSL) contributed significantly to NPP, with contributions of 12.64%, 11.50%, 11.05%, and 7.4%, respectively. Among them, the CSDI, DTR, and GSL showed negative contribution to NPP changes, while ID6.4 showed positive contribution. Most extreme climate indices contributed less than 5% to the NPP changes. ID6.4 had the greatest impact on NDVI, followed by warm nights (TN90p), warm spell duration indicator (WSDI), and warm days (TX90p), with contribution rates of 13.67%, 13.54%, 12.95%, and 10.02%, respectively. On the whole, the study stressed that the hot and rainy climate in the Yinjiang River basin promoted the growth of NDVI during 2000-2015, but caused a significantly negative impact on the accumulation of NPP due to the increase of temperature and rainfall. The results can provide reference for climate prediction, water resources management and ecological environment restoration in a basin of karst trough valley.

基于夜间灯光数据的甘青宁城市扩展及其建成区植被变化特征

PDF HTML阅读 XML下载 导出引用 引用提醒 基于夜间灯光数据的甘青宁城市扩展及其建成区植被变化特征 DOI: 10.5846/stxb202005141227 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 甘肃农业大学大学生创新创业计划项目（202102045）；国家自然科学基金青年项目（41907406）；甘肃农业大学科技创新基金（GAU-KYQD-2018-23） Characteristics of urban expansion and vegetation change in built-up areas of Gan-Qing-Ning based on nighttime light data Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:开展针对黄河上游甘青宁地区城市建成区空间发展模式及其植被变化特征的研究，可为构建科学合理的城市发展体系、维护区域生态安全屏障提供重要的参考依据。以DMSP/OLS和统计数据为基础，运用统计数据比较法提取了甘青宁地区21座地级市（州）的城市建成区边界，并从城市空间扩展特征、扩展速度、动态度、紧凑度和重心迁移5个方面对城市扩展特征进行分析；在此基础上，引入SPOT/VEGETATION NDVI数据，以年均NDVI作为表征城市建成区植被状况的指标，从NDVI总和、均值、比值及其变化量、年际变化率及其变化百分率和稳定性5个方面对研究区2002-2013年城市建成区的植被变化进行研究。结果表明：1）采用统计数据比较法提取获得的研究区城市建成区精度较高，误差率在6.7%以内；2）城市用地围绕河西走廊及黄河上游地区形成不连续的"几"字型空间形态，并具有点状、线状和面状3种不同的分布模式；3）城市扩展年均速及动态度大体呈上升趋势，且2010-2013年的城市扩展速度（4.53 km2/a）是2006-2010年的2倍；4）除酒泉、海西、白银、天水、石嘴山和兰州外，其他城市建成区紧凑度指数均较高；5）城市重心在第一（2002-2006）和第二阶段（2006-2010）均向北发生迁移，在第三阶段（2010-2013）则向南发生迁移；6）城市建成区NDVI总和随建成区面积的增加呈上升趋势；7）城市建成区植被整体呈向好趋势，从NDVI均值看，2002-2006年植被状况变差，而2006-2013年又有所改善，且NDVI均值分布具有明显的空间差异，中西部地区普遍小于东部；从NDVI年际变化率和变化百分率来看，各城市建成区均大于外围背景区；8）城市建成区植被状况普遍较背景区差，且2002-2013年近4/7的城市建成区NDVI比值及其变化量均呈下降趋势；9）城市建成区植被稳定性普遍较差，半数以上城市的变异系数在12年间均有不同程度的增加。 Abstract:Carrying out research on the spatial development pattern and vegetation change characteristics of urban built-up areas in the Gan-Qing-Ning region on the upper reaches of the Yellow River can provide an important reference for building a scientific and reasonable urban development system and maintaining regional ecological security barriers. Based on DMSP/OLS and statistical data, this paper extracted the urban built-up area boundaries of 21 prefecture-level cities (prefectures) in Gan-Qing-Ning region by using statistical data comparison method, and analyzed the urban expansion characteristics from five aspects:urban spatial expansion characteristics, expansion speed, dynamic degree, compactness and center of gravity transfer. On this basis, the SPOT/VEGETATION NDVI (Normalized Difference Vegetation Index) data was introduced, and the annual average NDVI was used as an indicator to represent the vegetation status in urban built-up areas. The vegetation change of urban built-up areas in the study area from 2002 to 2013 was studied from five aspects:sum, mean, ratio and its change amount, interannual change rate, percentage and stability of NDVI. The results showed that:1) As one of the effective methods to extract urban built-up area, the statistical data comparison method had high accuracy in our study area, and the error rate was within 6.7%. 2) The urban land formed a discontinuous spatial pattern of "几" around the Hexi Corridor and the upper reaches of the Yellow River, and had three different distribution patterns:point, line and plane. 3) The annual average rate and dynamic degree of urban expansion showed an upward trend, and the rate of urban expansion during 2010-2013 (4.53 km2/a) was twice as high as that during 2006-2010. 4) Except for Jiuquan, Haixi, Baiyin, Tianshui, Shizuishan and Lanzhou, the compactness index of other urban build-up areas was relatively high. 5) In the first stage (2002-2006) and the second stage (2006-2010), the center of gravity shifted northward, and in the third stage (2010-2013), the center of gravity shifted southward. 6) The total NDVI of urban built-up area increased with the increase of built-up area. 7) The vegetation in urban built-up areas showed a positive trend on the whole. From the perspective of the mean NDVI, the vegetation condition got worse from 2002 to 2006, and improved from 2006 to 2013. Moreover, the mean NDVI distribution showed obvious spatial differences, and the central and western regions were generally smaller than the eastern ones. From the perspective of the interannual variation rate and percentage of NDVI, the built-up area of each city was larger than the peripheral background area. 8) The vegetation status in urban built-up areas was generally worse than that in background areas, and the NDVI ratio and its variation in nearly 4/7 urban built-up areas showed a downward trend from 2002 to 2013. 9) Vegetation stability in urban built-up areas was poor, and the variation coefficients of more than half of the cities increased to varying degrees in the past 12 years. 参考文献 相似文献 引证文献

Landsat8 위성영상을 이용한 지표면 온도와 식생피복의 관계 분석 : 대구광역시를 대상으로

This study used Landsat 8 satellite imagery to identify land surface temperature (LST) and to create a normalized difference vegetation index (NDVI) for Daegu City for analysis of different land cover types. LST for Daegu showed distinctive patterns depending on the NDVI and land cover had negative correlations with LST and NDVI. In addition, urban and industrial areas accounted for the top 20% of LSTs while forests and waters accounted for the bottom 20%. Forest areas had a high NDVI value of 0.447, average NDVI values were highest for deciduous and lowest for coniferous forests. The results of this study provide a better understanding of the role of vegetative cover in the thermal environment in urban areas which has deteriorated due to climate change. This information will help validate urban planning policies that protect green spaces.

High altitudinal vegetation dynamics including treeline ecotone in Langtang National Park, Nepal

Identification of high altitudinal vegetation dynamics using remote sensing is important because of the complex topography and environment in the Himalayas. Langtang National Park is the first Himalayan park in Nepal representing the best area to study vegetation change in the central Himalaya region because of the high altitudinal gradient and relatively less disturbed region. This study aimed at mapping vegetation in Langtang National Park and its treeline ecotone using Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI). Two treeline sites with an altitude of 3927 and 3802 meters above sea level (masl) were selected, and species density was measured during the field survey. The linear slope for each pixel and the Mann Kendall test to measure significant trends were used. The results showed that NDVI has significantly increased at the rate of 0.002yr-1 in Langtang National Park and 0.003yr-1 in treeline ecotone during 2000-2017. The average 68.73% equivalents to 1463 km2 of Langtang National Park are covered by vegetation. At the same time, 16.45% equivalents to 350.43 km2 are greening, and 0.25%, i.e., 5.43 km2 are found browning. In treeline ecotone, the vegetation is mostly occupied by grasses, shrublands and small trees where the NDVI was found from 0.1 to 0.5. The relative changes of NDVI in barren lands are negative and vegetative lands above 0.5 NDVI are positive between 2000 and 2017. The dominant treeline vegetation were Abies spectabilis, Rhododendron campanulatum, Betula utilis and Sorbus microphyla, with the vegetation density of 839.28 and 775 individuals per hectare in sites A and B, respectively. The higher average NDVI values, significantly increased NDVI, and higher density of vegetation in both A and B sites indicate that the vegetation in treeline ecotone is obtaining a good environment in the Himalayas of Nepal.

Individual and joint effects of prenatal green spaces, PM2.5 and PM1 exposure on BMI Z-score of children aged two years: A birth cohort study

BackgroundFew studies examined the association of prenatal exposure to green spaces with children's body mass index (BMI) Z-score, and no study evaluated the joint effect of prenatal green spaces and PM2.5 or PM1 exposure on children's BMI Z-score. We aimed to assess the individual and joint effects of prenatal green spaces, PM2.5, and PM1 exposure on BMI Z-score of children aged two years. MethodsThe study was based on a birth cohort in Beijing, China, in which 13,253 mothers (LMP from 2014 to 2017) and their children were included. We estimated prenatal green spaces exposure by calculating average normalized difference vegetation index with 500 m buffers (NDVI-500), prenatal PM2.5 and PM1 exposure based on maternal residential addresses. Weight and height of children were measured at 2 years old. We calculated children's BMI Z-score based on the WHO Standards. Generalized linear regression was used to examine the individual and joint effects of prenatal NDVI-500, PM2.5 and PM1 exposure on children's BMI Z-score. ResultsA 0.1 increase in prenatal NDVI-500 exposure, a 10 μg/m3 decrease in PM2.5, a 10 μg/m3 decrease in PM1 were associated with 0.185 [95% confidence interval (95%CI): 0.155, 0.216], 0.034 (95%CI: 0.015, 0.052) and 0.041 (95%CI: 0.020, 0.061) increase of children's BMI Z-score, respectively. Compared with those exposed to low-level NDVI-500 (not greater than median) and high-level PM2.5 (greater than median), the BMI Z-score was higher in children whose mother exposed to high-level of NDVI-500 and low-level PM2.5 [β:0.172 (95%CI: 0.131, 0.214), Pinteraction = 0.003]. Compared with those exposed to low-level NDVI-500 and high-level PM1, the BMI Z-score was higher in children whose mother exposed to high-level of NDVI-500 and low-level PM1 [β:0.169 (95%CI: 0.127, 0.210), Pinteraction<0.001]. In the trimester-specific analysis, NDVI-500 and PM exposure during the second trimester have a consistent individual effect, together with a joint effect, on child growth. ConclusionThe study suggested the beneficial effect of prenatal exposure to green spaces on child growth and its interaction with PM2.5 and PM1, especially in the second trimester. The findings call for developing public health policy to improve green infrastructure and control PM2.5 and PM1 concentrations, in order to promote child growth.

Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China

Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the relative contribution of natural and anthropogenic factors to vegetation change due to their complicated interaction effects. In this study, we selected the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth and used trend analysis and the Mann-Kendall test to analyze its spatiotemporal change in the PYLB from 2000 to 2020. Then we applied the Geodetector model, a novel spatial analysis method, to quantify the effects of natural and anthropogenic factors on vegetation change. The results showed that most regions of the basin were experiencing vegetation restoration and the overall average NDVI value in the basin increased from 0.756 to 0.809 with an upward yearly trend of +0.0026. Land-use type exerted the greatest influence on vegetation change, followed by slope, elevation, and soil types. Except for conversions to construction land, most types of land use conversion induced an increase in NDVI in the basin. The influence of one factor on vegetation NDVI was always enhanced when interacting with another. The interaction effect of land use types and population density was the largest, which could explain 45.6% of the vegetation change, indicating that human activities dominated vegetation change in the PYLB. Moreover, we determined the ranges or types of factors most suitable for vegetation growth, which can be helpful for decision-makers to optimize the implementation of ecological projects in the PYLB in the future. The results of this study could improve the understanding of the driving mechanisms of vegetation change and provide a valuable reference for ecological restoration in subtropical humid regions.

A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

Urban greenness and survival in lung cancer patients: A registry-based cohort study in Beijing

BackgroundPrevious studies have reported inverse associations between urban greenness and mortality. However, findings regarding contemporaneous and cumulative average greenness exposure are inconsistent. A potential explanation is the weak effects of greenness among the general population. This study assessed the associations between contemporaneous and cumulative average greenness exposure and survival in lung cancer patients. MethodsAn open cohort consisting of lung cancer patients enrolled from 1 January 2001 was studied. The cohort was followed up until 31 December 2018. We evaluated contemporaneous and cumulative greenness exposure using the normalized difference vegetation index (NDVI) within a 250 m buffer of each patient’s geocoded residential address. The association between NDVI and mortality was evaluated using both time-fixed and time-varying Cox models. Their difference was compared either. ResultsIn time-fixed Cox models, a 0.1-unit increment in contemporaneous and cumulative average NDVI was associated with 17% (95% confidence interval [CI], 15–18%) and 7% (95% CI, 5–8%) reductions in mortality rate, respectively. Time-varying Cox models consistently revealed significant associations, but their magnitudes were lower, at 2% (95% CI, 1–3%) for both contemporaneous and cumulative average NDVI. Compared to the time-fixed models, the time-varying ones exhibited better fit for the proportional hazard assumption in Cox regressions with more conservative results. ConclusionUrban greenness is positively associated with an increased chance of survival in lung cancer patients. Compared to time-fixed Cox models, the results of time-varying Cox models were more conservative. This study confirms the beneficial health effects of urban greenness, which may facilitate the improvement of public health by urban green space planning.

The interaction of environmental factors increases the risk of spatiotemporal transmission of pine wilt disease

The Yangtze River basin contains one-third of the important ecological function areas in China. In recent years, pine wilt disease (PWD) has broken out in many provinces in this region, causing serious damage to the forest ecological environment. There have been many studies on the risk attribution of PWD at the stand scale but few macroscale studies. In this study, 1104 county-level administrative regions in the Yangtze River basin constituted the study area. We surveyed the epidemic of PWD and environmental factor data for the region. Taking into account the spatial heterogeneity of the risk of PWD occurrence, we conducted a macroscale attribution study on the risk of PWD outbreaks using GeoDetector. In 2000, 2005, 2010 and 2015, there were 51, 100, 101 and 126 affected counties in the Yangtze River basin, and the global Moran's I index of the incidence of PWD was 0.445, 0.220, 0.151 and 0.143, respectively. The spatial autocorrelation of the incidence of PWD has been decreasing annually, showing a trend with multi-point bursts. The number of affected counties has continued to increase in the central and western parts of the Yangtze River basin, and the mean centre of the affected areas has continued to move to the southwest. The risk detector shows that areas with high wind speed and many sunshine hours are the potential high-risk areas for PWD. The factor detector shows that the normalized difference vegetation index (NDVI), average wind speed and sunshine hours have the strongest effect on the incidence of PWD, with explanatory power of 20.5%, 20.3% and 10.4%, respectively. The interaction between average wind speed and NDVI and the interaction between average relative humidity and sunshine hours show a significant nonlinear enhancement effect, with their explanatory power reaching 64.7% and 60.8%, respectively. Analysis of the spatiotemporal data on PWD in the Yangtze River basin revealed a westward transmission pattern of PWD in the study area, and the interaction of environmental factors could increase the risk of this disease.

The Impact of Groundwater Variability on Mangrove Greenness in Karimunjawa National Park based on Remote Sensing Study

This study aims to understand the impact of groundwater table on soil moisture and mangrove greenness in different seasons in Karimunjawa National Park (KNP). We used Sentinel-2 L2A satellite imagery, Global Precipitation Measurement (GPM) satellite rainfall data, and water table observations at KNP. This study estimates Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on time series Sentinel-2 imagery in 2019-2020 using Google Earth Engine. In addition, we compared the monthly average rainfall data, the monthly average water table data, and the monthly average NDVI, NDWI data extracted at the water table observation points. NDVI is a method to estimate mangrove greenness, and NDWI to estimate soil moisture. The obtained results indicate that NDVI and NDWI in the near shoreline area show a higher value than in the middle area of the KNP that is far from the shoreline. In addition, the value of the NDVI and NDWI correlation coefficients is 0.94, which indicates a positive and strong correlation. Moreover, The NDWI and water table correlation coefficients are 0.79, which indicates a relatively strong positive correlation. Furthermore, the correlation between rainfall and the water table is 0.61, which indicates a relatively strong positive correlation. Thus, these findings show that the water table influences soil moisture and then affects the mangrove greenness. Besides that, the water table change is governed by rainfall, and therefore, the mangrove greenness in KNP depends on seasons and is vulnerable to drought.

Eucalyptus forest plantation assessment of vegetation health using satellite remote sensing techniques

Early examination of the water condition of the plants utilizing remote sensing technology can be used to assess the health of the vegetation in the Eucalyptus forest plantation. To preserve a sustainable wood supply and wooded region that is necessary to human life and vital wood supplies, the forested region should be protected from disease and environmental damage. Disease and environmental impacts are two of the most critical challenges in Eucalyptus forest management. To calculate the vegetation index and identify land cover in the research region, remote sensing with Catalyst Professional software based on Object Analyst (OBIA) tools was utilized. The NDVI (Normalized Difference Vegetation Index) is a valuable index for assessing early vegetation health. For atmospheric correction and haze removal, the image was first pre-processed with ATCOR tools. Second, the image was converted to NDVI using algorithm library tools. In addition, for land cover classification in the area, an OBIA based on Support Vector Machine (SVM) was utilized, followed by an accuracy assessment. Using ArcGIS software, zonal statistics were used to calculate the NDVI value for each land cover category. According to the method, the map produced roads, plantations, buildings, low-density vegetation, oil palm, and open area classifications. Based on accuracy assessment in OBIA, plantation, oil palm, and open area were all 100% accurate, whereas low-density vegetation and oil palm were 100% accurate according to the user. Producer accuracy was lowest on roads, whereas user accuracy was lowest in open areas. Non-vegetated land is difficult to classify at this site, according to the accuracy assessment results. The map improved accuracy since the study used a lower segmentation scale factor of 50, which produced fine vectors ascribed for classification. The average NDVI for oil palm area was 0.71, and 0.69 for plantation. Because it was difficult to classify open areas and roads, the NDVI for the class was low, at 0.37 and 0.22, respectively. From land use classification, the plantation was classified (37%), low-density vegetation area (28%), and oil palm (21%). Others make up only 2 to 7% of the site’s overall area. According to the study, NDVI is a useful indicator for assessing the health of vegetation in areas where NDVI values are larger than 0.70 and presents pf mixed landscape and non-vegetated features. A higher NDVI value implies that the plant is in good enough shape to conduct photosynthetic activities thus producing biomass for sustaining vegetation health. This type of inquiry can forecast more indices to produce higher accuracy of land use maps for the Eucalyptus plantation. At the same time, this type of research can assist forest managers in detecting large areas in their plantation for vegetation health assessment such as for early disease detection.

Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation

This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (&lt;0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data.