Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique
The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The N...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/12/5/593 |
| _version_ | 1797535436169543680 |
|---|---|
| author | Javed Mallick Mohammed K. AlMesfer Vijay P. Singh Ibrahim I. Falqi Chander Kumar Singh Majed Alsubih Nabil Ben Kahla |
| author_facet | Javed Mallick Mohammed K. AlMesfer Vijay P. Singh Ibrahim I. Falqi Chander Kumar Singh Majed Alsubih Nabil Ben Kahla |
| author_sort | Javed Mallick |
| collection | DOAJ |
| description | The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting. |
| first_indexed | 2024-03-10T11:44:28Z |
| format | Article |
| id | doaj.art-9800e80036f8446fb9028271d9306168 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T11:44:28Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-9800e80036f8446fb9028271d93061682023-11-21T18:13:14ZengMDPI AGAtmosphere2073-44332021-05-0112559310.3390/atmos12050593Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling TechniqueJaved Mallick0Mohammed K. AlMesfer1Vijay P. Singh2Ibrahim I. Falqi3Chander Kumar Singh4Majed Alsubih5Nabil Ben Kahla6Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi ArabiaDepartment of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi ArabiaDepartment of Biological and Agricultural Engineering, Texas A & M University, College Station, TX 77843-2117, USADepartment of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi ArabiaDepartment of Energy and Environment, TERI School of Advanced Studies, New Delhi 110070, IndiaDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi ArabiaThe Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.https://www.mdpi.com/2073-4433/12/5/593geographically weighted regressionglobal OLS regressionsatellite observation rainfall CHIRPS product |
| spellingShingle | Javed Mallick Mohammed K. AlMesfer Vijay P. Singh Ibrahim I. Falqi Chander Kumar Singh Majed Alsubih Nabil Ben Kahla Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique Atmosphere geographically weighted regression global OLS regression satellite observation rainfall CHIRPS product |
| title | Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique |
| title_full | Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique |
| title_fullStr | Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique |
| title_full_unstemmed | Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique |
| title_short | Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique |
| title_sort | evaluating the ndvi rainfall relationship in bisha watershed saudi arabia using non stationary modeling technique |
| topic | geographically weighted regression global OLS regression satellite observation rainfall CHIRPS product |
| url | https://www.mdpi.com/2073-4433/12/5/593 |
| work_keys_str_mv | AT javedmallick evaluatingthendvirainfallrelationshipinbishawatershedsaudiarabiausingnonstationarymodelingtechnique AT mohammedkalmesfer evaluatingthendvirainfallrelationshipinbishawatershedsaudiarabiausingnonstationarymodelingtechnique AT vijaypsingh evaluatingthendvirainfallrelationshipinbishawatershedsaudiarabiausingnonstationarymodelingtechnique AT ibrahimifalqi evaluatingthendvirainfallrelationshipinbishawatershedsaudiarabiausingnonstationarymodelingtechnique AT chanderkumarsingh evaluatingthendvirainfallrelationshipinbishawatershedsaudiarabiausingnonstationarymodelingtechnique AT majedalsubih evaluatingthendvirainfallrelationshipinbishawatershedsaudiarabiausingnonstationarymodelingtechnique AT nabilbenkahla evaluatingthendvirainfallrelationshipinbishawatershedsaudiarabiausingnonstationarymodelingtechnique |