Normalized Difference Vegetation Index Research Articles

Evaluations of ground surface heat fluxes and its potential vegetation effects in the permafrost region of northeastern China

Ground surface-air temperature difference (GATD) is the primary source of sensible heat flux from the ground surface, and vegetation inevitably affects it by altering the ground surface characteristics. However, the relationship between vegetation and GATD in the permafrost region has not been well-elucidated. Using meteorological station data, National Oceanic and Atmospheric Administration Climate Data Record Normalized Difference Vegetation Index (NOAA CDR NDVI) data, and some auxiliary data, this study aims to investigate the patterns of GATD in the Northeast China permafrost region and quantify the response of GATD to vegetation changes. The study employs trend analysis, Geodetector, and correlation analysis methods. The results show that the GATD in the Northeast China permafrost region decreased from west to east and the annual mean GATD exhibited an upward from 1982 to 2020. The relationship between NDVI and GATD trend is non-linear, in areas with low vegetation, vegetation changes amplify the GATD trend, while in areas with high vegetation, vegetation changes inhibit the GATD trend. Furthermore, the rate of GATD increase is higher in the discontinuous permafrost (DP) region than in the sporadic permafrost (SP) and isolated permafrost (IP) regions, suggesting that the DP region may have experienced a faster ground surface state change process. The findings of this study will provide new insights for evaluating permafrost degradation and studying the resulting ecological environmental effects in the permafrost region.

Exploring the pathways linking prenatal and early childhood greenness exposure to attention-deficit/hyperactivity disorder symptoms during childhood: An approach based on robust causal inference

BackgroundEpidemiological studies suggest that exposure to greenness during childhood may protect children from developing attention-deficit hyperactivity disorder (ADHD). ObjectiveWe analyzed the effect of both prenatal (pregnancy) and early childhood (4-5-year follow-up) residential greenness exposure and green space availability on ADHD symptoms during childhood (up to the age of 12 years) and further explored the potential mediating role of PM2.5 and physical activity in the association. MethodsThe study population included participants from the INfancia y Medio Ambiente (INMA) prospective birth cohort (Gipuzkoa, Sabadell, and Valencia). Average Normalized Difference Vegetation Index (NDVI) in buffers of 100-, 300- and 500-m around the residential addresses was used as an indicator of greenness, while green space availability was determined based on the presence of a major green space within 150-m from the residence. Childhood ADHD symptoms were assessed at the 6-8- and 10-12-year follow-ups using Conners Parents Rating Scale-Revised: Short Form. ResultsAlthough no association was found for the prenatal exposure period, increased early childhood NDVI inversely associated with the OR of clinically significant ADHD symptoms during the 6-8-year follow-up at the 100-m (OR 0.03, 95% CI: 0.003 to 0.44), 300-m (OR 0.04, 95% CI: 0.003 to 0.42) and 500-m (OR 0.08, 95% CI: 0.01 to 0.76) buffers, but exclusively in the context of direct effects. Additionally, the 10-12-year follow-up analysis found moderate to weak evidence of potential total and direct effects of NDVI at both 100- and 300-m buffers on inattention scores, as well as for NDVI at the 300-m buffer on ADHD index scores. The analysis did not reveal evidence of mediation through PM2.5 or physical activity. ConclusionsThe evidence suggests that early childhood greenness exposure may reduce the risk of developing ADHD symptoms later in childhood, and that this association is not mediated through PM2.5 and physical activity.

Pollen as a mediator between environmental greenness during pregnancy and infancy periods and childhood persistent asthma: A nationwide retrospective birth cohort study in Japan

The association between environmental greenness and childhood asthma remains unclear. Pollen has been proposed as a potential mechanism of detrimental associations. This study aimed to elucidate the mechanisms underlying the association between environmental greenness during the pre-, peri-, and postnatal periods and childhood persistent asthma. A nationwide retrospective birth cohort study was conducted using data from the Japan Medical Data Center (JMDC). Child-mother dyads between January 2010 and January 2017 were identified, and four subcohorts were developed based on the timing of exposure to the highest greenness season. The exposure of interest was environmental greenness levels between June and September, quantified using the normalized difference vegetation index (NDVI). The primary outcome was persistent asthma in children aged 4–5 years. Causal mediation analysis was conducted to evaluate pollen as a mediator between NDVI and asthma. In these analyses, linear and modified Poisson regression models were used to evaluate the association of NDVI with pollen levels and childhood persistent asthma. The analyses were stratified by metropolitan and non-metropolitan areas. Clinically meaningful confounders and predictors of asthma were adjusted in the statistical models. A total of 100,273 child-mother dyads were included in the entire cohort, with 24.1% of the children having persistent asthma at the ages of 4–5 years. Higher environmental greenness was associated with higher pollen levels. The modified Poisson regression models showed higher environmental greenness was associated with a slightly higher risk of persistent childhood asthma. In metropolitan areas, significant detrimental natural direct effects of NDVI were observed; however, the natural indirect effects were uncertain. A large part of the association between environmental greenness and childhood persistent asthma in metropolitan areas was attributed to mechanisms other than those involving pollen. The associations in non-metropolitan areas remain uncertain. Further studies are required to elucidate the underlying mechanisms.

Estimation and Spatial Distribution of Individual Tree Aboveground Biomass in a Chinese Fir Plantation in the Dabieshan Mountains of Western Anhui, China

Understanding aboveground biomass (AGB) and its spatial distribution is key to evaluating the productivity and carbon sink effect of forest ecosystems. In this study, a 123-year-old Chinese fir forest in the Dabieshan Mountains of western Anhui Province was used as the research subject. Using AGB data calculated from field measurements of individual Chinese fir trees (diameter at breast height [DBH] and height) and spectral vegetation indices derived from unmanned aerial vehicle (UAV) remote sensing images, a random forest regression model was developed to predict individual tree AGB. This model was then used to estimate the AGB of individual Chinese fir trees. Combined with digital elevation model (DEM) data, the effects of topographic factors on the spatial distribution of AGB were analyzed. We found that remote sensing spectral vegetation indices obtained by UAVs can be used to predict the AGB of individual Chinese fir trees, with the normalized difference vegetation index (NDVI) and the optimized soil-adjusted vegetation index (OSAVI) being two important predictors. The estimated AGB of individual Chinese fir trees was 339.34 Mg·ha−1 with a coefficient of variation of 23.21%. At the local scale, under the influence of elevation, slope, and aspect, the AGB of individual Chinese fir trees showed a distribution pattern of decreasing from the middle to the northwest and southeast along the northeast-southwest trend. The effect of elevation on AGB was influenced by slope and aspect; AGB on steep slopes was higher than on gentle slopes, and the impact of slope on AGB was influenced by aspect. Additionally, AGB on north-facing slopes was higher than on south-facing slopes. Our results suggest that local environmental factors such as elevation, slope, and aspect should be considered in future Chinese fir plantation management and carbon sink assessments in the Dabieshan Mountains of western Anhui, China.

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method

Abstract. Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies in the spatial distribution and interannual variability within GPP datasets pose challenges to a comprehensive understanding of the terrestrial carbon cycle. In contrast to previous models that rely on remote sensing and environmental variables, we developed an ensemble model based on the random forest method (denoted ERF model). This model used GPP outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), GPP estimate model based on Kernel Normalized Difference Vegetation Index (GPP-kNDVI), GPP estimate model based on Near-Infrared Reflectance of Vegetation (GPP-NIRv), Revised-EC-LUE, Vegetation Photosynthesis Model (VPM), and GPP estimate model based on the Moderate Resolution Imaging Spectroradiometer (MODIS). These outputs were used as inputs to estimate GPP. The ERF model demonstrated superior performance, explaining 85.1 % of the monthly GPP variations at 170 sites and surpassing the performance of selected GPP estimate models (67.7 %–77.5 %) and an independent random forest model using remote sensing and environmental variables (81.5 %). Additionally, the ERF model improved accuracy across each month and with various subranges, mitigating the issue of “high-value underestimation and low-value overestimation” in GPP estimates. Over the period from 2001 to 2022, the global GPP estimated by the ERF model was 132.7 PgC yr−1, with an increasing trend of 0.42 PgC yr−2, which is comparable to or slightly better than the accuracy of other mainstream GPP datasets in terms of validation results of GPP observations independent of FLUXNET (i.e., ChinaFLUX). Importantly, for a growing number of GPP datasets, our study provides a way to integrate these GPP datasets, which may lead to a more reliable estimate of global GPP.

Permafrost Degradation Induces the Abrupt Changes of Vegetation NDVI in the Northern Hemisphere

AbstractPermafrost, widely distributed in the Northern Hemisphere, plays a vital role in regulating heat and moisture cycles within ecosystems. In the last four decades, due to global warming, permafrost degradation has accelerated significantly in high latitudes and altitudes. However, the impact of permafrost degradation on vegetation remains poorly understood to date. Based on active layer thickness (ALT) monitoring data, meteorological data and normalized difference vegetation index (NDVI) data, we found that most ALT‐monitored sites in the Northern Hemisphere show an increasing trend in NDVI and ALT. This suggests an overall increase in NDVI from 1980 to 2021 while permafrost degradation has been occurring. Permafrost degradation positively influences NDVI growth, with the intensity of the effects varying across land cover types and permafrost regions. Furthermore, based on Mann‐Kendall trend test, we detected abrupt changes in NDVI and environmental factors, further confirming that there is a strong consistency between the abrupt changes of ALT and NDVI, and the consistency between the abrupt change events of ALT and NDVI is stronger than that of air temperature and precipitation. These findings work toward a better comprehending of permafrost effects on vegetation growth in the context of climate change.

Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) and another that is independent of climate data (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89–0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82–0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years

The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982–2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M-K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982–2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982–2022 trend, and sustainable improvement will dominate in the future; however, 17.69% of vegetation will degrade, that is, NDVI will degrade instead of improvement.. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had > 70% explanatory power (73.5, 79.9, 79.0, and 74.9%, respectively). The explanatory power of edaphic factors was > 30%, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of < 30%. Thus, climatic factors were the dominant factors influencing the spatial variability of NDVI in the study area. The results of the interaction detector analysis showed nonlinear strengthening for any two factors, and the interaction between Rhu and barometric pressure had the highest explanatory power. There were optimal ranges or characteristics of each factor that promoted vegetation growth. This study investigated the differences in the explanatory power of different factors on the NDVI and the optimal range of individual factors to promote vegetation growth, which can provide a basis for the development of vegetation resource management programs.

Monitoring Spatio-Temporal of Vegetation Indices Using NDVI and EVI Algorithms on Google Earth Engine (GEE) in Tuntang Watershed Area, Indonesia

Abstract Climate change affects increase rate in flood fluctuations, drought, and deforestation in watershed areas. The condition of Tuntang watershed has decreased in carrying capacity due to several factors. Google Earth Engine (GEE) is one of the significant technological breakthroughs in remote sensing and GIS. The aims to analyze the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in the Tuntang watershed using GEE and analyze spatio-temporal variations in vegetation density conditions. The method is processing NDVI and EVI algorithms for five years (2018-2022). This research to the full set of images acquired with the Sentinel-2A sensor between August 2018 and September 2022. The findings indicated that the highest forest area for five years with the NDVI method was in 2018, namely dense to very dense density of 716.92 Ha (54.78 %), while in the EVI method, the highest in 2020 was moderate to dense density of 504.58 Ha (38.55%). Based on the accuracy test, NDVI and EVI have the same accuracy overall of 83.33%, with a kappa index of 0.78 NDVI and 0.79 for EVI. Based on the correlation test, both methods have a correlation value of 0.799, indicating a strong and unidirectional relationship.

Remote-Sensed Determination of Spatiotemporal Properties of Drought and Assessment of Influencing Factors in Ordos, China

Ordos drought impacts are complex; the Geodetector model is able to explore the interaction between impact factors. Based on the drought severity index (DSI), this study explored the spatio-temporal dynamics and changing trends of drought, and analyzed the driving factors of DSI spatial differentiation by using the Geodetector model. The results show that: the evapotranspiration (ET) and normalized difference vegetation index (NDVI) in Ordos showed a significant increasing trend (p &lt; 0.05). The increasing rates were ET (4.291 mm yr−1) and NDVI (0.004 yr−1). In addition, the interannual variation of the DSI also showed a significant increase, with a trend change rate of 0.089. The spatial pattern of ET and the NDVI was low in the southwest and high in the northeast, and the spatial pattern of potential evapotranspiration (PET) was high in the southwest and low in the northeast, while the distribution of the DSI was dry in the west and wet in the east. The spatial differentiation of the DSI was mainly affected by five factors: air temperature, precipitation, land use type, soil type, and the digital elevation model (DEM), with q exceeding 0.15, which were the main driving factors of drought in the Loess Plateau. Under the interaction of multiple factors, the four combinations of temperature and the DEM, precipitation and the DEM, sunshine duration and the DEM, and relative humidity and the DEM jointly drive drought, in which precipitation (0.156) ∩ DEM (0.248) has the strongest influence on drought occurrence, and q reaches 0.389. This study directly informs specific drought management strategies or ecological conservation efforts in the region.

The impact of green space on nonaccidental and cause-specific mortality in the Adventist Health Study-2 population.

There is growing interest in evaluating the long-term health effects of neighborhood environments, particularly green space. However, only a limited body of research further incorporates multiple ambient air pollutants. This study looked at the relationship between green space, as measured by the Normalized Difference Vegetation Index, and mortality adjusted by key confounders in the Adventist Health Study-2, a longitudinal cohort study from 2002 to 2015, across the contiguous United States (N = 67,400). We used Cox proportional hazard regression models to assess the risk of nonaccidental, cardiovascular disease (CVD), ischemic heart disease (IHD), and respiratory disease mortality from green space around subjects' home address under multiple covariate and pollutant adjustments. We found a 0.1 unit increase in the Normalized Difference Vegetation Index was associated with nonaccidental (hazard ratio [HR]: 0.96 [95% confidence interval (CI): 0.93, 0.99]), CVD (HR: 0.94 [95% CI: 0.90, 0.98]), and IHD (HR: 0.87 [95% CI: 0.81, 0.94]) mortality, with the greatest precision in fully adjusted three-pollutant models using the 1000-m buffer. Effect estimates were strengthened in urban areas, when incorporating seasons, and for females. However, all associations between green space and respiratory mortality were null. This study supports evidence that increased neighborhood green space is inversely associated with nonaccidental, CVD, and IHD mortality, where the inclusion of multiple environmental covariates had a greater impact on effect estimate magnitude and precision than adjustment by individual lifestyle and health factors.

Impacts of climate change and human activities on three Glires pests of the Qinghai-Tibet Plateau.

The range of Glires is influenced by human activities and climate change. However, the extent to which human activities and environmental changes have contributed to this relationship remains unclear. We examined alterations in the distribution changes and driving factors of the Himalayan marmot, plateau pika, and plateau zokor on the Qinghai-Tibet Plateau (QTP) using the maximum entropy (MaxEnt) model and a geographical detector (Geodetector). The MaxEnt model showed that the contribution rates of the human footprint index (HFI) to the distribution patterns of the three types of Glires were 46.70%, 58.70%, and 59.50%, respectively. The Geodetector results showed that the distribution pattern of the Himalayan marmot on the QTP was influenced by altitude and the normalized difference vegetation index (NDVI). The distribution patterns for plateau pikas and plateau zokors were driven by HFI and NDVI. Climate has played a substantial role in shaping suitable habitats for these three Glires on the QTP. Their suitable area is expected to decrease over the next 30-50 years, along with their niche breadth and overlap. Future suitable habitats for the three Glires tended to shift toward higher latitudes on the QTP. These findings underscore the impacts of environmental and human factors on the distribution of the three Glires on the QTP. They have enhanced our understanding of the intricate relationships between Glires niches and environments. This can aid in identifying necessary interventions for developing effective early warning systems and prevention strategies to mitigate Glires infestations and plague epidemics on the QTP. © 2024 Society of Chemical Industry.

Multi-temporal assessment of a wildfire chronosequence by remote sensing

The study aimed to develop a methodological framework to identify forest ecosystems affected by wildfires and evaluate their recovery chronologically. To do this remote sensing analysis, sites with burn scars were selected based on various criteria (fire severity, affected area, vegetation and soil type, slope, aspect, and one-time occurrence of wildfire in the last 23 years). Spectral vegetation indices (VIs) from satellite imagery were used to estimate burn severity and vegetation cover changes. Images of surface reflectance were obtained from the collection of Landsat 5 ETM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, available and processed on the Google Earth Engine Platform (GEE). Indices VIs (i) the normalized difference vegetation index (NDVI), (ii) the normalized burn ratio (NBR), and (iii) the differenced normalized burn ratio (dNBR) were calculated to classify burn severity. The one-time occurrence selection was performed using the LandTrendr algorithm to monitor changes in land cover and burned areas. To validate the selection, the chosen sites within the chronosequence were clustered on 4 seasons of soil properties and litter accumulation recovery. Our result can guide methodological comparisons and forest management practices on large surfaces by comparing parches of different time-affected ecosystems. Validation sites of the cluster chronosequence shows consistent recovery of soil properties as soil carbon, bulk density and litter accumulation through the studied years•The study developed a framework to identify wildfire-affected forest ecosystems and evaluate their recovery using remote sensing and local data.•Vegetation indices (NDVI, NBR, dNBR) from Landsat satellite imagery processed on the Google Earth Engine were used to assess burn severity and vegetation changes over time.•Selected sites were validated using the LandTrendr algorithm and monitored for seasonal changes in soil properties and litter accumulation.

Association of fine particulate matter and residential green space with rheumatoid arthritis

BackgroundFine particulate matter (PM2.5) is a recognized risk factor for respiratory and cardiovascular diseases, but the association between PM2.5 and rheumatoid arthritis (RA) is still controversial. Additionally, evidence on the relationship of green space with RA is scarce. This study aimed to investigate the separate and combined associations of PM2.5 and green space with risk of RA. MethodsOur study involved 30,684 participants from the Yinzhou cohort in Ningbo, China. PM2.5 concentrations were determined using a land-use regression model. Residential green space was assessed using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from satellite images. We employed the Cox proportional hazard model to evaluate the relationships of PM2.5 and green space with RA. ResultsDuring the 176,894 person-years of follow-up period, 354 cases of RA were identified. Hazard ratio (HR) and the corresponding 95% confidence interval (95% CI) for every interquartile range (IQR) increase in PM2.5 were 1.23 (95% CI: 1.02, 1.49). Compared with lower exposure to residential green space, individuals living in areas with more green space had a decreased risk of RA (HR was 0.80 (95% CI: 0.70, 0.92), 0.80 (95% CI: 0.70, 0.92), and 0.79 (95% CI: 0.70, 0.89) for 250m, 500m, and 1000m NDVI buffers, respectively). Similar results were observed for the association between EVI and RA. Furthermore, a significant multiplicative interaction was observed between PM2.5 and green space (NDVI 250m and EVI 250m). No mediating effect of PM2.5 on the relationship between green space and RA was observed. ConclusionOur findings indicated that living in areas with higher green space was linked to a reduced risk of RA, whereas living in areas with higher PM2.5 was associated with an increased risk of RA. The beneficial effect of high green space may be offset by exposure to PM2.5.

Remote sensing assessment of wildfire using high-resolution PlanetScope satellite observations: A case study on Co Tien Mountain, Nha Trang City, Vietnam

In this study, high spatial resolution (3 m) PlanetScope (PS) imagery was utilized to map burned areas caused by a wildfire occurring on January 10, 2024, on Co Tien Mountain in Nha Trang city, Khanh Hoa province, South Central Coast of Vietnam. A pre-fire image, acquired ten days earlier, on December 31, 2023, and a post-fire one, acquired nearly one month after, on February 04, 2024, were used to create pre- and post-fire Normalized Difference Vegetation Index (NDVI) maps of the study area, then the difference of NDVI (dNDVI). A threshold (T = 0.20), proposed by the author, was applied to the histogram of the dNDVI product to classify the study area into two clusters: burned pixels (dNDVI &gt; T) and unburned pixels (dNDVI &lt;= T). Classification results estimate that a total of 16.11 ha of grass, reeds, small shrubs and vegetation have been burned out during the wildfire. A field trip is required to map the burned areas using unmanned aerial vehicles (UAVs) for an accurate validation of results derived purely from PS satellite observations. Although lacking a ground truth dataset for validation is a significant limitation, the proposed approach remains beneficial for local managers and decision-makers. It enables the rapid assessment of damages caused by small wildfires and provides essential data for effective disaster management and recovery planning, particularly in remote areas.

Evaluating Land Surface Temperature Variation and Its Responses to Climate Change and Human Activities on the Southeastern Tibetan Plateau

ABSTRACTThe Yarlung Zangbo River Basin (YZRB), situated within the Qinghai‐Tibetan Plateau, has experienced significant alterations due to global warming and vegetation greening. This region serves as a critical indicator of the interplay between vegetation growth and climatic fluctuations, as evidenced by substantial changes in spatiotemporal land surface temperature (LST) over recent decades. In this research, we assessed the components of the water and energy cycles from 1980 to 2015 utilising the variable infiltration capacity (VIC) model to generate a continuous daily LST data over a 35‐year period. Subsequently, we analysed the variations in LST and identified the influence of environmental factors on temperature changes. Notably, while greening was observed, LST exhibited an upward trend. By differentiating the effects of climatic and anthropogenic factors on LST, we found that climate was the predominant influence, accounting for a contribution rate of 70.36% from 1980 to 1995. In contrast, human activities became the primary driver of LST changes, contributing 55% after 1995. Grasslands with moderate coverage demonstrated potential cooling effects. Among the various environmental factors examined, albedo exhibited a negative and delayed impact on LST, while temperature, precipitation and evapotranspiration were positively correlated with LST, displaying relatively synchronous variations. Additionally, soil moisture and the normalised difference vegetation index (NDVI) were identified as leading contributors to positive changes in LST. This study enhances the understanding of the mechanisms influencing LST and provides essential insights for socio‐economic development in areas with sensitive ecosystem.

Flood Susceptibility Mapping Using Analytic Hierarchy Process and Geographic Information System in Baitarani Basin of Odisha, India

Baitarani, the 6th largest river basin in Odisha, eastern India, faces water related challenges in its catchments. As one of the most frequent environmental disasters, floods are consistently cited in hotspots of the eastern region in India. Hence, in this study, flood susceptibility maps were generated for Baitarani River basin by using analytical hierarchy process technique and geographic information system. Eleven thematic maps of topographic ruggedness index, topographic wetness index, stream power index, elevation, lithology, soil, slope, drainage density, distance from drainage network, land use/land cover and normalized difference vegetation index were prepared for flood vulnerability mapping. The themes were assigned suitable weights depending on relative influence of each of the themes on flood risk. The results indicated that 87% of the basin falls under intermediate flood susceptibility zone, while 10% is classified under high flood hazard zone. Areas in the lower elevation catchment, particularly those with flat topography near the coastline, were identified as being highly susceptible to flooding. The impacts of severe flooding in the basin are most pronounced in specific blocks in Jajpur district (Jajpur, Dasarathpur, and Korei blocks), Bhadrak district (Dhamnagar and Bhandaripokhari blocks), and Anandapur block in the Keonjhar district of Odisha. Additionally, changes in land use/land cover during 1995-2020 revealed 8% decline in agricultural land and 13.93% increase in fallow land in the area. The outcomes of this research can help researchers, planners and managers in managing natural resources, find locations that could be vulnerable to flooding, and lessen the damage.

The role of early life factors and green living environment in the development of gut microbiota in infancy: Population-based cohort study

ObjectiveEarly life microbial exposure influences the composition of gut microbiota. We investigated how early life factors, and the green living environment around infants’ homes, influence the development of gut microbiota during infancy by utilizing data from the Steps to Healthy Development follow-up study (the STEPS study). MethodsThe gut microbiota was analyzed at early (∼3 months, n = 959), and late infancy (∼13 months, n = 984) using 16S rRNA amplicon sequencing, and combined with residential green environment, measured as (1) Normalized Difference Vegetation Index, (2) Vegetation Cover Diversity, and (3) Naturalness Index within a 750 m radius. We compared gut microbiota diversity and composition between early and late infancy, identified significant individual and family level early life factors influencing gut microbiota, and determined the role of the residential green environment measures on gut microbiota development. ResultsAlpha diversity (t-test, p < 0.001) and beta diversity (PERMANOVA, R2 = 0.095, p < 0.001) differed between early and late infancy. Birth mode was the strongest contributor to the gut microbiota community composition in early infancy (PERMANOVA, R2 = 0.005, p < 0.01) and the presence of siblings in late infancy (PERMANOVA, R2 = 0.007, p < 0.01). Residential green environment showed no association with community composition, whereas time spend outdoors did (PERMANOVA, R2 = 0.002, p < 0.05). Measures of greenness displayed a statistically significant association with alpha diversity during early infancy, not during late infancy (glm, p < 0.05). In adjusted analysis, the associations remained only with the Naturalness Index, where higher human impact on living environment was associated with decreased species richness (glm, Observed richness, p < 0.05). ConclusionsThe role of the residential green environment to the infant gut microbiota is especially important in early infancy, however, other early life factors, such as birth mode and presence of sibling, had a more significant effect on the overall community composition.

Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method

The Advanced Geostationary Radiation Imager (AGRI) on board Fengyun-4B (FY-4B) has been found to have significant advantages in aerosol dynamic monitoring. This study proposed a surface-related dynamic spectral reflectance ratio (SDSRR) method for FY-4B AGRI to solve the problem of inaccurate surface reflectance estimation in Aerosol Optical Depth (AOD) retrieval. This method introduced Moderate-resolution Imaging Spectroradiometer (MODIS) aerosol product to assist in calculating the surface reflectance of the AGRI blue channel and the spectral reflectance ratio between the shortwave infrared (SWIR) channel and the blue channel, then constructed a surface-related dynamic spectral reflectance ratio series by combining the spectral reflectance ratio, Normalized Difference Vegetation Index (NDVI) and Scattering Angle (SCA) to obtain aerosol retrieval results. To verify the accuracy of the SDSRR method, the AOD dataset of the SDSRR method, the official aerosol products of AGRI (LDA) and Advanced Himawari Imager (AHI) AOD datasets were compared with the ground-based observations of Aerosol Robotic Network (AERONET) and Sun-shy radiometer Observation Network (SONET) in East Asia. The results indicate that the SDSRR method performs more consistently in East Asia compared to the official ARGI aerosol products. The root-mean-square-error (RMSE), mean error (ME), and correlation coefficient (R) between SDSRR AOD and ground-based measurements are 0.286, 0.180 and 0.70, which is better than that of LDA AOD (RMSE = 0.508, ME = 0.292, R = 0.69). Additionally, the RMSE, ME, and R of AHI AOD were 0.253, 0.168, and 0.74, respectively.