Duration and variability of spring green-up mediate population consequences of climate change.

Single phenological measures, like the average rate of phenological advancement, may be insufficient to explain how climate change is driving trends in animal populations. Here, we develop a multifactorial concept of spring phenology-including the onset of spring, spring duration, interannual variability, and their temporal changes-as a driver for population dynamics of migratory terrestrial species in seasonal environments. Using this conceptual model, we found that effects of advancing spring phenology on animal populations may be buffered or amplified depending on the duration and interannual variability of spring green-up, and those effects are modified by evolutionary and plastic adaptations of species. Furthermore, we compared our modelling results with empirical data on normalized difference vegetation index-based spring green-up phenology and population trends of 106 European landbird finding similar associations. We conclude how phenological changes are expected to affect migratory bird populations across Europe and identify regions that are particularly prone to suffer population declines.

Why do avian responses to change in Arctic green-up vary?

Global climate change has altered the timing of seasonal events (i.e., phenology) for a diverse range of biota. Within and among species, however, the degree to which alterations in phenology match climate variability differ substantially. To better understand factors driving these differences, we evaluated variation in timing of nesting of eight Arctic-breeding shorebird species at 18 sites over a 23-year period. We used the Normalized Difference Vegetation Index as a proxy to determine the start of spring (SOS) growing season and quantified relationships between SOS and nest initiation dates as a measure of phenological responsiveness. Among species, we tested four life history traits (migration distance, seasonal timing of breeding, female body mass, expected female reproductive effort) as species-level predictors of responsiveness. For one species (Semipalmated Sandpiper), we also evaluated whether responsiveness varied across sites. Although no species in our study completely tracked annual variation in SOS, phenological responses were strongest for Western Sandpipers, Pectoral Sandpipers, and Red Phalaropes. Migration distance was the strongest additional predictor of responsiveness, with longer-distance migrant species generally tracking variation in SOS more closely than species that migrate shorter distances. Semipalmated Sandpipers are a widely distributed species, but adjustments in timing of nesting relative to variability in SOS did not vary across sites, suggesting that different breeding populations of this species were equally responsive to climate cues despite differing migration strategies. Our results unexpectedly show that long-distance migrants are more sensitive to local environmental conditions, which may help them to adapt to ongoing changes in climate.

Thirty-six years of butterfly monitoring, snow cover, and plant productivity reveal negative impacts of warmer winters and increased productivity on montane species.

Climate change is contributing to declines of insects through rising temperatures, altered precipitation patterns, and an increasing frequency of extreme events. The impacts of both gradual and sudden shifts in weather patterns are realized directly on insect physiology and indirectly through impacts on other trophic levels. Here, we investigated direct effects of seasonal weather on butterfly occurrences and indirect effects mediated by plant productivity using a temporally intensive butterfly monitoring dataset, in combination with high-resolution climate data and a remotely sensed indicator of plant primary productivity. Specifically, we used Bayesian hierarchical path analysis to quantify relationships between weather and weather-driven plant productivity on the occurrence of 94 butterfly species from three localities distributed across an elevational gradient. We found that snow pack exerted a strong direct positive effect on butterfly occurrence and that low snow pack was the primary driver of reductions during drought. Additionally, we found that plant primary productivity had a consistently negative effect on butterfly occurrence. These results highlight mechanisms of weather-driven declines in insect populations and the nuances of climate change effects involving snow melt, which have implications for ecological theories linking topographic complexity to ecological resilience in montane systems.

Pixel walking along the boreal forest-Arctic tundra ecotone: Large scale ground-truthing of satellite-derived greenness (NDVI).

In this Technical Advance, we describe a novel method to improve ecological interpretation of remotely sensed vegetation greenness measurements that involved sampling 24,395 Landsat pixels (30 m) across 639 km of Alaska's central Brooks Range. The method goes well beyond the spatial scale of traditional plot-based sampling and thereby more thoroughly relates ground-based observations to satellite measurements. Our example dataset illustrates that, along the boreal-Arctic boundary, vegetation with the greatest Landsat Normalized Difference Vegetation Index (NDVI) is taller than 1 m, woody, and deciduous; whereas vegetation with lower NDVI tends to be shorter, evergreen, or non-woody. The field methods and associated analyses advance efforts to inform satellite data with ground-based vegetation observations using field samples collected at spatial scales that closely match the resolution of remotely sensed imagery.

Long-term changes in the daytime growing season carbon dioxide exchange following increased temperature and snow cover in arctic tundra.

Increasing temperatures and winter precipitation can influence the carbon (C) exchange rates in arctic ecosystems. Feedbacks can be both positive and negative, but the net effects are unclear and expected to vary strongly across the Arctic. There is a lack of understanding of the combined effects of increased summer warming and winter precipitation on the C balance in these ecosystems. Here we assess the short-term (1-3 years) and long-term (5-8 years) effects of increased snow depth (snow fences) (on average + 70 cm) and warming (open top chambers; 1-3°C increase) and the combination in a factorial design on all key components of the daytime carbon dioxide (CO2 ) fluxes in a wide-spread heath tundra ecosystem in West Greenland. The warming treatment increased ecosystem respiration (ER) on a short- and long-term basis, while gross ecosystem photosynthesis (GEP) was only increased in the long term. Despite the difference in the timing of responses of ER and GEP to the warming treatment, the net ecosystem exchange (NEE) of CO2 was unaffected in the short term and in the long term. Although the structural equation model (SEM) indicates a direct relationship between seasonal accumulated snow depth and ER and GEP, there were no significant effects of the snow addition treatment on ER or GEP measured over the summer period. The combination of warming and snow addition turned the plots into net daytime CO2 sources during the growing season. Interestingly, despite no significant changes in air temperature during the snow-free time during the experiment, control plots as well as warming plots revealed significantly higher ER and GEP in the long term compared to the short term. This was in line with the satellite-derived time-integrated normalized difference vegetation index of the study area, suggesting that more factors than air temperature are drivers for changes in arctic tundra ecosystems.

Estimation of plant pollution removal capacity based on intensive air quality measurements.

This study investigated the role of present vegetation in improving air quality in Bucharest (Romania) by analyzing six years of air quality data (PM10 and NO2 ) from multiple monitoring stations. The target value for human health protection is regularly exceeded for PM10 and not for NO2 over time. Road traffic has substantially contributed (over 70%) to ambient PM10 and NO2 levels. The results showed high seasonal variations in pollutant concentrations, with a pronounced effect of vegetation in reducing PM10 and NO2 levels. Indeed, air quality improvements of 7% for PM10 and 25% for NO2 during the growing season were reported. By using Principal Component Analysis and pollution data subtraction methodology, we have disentangled the impact of vegetation on air pollution and observed distinct annual patterns, particularly higher differences in PM10 and NO2 concentrations during the warm season. Despite limitations such as a lack of full tree inventory for Bucharest and a limited number of monitoring stations, the study highlighted the efficiency of urban vegetation to mitigate air pollution.

Different greenness exposure in Europe and respiratory outcomes in youths. A systematic review and meta-analysis.

The existing evidence on the association between greenness and respiratory outcomes remains inconclusive. We aimed at systematically summarizing existing literature on greenness exposure and respiratory outcomes in European children and adolescents, with a preliminary attempt to qualify the distribution of dominant tree species across different geographical areas and bioclimatic regions. Overall, 4049 studies were firstly identified by searching PubMed/MEDLINE, EMBASE, Scopus, Web of Science, GreenFile and CAB direct, up to 29 August 2023. Eighteen primary studies were included in the systematic review and six were meta-analyzed. No overall significant association was observed between the Normalized Difference Vegetation Index, assessed within 500-m buffers (i.e. NDVI-500), and the odds of asthma for 0.3-increase in the exposure (OR: 0.97, 95% CI from 0.53 to 1.78). Similarly, an overall exposure to the NDVI-300 highest tertile, as compared to the lowest tertile, was not significantly associated with asthma (OR: 0.65, 95% CI from 0.22 to 1.91): heterogeneity among studies was significant (p = 0.021). We delineated some key elements that might have mostly contributed to the lack of scientific consensus on this topic, starting from the urgent need of harmonized approaches for the operational definition of greenness. Additionally, the complex interplay between greenness and respiratory health may vary across different geographical regions and climatic conditions. At last, the inconsistent findings may reflect the heterogeneity and complexity of this relationship, rather than a lack of scientific consensus itself. Future research should compare geographical areas with similar bioclimatic parameters and dominant or potentially present vegetation species, in order to achieve a higher inter-study comparability.

Exposure to residential greenness, perceived stress, and depressive symptoms in a North American preconception cohort.

BACKGROUND: Studies suggest that greater exposure to natural vegetation (i.e., greenness) is associated with better mental health. However, there is limited research on greenness and mental health in the preconception period, a critical window of exposure in the life course. We investigated the associations of residential greenness with perceived stress and depressive symptoms using cross-sectional data from a cohort of pregnancy planners. METHODS: From 2013 to 2019, we enrolled female-identified participants aged 21-45 years who were trying to conceive without the use of fertility treatment into a North American preconception cohort study (Pregnancy Study Online [PRESTO]). On the baseline questionnaire, participants completed the 10-item Perceived Stress Scale (PSS) and the Major Depression Inventory (MDI). Using geocoded addresses, we estimated residential greenness exposure via satellite imagery (Normalized Difference Vegetation Index [NDVI]) in a 100m buffer. We estimated mean differences and 95% confidence intervals for the association of greenness with perceived stress and depression scores using linear regression models, adjusting for individual and neighborhood sociodemographic characteristics. We also evaluated the extent to which associations were modified by urbanicity and neighborhood socioeconomic status (SES). RESULTS: Among 9718 participants, mean age was 29.9 years, 81.5% identified as non-Hispanic White, 25% had household incomes <$50,000, and mean neighborhood income was $61,932. In adjusted models, higher greenness was associated with lower stress and depression scores (mean difference per interquartile range in greenness: -0.20, 95% CI: -0.39, -0.01; and -0.19, 95% CI: -0.48, 0.10, respectively). The association was stronger among residents of lower SES neighborhoods in urban areas (PSS: -0.57, 95% CI: -1.00, -0.15; MDI: -0.72, 95% CI: -1.40, -0.04). CONCLUSIONS: Higher greenness exposure was associated with lower stress and depressive symptoms among pregnancy planners, particularly in lower-SES neighborhoods.

Sensing the dependable surficial signatures of temporal groundwater variations in arid coastal regions through geospatial techniques with respect to microclimate changes.

Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 µS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

Ecological niche modelling of Culicoides imicola and future range shifts under climate change scenarios in Italy.

Culicoides imicola is the main vector of viral diseases of livestock in Europe such as bluetongue (BT), African horse sickness and epizootic haemorrhagic disease. Climatic factors are the main drivers of C. imicola occurrence and its distribution might be subject to rapid shifts due to climate change. Entomological data, collected during BT surveillance, and climatic/environmental variables were used to analyse ecological niche and to model C. imicola distribution and possible future range shifts in Italy. An ensemble technique was used to weigh the performance of machine learning, linear and profile methods. Updated future climate projections from the latest phase of the Climate Model Intercomparison Project were used to generate future distributions for the next three 20-year periods, according to combinations of general circulation models and shared socioeconomic pathways and considering different climate change scenarios. Results indicated the minimum temperature of the coldest month (BIO 6) and precipitation of the driest-warmest months (BIO 14) as the main limiting climatic factors. Indeed, BIO 6 and BIO 14 reported the two highest values of variable importance, respectively, 9.16% (confidence interval [CI] = 7.99%-10.32%), and 2.01% (CI = 1.57%-2.44%). Under the worst-case scenario of climate change, C. imicola range is expected to expand northward and shift away from the coasts of central Italy, while in some areas of southern Italy, environmental suitability will decrease. Our results provide predictions of C. imicola distribution according to the most up-to-date future climate projections and should be of great use to surveillance management at regional and national scales.

Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow.

Riparian ecosystems fundamentally depend on groundwater, especially in dryland regions, yet their water requirements and sources are rarely considered in water resource management decisions. Until recently, technological limitations and data gaps have hindered assessment of groundwater influences on riparian ecosystem health at the spatial and temporal scales relevant to policy and management. Here, we analyze Sentinel-2-derived normalized difference vegetation index (NDVI; n = 5,335,472 observations), field-based groundwater elevation (n = 32,051 observations), and streamflow alteration data for riparian woodland communities (n = 22,153 polygons) over a 5-y period (2015 to 2020) across California. We find that riparian woodlands exhibit a stress response to deeper groundwater, as evidenced by concurrent declines in greenness represented by NDVI. Furthermore, we find greater seasonal coupling of canopy greenness to groundwater for vegetation along streams with natural flow regimes in comparison with anthropogenically altered streams, particularly in the most water-limited regions. These patterns suggest that many riparian woodlands in California are subsidized by water management practices. Riparian woodland communities rely on naturally variable groundwater and streamflow components to sustain key ecological processes, such as recruitment and succession. Altered flow regimes, which stabilize streamflow throughout the year and artificially enhance water supplies to riparian vegetation in the dry season, disrupt the seasonal cycles of abiotic drivers to which these Mediterranean forests are adapted. Consequently, our analysis suggests that many riparian ecosystems have become reliant on anthropogenically altered flow regimes, making them more vulnerable and less resilient to rapid hydrologic change, potentially leading to future riparian forest loss across increasingly stressed dryland regions.

Decadal-scale variability and warming affect spring timing and forest growth across the western Great Lakes region.

The Great Lakes region of North America has warmed by 1-2 °C on average since pre-industrial times, with the most pronounced changes observable during winter and spring. Interannual variability in temperatures remains high, however, due to the influence of ocean-atmosphere circulation patterns that modulate the warming trend across years. Variations in spring temperatures determine growing season length and plant phenology, with implications for whole ecosystem function. Studying how both internal climate variability and the "secular" warming trend interact to produce trends in temperature is necessary to estimate potential ecological responses to future warming scenarios. This study examines how external anthropogenic forcing and decadal-scale variability influence spring temperatures across the western Great Lakes region and estimates the sensitivity of regional forests to temperature using long-term growth records from tree-rings and satellite data. Using a modeling approach designed to test for regime shifts in dynamic time series, this work shows that mid-continent spring climatology was strongly influenced by the 1976/1977 phase change in North Pacific atmospheric circulation, and that regional forests show a strengthening response to spring temperatures during the last half-century.

Responses of stem growth and canopy greenness of temperate conifers to dry spells.

Dry spells strongly influence biomass production in forest ecosystems. Their effects may last several years following a drought event, prolonging growth reduction and therefore restricting carbon sequestration. Yet, our understanding of the impact of dry spells on the vitality of trees' above-ground biomass components (e.g., stems and leaves) at a landscape level remains limited. We analyzed the responses of Pinus sylvestris and Picea abies to the four most severe drought years in topographically complex sites. To represent stem growth and canopy greenness, we used chronologies of tree-ring width and time series of the Normalized Difference Vegetation Index (NDVI). We analyzed the responses of radial tree growth and NDVI to dry spells using superposed epoch analysis and further explored this relationship using mixed-effect models. Our results show a stronger and more persistent response of radial growth to dry spells and faster recovery of canopy greenness. Canopy greenness started to recover the year after the dry spell, whereas radial tree growth remained reduced for the two subsequent years and did not recover the pre-drought level until the fourth year after the event. Stem growth and canopy greenness were influenced by climatic conditions during and after drought events, while the effect of topography was marginal. The opposite responses of stem growth and canopy greenness following drought events suggest a different impact of dry spells on trees´ sink and source compartments. These results underscore the crucial importance of understanding the complexities of tree growth as a major sink of atmospheric carbon.

Effect of drought stress during seed development on charcoal rot and yield of soybean.

The primary controls for charcoal rot in soybean, caused by the fungal pathogen Macrophomina phaseolina, are to avoid drought stress and to plant a moderately resistant cultivar. The effects of irrigation and cultivar were determined in 2011 and 2013 at the Lon Mann Cotton Research Station, Marianna, AR. Four soybean cultivars (Hutcheson, Osage, Ozark, and R01581F), were planted in plots with or without added M. phaseolina inoculum and subjected to three furrow irrigation regimes: full season irrigation (Full), irrigation terminated at R5 (CutR5), and non-irrigated (NonIrr). Normalized difference vegetative index (NDVI) was measured at R3 and R6. At harvest, plants and yields were collected. Roots and stems were split and the extent of visible colonization by M. phaseolina microsclerotia was assessed in the roots with a 1-5 scale (RSS) and the percent plant height stem discoloration (PHSD) measured. Precipitation in September and October was 54 and 65% below the 30-year average in 2011 and 2013, respectively. The CutR5 irrigation treatment resulted in one less irrigation than Full each year, but CutR5 NDVI's at R6 and yields were significantly lower than those with Full and not significantly different than those of NonIrr. The CutR5 RSS ratings were greater than either Full or NonIrr. Plant colonization by M. phaseolina was negatively correlated to yield in 2011 but not in 2013. No premature plant death caused by charcoal rot was observed in either year. These results indicated that late season drought stress may be more important to charcoal rot development than drought stress throughout the season, but other factors are needed to trigger early plant death and subsequent yield losses observed in grower fields.

3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements.

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications.

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea.

As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.

The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery.

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits.

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012-2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha-1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (-25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

Spatiotemporal geographically weighted regression analysis for runoff variations in the Weihe River Basin.

In order to investigate the effects of vegetation changes on runoff and to obtain recommendations for improving runoff in the Weihe River Basin (. In this study, a spatiotemporal geographic autocorrelation weighted regression analysis (SGAWRA) approach was newly developed based on previous studies. This approach investigates spatial non-stationarity of the dynamic response from vegetation variations to climatic change and human activity. Implications of spatial non-stationarity related to runoff variability were also discussed, which in turn yield the effect that vegetation changes have on runoff. The method systematically analysed the spatial non-stationarity of vegetation variations and its associated effects on runoff. Therefore, more closely related results with less error were produced at each step, and results with more accuracy were obtained. These results indicated that the average trend rates of NDVI in the annual average, each season, and the growing season (Growing season refers to April to September) exceeded 0. Areas where NDVI show a growing trend cover more than 50%, which is greater than the area with a decreasing trend. The GWR regression parameters of precipitation, average temperature, and NDVI are all greater than 0. The GWR regression parameters of human activities and NDVI also have more than 50% of the area greater than 0. Based on the visual analysis of the calculation results, it can be seen that there are obvious spatial trends in the data, and the spatial data are significantly different between different regions. Therefore, WRB can be regarded as spatio-temporally non-stationary. In the WRB, the underlying surface change with vegetation change as the prominent feature is the leading cause (about 60%) of the runoff attenuation. The results showed that WRB has spatial and temporal non-stationarity. The spatial non-stationarity of vegetation has a greater effect on runoff changes. The results of this study support recommendations for improving runoff in the WRB.

Community-based assessment of the dynamics of urban landscape characteristics and ecosystem services in the rainforest and guinea savanna ecoregions of Nigeria.

Understanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI). A household survey involving the administration of a semi-structured questionnaire to 1552 participants was conducted. Diminishing NDVI and increasing NDBI were observed due to the rising trend of urban expansion, corroborating the perception of over 54% of the respondents who noted a decline in landscape ecological health. Residential expansion, agricultural practices, transport and infrastructural development, and fuelwood production were recognised as the principal drivers of landscape changes. Climate variability/change reportedly makes a 28.5%-34.4% (Negelkerke R2) contribution to the changing status of natural landscapes in Akure and Makurdi as modelled by multinomial logistic regression, while population growth/in-migration and economic activities reportedly account for 19.9%-36.3% in Owerri and Minna. Consequently, ecosystem services were perceived to have declined in their potential to regulate air and water pollution, reduce soil erosion and flooding, and mitigate urban heat stress, with a corresponding reduction in access to social services. We recommend that urban residents be integrated into management policies geared towards effectively developing and enforcing urban planning regulations, promoting urban afforestation, and establishing sustainable waste management systems.