Normalized Difference Vegetation Index Research Articles

Wetting or greening? Probing the global trends in Vegetation Condition Index (VCI)

Vegetation Condition Index (VCI), as a widely used drought index for monitoring vegetation drought stress and estimating drought trends, is constructed by normalizing the long-term satellite-based Normalized Difference Vegetation Index (NDVI) data. However, under global greening, vegetation across different regions has shown an increasing trend in greenness, which may cause VCI to inherit the greening trend in NDVI and further hamper its ability in drought trend analysis. Therefore, this study quantitatively explored the underlying relationship among VCI, global greening, and drought from 2001 to 2021 to examine the utility of VCI in a changing environment. Multi-source indicators were employed as surrogates for vegetation greenness and drought, respectively. Particularly, the Leaf Area Index (LAI) and Net Primary Production (NPP) were used as proxies for vegetation greenness while the Root-Zone Soil Moisture (RZSM), Palmer Drought Severity Index (PDSI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were used for drought indices. Based on Sen’s slope estimator, Mann-Kendall (MK) test, and partial correlation analysis, our results show that the proportion of pixels with an increasing trend in VCI is approximately 38.5%, which is similar to the greenness-related indices (i.e., NPP [26.47%] and LAI [59.14%]), and is significantly higher than the other drought indices (i.e., RZSM [19.83%], PDSI [10.38%], and SPEI [9.32%]). Furthermore, our results demonstrate that the VCI, as a drought index, exhibits a closer correlation with greenness-related indicators (LAI and NPP) than other drought indices (RZSM, PDSI, and SPEI). These results reveal the potential limitations in the practical applications of VCI and could enhance our understanding of vegetation dynamics and drought trends, especially in the current greening globe. Additionally, this study serves as a cautionary note for the scientific community involved in drought monitoring, emphasizing that the VCI may not be a suitable tool for evaluating long-term trends in drought impacts on vegetation.

Seasonal Response of the NDVI to the SPEI at Different Time Scales in Yinshanbeilu, Inner Mongolia, China

Recently, the frequent occurrence of droughts has caused a serious impact on vegetation growth and progression. This research is based upon the normalized difference vegetation index (NDVI) from 2001 to 2020. The correlation between the NDVI and standardized precipitation evapotranspiration index (SPEI) at disparate time scales was used to assess the response of vegetation growth to drought in the Yinshanbeilu region. The drought levels of SPEI1, SPEI3, SPEI6, and SPEI12 increased prominently in the eastern region of the country, while the NDVI decreased significantly from east to west in spring, summer, and autumn but was reversed in the winter. The area with an upward trend (33.86%) was slightly lower than that with a downward trend (66.14%). The correlation coefficients between the NDVI and SPEI over the entire year increased with the SPEI timescale. The elevated values were concentrated in the southeastern and western regions of the survey region. Additionally, the best correlation timescales were SPEI6 and SPEI12. Grassland was the most sensitive vegetation type to the SPEI response in the NDVI. The correlation coefficients of NDVI and SPEI1–12 were 0.313, 0.459, 0.422, and 0.406. Both spring and summer were more responsive to SPEI12, whereas autumn and winter were more responsive to SPEI3. The correlation of disparate time scales exhibited complex soil texture features with respect to different seasonal scales, and the soil texture showed a strong response to vegetation in both summer and autumn. Loam, sandy loam, and silty loam all exhibited the highest response to SPEI12, with coefficients of 0.509, 0.474, and 0.403, respectively.

Development of long‐term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China

AbstractBackgroundThe time‐series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long‐term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time‐series data sets.MethodsIn this study, a long time‐series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High‐Resolution Radiometer) time‐series NDVI products using the Random Forest (RF) downscaling model.ResultsCompared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7.ConclusionsThe long time‐series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long‐term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.

Normalized Difference Vegetation Index (NDVI) marked reliable value of Ulu Muda Forest Reserve

Water resources must be preserved because they are vital for agricultural, industrial, and domestic use, especially in tropical forest regions. Given the importance of the Ulu Muda Forest Reserve to the Kedah state in sustaining paddy cultivation for the country, this study aimed to provide reliable forest classification method and produce Normalized Difference Vegetation Index-Maximum Likelihood Classification of forest density map for Ulu Muda Forest Reserve. Here, the study reports measurements of the satellite remote sensing index for the forest based the image date. The forest recorded NDVI index of 0.72 to 0.98 for high density, NDVI index of 0.61 to 0.71 for moderate and NDVI index of 0.26 to 0.60 for low density area. The index showed higher value, which is comparable with reports of other tropical forest elsewhere. The results were influenced using satellite remote sensing pre-processing, processing, and classification. As a result, based on the satellite image indices approach, upper threshold of the higher density, the forest can exhibit higher capability for sustaining its ecological and hydrological functions. Given that most of the tropical forest biome has sustained this threshold, forest management should be sustained at this state, and more measures should be taken for example increase of protection forest classification in Rancangan Pengurusan Hutan (RPH) that is one of yearly planning report in states of Peninsular Malaysia. In the meantime, Geographical Information System (GIS) capacity should be mandate looking forwards for more utilisation in mapping for planning and projection of forest resource management activities.

LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B < 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D’IVOIRE

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained.

Long-term vegetation trends and driving factors of NDVI change on the slopes of Mount Kilimanjaro

ABSTRACT This study is the first to examine vegetation change on Kilimanjaro from 2000 to 2022, using the Normalised Difference Vegetation Index (NDVI) at a spatial resolution of 250 m via the Moderate Resolution Imaging Spectrometer (MODIS). There is a long-term increase in vegetation on an annual basis, but with decreases in the long/short rains at low/high elevations. Temperature significantly influences NDVI across all elevations and seasons. Precipitation effects are more variable and often delayed, especially in the forest zone. The study explores how El Nino Southern Oscillation (ENSO) events affect NDVI differently at contrasting elevations, indicating the need for further elevation-specific remote sensing research and field data.

The effects of urban green space and road proximity to indoor traffic-related PM2.5, NO2, and BC exposure in inner-city schools.

Since there are known adverse health impacts of traffic-related air pollution, while at the same time there are potential health benefits from greenness, it is important to examine more closely the impacts of these factors on indoor air quality in urban schools. This study investigates the association of road proximity and urban greenness to indoor traffic-related fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in inner-city schools. PM2.5, NO2, and BC were measured indoors at 74 schools and outdoors at a central urban over a 10-year period. Seasonal urban greenness was estimated using the Normalized Difference Vegetation Index (NDVI) with 270 and 1230 m buffers. The associations between indoor traffic-related air pollution and road proximity and greenness were investigated with mixed-effects models. The analysis showed linear decays of indoor traffic-related PM2.5, NO2, and BC by 60%, 35%, and 22%, respectively for schools located at a greater distance from major roads. The results further showed that surrounding school greenness at 270 m buffer was significantly associated (p < 0.05) with lower indoor traffic-related PM2.5: -0.068 (95% CI: -0.124, -0.013), NO2: -0.139 (95% CI: -0.185, -0.092), and BC: -0.060 (95% CI: -0.115, -0.005). These associations were stronger for surrounding greenness at a greater distance from the schools (buffer 1230 m) PM2.5: -0.101 (95% CI: -0.156, -0.046) NO2: -0.122 (95% CI: -0.169, -0.075) BC: -0.080 (95% CI: -0.136, -0.026). These inverse associations were stronger after fully adjusting for regional pollution and meteorological conditions. More than 90% of children under the age of 15 worldwide are exposed to elevated air pollution levels exceeding the WHO's guidelines. The study investigates the impact that urban infrastructure and greenness, in particular green areas and road proximity, have on indoor exposures to traffic-related PM2.5, NO2, and BC in inner-city schools. By examining a 10-year period the study provides insights for air quality management, into how road proximity and greenness at different buffers from the school locations can affect indoor exposure.

Monitoring the Invasion of S. alterniflora on the Yangtze River Delta, China, Using Time Series Landsat Images during 1990–2022

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands.

Anthropogenic activities dominated the spatial and temporal changes of normalized difference vegetation index (NDVI) in the Hehuang valley in the northeastern Qinghai Province between 2000 and 2020

The Hehuang Valley (HV) is a key development area in the Qinghai Province; understanding changes in the vegetation within this area is of great significance if we are to maintain the ecological quality of this regional environment. Based on the 30 m spatial resolution Normalized difference vegetation index (NDVI) time series dataset, this paper analyzes the spatial and temporal characteristics and evolutionary trends of NDVI in the HV from 2001 to 2020 under the influences of climate change and human activities, by applying Mann-Kendall trend analysis, the Hurst index, and residual analysis. Analysis showed that firstly, high NDVI values (&amp;gt;0.5) were distributed in the low elevation areas of the HV except for towns and cropland, while the low NDVI values (&amp;lt;0.5) were mainly distributed in the high elevation regions; the NDVI exhibited an increasing trend over the study period. Second, human activities promoted NDVI growth in the HV by changing land-use types, although there is a risk of vegetation degradation in the future. Third, the proportion of NDVI changes affected by climate change and human activities was determined to be 87.24% of the HV; furthermore, the contribution of human activities was three-fold higher than that of climate change. Fourth, managers should scientifically manage grasslands and forests and implement specific anthropogenic interventions based on the characteristics of regional NDVI degradation, to improve ecosystem resilience. These results can be used to quantitatively analyze the relative contributions of natural and anthropogenic factors to the ecological changes in the HV, and provide reference guidelines for the management of ecological environments.

Monitoring Plant Height and Spatial Distribution of Biometrics with a Low-Cost Proximal Platform

Measuring canopy height is important for phenotyping as it has been identified as the most relevant parameter for the fast determination of plant mass and carbon stock, as well as crop responses and their spatial variability. In this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode. The tool was lab-tested and field-tested on crop systems of different geometry and spacings: in a static setting on faba bean (Vicia faba L.) and in an on-the-go setting on chia (Salvia hispanica L.), alfalfa (Medicago sativa L.), and wheat (Triticum durum Desf.). Cross-correlation (CC) or a dynamic time-warping algorithm (DTW) was used to analyze and correct shifts between manual and sensor data in chia. Sensor data were able to reproduce with minor shifts in canopy profile and plant status indicators in the field when plant heights varied gradually in narrow-spaced chia (R2 = 0.98), faba bean (R2 = 0.96), and wheat (R2 = up to 0.99). Abrupt height changes resulted in systematic errors in height estimation, and short-scale variations were not well reproduced (e.g., R2 in widely spaced chia was 0.57 to 0.66 after shifting based on CC or DTW, respectively)). In alfalfa, ultrasound data were a better predictor than NDVI (Normalized Difference Vegetation Index) for Leaf Area Index and biomass (R2 from 0.81 to 0.84). Maps of ultrasound-determined height showed that clusters were useful for spatial management. The good performance of the tool both in a static setting and in the on-the-go setting provides flexibility for the determination of plant height and spatial variation of plant responses in different conditions from natural to managed systems.

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites.

Geological Hazard Susceptibility Analysis and Developmental Characteristics Based on Slope Unit, Using the Xinxian County, Henan Province as an Example

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012–2022), average annual rainfall (2012–2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area’s total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county’s geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses’ strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory.

Prediction of Thrips Damage Distribution in Mango Orchards Using a Novel Maximum Likelihood Classifier

Thrips constitute the primary pest responsible for reducing mango yield and quality every year in Asia. Therefore, the efficient monitoring of thrips damage across mango orchards on a large scale to aid farmers in devising rational pesticide application strategies poses a significant challenge within the current mango industry. This study designs a mango thrips damage inversion prediction method based on the maximum likelihood classifier (MLC). Initially, drone multispectral remote sensing technology is utilized to acquire multispectral data from mango orchards, which are then combined with ground hyperspectral information to identify sensitive bands indicative of mango leaf damage caused by thrips. Subsequently, correlation analysis is conducted on various vegetation indices, leading to the selection of the Greenness Normalized Difference Vegetation Index (GNDVI), which exhibits a strong correlation coefficient of 0.82, as the spectral characteristic parameter for the inversion prediction model. The construction of a remote sensing prediction model for thrips damage distribution in mango orchards is then undertaken based on the MLC. Acknowledging the bias-variance trade-off inherent in the MLC when processing spectral data and its potential limitations in feature extraction and robustness, this study proposes a modification wherein neighboring pixels are weighted differently to enhance the model’s feature extraction capabilities. Experimental results show that the novel MLC maintains stable estimation levels across various numbers of domain pixels, achieving an inversion accuracy of 91.23%. Through the reconstruction of the pixel matrix, the damage distribution of thrips in mango orchards can be swiftly and comprehensively visualized over extensive areas.

Green Space and Internalizing or Externalizing Symptoms Among Children

Evidence suggests that living near green space supports mental health, but studies examining the association of green space with early mental health symptoms among children are rare. To evaluate the association between residential green space and early internalizing (eg, anxiety and depression) and externalizing (eg, aggression and rule-breaking) symptoms. Data for this cohort study were drawn from the Environmental Influences on Child Health Outcomes cohort; analysis was conducted from July to October 2023. Children born between 2007 and 2013 with outcome data in early (aged 2-5 years) and/or middle (aged 6-11 years) childhood who resided in 41 states across the US, drawing from clinic, hospital, and community-based cohorts, were included. Cohort sites were eligible if they recruited general population participants and if at least 30 children had outcome and residential address data to measure green space exposure. Nine cohorts with 13 sites met these criteria. Children diagnosed with autism or developmental delay were excluded, and 1 child per family was included. Green space exposure was measured using a biannual (ie, summer and winter) Normalized Difference Vegetation Index, a satellite image-based indicator of vegetation density assigned to monthly residential history from birth to outcome assessment. Child internalizing and externalizing symptoms were assessed using the Child Behavior Checklist for Ages 1½ to 5 or 6 to 18. The association between green space and internalizing and externalizing symptoms was modeled with multivariable linear regression using generalized estimating equations, adjusting for birthing parent educational level, age at delivery, child sex, prematurity, and neighborhood socioeconomic vulnerability. Models were estimated separately for early and middle childhood samples. Among 2103 children included, 1061 (50.5%) were male; 606 (29.1%) identified as Black, 1094 (52.5%) as White, 248 (11.9%) as multiple races, and 137 (6.6%) as other races. Outcomes were assessed at mean (SD) ages of 4.2 (0.6) years in 1469 children aged 2 to 5 years and 7.8 (1.6) years in 1173 children aged 6 to 11 years. Greater green space exposure was associated with fewer early childhood internalizing symptoms in fully adjusted models (b = -1.29; 95% CI, -1.62 to -0.97). No associations were observed between residential green space and internalizing or externalizing symptoms in middle childhood. In this study of residential green space and children's mental health, the association of green space with fewer internalizing symptoms was observed only in early childhood, suggesting a sensitive period for nature exposure. Policies protecting and promoting access to green space may help alleviate early mental health risk.

Variation of the Start Date of the Vegetation Growing Season (SOS) and Its Climatic Drivers in the Tibetan Plateau

Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY–170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP.

Comparison of Different Green Space Measures and Their Impact on Dementia Cases in South Korea: A Spatial Panel Analysis

Dementia has become a profound public health problem due to the number of patients increasing every year. Previous studies have reported that environmental factors, including greenness, may influence the development and progression of dementia. Studies have found that exposure to green space is associated with a lower incidence of dementia. However, many definitions of green space exist, and the effects of its use may differ with the type of green space. Therefore, two types of green space measures were considered in this study to assess the differences in their impact on the prevalence of dementia among females and males. This study used five years of data (2017–2021) from 235 districts in South Korea. The two green space measures used were open space density and normalized difference vegetation index (NDVI), which were derived from satellite images. The analysis utilized a combination of traditional and spatial panel analyses to account for the spatial and temporal effects of independent variables on dementia prevalence. The spatial autocorrelation results revealed that both measures of greenness were spatially correlated with dementia prevalence. The spatial panel regression results revealed a significant positive association between NDVI and dementia prevalence, and open space had a negative association with dementia prevalence in both genders. The difference in the findings can serve as the basis for further research when choosing a greenspace measure, as it affects the analysis results, depending on the objective of the study. This study adds to the knowledge regarding improving dementia studies and the application of spatial panel analysis in epidemiological studies.

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

Comparing ML Methods for Downscaling Near-Surface Air Temperature over the Eastern Mediterranean

Near-surface air temperature (Ta) is a key variable in global climate studies. Global climate models such as ERA5 and CMIP6 predict various parameters at coarse spatial resolution (&gt;9 km). As a result, local phenomena such as the urban heat islands are not reflected in the model’s outputs. In this study, we address this limitation by downscaling the resolution of ERA5 (9 km) and CMIP6 (27 km) Ta to 1 km, employing two different machine learning algorithms (XGBoost and Deep Learning). Our models leverage a diverse set of features, including data from satellites (land surface temperature and normalized difference vegetation index), from ERA5 and CMIP6 climate models (e.g., solar and thermal radiation, wind), and from digital elevation models to develop accurate machine learning prediction models. These models were rigorously validated against observations from 98 meteorological stations in the East Mediterranean (Israel) using a standard cross-validation technique as well as a leave-one-group-out on the station ID evaluation methodology to avoid overfitting and dependence on geographic location. We demonstrate the sensitivity of the downscaled Ta to local land cover and topography, which is missing in the climate models. Our results demonstrate impressive accuracy with the Deep Learning-based models, obtaining Root Mean Squared Error (RMSE) values of 0.98 °C (ERA5) and 1.86 °C (CMIP6) for daily Ta and 2.20 °C (ERA5) for hourly Ta. Additionally, we explore the impact of the various input features and offer an extended application for future climate predictions. Finally, we propose an enhanced evaluation framework, which addresses the problem of model overfitting. This work provides practical tools and insights for building and evaluating Ta downscaling models. The code and data are publicly shared online.

Integrating RUSLE Model with Cloud-Based Geospatial Analysis: A Google Earth Engine Approach for Soil Erosion Assessment in the Satluj Watershed

This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed’s land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes.

An Analysis on the Impact of Normalized Difference Vegetation Index (NDVI) Changes on the Land Surface Temperature (LST) using Satellite Imagery in the West Bank, Palestine

Abstract&#x0D; This study investigated the impact of the Normalized Difference Vegetation Index (NDVI) on the Land Surface Temperature (LST) in the West Bank, Palestine, using multitemporal satellite imagery. Landsat 5 TM and Landsat 8 OLI data were used in the study. The Landsat 5 TM data was acquired on March 29, 2001, while the Landsat 8 OLI data was acquired on April 05, 2021. The NDVI and the LST for the two images were then calculated from the two images using ArcGIS 10.8 software. Results of the study showed that the NDVI of the 2021 image was much higher than that of the 2001 image. The mean NDVI of the 2001 image was 0.25, while that of the 2021 image was 0.32. As for the LST, the mean LST of the 2001 image was 29.85 °C, while that of the 2021 image was 26.98 °C. Around 74.7% of the West Bank lands recorded an inverse relationship between the NDVI change and the LST change between 2001 and 2021. Around 25.3% of the West Bank lands recorded a positive relationship. In other words, the study depicted the impact of the NDVI on the LST in the West Bank. The study recommends that policymakers need to increase the green areas in the West Bank to reduce the LST as this is seen to affect the climate conditions in the area.&#x0D; Keywords: LST, NDVI, West Bank, Palestine, Landsat data.