Mean Normalized Difference Vegetation Index Research Articles

“Residential greenness and site-specific cancer: A registry based cohort of 144,427 participants with a 21-years of follow-up, Tel-Aviv district, Israel”

BackgroundFew longitudinal studies evaluated the beneficial associations between cumulative residential greenness and site-specific cancer. Our objective was to evaluate the associations between cumulative residential greenness exposure and site-specific cancer incidence (lung, bladder, breast, prostate, and skin cancer) within a registry-based cohort study. MethodsThis study was based on 144,427 participants who lived in the Tel Aviv district during 1995–2015. The residential greenness exposure was estimated for every participant, as the weighted mean residential greenness exposure, based on the mean Normalized Difference Vegetation Index (NDVI) in the residential area and the duration of the residence in this area. Cox regression models were used to evaluate the unadjusted and adjusted associations between exposure to greenness and cancer incidence during 1998–2015 (Hazard Ratios (HRs) and 95% Confidence Intervals (CIs)). Covariates included in adjusted models were selected based on prior knowledge and directed acyclic graphs. We imputed missing data and further sensitivity analyses were conducted. ResultsAfter adjustments, beneficial associations between exposure to greenness and cancer incidence were observed. An interquartile range (IQR) increase in NDVI was associated with a lower HRs for lung cancer (HRadj. = 0.75 95% CI: 0.66–0.85), bladder cancer (HRadj. = 0.71, 95% CI: 0.62–0.82), breast cancer (HRadj. = 0.81, 95% CI: 0.74–0.88), prostate cancer (HRadj. = 0.77, 95% CI: 0.70–0.86) and skin cancer (HRadj. = 0.78, 95% CI: 0.69–0.88). Generally, the patterns of associations were consistent between complete-case models and imputed models, when estimated for participants aged 16 years or 40 years and older at baseline, when stratified by area level socioeconomic status, when evaluated for non-movers participants and after further adjustment to social determinants of health. ConclusionResidential greenness may reduce the risk for lung, bladder, breast, prostate, and skin cancers. If our observations will be replicated, it may present a useful avenue for public-health intervention to reduce cancer burden.

Air pollution, residential greenness, and metabolic dysfunction biomarkers: analyses in the Chinese Longitudinal Healthy Longevity Survey

BackgroundWe hypothesize higher air pollution and fewer greenness exposures jointly contribute to metabolic syndrome (MetS), as mechanisms on cardiometabolic mortality.MethodsWe studied the samples in the Chinese Longitudinal Healthy Longevity Survey. We included 1755 participants in 2012, among which 1073 were followed up in 2014 and 561 in 2017. We used cross-sectional analysis for baseline data and the generalized estimating equations (GEE) model in a longitudinal analysis. We examined the independent and interactive effects of fine particulate matter (PM2.5) and Normalized Difference Vegetation Index (NDVI) on MetS. Adjustment covariates included biomarker measurement year, baseline age, sex, ethnicity, education, marriage, residence, exercise, smoking, alcohol drinking, and GDP per capita.ResultsAt baseline, the average age of participants was 85.6 (SD: 12.2; range: 65–112). Greenness was slightly higher in rural areas than urban areas (NDVI mean: 0.496 vs. 0.444; range: 0.151–0.698 vs. 0.133–0.644). Ambient air pollution was similar between rural and urban areas (PM2.5 mean: 49.0 vs. 49.1; range: 16.2–65.3 vs. 18.3–64.2). Both the cross-sectional and longitudinal analysis showed positive associations of PM2.5 with prevalent abdominal obesity (AO) and MetS, and a negative association of NDVI with prevalent AO. In the longitudinal data, the odds ratio (OR, 95% confidence interval-CI) of PM2.5 (per 10 μg/m3 increase) were 1.19 (1.12, 1.27), 1.16 (1.08, 1.24), and 1.14 (1.07, 1.21) for AO, MetS and reduced high-density lipoprotein cholesterol (HDL-C), respectively. NDVI (per 0.1 unit increase) was associated with lower AO prevalence [OR (95% CI): 0.79 (0.71, 0.88)], but not significantly associated with MetS [OR (95% CI): 0.93 (0.84, 1.04)]. PM2.5 and NDVI had a statistically significant interaction on AO prevalence (pinteraction: 0.025). The association between PM2.5 and MetS, AO, elevated fasting glucose and reduced HDL-C were only significant in rural areas, not in urban areas. The association between NDVI and AO was only significant in areas with low PM2.5, not under high PM2.5.ConclusionsWe found air pollution and greenness had independent and interactive effect on MetS components, which may ultimately manifest in pre-mature mortality. These study findings call for green space planning in urban areas and air pollution mitigation in rural areas.

Land-Greening Hotspot Changes in the Yangtze River Economic Belt during the Last Four Decades and Their Connections to Human Activities

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI changes using global inventory modelling and mapping studies (GIMMS) NDVI data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data. Secondly, the Mann–Kendall test, regression residual analysis and cluster analysis are used to diagnose the potential causes of the NDVI changes. The results show that the regional mean NDVI exhibited an uptrend from 1982 to 2019, which consists of two prominent uptrend periods, i.e., 1982–2003 and 2003–2019. There has been a shift of greening hotspots. The first prominent greening trend from 1982 to 2003 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2003 to 2019 mainly occurred at the junction of Chongqing, Guizhou and Yunnan. The greening trend and shift of greening hotspots were slightly caused by climate change, but mainly caused by human activities. The first greening trend was closely related to the agricultural progress, and the second greening trend was associated with the rapid economic development and implementation of ecology restoration policies.

Spatiotemporal Variation in Land Use Land Cover in the Response to Local Climate Change Using Multispectral Remote Sensing Data

Climate change is likely to have serious social, economic, and environmental impacts on farmers whose subsistence depends on nature. Land Use Land Cover (LULC) changes were examined as a significant tool for assessing changes at diverse temporal and spatial scales. Normalized Difference Vegetation Index (NDVI) has the potential ability to signify the vegetation structures of various eco-regions and provide valuable information as a remote sensing tool in studying vegetation phenology cycles. In this study, we used remote sensing and Geographical Information System (GIS) techniques with Maximum Likelihood Classification (MLC) to identify the LULC changes for 40 years in the Sahiwal District. Later, we conducted 120 questionnaires administered to local farmers which were used to correlate climate changes with NDVI. The LULC maps were prepared using MLC and training sites for the years 1981, 2001, and 2021. Regression analysis (R2) was performed to identify the relationship between temperature and vegetation cover (NDVI) in the study area. Results indicate that the build-up area was increased from 7203.76 ha (2.25%) to 31,081.3 ha (9.70%), while the vegetation area decreased by 14,427.1 ha (4.5%) from 1981 to 2021 in Sahiwal District. The mean NDVI values showed that overall NDVI values decreased from 0.24 to 0.20 from 1981 to 2021. Almost 78% of farmers stated that the climate has been changing during the last few years, 72% of farmers stated that climate change had affected agriculture, and 53% of farmers thought that rainfall intensity had also decreased. The R2 tendency showed that temperature and NDVI were negatively connected to each other. This study will integrate and apply the best and most suitable methods, tools, and approaches for equitable local adaptation and governance of agricultural systems in changing climate conditions. Therefore, this research outcome will also meaningfully help policymakers and urban planners for sustainable LULC management and strategies at the local level.

The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM2.5 using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM2.5 personal exposure of 133.9 (standard deviation = 114.8) μg/m3. The within-trip analysis showed weak inverse associations between greenspace markers and PM2.5 concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM2.5 during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM2.5 of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM2.5, which vary by season.

Identifying trend shifts in vegetation greenness in China from 1982 to 2015

AbstractUnderstanding vegetation evolution is critical for exploring changes in terrestrial ecosystems and identifying future challenges. However, analyses that simultaneously examine trend shifts in vegetation greenness at the national, regional, biome, and pixel scales in China are still rare. Using long‐term (1982–2015) satellite data and the breaks for additive season and trend (BFAST) technique, we identified breakpoints in normalized difference vegetation index (NDVI) in China. The results showed that the annual mean NDVI gradually increased. A total of 68.8% of the vegetated area exhibited upward trends in NDVI, most of which was distributed in Southeast China and the Loess Plateau. Changes in NDVI trends occurred in 78.7% of the total vegetated areas, while hotspots were concentrated in Northwest and North China. A rapid increase in breakpoints was detected after 1999, mainly concentrated in North and Northwest China, and corresponding to the times and areas with the highest ecological engineering efforts. Positive shifts in NDVI trends were more common and generally distributed on the eastern side of the Hu Huanyong line, while browning (negative) shifts were mainly distributed on the western side and were gradually expanding, indicating a possible tendency toward environmental degradation. Although unstable vegetation areas that underwent land cover type changes had higher frequencies of breakpoints, the proportion of stable vegetation experiencing NDVI trend shifts was higher after 2000, probably because human intervention buffered external disturbances in unstable areas. Identifying hotspot areas of shifts in vegetation greenness can help setting priorities when taking measures for ecosystem conservation, thus provide scientific reference for sustainable land development.

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

It is highly necessary to apply unmanned aerial vehicle (UAV) remote sensing technology to forest health assessment. To prove the feasibility of quantitative inversion of photosynthetic pigment content (PPC) in Populus euphratica Oliv. individual tree canopy (PeITC) by using multispectral UAV images, in this study, Parrot Sequoia+ multispectral UAV system was manipulated to collect the images of Populus euphratica (Populus euphratica Oliv.) sample plots in Daliyabuyi Oasis from 2019 to 2020, and the canopy PPCs of five Populus euphratica sample trees per plot were determined in six plots. The Populus euphratica crown regions were extracted by grey wolf optimizer-OTSU (GWO-OTSU) multithreshold segmentation algorithm from the normalized difference vegetation index (NDVI) images of Populus euphratica sample plots obtained after preprocessing, and the PeITCs were segmented by multiresolution segmentation algorithm. The mean values of 27 spectral indices in the PeITCs were calculated in each plot, and the optimal model was constructed for quantitative estimation of the PPCs in the PeITCs, then the inversion results were compared and verified based on GF-6 and ZY1-02D satellite imageries respectively. The results were as follows. (1) The average value of canopy chlorophyll content (Chl) was 2.007 mg/g, the mean value of canopy carotenoid content (Car) was 0.703 mg/g. The coefficient of variation (C.V) of both were basically the same and they were both of strong variability. The measured PPCs of the PeITCs in Daliyabuyi Oasis was generally low. The average contents of chlorophyll and carotenoid in PeITC in June were more than twice those in August, while the mean ratio between them was significantly lower in June than in August. The measured PPCs had no obvious spatial distribution law. However, that could prove the rationality of sample selection in this study. (2) NDVI had the best effect of highlighting vegetation among all quadrats in the study area. Based on the GWO-OTSU multithreshold segmentation method, the canopy area of Populus euphratica could be quickly and effectively extracted from the quadrat NDVI map. The best segmentation effect of PeITCs was obtained based on a multiresolution segmentation method when the segmentation scale was 120, the shape index was 0.7, and the compactness index was 0.5. Compared with manual vectorization method of visual interpretation, the root mean square error (RMSE) and Pearson correlation coefficient (R) values of the mean NDVI values in PeITCs obtained by these two methods were 0.038 and 0.951. (3) Only 12 of the 27 spectral indices were significantly correlated with Chl and Car at the significance level of 0.02. Characteristics of the calibration set and validation set were basically consistent with those of the entire set. The classification and regression tree-decision tree (CART-DT) model performed best in the estimation of the PPCs in the PeITCs, in which, when estimating the Car, the calibration coefficient of determination (R2C) was 0.843, the calibration root mean square error (RMSEC) was 0.084, the calibration residual prediction deviation (RPDC) was 2.525, the validation coefficient of determination (R2V) was 0.670, the validation root mean square error (RMSEV) was 0.251, the validation residual prediction deviation (RPDV) was 1.741. (4) Qualitative comparison of spectral reflectance and NDVI values between GF-6 multispectral imagery and Parrot Sequoia+ multispectral image on the 172 PeITCs can show the reliability of Parrot Sequoia+ multispectral image. The comparison results of five PeITCs relative health degree judged by field vision judgment, measured SPAD value, predicted value of Chl (Chlpre), the red edge value calculated by ZY1-02D (ZY1-02Dred edge) and the Carotenoid Reflection Index 2 (CRI2) value calculated by ZY1-02D (ZY1-02DCRI2) can further prove the scientificity of inversion results to a certain extent. These results indicate that multispectral UAV images can be applied for quantitative inversion of PPC in PeITC, which could provide an indicator for the construction of a Populus euphratica individual tree health evaluation indicator system based on UAV remote sensing technology in the next step.

Classification of Geomorphic Units and Their Relevance for Nutrient Retention or Export of a Large Lowland Padma River, Bangladesh: A NDVI Based Approach

Geomorphic classification of large rivers identifies morphological patterns, as a foundation for estimating biogeochemical and ecological processes. In order to support the modelling of in-channel nutrient retention or export, the classification of geomorphic units (GUs) was done in the Padma River, Bangladesh, a large and geomorphically-complex lowland river. GUs were classified using the normalized difference vegetation index (NDVI) four times over a year, so as to cover the seasonal variation of water flows. GUs were categorized as primary and secondary channels (C & S); longitudinal bar (L); transverse bar (T); side bar (SB); unvegetated bank (EK); dry channel (ED); island (VI); and water depression (WD). All types of GUs were observed over the four distinct annual seasons, except ED, which was absent during the high flow, monsoon season. Seasonal variation of the surface area of GUs and discharge showed an inverse relation between discharge and exposed surface areas of VI, L, T, and SB. Nutrients mainly enter the river system through water and sediments, and during monsoon, the maximum portion of emergent GUs were submerged. Based on the assumption that nutrient retention is enhanced in the seasonally inundated portions of GUs, nutrient retention-/export-relevant geomorphic units (NREGUs) were identified. Seasonal variation in the area of NREGUs was similar to that of GUs. The mean NDVI values of the main identified NREGUs were different. The variation of NDVI values among seasons in these NREGUs resulted from changes of vegetation cover and type. The variation also occurred due to alteration of the surface area of GUs in different seasons. The changes of vegetation cover indicated by NDVI values across seasons are likely important drivers for biogeochemical and ecological processes.

Socioeconomic Disparities in Hypertension by Levels of Green Space Availability: A Cross-Sectional Study in Philadelphia, PA.

Green spaces have been proposed as equigenic factors, potentially mitigating health disparities. We used data from the 3887 participants residing in Philadelphia who participated in the Public Health Management Corporation’s Southeastern Pennsylvania Household Health Survey in 2014–2015 to assess whether socioeconomic disparities in hypertension are modified by availability of neighborhood-level green spaces. Socioeconomic status (SES) was measured using individual-level education and neighborhood-level median household income. Green space availability was measured using surrounding percent tree canopy cover, mean normalized difference vegetation index (NDVI), and proximity to nearest park. Using logistic regression models adjusted for age, sex, and race/ethnicity, we found that adults with higher educational attainment had significantly lower levels of hypertension (OR = 0.63, 0.57, and 0.36 for high school, some college, and college graduates, respectively, as compared to those with less than high school education), and this pattern was similar for median household income (higher prevalence in lower income areas). We found no significant interaction between education and percent tree canopy cover (p = 0.83), meaning that educational disparities in hypertension were similar across all levels of green space availability. These results held when using mean NDVI or distance to nearest park as availability measures, or when considering neighborhood-level median household income as the socioeconomic measure, although the specific patterns and significance of interactions varied by exposure and modifier. While socioeconomic disparities in hypertension are strong for adults residing in Philadelphia, green spaces did not seem to modify them.

Using geographical detection to analyze responses of vegetation growth to climate change in the Loess Pla-teau, China

In order to explore the responses of different vegetation types to climatic change in the Chinese Loess Plateau (CLP), we analzyed the changes of different vegetation types and their relationships with meteorological factors using trend analysis, Hurst index, and geographical detector model based on normalized difference vegetation index (NDVI). The results showed that NDVI of different vegetation types from 2002 to 2019 was dominated by a growing trend and codirectional moderate persistence. The NDVI of crops in the built-up and adjacent areas decreased significantly. Except for grassland or meadow that was affected by mixed pixels, the spatial variation of NDVI was significant in the growing season (from April to October). The mean NDVI of different vegetation types followed an oder: coniferous forest > broadleaved forest > scrub > meadow > grassland > crop > steppe > desert. The interactions between meteorological factors were synergistic and non-linear enhancement in the CLP. Moreover, the interaction was more prominent under steppe and desert where habitat was fragile. The synergistic effect of precipitation and temperature had a great influence on all vegetation types. Water vapor, relative humidity, sunshine duration, atmospheric pressure, and wind speed had different explanatory powers on NDVI through indirectly affec-ting hydrothermal conditions.

Qinghai-Tibetan Plateau Greening and Human Well-Being Improving: The Role of Ecological Policies

Appropriate human activities can have significantly positive effects on vegetation dynamics. In the past 50 years, various ecological policies have improved both ecological change and human well-being in the Qinghai–Tibetan Plateau (QTP), efficiently achieving multiple Sustainable Development Goals (SDGs) of the United Nations’ 2030 Agenda for Sustainable Development. During 1981–2017, the annual mean normalized difference vegetation index (NDVI) of the protected areas (PAs) tended to increase significantly at a rate of 2.93 × 10−4/a (p < 0.01), while non-PAs only increased by 0.6 × 10−4/a (p < 0.5). Improvement in the NDVI of the PAs is more obvious than that of non-PAs. Specifically, the earlier the establishment of the Pas is, the more significant the greening effect will be. Moreover, ecological protection has not slowed improvements in human welfare; on the contrary, the Human Development Index (HDI) has nearly doubled in the past 40 years. In terms of global ecological construction, the Chinese government has demonstrated the responsibilities of a large country in global ecological governance. Chinese initiatives can guide other nations in contributing to the global sustainability aspirations embodied in the 2030 SDGs Agenda. This study can be used as a reference for other countries in the world to coordinate the development of ecological protection and well-being.

Vulnerability assessment and its driving forces in terms of NDVI and GPP over the Loess Plateau, China

With the impact of climate change and the implementation of the “Grain-for-Green” ecological restoration project, the dynamics of water and heat transport and storage in the Loess Plateau (LP) have changed substantially. However, studies on the vulnerability of the LP ecosystem in response to ecological restoration are less concerned. Based on the normalized difference vegetation index (NDVI) and the gross primary production (GPP), this study assessed the spatial change of ecological vulnerability (EV) in the LP during 2001–2019. Results showed that (1) the annual mean NDVI and GPP was high in the southeast of LP and low in the northwest. The trend of NDVI and GPP presented a significant upward trend over approximately 85% of the regions; (2) the ecological vulnerability of LP displayed a low-to-high gradient from southeast to the northwest. Medium (0.3–0.45) and heavy (≥0.45) vulnerability levels occurred in the northwest of LP, which was attributed to less vegetation and serious land degradation; (3) forestland has the lowest vulnerability whereas grassland, sparse vegetation and urban land exhibited higher vulnerability, with the respective percentages of vulnerable areas of 63.8%, 90.4%, 70% in terms of NDVI and 87.1%, 49.8%, 38.4% in terms of GPP; and (4) the EV of LP was impacted by climate and terrain. The magnitude of vulnerability was negatively correlated with precipitation, temperature and slope, and positively correlated with drought. It is suggested that precipitation, slope and drought stress were the dominant factors of EV. The findings provide guidance for appropriate planning of ecological restoration in the LP.

The Relationship between Climate Change, Variability, and Food Security: Understanding the Impacts and Building Resilient Food Systems in West Pokot County, Kenya

This study integrated local and scientific knowledge to assess the impacts of climate change and variability on food security in West Pokot County, Kenya from 1980–2012. It characterized rainfall and temperature from 1980–2011 and the phenology of agricultural vegetation, assessed land use and land cover (LULC) changes, and surveyed local knowledge and perceptions of the relationships between climate change and variability, land use decisions, and food (in)security. The 124 respondents were aware of long-term changes in their environment, with 68% strongly believing that climate has become more variable. The majority of the respondents (88%) reported declining rainfall and rising temperatures, with respondents in the lowland areas reporting shortened growing seasons that affected food production. Meteorological data for 1980–2011 confirmed high inter-annual rainfall variability around the mean value of 973.4 mm/yr but with no notable trend. Temperature data showed an increasing trend between 1980 and 2012 with lowlands and highlands showing changes of +1.25 °C and +1.29 °C, respectively. Land use and land cover changes between 1984 and 2010 showed cropland area increased by +4176% (+33,138 ha), while grassland and forest areas declined by –49% (–96,988 ha) and –38% (–65,010 ha), respectively. These area changes illustrate human-mediated responses to the rainfall variability, such as increased stocking after good rainfall years and crop area expansion. The mean Normalized Difference Vegetation Index (NDVI) values ranged from 0.36–0.54 within a year, peaking in May and September. For weather-related planning, respondents relied on radio (64%) and traditional forecasters (26%) as predominant information sources. Supporting continuous climate change monitoring, intensified early warning systems, and disseminating relevant information to farmers could help farmers adopt appropriate adaptation strategies.

Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

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

Soil Property Plays a Vital Role in Vegetation Drought Recovery in Karst Region of Southwest China

AbstractSouthwest China is the world's largest continuous karst region, where drought events are expected to intensify in the future. Understanding the postdrought recovery of vegetation growth sheds light on the resilience and stability of ecosystems and is of great importance to the sustainable management. However, the effects of the soil properties, beyond the climate variations, on the vegetation postdrought recovery are often overlooked and remain unknown. In this study, we characterized both the patterns and the underlying processes governing the postdrought recovery trajectories of vegetation growth after extreme droughts in karst regions of southwest China. Linear mixed‐effects model analyses revealed that higher mean Normalized Difference Vegetation Index (NDVI) preceding the spring/summer drought leads to shorter drought recovery time for both forests and grasslands, while opposite pattern appears in autumn/winter drought, particularly in forests. Significant effects of precipitation during recovery period were only found in grasslands but not in forests. More importantly, we discovered a crucial impact of the depth to bedrock on vegetation postdrought recovery in forests but not in grasslands, with deeper depth to bedrock associated to a shorter drought recovery. The field capacity is more important for postdrought recovery in karst regions than in nonkarst regions, which can be most likely attributed to differences in the water‐holding capacities and water use strategies of vegetation. Our understanding of the postdrought recovery of vegetation growth in karst regions will facilitate the reasonable prediction of ecosystem resilience and health to extreme climate events in such fragile ecological environments.

Evaluation of consistency among three NDVI products applied to High Mountain Asia in 2000–2015

The current study evaluates consistency among three Normalized Difference Vegetation Index (NDVI) datasets, namely GIMMS, MODIS and SPOT, to characterize alpine vegetation dynamics (greening and browning) across High Mountain Asia (HMA) in 2001–2015. The utility of these datasets is explored to evaluate the vegetation's variability at different spatial-temporal scales and, elevation, and to compare their spatial trends and distribution patterns. In addition to the Pearson correlation coefficients performed to quantitatively analyze the consistency and inconsistency of each dataset, an NDVI quality control (QC) layer and Landsat NDVI are also used to evaluate the findings. The results indicate that the GIMMS has the highest NDVI mean, while SPOT has the lowest. However, GIMMS also showed a browning trend for both Tianshan (TS) and the Qinghai Tibet Plateau (TP) at a rate of −0.3 × 10−3 per year, whereas MODIS and SPOT exhibit a greening trend (TSMODIS = 0.5 × 10−3 per year, TSSPOT = 0.6 × 10−3 per year, TPMODIS = 0.9 × 10−3 per year, TPSPOT = 1.6 × 10−3 per year). Furthermore, MODIS-SPOT shows the highest correlation (RGREEN = 0.73; RBROWN = 0.47), followed by MODIS-GIMMS, and GIMMS-SPOT. The overall, NDVI trend consistency appears to be higher in TS. Finally, the consistent greening pixels mainly distributed in central TP stretching to the northeastern part, and in western stretching to eastern TS, account for 32.14%, while 8.32% of consistent browning pixels are concentrated in southwestern TP and central TS. The inconsistent pixels account for 59.54%, with 39.21% of inconsistent greening pixels being widely distributed across HMA, and 20.58% of inconsistent browning pixels being relatively pronounced in central TS and southern TP. This study provides baseline inferences for the selection and reconstruction of data in follow-up studies on vegetation dynamics.

Open space and adult's chronic kidney disease, overweight and diabetes in the metropolitan area of New Taipei City

The beneficial effects of urban open green space on residential health had few investigations in Taiwan. A total of 40,375 participants older than 30-year-old attended the health screening program during 2007–2009 in the Metropolitan area in the New Taipei City. We defined urban open green spaces (UOGS) if land use belongs to parks, green, plaza, public schools and sport venues. Small public urban open space (SPUOS) is defined as UOGS with area less than 1 hector and with at least three of the followings: vegetations, sport facilities and benches. Greenness is defined as the mean Normalized Difference Vegetation Index (NDVI). Air quality was accessed by land use regression model. Logistic regression model was used to calculate odds ratios of diseases for proximity to UOGS or greenness. We found that CKD was significantly associated with proximity to SPUOS. No relationship was found between proximity to UOGS or NDVI and overweight or diabetes. The adjusted odds ratio of CKD comparing distance to SPUOS >200 m and ≤200 m was 1.144 (95% CI, 1.059–1.237). The effect of open space on CKD was similar if we incorporated public schools to SPUOS. Greenness was not associated with CKD. Subgroups analyses revealed the effect of SPUOS on CKD was more prominent in health residents, including in those who never smoke, no hypertension, no diabetes and no hypertriglyceridemia. Proximity to SPUOS or schools is associated with lower risk of CKD for adults living in the Metropolitan area in New Taipei City.

Mapping distribution and identifying gaps in protected area coverage of vulnerable clouded leopard (Neofelis nebulosa) in Nepal: Implications for conservation management

Clouded leopard (Neofelis nebulosa) is listed as vulnerable by the IUCN red list and is a protected species in Nepal. However, there is limited information on its status and distribution, which is a critical first step in conservation planning and management. To address this knowledge gap, this study attempts to: (a) predict the potential suitable habitat for the clouded leopard in Nepal with available occurrence point, (b) determine important variables (bioclimatic and environmental) responsible for its distribution and range limits, and (c) evaluate coverage of existing protected areas (PAs) in Nepal using MaxEnt modeling. The current model with a high discriminative ability (AUC = 0.945, TSS = 0.780) predicts 11,794 km2 suitable habitat for clouded leopard in Nepal, of which 72% of the area is distributed outside the current protected areas network of Nepal. Most of these habitats lie in the Middle mountain and Hilly regions, distributed from the Pachthar Ilam Taplejung corridor in the east to Annapurna Conservation Area in the mid-central part of Nepal. The most important variables responsible for its distribution were the mean Normalized Difference Vegetation Index (NDVI), Isothermality, and the precipitation of the coldest quarter associated with the forest cover landscape and climate. Deforestation and fragmentation of habitat and climate change in the Middle mountain and Hilly region, and occasional poaching are significant concerns, and may pose threats for the long-term conservation of this species. We recommend establishing community-based conservation areas in the Middle mountain and Hilly regions and investigating species distribution, status, and ecology, including its prey and habitat, across the Nepalese landscape. Identification of habitats and conservation opportunities is crucial for species' long-term conservation. Hence, the map produced here can serve as a reference for future investigations into clouded leopard distribution and could be a significant tool for formulating and implementing conservation strategies in Nepal.

Residential greenness and hair cortisol levels during the first trimester of pregnancy

Background and aimStudies have shown that increased maternal cortisol level is associated with child adverse health outcomes. Hair cortisol (HC) is suitable for assessing long-term circulating cortisol concentration. Only two previous studies reported beneficial associations between cortisol and residential greenness during pregnancy and no study focused on the first trimester. Our aim was to evaluate the association between residential greenness and first trimester HC levels among pregnant women in Israel. MethodsWomen were recruited during second and third trimesters. Hair samples were collected from the scalp and retrospective HC levels during the first trimester were quantified for 217 women. HC levels were natural log transformed and outliers were excluded. Based on geocoded birth address, small area sociodemographic status (SES) and mean residential surrounding greenness were calculated using high-resolution satellite-based Normalised Difference Vegetation Index (NDVI) data at 100, 300 and 500-m buffers in a cross-sectional approach. In addition, longitudinal exposure to mean greenness during a week preconception and during the first trimester were calculated. Missing covariates were imputed and linearity of the associations were evaluated. Generalized linear models were used to estimate the crude and adjusted associations controlled for the relevant covariates. ResultsAfter exclusion of outliers, for 211 women, crude and adjusted beneficial associations between exposure to higher mean NDVI and HC levels were observed for all the exposure measures. An increase in 1 interquartile range of greenness (100 m buffer) was associated with a statistically significant lower estimated natural log mean HC level (−0.27 95% CI: −0.44; −0.11). The associations were robust to adjustment for covariates. The findings were consistent for different buffers, for the longitudinal approach, when all observations were included in the analysis and slightly stronger associations were observed for women with addresses geocoded at the home or street level. For most of the exposure measures, stronger associations were observed among those of lower sociodemographic status. ConclusionOur findings that more greenness associated with reduced maternal cortisol levels measured in the hair during the first trimester, could have substantial implications for urban planners and public health professional. If our observations will be replicated, it may present a useful avenue for public-health intervention to promote health through the provision of greenness exposure during early pregnancy, specifically to disadvantage populations.