Enhanced Vegetation Index Values Research Articles

Identification of high-performing soil groups in grazing lands using a multivariate analysis method

Understanding and quantifying the complex relationships between soil properties and vegetation health is important for sustainable land management and optimising agricultural productivity. This study tested a spatial data-driven framework to identify the soil groups associated with pasture health using publicly available gridded soil attribute layers from Soil and Landscape Grid of Australia (SLGA) over two adjacent southeast Australian river catchments. Principal component analysis (PCA) followed by isocluster unsupervised classification was applied to seventeen SLGA soil attribute layers to identify dominant soil patterns, which showed good spatial agreement with the Enhanced Vegetation Index (EVI) data derived from Landsat-8 imagery. The soil class demonstrating the highest EVI values (HVR class) and the lowest EVI values (LVR class) were determined. A comparison of these classes with soil types defined in the New South Wales Soil Landscape maps confirmed that the HVR class is predominated by agriculturally productive, basalt-derived 'Ant Hill' soils. The cation exchange capacity (CEC) ranging from ∼10 to 20 meq/100 g, clay content ranging from 20 % to 30 %, pHc (pH in calcium chloride solution) between 5 and 6, pHw (pH in water) between 5.5 and 6.5 and SOC between 2.5 % to 4 % were associated with higher EVI values in the HVR soil class within the study area. This study demonstrated an effective framework for identifying key soil attributes that affect agricultural productivity and pinpointing critical locations for grazing land productivity.

Intra-annual vegetation changes and spatial variation in China over the past two decades based on remote sensing and time-series clustering.

Vegetation is an important link between land, atmosphere, and water, making its changes of great significance. However, existing research has predominantly focused on long-term vegetation changes, neglecting the intra-annual variations of vegetation. Hence, this study is based on the Enhanced Vegetation Index (EVI) data from 2000 to 2022, with a time step of 16days, to analyze the intra-annual patterns of vegetation changes in China. The average intra-annual EVI values for each municipal-level administrative region were calculated, and the time-series k-means clustering algorithm was employed to divide these regions, exploring the spatial variations in China's intra-annual vegetation changes. Finally, the ridge regression and random forest methods were utilized to assess the drivers of intra-annual vegetation changes. The results showed that: (1) China's vegetation status exhibits a notable intra-annual variation pattern of "high in summer and low in winter," and the changes are more pronounced in the northern regions than in the southern regions; (2) the intra-annual vegetation changes exhibit remarkable regional disparities, and China can be optimally clustered into four distinct clusters, which align well with China's temperature and precipitation zones; and (3) the intra-annual vegetation changes demonstrate significant correlations with meteorological factors such as dew point temperature, precipitation, maximum temperature, and sea-level pressure. In conclusion, our study reveals the characteristics, spatial patterns and driving forces of intra-annual vegetation changes in China, which contribute to explaining ecosystem response mechanisms, providing valuable insights for ecological research and the formulation of ecological conservation and management strategies.

Invasibility framework to predict the early colonization of alien Sonneratia in mangrove: Implications for coastal area management

Invasibility, or an ecosystem's susceptibility to invasion, plays a critical role in managing biological invasions but is challenging to quantify due to its dependence on specific ecosystem variables. This limitation restricts the practical application of this concept in the control of alien species. This study aims to simplify invasibility into measurable components and develop an applicable framework to predict early colonization of alien plants within the coastal mangrove ecosystem. We used the unchanneled path length (UPL), a widely applied hydrological connectivity-related indicator, to assess the accessibility of the mangrove. The enhanced vegetation index (EVI), positively correlated with above-ground biomass, was used to evaluate the potential competitive intensity. Firstly, building on existing studies, we developed a four-quadrant concept model integrating the effects of EVI and UPL on the early colonization of the alien species Sonneratia apetala. Our results revealed significant differences in EVI and UPL values between colonized and uncolonized areas, with colonized regions displaying markedly lower values (P < 0.001). Additionally, logistic regression showed a significant negative association between the probability of successful colonization by S. apetala and both indicators (P < 0.001). These results validate the effectiveness of our conceptual model. Furtherly, we identified four key niche opportunities for exotic species in mangrove: mudflats outside the mangrove forest, tidal creeks, canopy gaps, and unmanaged abandoned aquaculture ponds. Overall, this study provides important insight into the ecological processes of alien S. apetala colonization and practical information for management of coastal areas susceptible to invasion. Additionally, it presents a case study on the practical application of the concept of invasibility in the management of alien species.

Vegetation indices monitoring by using copernicus data in the old-growth forests of the Republic of Srpska/Bosnia and Herzegovina

IntroductionOld-growth forests (OGFs) are vital for global biodiversity, acting as irreplaceable carbon sinks, fostering ecosystem stability, and preserving unique ecological and cultural heritage. There are three old-growth forests in the Republic of Srpska/Bosnia and Herzegovina (Lom, Janj, and Perućica). They are dominated by mixed forests of beech, fir, and spruce (Piceo-Abieti-Fagetim illyricum). The satellite imagery results confirm that the vegetational indices are crucial for monitoring old-growth forests. Vegetation indices provide significant information for monitoring forest ecosystems. Based on Copernicus Sentinel-2 images, remote sensing is enabled, providing data in various spatial and spectral resolutions. The analysis of images related to old-growth forests allows for monitoring the spatial dynamics of changes in vitality and the health status of forests. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests.MethodsThe research methodology includes the application of Sentinel 2A satellite imagery, which is taken from the European Space Agency (ESA) database. The Sentinel 2 mission consists of two identical satellites (Sentinel 2A and Sentinel 2B) that have multispectral sensors with 13 spectral channels (bands) at spatial resolutions of 10, 20, and 60 m. The analysis included the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and visible atmospheric resistance index (VARI). For determining the significance of differences in vegetation index magnitudes between old-growth forests, a one-way analysis of variance with equal repetitions was employed.Results and discussionThe parts of old-growth forests are located in different stages of development, and the trees in them are characterized by different degrees of vitality and have different values of vegetation indices. It is assumed that parts of old-growth forests in the terminal phase of development with a large number of diseased and rotting trees have the smallest sizes of vegetation indices. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests. Using satellite imagery and their analysis of the obtained sizes of vegetation indices, it is observed that there is a significant difference in their sizes between old-growth forests. The Janj old-growth forest stands out as the area with the highest NDVI values, followed by the Lom old-growth forest, while the Perućica old-growth forest has the lowest NDVI values. However, the Perućica old-growth forest has significantly higher values of EVI and SAVI vegetation indices, while the other two old-growth forests have approximately similar values. The VARI index values are highest in the Lom old-growth forest, slightly lower in the Perućica old-growth forest, and lowest in the Janj old-growth forest. The results of these studies indicate statistically significant differences in vegetation index magnitudes between old-growth forests. The application of Copernicus satellite data enables the monitoring of changes in vegetation index values, which is of great assistance in spatially defining the developmental stages in old-growth forests (OGFs). Considering the relatively small number of sample areas covered by these studies, similar research needs to be conducted on a larger number of sample areas (replications), especially during the vegetation period.

Geo-ecological, shoreline dynamic, and flooding impacts of Cyclonic Storm Mocha: A geospatial analysis

This research comprehensively assesses the aftermath of Cyclonic Storm Mocha, focusing on the coastal zones of Rakhine State and the Chittagong Division, spanning Myanmar and Bangladesh. The investigation emphasizes the impacts on coastal ecology, shoreline dynamics, flooding patterns, and meteorological variations. Employed were multiple vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), Disaster Vegetation Damage Index (DVDI), and Fractional Vegetation Cover (FVC)—to evaluate ecological consequences. The Digital Shoreline Assessment System (DSAS) aided in determining shoreline alterations pre- and post-cyclone. Soil exposure and flood extents were scrutinized using the Bare Soil Index (BSI) and Modified Normalized Difference Water Index (MNDWI), respectively. Additionally, the study encompassed an analysis of microclimatic variables, comparing meteorological data across pre- and post-cyclone periods. Findings indicate significant ecological impacts: an estimated 8985.46 km2 of dense vegetation (NDVI >0.6) was adversely affected. Post-cyclone, there was a discernible reduction in EVI values. The mean mVCI shifted negatively from −0.18 to −0.33, and the mean FVC decreased from 0.39 to 0.33. The DVDI underscored considerable vegetation damage in various areas, underscoring the cyclone's extensive impact. Meteorological analysis revealed a 245 % increase in rainfall (20.22 mm on May 14, 2023 compared to the May average of 5.86 mm), and significant increases in relative humidity (14 %) and wind speed (205 %). Erosion was observed along 74.60 % of the studied shoreline. These insights are pivotal for developing comprehensive strategies aimed at the rehabilitation and conservation of critical coastal ecosystems. They provide vital data for emergency response initiatives and offer resources for entities engaged in enhancing coastal resilience and protecting local community livelihoods.

The Use of EVI Algorithm in Predicting Rice Productivity, Harau District, Lima Puluh Kota Regency, West Sumatra

Using remote sensing data in predicting rice productivity can provide information quickly and easily for relatively large areas. Harau District is a rice center in Lima Puluh Kota Regency and is one of the many rice-producing areas in West Sumatra Province. This study aims to determine a mathematical model to estimate rice productivity in Harau District, Lima Puluh Kota Regency, using the EVI (Enhanced Vegetation Index) algorithm. This study used two methods, field observation and MODIS image data processing (MOD13A1 v006). The results of the analysis obtained a mathematical model to predict rice productivity in Harau District, namely y = 5393.3x4 - 8140.7x3 + 4518.9x2 - 1121.6x + 113.55 where y is the productivity value (tons/ha) and x as the EVI value. The percentage of errors in observation and estimation data was 15.284%, and the results of the observation and estimation data validation test (NSE) were 0.734, a good category.

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period.

Effect of neighbourhood greenness on the association between air pollution and risk of stroke first onset: A case-crossover study in shandong province, China.

Higher neighbourhood greenness is associated with beneficial health outcomes, and short-term exposure to air pollution is associated with an elevated risk of stroke onset. However, little is known about their interactions. Daily data on stroke first onset were collected from 20 counties in Shangdong Province, China, from 2013 to 2019. The enhanced vegetation index (EVI) and concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were calculated for each individual at the village or community level based on their home address to measure their neighbourhood exposure to greenness and air pollution. EVI was categorised as low or high, and a time-stratified case-crossover design was used to estimate the percent excess risk (ER%) of stroke associated with short-term exposure to air pollution. We further stratified greenness on the basis of EVI values into quartiles and introduced interaction terms between air pollutant concentrations and the median EVI values of the quartiles to assess the effect of greenness on the associations between short-term exposure and stroke. Individuals living in the high-greenness areas had weaker associations between total stroke risk and exposure to NO2 (low greenness: ER%=1.765% [95% CI 1.205%-2.328%]; high greenness: ER%=0.368% [95% CI -0.252% to 0.991%]; P=0.001), O3 (low greenness: 0.476% [95% CI 0.246%-0.706%]; high greenness: ER%=0.085% [95% CI -0.156% to 0.327%]; P=0.011), and SO2 (low greenness: 0.632% [95% CI 0.138%-1.129%]; high greenness: ER%=-0.177% [95% CI -0.782% to 0.431%]; P=0.035). Residence in areas with higher greenness was related to weaker associations between air pollution and stroke risk, suggesting that effectively planning green spaces can improve public health.

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

The motivation for this study arises from the need to monitor the condition of a rehabilitated post-mining areas even decades after the end of the recovery phase. This can be facilitated with satellite derived spectral vegetation indices and Geographic Information System (GIS) based spatiotemporal analysis. The study area described in this work is located in Western Poland and has unique characteristics, as it was subjected to the combined underground and open pit mining of lignite deposits that had been shaped by glaciotectonic processes. The mining ended in early 1970’ties and the area was subjected to reclamation procedures that ended in the 1980’ties. We used the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) spectral indices derived from Sentinel-2 data for the 2015–2022. period. Then, we applied a combination of GIS-based map algebra statistics (local, zonal and combinatorial) and GI* spatial statistics (hot spot and temporal hot spot) for a complex analysis and assessment of the vegetation cover condition in a post-mining area thought to be in the rehabilitated phase. The mean values of NDVI and EVI for the post-mining study area range from 0.48 to 0.64 and 0.24 to 0.31 and are stable in the analyzed 8 year period. This indicates general good condition of the vegetation and post-recovery phase of the area of interest. However, the combination of spatiotemporal analysis allowed us to identify statistically significant clusters of higher and lower values of the vegetation indices and change of vegetation cover classes on 3% of the study area. These clusters signify the occurrence of local processes such as, the encroachment of aquatic vegetation in waterlogged subsidence basins, and growth of low vegetation in old pits filled with waste material, barren earth zones on external waste dumps, as well as present-day forest management activities. We have confirmed that significant vegetation changes related to former mining occur even five decades later. Furthermore, we identified clusters of the highest values that are associated with zones of older, healthy forest and deciduous tree species. The results confirmed applicability of Sentinel-2 derived vegetation indices for studies of post-mining environment and for the detection of local phenomena related to natural landscaping processes still taking place in the study area. The methodology adopted for this study consisting of a combination of GIS-based data mining methods can be used in combination or separately in other areas of interest, as well as aid their sustainable management.

Determining fire frequency and its relationship with rangeland aboveground grass biomass using MODIS and Landsat imagery

ABSTRACT High frequency burned area (BA) images provide an opportunity to monitor rangeland fire frequency (FF). This study related grass aboveground biomass (AGB) to FF in a 4800 ha biodiversity conservation savannah-grassland rangeland. Archived (2000–2021), 500 m resolution MODIS burned area monthly images were used. Following Boolean coding (fire event pixel = 1, no-fire pixel = 0), addition GIS overlay analysis yielded total fires per pixel location. Fire detection accuracy was assessed using 2018–2021 management fire event records. Sample grass AGB data were obtained at the end of the 2020 and 2021 growing seasons from widely dispersed sampling sites with wide grass cover uniformity, where a 1 m quadrat was tossed randomly in a 20 m × 20 m plot up to three times. The quadrat-enclosed grass was harvested to soil level, air-dried, and weighed to generate site average AGB values, which were correlated with vegetation index (VI) values from sampling near-concurrent surface reflectance (L2SP) Landsat-8 OLI images. Four biomass-sensitive VIs utilising Landsat sensor spectral ranges were tested. The Enhanced Vegetation Index (EVI) yielded the strongest relationship (r = 0.410, p < 0.01). A linear model predicting grass AGB from EVI values was developed using 58% of sample data for training (R 2 = 0.3062, p < 0.01) and 42% for validation (R 2 = 0.5225, p < 0.001). Using the model, sampling site historical grass AGB values were predicted on same season, L2SP Landsat (TM, ETM+, OLI) images from 2000, 2002, 2006, 2009, 2013 and 2016, whose dates were selected by comparing rainfall. The MODIS images detected 73% of fires larger than 25 ha (one pixel). Most sites experienced long-term AGB gains, at faster rates in high FF (4–5 fires), low grazing sites. Most fires occurred as ecologically undesired late burns, indicating the utility of archived high-frequency BA images for rangeland management.

Monitoring of Carbon Monoxide (CO) changes in the atmosphere and urban environmental indices extracted from remote sensing images for 932 Iran cities from 2019 to 2021

ABSTRACT Carbon Monoxide (CO) is an important urban pollutant with a relation to transition economies based on emission intensities. In this study, Sentinel-5, MODerate resolution Imaging Spectroradiometer (MODIS), and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them. From the assessed correlations for 932 Iranian cities, it occurred that the assessed indices were all correlated. The highest CO levels were 0.031 in the spring of 2019 and 2020, whereas in 2021 it was equal to 0.030 in both the spring and winter, respectively. In 2019 and 2020 the maximum values of the Enhanced Vegetation Index (EVI) in the spring were 0.181 and 0.183. Exceptionally high Absorbing Aerosol Index (AAI) values of – 0.834 and – 1.0, along with Urban Index (UI) of 0.102 and 0.092, were correlated with recorded spikes in CO level, despite that these seasons’ EVI values were not so abnormal. It was forecasted that in 2030 rises in the CO level by 13.2% in the winter and by 17.5% in the fall are expected, with the simultaneous increase of AAI by 204.5% and 980.2%, and Aerosol Optical Depth (AOD) by 27% and 5% in the winter and spring, respectively.

Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm

Land surface phenology (LSP), the study of the seasonal vegetation dynamics from remote sensing imagery, provides crucial information for plant monitoring and reflects the responses of ecosystems to climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) provides global LSP information, but it has large spatial gaps in many regions, especially in ecosystems where rainfall influences phenology more than temperature. This study aimed to improve spatial coverage of LSP retrieval in these ecosystems. To do so, we used a regionally modified threshold algorithm for LSP retrievals, which were tested over continental Australia as it includes diverse landscapes of arid, mesic, and forest environments. We generated LSP metrics annually from 2003 to 2018 using satellite Enhanced Vegetation Index (EVI) time series at 500 m resolution, including the start, peak, end, and length of growing seasons, the minimum EVI value prior to and after the peak date, the seasonal maximum EVI value, the integral EVI value during the growing season (an approximation of productivity), and seasonal amplitude (maximum EVI value minus minimum EVI). Our regionally optimised algorithm improved the spatial coverage of LSP information in Australia from only 26 % of the continent to 70 % averaged across 16 years. Our results showed that the growing season amplitude was low (EVI < 0.1) over arid/semi-arid shrublands and savannas, tropical and subtropical savannas, and temperate evergreen forests, whose LSP metrics were captured by our regional algorithm and not by the global product. Some ecosystems, such as arid/semi-arid shrublands and savannas, showed more irregular phenology with low seasonal dynamics, and the growing seasons could skip a year or occur more than once in a year depending on climate conditions. Our algorithm was more sensitive to ecosystems with low seasonal amplitudes. We found that the detectability of LSP increases as the growing season amplitude increases, regardless of vegetation cover. Evaluation of the LSP metrics using eddy covariance flux tower measurements of gross primary productivity (GPP) demonstrated the reliability and accuracy of the algorithm. These improved LSP retrievals provide a greater understanding of the vegetation phenology across diverse ecosystems, especially savanna, shrubland, and evergreen forest ecosystems that cover more than 30 % of the land globally. The LSP provides essential information for ecological and agricultural studies such as quantifying bushfire fuel accumulation and forest carbon cycling, whilst enhancing our capacity for quantifying ecological responses to climate change.

EVI and NDVI as proxies for multifaceted avian diversity in urban areas.

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-m buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities.

Vegetation productivity of the Lake Poyang Wetland in the post Three Gorges Dam era

三峡运行以来,气候变化与人类活动共同驱动了鄱阳湖水文情势的急剧变化,并对其湿地植被产生了显著影响,但当前研究尚未系统量化鄱阳湖湿地植被在此条件下的演变规律与趋势。因此,本研究以增强型植被指数EVI(enhanced vegetation index)的时段最高值作为表征湿地植被生产力的代用指标,定量揭示了后三峡工程时代鄱阳湖湿地植被生产力的气候态特征及其变化,包括变化的量级、显著性及阶段性。结果表明:(1)2000-2020年,鄱阳湖湿地植被EVI最高值在全年及春、秋两季分别为0.37、0.30和0.33,入湖河流三角洲及碟形洼地边滩是其生产力中心;春季EVI最高值在此区域高于秋季,而在湖心区低于秋季。(2)三峡运行后的湿地植被生产力变化在全年及春、秋两季均有极大空间异质性,在湖心深泓线附近显著增加,而在入湖河流三角洲及湿地边缘下降。春季EVI增长区域面积占比极大(84.2%),而秋季增长区面积占比较小(71.2%),即有较大面积区域EVI在秋季下降(38.8%)。(3)三峡运行后,鄱阳湖湿地植被生产力中心经历了原位增长、空间扩张以及湖心向转移3个阶段,最终完成了由支流三角洲向湖心区的转移。从总体来看,鄱阳湖湿地植被生产力在全年及春、秋季均呈增长趋势,增速分别为0.0035、0.0049和0.0028 EVI/a。本研究量化了后三峡工程时代的鄱阳湖湿地植被生产力变化,为该区域进一步的综合管理提供了数据支撑。;Since the operation of the Three Gorges Dam (TGD), the superposition of human activities and climate change has dramatically changed hydrological regimes of the Lake Poyang. These changes significantly affected the structure and function of the surrounding wetland ecosystems. However, vegetation variations in the Lake Poyang Wetland under this changing condition has not yet been systematically quantified. Hence, this study took the maximum EVI (enhanced vegetation index) as a proxy index to characterize vegetation productivity of the Lake Poyang Wetland, aiming to quantitatively reveal its changes in the post TGD era. The results show that: (1) During 2000 to 2020, the regional averaged maximum EVI of the wetland was 0.37, 0.30 and 0.33 in the whole year, spring, and autumn, respectively. River deltas of the lake tributaries and the dished depressions in the wetland were generally with higher EVI values compared to other areas, thus regarding as high productivity center of the wetland. In springs, the maximum EVI in the productivity center was higher than that in autumn, whereas it was lower than that in autumn in other areas with low EVI values such as the center of the lake. (2) Changes of vegetation productivity had great spatial heterogeneity for all the periods including the whole year, springs, and autumns. The maximum EVI values increased significantly in the central of the wetland (surrounding the thalweg), while the maximum EVI values decreased in river deltas of lake tributaries and wetland edge. The areas with increasing EVI values in spring accounted for about 84.2% of the whole wetland, while the area with increasing EVI in autumn accounted merely 71.2% of the wetland. That is, there was still a large proportion of the wetland with decreasing EVI in autumn (38.8%). (3) After TGD, the high productivity center of the wetland experienced three stages: in-situ growth, spatial expansion and towards lake center transfer, thus moved from river deltas to lake center areas. Overall, the maximum EVIs of the wetland showed an increasing trend, with a rate of 0.0035, 0.0049, and 0.0028 EVI/year for the annual, spring and autumn, respectively. The findings systematically quantified vegetation productivity changes of the Lake Poyang Wetland in the post TGD era. It also provided a scientific basis for proposing water resource management strategies to maintain the stability of wetland ecosystem.

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China.

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

Classification of forest types in Cuigang Forest Farm based on time series data of Landsat 8

To explore the practical role of enhanced vegetation index (EVI) time series data in improving the accuracy of forest type recognition could promote the deep application of optical remote sensing data in forest resources investigation and monitoring. With Cuigang Forest Farm of Xinlin Forestry Bureau in Daxing'anling as the object, we constructed six classification schemes, using random forest algorithm with spectral feature, texture feature and EVI time series feature. The data sources were 20-view Landsat 8 OLI time series data from 2014 to 2018, 56 fixed plots data from 2017-2019, and the 2016 Class II survey data. Our aims were to realize the classification of forest types in Cuigang Forest Farm and to evaluate the accuracy of different classification schemes. The results showed the EVI values of Larix gmelinii forest, Betula platyphylla forest, coniferous-broadleaved mixed forest, coniferous mixed forest and broadleaved mixed forest were significantly different in non-growing seasons (36-111 days and 287-367 days), with the EVI value of mixed conifer forest being significantly higher, and that of mixed broadleaf forest being always lower than the other four forest types. In the early growing season (111-143 days), the EVI value of B. platyphylla forest were higher than L. gmelinii forest, which could effectively distinguish the two forests. Among the six classification schemes, spectral feature, texture feature, and EVI time series feature had the highest classification accuracy, with a Kappa of 0.82 and a classification accuracy of 86.1%. The comparison results showed that the overall accuracy of adding vegetation index time series feature was improved by 14.3% compared with that of spectral feature. The random forest algorithm with combined spectral, texture and EVI time series features could effectively classify forest stand types in Cuigang Forest Farm, with good recognition accuracy and confidence.

The associations between residential greenness and allergic diseases in Chinese toddlers: A birth cohort study

BackgroundGreen space in the living environment has been linked to the development of allergic diseases. However, evidence regarding early-onset allergy in toddlers was limited, and the critical exposure window remained unclear. We aimed to investigate associations between residential greenness with allergic diseases in early life. MethodsThis prospective birth cohort study included 522 mother-child pairs in Guangzhou, China. We quantified prenatal, postnatal, and early-life (i.e., the first 1000 days of life) residential greenness, estimated from remote satellite data using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and tree cover. We identified physician-diagnosed allergic diseases (eczema, atopic dermatitis, urticaria, allergic rhinitis, allergic conjunctivitis, food allergy, and asthma) based on medical records at age 2 years. Generalized linear regression was conducted to examine the associations of greenness with allergic outcomes. ResultsThe ranges of residential NDVI and EVI values in 500-m buffer during early life were 0.06–0.70 and 0.03–0.46, respectively. We found a 0.1 unit increase of NDVI in 500-m buffer throughout early life was associated with higher odds of any allergic diseases (prenatal: OR [odds ratio], 1.25; 95%CI, 1.02–1.53; postnatal: OR, 1.24; 95%CI, 1.02–1.52; early-life: OR, 1.25, 95%CI: 1.02–1.53) and higher odds of eczema (prenatal: OR, 1.28; 95%CI, 1.04–1.59; postnatal: OR, 1.24; 95%CI, 1.01–1.54; early-life: OR, 1.26, 95%CI: 1.02–1.56). The results were consistent when using EVI as a proxy for greenness. We only observed that prenatal exposure to the highest tertile of NDVI-500 was adversely associated with any allergic diseases (OR, 1.63; 95%CI, 1.03–2.58) and eczema (OR, 1.70; 95%CI: 1.04–2.78) compared with the lowest tertile. ConclusionsThis study identified detrimental associations of residential greenness with allergic diseases especially eczema among toddlers, and pregnancy appears to be the critical exposure window. Our findings highlighted the importance of urban planning to develop friendly-green neighborhood to improve maternal and child health.

Long-term exposure to residential surrounding greenness and incidence of diabetes: A prospective cohort study

Exposure to residential greenness might affect population health through increasing physical activity and social engagement, improving mental health, and reducing harmful environmental exposure. However, evidence on the association of greenness with risk of diabetes is still controversial. In this study, we recruited a total of 22,535 participants aged ≥18 years from Yinzhou District, Ningbo, Zhejiang Province, China to investigate the associations between residential greenness and risk of diabetes incidence. Residential greenness was estimated using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Vegetation Continuous Field (VCF). We also calculated cumulative average NDVI, EVI and VCF values, and changes in NDVI, EVI and VCF during the follow-up period. We used Cox proportional hazards models controlling for demographic characteristics, lifestyles, individual socioeconomic status, history of diseases and particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) to examine hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assessed physical activity, body mass index (BMI) or PM2.5 as potential mediators. During 84,992.64 person-years of follow-up, a total of 1,154 incident cases of diabetes occurred. In multivariable models, living in the highest quartile of cumulative average NDVI, EVI and VCF within 250-m buffer was associated with 57% (HR = 0.43, 95% CI: 0.36, 0.52), 62% (HR = 0.38, 95% CI: 0.32, 0.45), and 55% (HR = 0.45, 95% CI: 0.38, 0.54) reduction in diabetes risk compared with the lowest quartile, respectively. Results remained similar for NDVI, EVI, and VCF within 500-m and 1000-m buffers. Stratified analyses showed stronger association for residential greenness and diabetes among older people. The association between greenness and diabetes did not appear to be mediated by physical activity, PM2.5 or BMI. Our findings suggested that higher residential greenness was significantly associated with lower risk of diabetes.

Identification of forest vegetation types in southern China based on spatio-temporal fusion of GF-1 WFV and MODIS data

It is difficult to obtain long time series of high spatial resolution remote sensing images in southern China because of the complex terrain and frequent cloudy and rainy weather. In contrast, the spatio-temporal fusion can sychonorously obtain remote sensing data with high spatial-temporal resolution, which is beneficial to extract forest vegetation type information. With Xingguo County of Jiangxi Province as the study area, we fused the Landsat8 OLI and GF-1 WFV images with high spatial resolution with high temporal resolution of MODIS09 A1 image on the basis of enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), reconstructed the time series data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI with 8 d step of enhanced vegetation index (EVI), obtained the phenology (PH) characteristics, and identified the forest vegetation types by using random forest classification model. The results showed that the correlation coefficients between the fusion data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI and the real images were all greater than 0.7, and had good consistency in spatial distribution, which could be used to supplement the missing data with high spatial resolution. The extraction accuracy of random forest classification with different combination modes was EVI+PH>EVI>PH and the classification accuracy of fusion data GF-1 was higher than that of Landsat8. A total of 43 variables were selected as the optimal feature variables for classification. The overall accuracy and Kappa coefficient were 95.6% and 94.9%, respectively, including 37 sequential EVI values and 6 phenological feature information. The sequential EVI data contributed more to the identification of forest vegetation types, while the phenological feature information was beneficial to improve the classification accuracy. The ESTARFM fusion algorithm was suitable for GF-1 and MODIS data, which could solve the problem of insufficient long-term sequence of high spatial resolution images. The GF-1 temporal fusion images had high accuracy in the identification of forest vegetation types in southern China under complex terrain and frequent cloudy and rainy weather.

Dormant Season Vegetation Phenology and Eddy Fluxes in Native Tallgrass Prairies of the U.S. Southern Plains

Carbon dioxide (CO2) fluxes and evapotranspiration (ET) during the non-growing season can contribute significantly to the annual carbon and water budgets of agroecosystems. Comparative studies of vegetation phenology and the dynamics of CO2 fluxes and ET during the dormant season of native tallgrass prairies from different landscape positions under the same climatic regime are scarce. Thus, this study compared the dynamics of satellite-derived vegetation phenology (as captured by the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI)) and eddy covariance (EC)-measured CO2 fluxes and ET in six differently managed native tallgrass prairie pastures during dormant seasons (November through March). During December–February, vegetation phenology (EVI and NDVI) and the dynamics of eddy fluxes were comparable across all pastures in most years. Large discrepancies in fluxes were observed during March (the time of the initiation of growth of dominant warm-season grasses) across years and pastures due to the influence of weather conditions and management practices. The results illustrated the interactive effects between prescribed spring burns and rainfall on vegetation phenology (i.e., positive and negative impacts of prescribed spring burns under non-drought and drought conditions, respectively). The EVI better tracked the phenology of tallgrass prairie during the dormant season than did NDVI. Similar EVI and NDVI values for the periods when flux magnitudes were different among pastures and years, most likely due to the satellite sensors’ inability to fully observe the presence of some cool-season C3 species under residues, necessitated a multi-level validation approach of using ground-truth observations of species composition, EC measurements, PhenoCam (digital) images, and finer-resolution satellite data to further validate the vegetation phenology derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during dormant seasons. This study provides novel insights into the dynamics of vegetation phenology, CO2 fluxes, and ET of tallgrass prairie during the dormant season in the U.S. Southern Great Plains.