The Abdi-Basid Courses Institute

Climatic and Environmental Drivers of NDVI Dynamics in an Arid Region: Predictor Importance, Partial Correlation, and Trend Analysis (1987–2022)

Understanding changes in mangrove ecosystems driven by human activities, climate change, and environmental variations is essential for effective ecological management. This study focuses on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) and examines its responses to parameters such as sea level (SL), Potential Evapotranspiration (PET), rainfall (RF), Standardized Precipitation Index (SPI-1 month), soil moisture (SM), minimum temperature (TN), and maximum temperature (TX) within the study area. Trends, relative influences, spatial autocorrelation, and relationships between NDVI and climatic-environmental variables, as well as partial correlations, were analyzed using the Mann-Kendall monotonic trend test (MKMT), Relative Weight Analysis (RWA), partial correlation coefficients (PCC), and Multiple Linear Regression (MLR) methods.The spatiotemporal patterns of NDVI reveal a reduction in bare soil and an increase in sparse and dense vegetation from 1987 to 2022. Nevertheless, zones of degradation were observed, particularly in southern Godoria in 2022 compared to 1987, as indicated by NDVI. A notable deterioration in NDVI (> 0.2) was recorded from 2000 to 2012, while the overall interannual trend shows a slight decline.Additionally, analyses with Mann-Kendall and Theil-Sen slope reveal that TN, TX, PET, and SPI-1 show increasing trends, though not statistically significant, while SM and LST show decreasing trends. For environmental variables, SL indicates an upward trend. Further, partial correlation analysis identifies SL, TN, SPI-1, TX, and PET as the primary climatic factors controlling vegetation dynamics during the JJAS season, with PCC values of -0.89, 0.87, 0.77, -0.76, -0.75, and 0.86 with NDVI, respectively.These findings highlight the significant influence of select environmental factors on the spatiotemporal dynamics of mangrove vegetation, providing insights critical for conservation and management efforts.

2.1.1. Partial correlation

The bivariate correlation coefficients may not effectively represent the complex relationships among variables in multivariate correlation analysis, given that multiple factors can influence these relationships. Therefore, partial correlation coefficients were computed to assess the spatiotemporal strength and direction of the linear relationship between NDVI and each climate variable, while controlling for the effects of the other climate variables (i.e, sea level, PET, SM, SPI, LST, TN and TX). The strongest correlation is close to 1, while the weakest is below 0.5. Thus, the partial correlation can be calculated as follow (Cheng et al., 2017):

(1)

Where x, y, and z represent three distinct variables. R_xy,zsignifies the partial correlation between variables x and y while accounting for the influence of variable z. Similarly, R_xy denotes the linear correlation coefficient between x and y, with R_xz and R_yz conveying analogous interpretations.

To explore the spatial autocorrelation of NDVI data, we used the "global and local autocorrelation analysis based on Moran’s I statistics." This method enables the evaluation of the average spatial differences between individual cells and their adjacent neighbors, thereby characterizing the spatial attributes of a specific property across the entire study area through global spatial autocorrelation analysis. In Moran’s statistics, the normalized z-score can range from -1 to +1. A Moran's I value exceeding 0 indicates a positive correlation, which suggests a clustering pattern, while a value below 0 points to a negative correlation, reflecting a dispersed arrangement. The calculation of Moran’s I statistics for examining spatial autocorrelation is provided by Xu et al. (2015):

(2)

Where N represents the number of observations, x_i denotes the observed value for cell i, x_j indicates the observed value for cell j, is the average of cell i or cell j, and w_ijis the weight assigned to the relationship between cells i and j.

While the global spatial autocorrelation through Moran's I statistics reveals the overall clustering pattern, it does not allow for the assessment of spatial association patterns across multiple locations. In contrast, Local Spatial Autocorrelation focuses on the significance of local statistics at each individual location and identifies the presence of spatial clusters, a capability that global spatial autocorrelation lacks. The mathematical equation of local spatial autocorrelation using Moran's I is described by (Anselin (2010)).

(3)

(a)

(b)

Fig. 1. (a)Estimation of relative importance of climate variables as predictors of NDVI. (b) Pearson correlation coefficient between NDVI and climate variables.

Table 1. Mann Kendall Trend and Theil-sen slope statistics of NDVI and climate variables from 1987 to 2022.

	Theil-sen slope	p.value	Mann kendall tau (τ)	p.value
NDVI	-0.001	0.858	-0.023	0.859
Sea level	0.001	0.646	0.057	0.646
Rainfall	0.000	NA	0.000	NA
SPI	0.000	0.917	0.000	0.917
Temp Min	0.057	0.109	0.194	0.109
Temp Max	0.072	0.173	0.166	0.172
Soil Moisture	-0.030	0.739	-0.057	0.739
PET	0.046	0.649	0.085	0.649
LST	-0.222	0.720	-0.111	0.720

Abdi-Basid ADAN, 2024

The Abdi-Basid Courses Institute (TABCI)

Exploiter les données SRTM pour la cartographie (ArcGIS)

Découvrez dans ce tutoriel complet comment exploiter les données d’élévation SRTM pour vos projets SIG et cartographiques. De la téléchargement des rasters à leur importation et extraction par shapefile, en passant par la représentation visuelle et la mise en page professionnelle, vous serez guidé pas à pas pour transformer des données brutes en cartes prêtes à l’analyse ou à la présentation. Que vous soyez étudiant, chercheur ou passionné de géomatique, ce tutoriel vous donnera les clés pour gagner du temps et améliorer la qualité de vos productions cartographiques.

Abdi-Basid ADAN, 2025

🎯 The detailed methodology and results can be accessed through this link:

👉Click here now: https://www.youtube.com

En collaboration avec :

Interannual Variability of Sea Level and Sea Surface Temperature in Djibouti and Yemen: Insights from the University of Hawaii Sea Level Center and NOAA OISST Data (1981–2022)

Figure 1. Interannual sea level changes in Djibouti and Yemen over the period 2007 to 2018 from University of Hawaii, Sea Level Center

Figure 2. Annual average Sea surface température (°C) from 1981 to 2022 , NOAA OISST.

Abdi-Basid ADAN, 2022

The Abdi-Basid Courses Institute

Spatiotemporal Analysis of the Impacts of Early 2024 Wildfires in North America: A Study of Correlations Between Meteorological Conditions, Air Quality, and Wind Dynamics in Stinnett and Canadian, T

This study provides a detailed spatiotemporal analysis of the impacts of early wildfires occurring between February 26 and March 16, 2024, in North America, with a focus on the localities of Stinnett (35.82°N, -101.44°W) and Canadian, TX (35.91°N, -100.38°W). Utilizing data from NetCDF files (v10m, u10m, AOD500nm, t2m, coplev, comlev), the analysis employs the xarray library to process meteorological and atmospheric variables, including aerosol optical depth (AOD at 550 nm), 2-meter temperature (converted to °F), carbon monoxide (CO) at 1000 hPa and model level 1 (in ppb), and 10-meter wind components (u10, v10). Visualizations, created using matplotlib and cartopy, include spatial maps of daily averages of AOD, temperature, and CO, overlaid with wind vectors to illustrate their role in pollutant dispersion. Time series analyses reveal the daily evolution of these variables for both localities, highlighting significant AOD peaks in Stinnett, indicative of smoke plume passage. Hourly heatmaps confirm the immediate impact of wildfires on air quality, showing a marked increase in AOD within hours. Wind rose diagrams, generated for each locality, quantify the frequency and intensity of winds, emphasizing their influence on pollutant spread. Finally, a pixel-by-pixel Pearson correlation analysis between AOD and CO reveals a strong positive relationship (R close to +1) in affected areas, confirming the common origin of aerosols and CO from biomass burning. These findings, contextualized within an extreme meteorological event characterized by high temperatures and dry conditions, underscore the complex interactions between meteorological and atmospheric dynamics, with implications for air quality monitoring and environmental crisis management in North America.