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Copyright of Agriculture / Poljoprivreda is the property of Josip Juraj Strossmayer University of Osijek, Faculty of Agriculture in Osijek and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drought is a major water resources management issue in Iran. Khuzestan Province is in a drought state due to water shortage. Therefore, identifying areas at high risk of drought and when drought occurs is essential for drought management. For this purpose, this study used precipitation and temperature data of 12 selected stations and MODIS sensor images from the United States Geological Survey database in 2000–2017. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Normalized Difference Vegetation Index (NDVI) were calculated using the Hargreaves–Samani method and ENVI software. Moreover, different spatial statistics techniques were used in the ArcGIS environment to analyze the results. Also, time series diagrams were drawn, and the trend was evaluated using the Mann–Kendall test. Finally, the distribution of NDVI values was investigated using EasyFit software, and the amount of drought damage was determined using NDVI. The investigation of the cluster maps of the Anselin Local Moran's Index along with hot and cold spots formed for both SPEI and NDVI showed that drought severity was higher at the southern stations than at the semi-northern and northwestern ones in the province. Moreover, the survey results using the EasyFit software showed that the southern stations, including the Ahvaz, Mahshahr, and Omidiyeh-Aghajari stations, were more at risk of drought than the other stations due to the drought threshold. Furthermore, the total damage caused by drought for the Ahvaz and Abadan stations showed a damage rate of 50%. [ABSTRACT FROM AUTHOR]

Urban vegetation plays a very important role in regulating urban climate and improving the urban environment. There is an urgent need to construct an effective index to quickly detect urban vegetation quality changes. In this study, a synthetic vegetation quality index (VQI) was proposed using a holistic approach based on the quality of vegetation itself and the spatial relationship with its surroundings, composed of four selected variables: normalized difference vegetation index (NDVI), patch aggregation index (AI), patch density (PD), and percentage of landscape (PLAND). Principal component analysis (PCA) was employed to calculate weights for each variable due to its objectivity. Then, taking Fuzhou City, southeast China as the case study, the scale effects of the VQI under different moving window sizes (500 m, 1 km, 2 km, ..., 5 km) and the spatiotemporal changes were explored. The results showed that a VQI with a window size of 3 km had the highest correlations with all the selected indicators. Meanwhile, the representativeness and the effectiveness of the VQI were validated by the percentage eigenvalues of PC1, as well as Pearson correlation analysis and bivariate spatial autocorrelation analysis. We also revealed that the proposed VQI had the greatest explanatory power for land surface temperature (LST) among all the factors in both studied years (2000 and 2016), with the VQI's interpretation of LST being 0–44% better than any individual indicator except for AI in 2000. Additionally, our work revealed that the location of vegetation has a great impact on the urban thermal environment. The VQI can assess urban vegetation quality effectively and quickly. [ABSTRACT FROM AUTHOR]

Copyright of Virtual Archaeology Review is the property of Virtual Archaeology Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are many ecologically fragile areas similar to China’s Mu Us Sandy Land in the world, which are facing ecological and environmental problems, and improvement of its vegetation cover is essential to those regions’ sustainable development. In this study, spatiotemporal patterns in the Sandy Land’s vegetation cover between 2000 and 2019 were monitored using the Normalized Difference Vegetation Index (NDVI) data (MOD13A1-NDVI). Correlation analyses of regional climate change (precipitation and temperature) and NDVI-related land cover parameters, and quantified respective contribution rates using the residual analysis, indicated that: (i) accounted for 43.5% of the Sandy Land by area, zones of significant improvement in vegetation cover occurred predominantly in the east and southeast. In contrast, the Sandy Land’s central and northwest regions, accounting for 56.5% of their area, showed little change in vegetation coverage. (ii) in terms of overall trends in vegetation improvement, interannual changes in vegetation cover were highly spatially consistent: vegetation coverage was high in the east and south, but low in the central and western regions. (iii) within the Sandy Land, a correlation existed between NDVI and precipitation, and between NDVI and temperature, with the former being the stronger with a positive correlation across 99% of the Sandy Land. (iv) since zones with unchanged land cover contributed 85% of the change in NDVI, changes in the Sandy Land’s NDVI values were not related to changes in land cover types, but rather to the improvement of vegetation within land cover types. (v) the contribution rate of human activities to vegetation improvement was 62.68%, while that of climate change was 37.32%. These results can hopefully provide support for local government in the development of an ecologically sound environment.

Mark–release–recapture (MRR) trials have been conducted in Northern Italy to evaluate the capacity of radio-substerilized Aedes albopictus males to survive, disperse, and engage in mating in the field. Two MRR sessions with the human landing collection method (HLC) were conducted with the simultaneous release of irradiated males marked with four different pigment colors. The survival and dispersal rates seem to be influenced more by environmental factors such as barriers, shading, and vegetation rather than weather parameters. In this study, we confirmed a positive linear relationship between the sterile adult male’s daily survival rate and the relative humidity previously reported in similar experimental conditions and a different dispersal capacity of the released A. albopictus males in low- (NDVI index 0.4)-vegetated areas. Consistent with previous studies, A. albopictus males have their maximal dispersion in the first days after release, while in the following days the males become more stationary. The similar field performances obtained with marked and unmarked radio-sterilized and untreated A. albopictus males on similar environments confirm the negligible effects of irradiation and marking procedures on the quality of the males released. The similar sterile to wild (S/W) male ratio measured in high- and low-vegetation areas in the release sites indicates a similar distribution pattern for the wild and the released sterile males. According to the MRR data collected, the Lincoln index estimated different A. albopictus mean population densities in the study areas equal to 7,000 and 3,000 male/ha, respectively.

Effect of meteorological drought on vegetation conditions and their relationships with each other was investigated in the Province of Lorestan. The standard precipitation index (SPI) as a meteorological drought index was used for 28 rain gauging stations during the years 1987-2017 in that province. Benefiting from the remote sensing and MODIS images, the normalized difference vegetation index (NDVI) was extracted for the years 2000-2017 and the vegetation condition index (VCI) was calculated. Using the SPI results, the dry, normal and wet years were identified and the index year was selected for them. The correlation between the SPI and VCI was investigated using linear regression. The results indicated that the highest correlation coefficient of Pearson was related to the VCI in March with a 9-month SPI in November was equal to 0.64; the correlation coefficient between the multivariate linear regressions of the SPI with VCI in June was 0.7. The results of multivariate linear regression indicated that the SPI had a significant correlation with the VCI at the five percent level over a period of 9 and 12 months. A confusion matrix was used to evaluate the compatibility of the SPI drought classes with the VCI; the results also indicated that the highest compliance of the VCI with the SPI was in the moderate drought class. Furthermore, the results of this study indicated a 9-month delay in the meteorological drought index with satellite drought index at the 5 % level significance of the SPI with the VCI, which indicates that the VCI may be used in the absence of meteorological indicators for the study area.

Mangrove forests have experienced a rapid decline. However, the rate of loss has decreased in recent years due to enhanced conservation and nature regeneration. The dynamics of mangrove forests in Panama have not been monitored since the year 2000, despite a significant loss during the 1980s. The objectives of our study were to quantify changes in mangrove cover and identify the dominant drivers of change in Parita Bay, Panama. Temporal changes in mangrove cover and the Normalized Difference Vegetation Index (NDVI) were determined using the supervised classification method on Landsat satellite images from 1987 to 2019. We identified a 4.7% increase in the mangrove area of Parita Bay during the 32 years; the mangrove forests were also considered healthy as reflected by high NDVI values. However, the conversion of mangroves to other land cover types resulted in a 1.26% decline in mangrove cover from 1987 to 1998. Moreover, the area of aquaculture and saltpans almost doubled during this period. During the following two decades, the conversion of other land cover classes (water, other vegetation, and bare soil) increased the mangrove area by 6%, and the annual rate of increase was greater during the second decade (0.43% year−1). From 2009 to 2019, mangroves declined at an annual rate of 0.11% in protected areas and increased at an annual rate of 0.50% in unprotected areas. Despite the regeneration potential of mangrove forests, our study highlights the need to continually manage and protect mangrove forests in order to facilitate their expansion in Parita Bay.

Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang–Huai–Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF’s capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R2 > 0.8, p < 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R2 > 0.5, p < 0.01 for SIF vs FAPAR and R2 > 0.6, p < 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought–vegetation productivity relationship.

[Display omitted] • The soil parameters differed widely, with a coefficient of variation varying from 8.75 to 118.98%. • The soils were strongly acidic to slightly alkaline with a slightly saline electrical conductivity. • The best-fit model of pH, N, P, and Ca was spherical. • The mathematical and exponential models were best fit for Ca, Mg and organic carbon (OC), respectively. • The region shows a moderate NDVI of 0.15, indicating dynamics in vegetation cover. A profound grasp of the quantitative spatial heterogeneity and distribution of the soil physicochemical attributes is crucial in understanding agricultural landscapes for ensuring the provisioning of soil ecosystem services. However, the analysis of data from remote sensing, like NDVI, can be of help in analysing the capacity of the landscape to provide supporting ecosystem services such as primary productivity. The research investigated and addressed the dispersion of important soil physico-chemical attributes in agricultural lands of the temperate Himalayan region of India using a geostatistical method and combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging method. A 206 soil samples were gathered and assessed for soil parameters like pH, EC, OC, and available N, P, K, Ca, and Mg from Kishtwar district of Jammu. The coefficient of variation (CV) for pH and electrical conductivity (EC) ranged notably from 8.75 % to 118.98 %, highlighting diverse soil characteristics critical for local management practices. Mean elevation averaged 2743.32 m (m), with a moderate NDVI of 0.15, indicating dynamics in vegetation cover. Soil pH ranged from intensely acidic to marginally alkaline, with varying EC levels. Seemingly high organic carbon (OC), nitrogen (N), and potassium (K) levels, accompanied by medium phosphorus (P), calcium (Ca), and magnesium (Mg) levels were found in the region. The study employed ordinary kriging (OK) to map the spatial distribution of soil parameters, utilizing mean square error (MSE), root mean square error (RMSE), and the Moran's I index. Exponential models were the best fit models for OC, while spherical models were fit for pH, EC, N, P, and Ca. Mathematical models were best fit for K and Mg. Spatial analysis using spherical and exponential models revealed distinct distribution patterns for pH, N, P, Ca, and Mg. The results of the degree of spatial dependence from the semi-variogram analyses indicated a strong (0.06 %) to moderate (0.51 %) to weak (2.81 %) dependence. The interpolated maps showed a distinct gradient in elevation (1053–4413 m), OC (0.13–2.80 %), NDVI (−0.16–0.54), pH (4.80–8.00), EC (0.03–9.80 dS m−1), N (201.15–993.19 kg ha−1), P (3.00–96.00 kg ha−1), K (124.88–1110.71 kg ha−1), Ca (7.00–46.00 meq 100 g soil−1), and Mg (2.30–21.50 meq 100 g soil−1) at the regional scale, indicating a wide range of spatial soil heterogeneity. The heterogeneity maps of soil parameters generated by this research can be effectively used by land planners and farm managers at a regional scale for crop nutrient management to reduce soil contamination risk. These maps serve as baseline materials and effective tools for suitable land management strategies such as conservation-effective tillage, integrated nutrient management, and organic farming based on the spatial distribution of soil properties and they can significantly enhance the long-term ecological sustainability of agro-ecosystems' management. [ABSTRACT FROM AUTHOR]

The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index—NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers.

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.

Walnut (Juglans regia) planting is the main economic pillar industry in southern Xinjiang. Based on the Google Earth Engine (GEE) cloud platform, the NDVI maximum synthesis method was used to estimate changes in the walnut cultivation area in Ganquan Town, South Xinjiang, from 2017 to 2021. The simultaneous difference between NDVI and meteorological conditions was also used to monitor the growth and correlation analysis of walnuts from April to September 2021. To improve the classification accuracy of the extracted walnut plantation area, Sentinel-2 image data were selected, and features were trained using the random forest algorithm, and by combining topographic features, texture features, NDVI, and EVI. The results show that, compared with Statistical Yearbook data, the average error of the extracted walnut planted area is less than 10%, the overall classification accuracy is 92.828%, the average kappa coefficient is 90.344%, and the average walnut classification accuracy is 94.4%. The accuracy of the data was significantly improved by adding vegetation indices EVI and NDVI compared with the single vegetation index. An analysis of the results from monitoring comparative growth shows that the growth of walnuts in Ganquan was better during the hardcore and oil transformation stages compared with 2020, and in the fruit development stage, the growth was the same as in 2020, and overall, the growth of walnuts in 2021 was better than in previous years.

• An extreme flash flood removed trees estimated to be 200 years old. • Vegetation removal was detected using satellite images in a hyper-arid wadi. • The restricted spatial extent of the damage was demonstrated by NDVI analysis. • Over 50 % of the vegetation patches were removed; loss increased downstream. • Correlation of accumulated drainage area and tree cover loss is strong and positive. On April 10, 2023, exceptional weather conditions over southern Israel produced an extreme rainstorm. The peak precipitation, with a maximum rainfall depth of 62 mm and rain intensities exceeding 50 mm h−1, fell over the hyper-arid southern Negev and Arava Valley and generated a mega flash flood in Wadi Zihor. This ephemeral stream channel supported a comparatively dense cover of vegetation, predominated by Acacia raddiana , Acacia pachyceras , Tamarix nilotica , and Tamarix aphylla. To determine the properties of the flash flood, we used post-flood ground-based measurements and calculations of shear stress and peak discharge along the wadi. Also, we used pre- and post-flood satellite images to assess the flood's impact on woody vegetation cover along the ∼17 km-long wadi bed. Calculations based on data collected by the Israel Water Authority implied a peak discharge of 585 m3/s at the wadi outlet, indicating a low-frequency flash flood of less than once in 150 years. The pre-flood's 12.7 % vegetation cover throughout the wadi bed decreased to 7.5 % after the flood, corresponding to a 41 % net loss. To detect differences in tree removal extent along the wadi, we divided its entire length into 24 equal segments, and separately assessed the vegetation cover before and after the flood. Vegetation cover loss was 10.8 % in the wadi's uppermost segment, whereas its lowermost segment lost 51.6 %. Overall, a significant and strongly positive (r = 0.88) correlation was recorded between the accumulated watershed area (downstream) and the extent of tree cover loss. Among other factors, this effect seems to be predominantly determined by the increasing shear stress and peak discharge downstream. Forecasted climatic change scenarios, with increasing magnitude and frequency of extreme rainstorms and floods across the world's drylands, highlight the need for more data collecting and analyzing of those events impact as described in this study. [ABSTRACT FROM AUTHOR]

• Long-term trends of vegetation greenness was analyzed in 889 global cities. • Vegetation greenness increased in European cities but decreased in African cities. • Vegetation greenness decreased in suburban and urban areas, but increased in exurban, and urban core areas. Long-term trends in urban vegetation greenness were systematically analyzed. However, long-term trends in vegetation greenness under different urban development intensities (UDIs) in global cities remain poorly understood. In this study, exurban, suburban, urban and urban core areas were defined according to their UDI in 2001. Subsequently, changes in the UDI and long-term trends in vegetation greenness were analyzed in these four areas in 889 global cities from 2001 to 2019. African and Asian cities have witnessed rapid urbanization, whereas European cities have experienced slow urbanization. The UDI increased significantly in suburban, exurban and urban areas, whereas it remained stable in urban core areas. The enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) increased in all four areas in European cities but decreased in all four areas of the African cities. The EVI and NDVI decreased in suburban and urban areas due to urbanization, but increased in exurban, and urban core areas. Changes in UDI were an important driver of EVI and NDVI trends in exurban and suburban areas. The results of this study enhance our understanding of long-term trends in vegetation greenness in urban and surrounding areas. [ABSTRACT FROM AUTHOR]

Several earth science investigations depend heavily on knowing the surface energy budget and determining surface temperature. The primary factor affecting the energy balance in the surface physical processes of the planet is the land surface temperature (LST). Even in the case of small-scale green areas like local parks, plants have a significant impact on the climate of cities. The goal of this study was to estimate the construction-related impacts of the Karizland green town (green belt) on the LST of its surroundings over time, for the years 2013 (before construction began), 2015, 2020 and 2022 (after construction was completed). LST values and hot spot analyses were employed for thermal condition evaluation purposes on Landsat-8 satellite images, and normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) indices were used for examining the vegetation change. The results showed that after the establishment of the green town, the mean NDVI and FVC grew by 275% and 950%, respectively, compared to the initial period, which resulted in the addition of approximately 208.35 ha of green space to the study area. In this regard, the results showed that after these changes, compared to the first period, the mean LST decreased by 8%. In addition, the area of the class of hotspot analysis with less than 90% confidence increased by 9%. The results illustrated that almost 20% of the data in the LST range was below 55 °C in 2013, near 57 °C in 2015, and around 51 °C in 2020 and 2022. The results also showed a negative relationship between the distance from the established settlement and the values of NDVI and FVC in 2022 of 91% and 89% and in 2020 of 67% and 69%, respectively. Every year, LST has had a significant negative relationship with the NDVI and FVC of that year and a positive relationship with the LST of the following years, such that the correlation decreases in later years. In order to control LST and the temperature surrounding cities, this research strongly advises managers to develop these green towns.

Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (<1.1 nW cm−2sr−1), higher elevation (>43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes. [ABSTRACT FROM AUTHOR]

Vegetation in the terrestrial ecosystem, sensitive to climate change and human activities, exerts a crucial influence on the carbon cycles in land, ocean, and atmosphere. Discrimination between climate and human-induced vegetation dynamics is advocated but still limited, especially in coastal China, which is characterized by a developed economy, a large population, and high food production, but also by unprecedented climate change and warming. Taking coastal China as the research area, our study used the normalized difference vegetation index (NDVI) in growing seasons, as well as precipitation, temperature, and sunlight hours datasets, adopted residual trend analysis at pixel and regional scales in coastal China from 2000–2019 and aims to (1) delineate the patterns and processes of vegetation changes, and (2) separate the relative contributions of climate and human activities by adopting residual trend analysis. The results indicated that (1) coastal China experienced the most vegetation greening (83.04% of the whole region) and partial degradation (16.86% of the whole region) with significant spatial heterogeneity; (2) compared with climate change, human activities have a greater positive impact on NDVI, and the regions were mainly located in the north of the North China Plain and the south of southern China; (3) the relative contribution rates of climate change and human activities were detected to be 0–60% and 60–100%, respectively; (4) in the northern coastal areas, the improvement of cultivated land management greatly promoted the greening of vegetation and thus the increase of grain yield, while in southern coastal areas, afforestation and the restoration of degraded forest were responsible for vegetation restoration; and (5) similar results obtained by partial correlation between nighttime lights and NDVI indicated the reliability of the residual trend analysis. The linear relationships of precipitation, temperature, and radiation on NDVI may limit the accurate estimation of climate drivers on vegetation, and further ecosystem process-modeling approaches can be used to estimate the relative contribution of climate change and human activities. The findings in our research emphasized that the attribution for vegetation dynamics with heterogeneity can provide evidence for the designation of rational ecological conservation policies. [ABSTRACT FROM AUTHOR]

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability. [ABSTRACT FROM AUTHOR]

Background: Green spaces reduce the risk of multiple adverse health outcomes by encouraging physical activity. This study examined correlations between urban green space and residents' health outcomes in hilly neighborhoods: if they are mediated by social cohesion, visual aesthetics, and safety. Methods: We used multiple green space indicators, including normalized difference vegetation index (NDVI) extracted from satellite imagery, green view index (GVI) obtained from street view data using deep learning methods, park availability, and perceived level of greenery. Hilly terrain was assessed by the standard deviation of the elevation to represent variations in slope. Resident health outcomes were quantified by their psychological and physiological health as well as physical activity. Communities were grouped by quartiles of slopes. Then a mediation model was applied, controlling for socio-demographic factors. Results: Residents who perceived higher quality greenery experienced stronger social cohesion, spent more time on physical activity and had better mental health outcomes. The objective greenery indicators were not always associated with physical activity and might have a negative influence with certain terrain. Conclusions: Perceived green space offers an alternative explanation of the effects on physical activity and mental health in hilly neighborhoods. In some circumstances, geographical environment features should be accounted for to determine the association of green space and resident health outcomes. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) contains important data for providing vegetation-cover information and supporting environmental analyses. However, understanding long-term vegetation cover dynamics remains challenging due to data outliers that are found in cloudy regions. In this article, we propose a sliding-window-based tensor stream analysis algorithm (SWTSA) for reconstructing outliers in NDVI from multitemporal optical remote-sensing images. First, we constructed a tensor stream of NDVI that was calculated from clear-sky optical remote-sensing images corresponding to seasons on the basis of the acquired date. Second, we conducted tensor decomposition and reconstruction by SWTSA. Landsat series remote-sensing images were used in experiments to demonstrate the applicability of the SWTSA. Experiments were carried out successfully on the basis of data from the estuary area of Salween River in Southeast Asia. Compared with random forest regression (RFR), SWTSA has higher accuracy and better reconstruction capabilities. Results show that SWTSA is reliable and suitable for reconstructing outliers of NDVI from multitemporal optical remote-sensing images. [ABSTRACT FROM AUTHOR]

The present study assesses the seasonal variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) on different types of land use/land cover (LULC) in Raipur City of India using 119 Landsat images of pre-monsoon, monsoon, post-monsoon and winter seasons from 1988 to 2019. The results show that the highest LST is found in the bare lands and built-up areas, whereas the lowest LST is observed in the green vegetation. The LST-NDVI correlation is strong negative in the monsoon (−0.51) and post-monsoon (−0.50) seasons, moderate negative (−0.46) in the pre-monsoon season and weak negative (−0.24) in the winter season. The different LULC types largely influence the nature and strength of the LST-NDVI correlation. The correlation is strong negative (−0.60) on green vegetation, moderate negative (−0.35) on the built-up area and bare land and nonlinear on the water bodies. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is the most effective means of monitoring the vegetation dynamic over large, scale. With the development of remote sensing technology in China, domestic Fengyun satellites has been gradually applied to the study of vegetation change. However, the performance of Fengyun satellites in depicting the vegetation dynamic is still unclear. The spatial and temporal consistency of normalized difference vegetation index (NDVI) products retrieved from FY-3C VIRR (FY-3C) and Terra MODIS (MO, DIS) was compared over Hunan and Jiangxi provinces. The results show as follows. High consistence in spatial distribution and temporal dynamic (i. e., annual, seasonal, and monthly) are characterized by FY-3C and MODIS NDVI products. Regional averaged annual mean NDVI of Jiangxi province is higher than that of Hunan province. The annual mean NDVI of 2018 is higher than that of 2017 and 2019 in both FY-3C and MODIS NDVI products. Three years regional averaged annual mean NDVI in Hunan and Jiangxi Provinces based on MODIS (0.58) is 60% higher than of FY-3C (0.36). FY-3C NDVI is lower than that of MODIS NDVI in all seasons with the largest difference occurred in winter. MODIS NDVI (0.42) is twice higher than FY-3C NDVI (0.21) in winter and this magnitude is about 50% in the rest seasons. The monthly variation of FY-3C NDVI and MODIS NDVI is also consistent. The difference is stable and small in annual maximum NDVI but larger in annual minimum NDVI (the annual minimum NDVI based on MODIS is about 100% higher than that of FY-3C). Scientific reference for the ability of domestic Fengyun satellites to detect vegetation dynamic characteristics in typical areas is unraveled [ABSTRACT FROM AUTHOR]

As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area's vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection. [ABSTRACT FROM AUTHOR]

Vegetation dynamics are sensitive to climatic warming and are affected by individual or combined climatic factors at different temporal scales with different intensities. Previous studies have unraveled the relationships between vegetation dynamics and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamics are dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore scale-specific univariate and multivariate controls on vegetation over the period 1982–2015 using bivariate wavelet coherence (BWC), multivariate wavelet coherence (MWC), and multidimensional empirical mode decomposition (MEMD). The results indicated that significant vegetation dynamics were located mainly at scales of 1, 0.5, and 0.3 years. Vegetation variations were divided into seasonal (≤ 1 year), short-term (1–4 years), medium-term (4–8 years), and long-term (> 8 years) scales. The combined explanatory powers of seven climatic factors on the vegetation were greater at the short-term and long-term scales, whereas individual climatic factors, such as precipitation or temperature, might affect vegetation dynamics in some climatic regions at the seasonal and medium-term scales. The combined effect of climatic factors in the grassland of the Tibetan Plateau (TP) and the temperate grassland of Inner Mongolia (TGIM) were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate on crop dynamics in both temperate humid and subhumid Northeast China and the TP were around 47%, whereas the controls of climate on crops in both the TGIM and the temperate and warm-temperate desert of Northwest China were around 39%. Cropland farming practices could alleviate the spatial variation of the relationships between climate and vegetation while enhancing the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly at the medium-term scale. Collectively, the results might provide an alternative perspective for understanding vegetation evolution in response to climatic changes in China. [ABSTRACT FROM AUTHOR]

The quantitative and qualitative assessment of post-disaster vegetation damage and recovery in the core area of the Wenchuan earthquake is of great significance for the restoration and reconstruction of natural ecosystems and the construction of human settlements in China. This study used time series analysis to determine the time of MODIS data and used the data to study the vegetation damage and restoration in the core area of the Wenchuan earthquake. The determined MODIS images were used to quantitatively analyze a series of vegetation damage changes and the vegetation recovery rate in the core area of the Wenchuan earthquake before and after the earthquake. By applying the topographic factors, we analyzed the spatial and temporal characteristics of the dynamic changes of vegetation damage and the recovery rate in the disaster area. The results show that the study area's vegetation damage was correlated to topographic factors and distance from towns. Besides, the overall vegetation restoration after the disaster was relatively optimistic. In some areas, the vegetation restoration level even exceeded the vegetation coverage level before the disaster. The recovery study of MODIS-NDVI showed a specific lag delay effect on the image of vegetation cover. The vegetation damage and the recovery rate of vegetation cover were significantly correlated with the distance from towns and the topographic factor. Overall, the results contribute to the theoretical support for the damage and recovery of vegetation in the core area affected by the earthquake. [ABSTRACT FROM AUTHOR]

Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansion in activities such as urbanization, socio-economic activities, and environmental changes. Hence, detecting the effects of spatio-temporal variability on rainfall-runoff transformation is crucial. In this study, the conventional Natural Resources Conservation Service Curve Number (NRCS-CN) method was modified using slope factor as the conventional NRCS-CN method does not consider slope. The maximum change in spatial extent was observed in the case of bare soil followed by built-up land, agricultural land, dense forest, open forest, and water body over the time interval of 13 years (1999 to 2011). The area under low vegetative coverage (LVC) was observed to be increased from 697.68 to 999.30 km2 over 13 years. The areas under medium vegetative coverage (MVC) and high vegetative coverage (HVC) were observed to be decreased from 1081.93 to 914.54 km2 and 137.56 to 3.33 km2, respectively over 13 years. These changes in the spatial extent of LVC, MVC, and HVC were found to be responsible to increase the curve number (CN) of the study area. Over the time interval of 13 years, the slope-corrected weighted curve number (CNwα) values under dry (AMC-I), normal (AMC-II), and wet (AMC-III) conditions were observed to be increased from 66.55 to 69.54, 80.65 to 82.34, and 90.23 to 91.05, respectively. It was observed that the average runoff coefficient has been increased from 0.41 to 0.43 over 13-year interval which is responsible to increase the runoff. The coefficient of determination (R2) between estimated and observed runoff was found to be about 0.77 which indicated the good predictive performance of the modified NRCS-CN method. This study is helpful for detecting the temporal land use/land cover change and its effects on runoff generation. This study provides importance of construction of rainwater harvesting structures for the purpose of groundwater recharge. [ABSTRACT FROM AUTHOR]

Economic development is a basic need for the growth of the region and it stimulates the rapid transformation of land use and land cover (LULC) units. Urbanization and industrialization are one of the major factors to increase temperature. Asansol sub-division is one of the important industrial and urbanized regions of eastern India. In this study, two different years viz. 1993 and 2018 have taken for the preparation of LULC and land surface temperature map. The kappa coefficient has been implied in this investigation to assess the accuracy of LULC maps. Temperature maps show that summer and winter surface temperature increases at the rate of 0.15 °C and 0.19 °C per year respectively. The result also reveals that temperature mainly increases due to the presence of urban, industrial and coal mine areas. The changing land use and land cover patterns show that the coal mine areas have been increased by 15% and urban areas also increased by 60%. Some correlations have been prepared to show the relationship between Land Surface Temperature (LST) and other spatial indices like NDBI, NDVI, and NDWI, where negative correlation prevails between LST and NDVI also with NDWI, but positive relation exists between LST and NDBI. Lastly, simulation of temperature for the year 2041 has been prepared, which shows that in the upcoming years’ temperature may be increased up to 0.21 °C/year.

Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.

Plant and scene three-dimensional (3-D) modeling, combined with radiative transfer (RT) modeling, are of great importance for mastering canopy reflectance characteristics and further developing target recognition and parameter retrieval in remote sensing images. However, 3-D RT simulation of large, complex landscapes is generally too demanding in terms of computing time and memory space. Simplifying plant models can significantly reduce the computational load, but with the accuracy reducing in radiation simulations. It is necessary to balance the complexity of plant models and the efficiency of 3-D RT simulation while maintaining high simulation accuracy. We investigated this issue for the vegetation of the Yangtze River estuary in eastern China. First, we used a series of created 3-D models of two species (Phragmites australis and Spartina alterniflora) to simulate canopy reflectance with the discrete anisotropic radiative transfer (DART) model. Then, we investigated how the simulated plant model complexity, plant density, and scene unit scale influence the accuracy and computation time of canopy normalized difference vegetation index (NDVI) simulation. The comparison of different parameterization simulations leads to three major conclusions. It is not necessary to simulate the actual vegetation density exactly, given the simplifications and approximations inherent in simulations. A specific 3-D model per species is needed for simulation since plants’ morphological structures different. Simplifying plant 3-D models and using a coarser DART scale of analysis shortens simulation time, but decreases the accuracy of the simulated canopy NDVI to varying degrees. Based on these results, we propose a universal optimization scheme that balances the accuracy and computation time of canopy NDVI simulation.

The benefits of urban blue-green infrastructures are well known: they intercept airborne three-atom particles, thus reducing pollution levels; and they provide shade and cooling by means of evapotranspiration. The focus of this paper is to demonstrate methods such as remote sensing and multi-spectral analysis, which can be a very useful addition to the quantification of blue-green infrastructures for cooling and shading, especially in the highly complex geometry of city blocks. The basic aim of this research is to attempt to reduce urban heat islands and in this way to indirectly increase the comfort of living. A cause/ effect relationship between the envelope of built up structures and the solar radiation distribution on the environment was established by means of multi-spectral analysis, and an estimation was made concerning the lack of vegetation on a specific parcel/block (an important tool for urban planners). This state-of-the-art methodology was applied to the optimized prediction concept of vegetation resources. Now it is possible to create a model that will incorporate this newly-added urban vegetation into urban plans, depending on the evaporation potential that will affect the microclimate of the urban area. Such natural cooling can be measured and adapted and hence aimed at a potential decrease in temperature in areas with UHI emissions. As a case study, part of a seacoast urban block (Abu Dhabi UE,) was analysed with and without a street treeline and green façades and roofs. It was concluded that green infrastructure reduced the land surface temperature by up to 4.5˚C.

This study analyzes the long-term monthly variation of land surface temperature (LST) and its relationship with normalized difference spectral indices in the Raipur City of India using one hundred and 23 Landsat images from 1988 to 2020. In specific, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and normalized difference bareness index (NDBaI) were used to show the relationship between LST and land surface materials. In terms of LST, the warmest month is April (38.49°C) and the coldest month is January (23.04°C). The standard deviation in LST is noticed as 1.1022°C throughout the period. The growth pattern of LST is increasing in the earlier stage while it is steady and decreasing in the later stage. The linear regression method is used to correlate LST with the spectral indices. The mean regression coefficients for LST-NDVI is −0.42, LST-NDBI are 0.68, LST-NDWI is 0.27, and LST-NDBaI is 0.32. It indicates that the high ratio of green vegetation and water bodies resist the raise of LST, whereas the bare rock surface and built-up land accelerate the LST. The value of the spectral indices and LST varies with the change of month due to the physical change of the land surface materials. Hence, the study will be an effective one for the town and country planners for their future estimation of land conversion.

Observing vegetation normalized difference vegetation index (NDVI) changes, climate change characteristics, and their response relationship have a great significance to the ecosystem's regulation and improvement the human settlements. Based on GIMMS AVHRR NDVI and MODIS NDVI datasets as well as temperature, precipitation, and sunshine duration data, this study used unitary linear trend analysis, correlation analysis, RS, and GIS data to analyze the spatiotemporal variation characteristics of vegetation NDVI in the middle and upper reaches of the Yellow River between 1989 and 2018. It also analyzed the spatiotemporal response between vegetation NDVI and climate factors (temperature, precipitation, and sunshine duration). The results showed that the vegetation NDVI in the study area had an increasing trend over the past 30 years, growing by 31.28%, and the NDVI change in 81.83% of the pixels was positive, the highest being 0.025. The temperature in the middle and upper reaches of the Yellow River showed an obvious upward trend, showing an overall distribution pattern of low temperature in the southwest and high temperature in the southeast. The precipitation showed a gentle upward trend and a spatial distribution pattern of a gradual decrease from southeast to northwest. The sunshine duration showed an obvious decreasing trend and a spatial distribution pattern of gradually increasing from southeast to northwest. In the past 30 years, the annual mean NDVI in the study area had a positive correlation with temperature and precipitation and a negative correlation with sunshine duration.

This study aims at characterizing agricultural drought in Ethiopia and understanding the effects of drought on crop yield. Monthly, seasonal and annual Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) values were calculated using MODIS (MOD13Q1) from the year 2003 to 2017. The relationships between NDVI, VCI, and crop yield were examined to predict the possibility of drought impacts on crop productivity. We found that VCI and NDVI data provides consistent and spatially explicit information for operational drought monitoring in Ethiopia. Results also indicated that the most extreme agricultural drought in recent years occurred in 2003, 2004, 2008, 2009, and 2015. These findings also show that mild to severe droughts have a great chance of occurrence in Ethiopia. However, only severe drought has significant impacts on crops. The food crops yield data used in this study include cereals, legumes, and tubers. It was observed that cereals such as (Zea mays), teff (Eragrostis tef), haricot beans (Phaseolus vulgaris) are more sensitive to agricultural drought when compared to the tubers such as sweet potato (Ipomoea batatas) and taro (Colocasia esculenta). Thus, drought preparedness programs need to pay more attention to the cultivation of these crops under severe drought conditions. Highlights NDVI and VCI patterns easily discriminate cereals and legumes when compared to tuber crops. 45% and 43% yield variability of respectively teff and maize is explained by the NDVI patterns. The studied crops (Teff, Maize, Sweet potato and Taro) are less discriminable to seasonal VCI variation. Drought preparedness strategies should encourage farmers to cultivate tubers instead of cereals. The PMARE value for Taro and sweet potato exceeded the model acceptable range.

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability.

Across the United States, cities are creating sustainability and climate action plans (CAPs) that call to increase local vegetation. These greening initiatives have the potential to not only benefit the environment but also human health. In epidemiologic literature, greenness has a protective effect on mortality through various direct and indirect pathways. We aimed to assess how an increase in greenness could decrease mortality in the largest urban areas in the United States. We conducted a nationwide quantitative health impact assessment to estimate the predicted reduction in mortality associated with an increase in greenness across two decades (2000, 2010, and 2019). Using a recently published exposure-response function, Landsat 30 m 16-day satellite imagery from April to September, and publicly available county-level mortality data from the CDC, we calculated the age-adjusted reduction in all-cause mortality for those 65 years and older within 35 of the most populated metropolitan areas. We estimated that between 34,000 and 38,000 all-cause deaths could have been reduced in 2000, 2010, and 2019 with a local increase in green vegetation by 0.1 unit across the most populated metropolitan areas. We found that overall greenness increased across time with a 2.86% increase from 2000 to 2010 to 11.11% from 2010 to 2019. These results can be used to support CAPs by providing a quantitative assessment to the impact local greening initiatives can have on mortality. Urban planners and local governments can use these findings to calculate the co-benefits of local CAPs through a public health lens and support policy development.

High spatial and temporal resolution normalized difference vegetation index (NDVI) time series are indispensable for monitoring land surfaces dynamics in spatiotemporally heterogeneous areas. Spatiotemporal fusion (STF) is one of the most common methods used for producing such data. These methods require the use of one or two pairs of fine images (with fine spatial but rough temporal resolution, such as Landsat images) and coarse images [with fine temporal but rough spatial resolution, such as Moderate Resolution Imaging Spectroradiometer (MODIS)]. A coarse image at the prediction date is also required to predict the corresponding missing fine image in the time series. Recently, the proposed deep learning (DL)-based STF methods have achieved promising fusion performance but are challenged in areas with frequent cloud contamination and landcover change prediction, while they also suffer from unstable fusion performance. Moreover, current STF methods lack a quality assessment process for the fusion results. To address these limitations, in this article, we propose a multitask DL framework for STF of NDVI time series, which integrates two types of DL-based STF methods. Four experiments in two study sites were conducted to test the effectiveness of the proposed method, and the results indicate that it achieves accurate and stable fusion capable of predicting landcover changes even when image pairs obtained at long intervals are used. In addition, a fusion uncertainty estimation method is proposed, which has the potential to be used as a quality assessment metric. [ABSTRACT FROM AUTHOR]

• The temporal LST has increased 2.5 times higher in KMA than the surrounding area. • Premonsoon was the warmest followed by monsoon, postmonsoon and winter periods. • The urban heat island growth and urban growths were 44.65 sq. km and 28.74 sq. km. • Highest urban heat island extent index noted in WSW direction while least in SSE. • City's center and 20 km ring buffer was highly unhealthy. This study examines the Urban Heat Island (UHI) phenomena in the Kolkata Metropolitan Area (KMA) region and its surrounding rural areas based on MODIS satellite data of the last two decades (2001-19). The temporal land surface temperature (LST) has increased in both the KMA and its neighborhood, with this rate of increase being 2.5 times higher in the KMA as compared to its surroundings. Seasonally, the pre-monsoon period was the warmest, with an average LST of 32.0 °C, followed by the monsoon (30.5 °C), post-monsoon (28.6 °C) and winter (25.0 °C) periods, respectively. Especially during the pre-monsoon, the ambient high air and LSTs extenuate the UHI scenario, causing much discomfort to residents. The human health comfort map identified that the city's center and a 20 km buffer around it contained stressful health conditions, with about 90% of the heat island situated within this zone. The total amount of UHI growth was 44.65 sq.km, with an urban expansion of 28.74 sq.km and the highest UHI extent index (UHIEI) was identified in the WSW direction, with it being least towards SSE. High-rise buildings and population density has increased rapidly with concomitant decline in greenery during the study period, and therefore the Normalized Difference Vegetation Index (NDVI) values are lower within the KMA but more enhanced in the surrounding rural areas. Spaces that enable quicker lowering of the LST are also more in areal extent in these surrounding rural areas. Practices of water roof and green roof wherever possible and planting of trees in available spaces within the city is urgently required to mitigate the UHI effect. This time series based study can guide future planning efforts towards decreasing the adverse effects of urbanization induced by heat islands and make possible an eco-friendly and sustainable urban development in this region. [ABSTRACT FROM AUTHOR]

The response of long-term vegetation changes and climate change has been a hot topic in recent research. Previously, a Landsat-based fusion model was developed and used to produce a dataset of normalized vegetation index (NDVI) for the Three-River Headwater region on the Qinghai-Tibet Plateau with a spatial resolution of 30 m and the time spanning the nearly 30 years from 1990 to 2018. In this study, the NDVI was applied to an analysis of the spatial and temporal changes in the alpine grassland and the impacts from climate change using the Theil-Sen Median method and linear regression. The results showed that: (1) The regional mean NDVI was 0.39 and showed a spatial pattern of decreasing from the southeast to the northwest in the recent three decades. Among the three parks, the Lancang River Park had the highest NDVI (0.43), followed by the Yellow River Park (0.38) and Yangtze River Park (0.23). (2) An upward trending was found in the NDVI time series at a rate of 0.0031 yr–1 (R2 =0.62, P < 0.01) over the whole period of 1990–2018. The increasing rate (0.00649 yr–1, R2 =0.71, P < 0.01) in the latter period of 2005–2018 was nearly 2.3 times of that (0.00284 yr–1, R2 =0.31, P < 0.01) in the previous period of 1990–2005. In the latest periods, the three parks experienced rates that were 2.3 to 63 times the corresponding values in the early period. (3) The NDVI is correlated more positively with temperature than precipitation. The impacts of climate change decreased along with the coverage fraction from the higher, median and then lower levels. The climate change can explain 34% of the variability in the NDVI time series of the areas with a higher fraction of grassland coverage, while it was 31% for the median fraction and 20% for the lower fraction. This study is the first to use the 30 m NDVI dataset spanning nearly 30 years to analyze the spatial and temporal variability and climate impacts in the alpine grasslands of the Three-River Headwater region of the Qinghai-Tibet Plateau. The results provide a basis for assessments on the ecological management effects and ecological quality based on long-term baseline data with a higher spatial resolution. [ABSTRACT FROM AUTHOR]

Haemosporidians are among the most common parasites of birds and often negatively impact host fitness. A multitude of biotic and abiotic factors influence these associations, but the magnitude of these factors can differ by spatial scales (i.e., local, regional and global). Consequently, to better understand global and regional drivers of avian‐haemosporidian associations, it is key to investigate these associations at smaller (local) spatial scales. Thus, here, we explore the effect of abiotic variables (e.g., temperature, forest structure, and anthropogenic disturbances) on haemosporidian prevalence and host–parasite networks on a horizontal spatial scale, comparing four fragmented forests and five localities within a continuous forest in Papua New Guinea. Additionally, we investigate if prevalence and host–parasite networks differ between the canopy and the understory (vertical stratification) in one forest patch. We found that the majority of Haemosporidian infections were caused by the genus Haemoproteus and that avian‐haemosporidian networks were more specialized in continuous forests. At the community level, only forest greenness was negatively associated with Haemoproteus infections, while the effects of abiotic variables on parasite prevalence differed between bird species. Haemoproteus prevalence levels were significantly higher in the canopy, and an opposite trend was observed for Plasmodium. This implies that birds experience distinct parasite pressures depending on the stratum they inhabit, likely driven by vector community differences. These three‐dimensional spatial analyses of avian‐haemosporidians at horizontal and vertical scales suggest that the effect of abiotic variables on haemosporidian infections are species specific, so that factors influencing community‐level infections are primarily driven by host community composition. [ABSTRACT FROM AUTHOR]

This research presents a novel approach of using open data multispectral imagery as indicators Normalized Difference Vegetation Index, Land Surface Temperature (LST), Green-Shortwave Infrared index and Soil Adjusted Vegetation Index to monitor and assess the impact of dumpsites on the environment in Yenagoa Metropolis, Bayelsa State Nigeria. The outcome uncovers that the LST at the dumpsites was higher than the immediate environment, and the SAVI, NDVI, and G-SWIR values were lower than the immediate surrounding. The high estimations of LST at the dumpsites portray the impact of gases released as a result of decomposition activities, while low values of SAVI and NDVI show vegetation reaction to soil and groundwater pollution due to leachate invasion, lastly G-SWIR indicates discriminations of moisture content of soil and vegetation in leachate infiltration. The outcome shows correlation is significant with the NDVI and G-SWIR value at each of the dumpsites which indicate indirect relationship R2. NDVI versus G-SWIR = 0.6271, NDVI versus SAVI = 0.9084, these values indicate highly significant correlated in dumpsite reflectance, while SAVI versus G-SWIR = 0.73 is strongly correlated, indicating increase in waste quantity expected to result in high decomposition, gas emissions and contamination in the environment. The selected heavy metals analyzed for river water are lead (Pb), Zinc (Zn), iron (Fe), cadmium (Cd), nickel (Ni) and chromium (Cr6+). Indicated that all the heavy metals in river water are above the detectable limit by WHO 2011 which imply Epie creek is highly polluted and is due to illegal dumpsites present. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) change has become a major concern for biodiversity, ecosystem alteration, and modifying the climatic pattern especially land surface temperature (LST). The present study assessed past and predicted future LULC and LST change in the Swabi District of Pakistan. LULC maps were generated from satellite data for years 1987, 2002, and 2017 using supervised classification. Mean LST and its areal change were estimated for different LULC classes from thermal bands of satellite images. LULC and LST were projected for the year 2047 using the integrated weighted evidence-cellular automata (WE-CA) model and a regression equation developed in this study, respectively. LULC change revealed an increase of > 5% in the built-up while a decrease in the agricultural area by ~ 9%. There was an increase of ~ 63% area in the LST class ≥ 27 °C which may create urban heat island (UHI). Simulation results indicated that the built-up area will further be increased by ~ 3% until 2047. Area associated with LST class > 30 °C indicated a further increase of ~ 38% till 2047 with reference to year 2017. Findings of this study suggested proper utilization of LULC in order to mitigate the creation of UHIs associated with urbanization and built-up areas. [ABSTRACT FROM AUTHOR]

Light pollution is one of the forms of pollution triggered by man's desire to lengthen the day. Its negative effects on the environment were discovered decades ago. However, the situation in Slovenia, despite the Regulation on limit values of light pollution, is still very alarming. The main source is public lighting. Often various buildings are too much or incorrectly illuminated. The consequences of light pollution are astronomical, biological, environmental, and economic. They can influence human health as well. Based on remote sensing data, field observations and spatial analysis, it was proved that light pollution in the area of the Voglajna valley and the upper Posotelje affects the leaf-development phenophase (leafing), both at the level of the habitat formed by small wooden landscape elements, and at the species level of the common birch (Betula pendula). [ABSTRACT FROM AUTHOR]

High spatial resolution precipitation data are important for hydrological modeling and meteorological applications, especially at regional scales. Statistical downscaling methods for satellite precipitation products using the normalized difference vegetation index (NDVI) have been carried out in many regions to provide high spatial resolution precipitation. These methods generally use NDVI and precipitation at the same time, assuming that there is a real-time response of vegetation to precipitation. However, this assumption does not hold in many scenarios. It is known that different vegetation types exhibit different response times to precipitation, i.e., there is a possible lag in the response of vegetation to precipitation depending on the vegetation/landcover type. Therefore, it is not appropriate to estimate precipitation using NDVI collected at the same time. To better represent the relationship between precipitation and vegetation, this article develops a new vegetation index based on adaptive lag phase (VIAL) estimated from a new growth rate that is adaptive to landcover type. Based on VIAL, a new local precipitation downscaling method called LPVIAL is proposed, which essentially considers the nonstationary relationship between precipitation and VIAL. The performance of LPVIAL is assessed by downscaling Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) from 0.1° to 1-km spatial resolution over the Pearl River Basin in Southern China from 2010 to 2017 at 16-day temporal resolution, and the downscaled products are validated against ground observations. Results indicate that the high-resolution precipitation data obtained from the new downscaling approach perform well, and the accuracy is higher than traditional approaches. With the enhancement of spatial resolution, LPVIAL downscaled products show more detailed spatial information of precipitation with smooth distribution, and the downscaled products have slightly higher accuracy compared with IMERG. It is, therefore, suggested that the adaptive lag phase should be considered in the satellite precipitation product downscaling process. [ABSTRACT FROM AUTHOR]

In this study, we quantified the vegetation changes in the Haihe river basin (HRB) and analyzed their drivers during 1982–2012 by using satellite normalized difference vegetation index (NDVI) data, observation-based climate data, satellite vegetation continuous fields (VCF) data, and soil moisture (SM) data. The results showed that the increases in NDVI was the largest in autumn (2.12 × 10−3), followed by growing-season (1.97 × 10−3), spring (1.82 × 10−3), annual (1.35 × 10−3), summer (1.15 × 10−3), and winter (0.32 × 10−3). The VCF, mean annual temperature (MAT), SM, and mean annual precipitation (MAP) determined the NDVI changes in 44.43%, 27.69%, 5.33%, and 2.01% of the HRB, respectively. In the western and southern HRB, MAT and VCF were the dominant factors, besides, SM was one of the important factors in the western HRB. Therefore, the study identified that SM, MAT, and human activities (Changes in VCF) were the main factors that determined the changes in vegetation over HRB. [ABSTRACT FROM AUTHOR]