Spatial Assessment of Drought Conditions Over Iraq Using the Standardized Precipitation Index (SPI) and GIS Techniques

Beg, Ayad Ali Faris; Al-Sulttani, Ahmed H.

doi:10.1007/978-3-030-21344-2_18

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Then sand dunes accumulated from 1527.0291 km2 to 1648.4679 km2 throughout the ten years from 2011 to 2021 in Baiji by Landsat-5 TM and Landsat-8 satellites. As the highest area observed in 2021, the reason is that land degradation was significant due to global climatic changes, which is reflected in Iraq as drought and expanding of desertification (Awadh et al, 2022;Beg and Al-Sulttani, 2020;Mail et al, 2016). Iraqi Geological Journal Al-Zubaidi et al 2022, 55 (1F), 102-121…”

Section: Validation and Change Detectionmentioning

confidence: 99%

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Al-Zubaidi

Al-Sulttani

Rabee

2022

IGJ

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Monitoring the propagation of dunes is essential for natural hazard management. Accurate dunes mapping is critical in this situation. Landscape elements such as vegetation, water, dunes, and built-up are commonly separated using spectral indices. The discovery of dune features using a spectral index is one of the most significant achievements in earth observation. In this research, it was suggested Drifting Sand Index (DSI) is a newly created index that can be used to extract the land of dunes. The DSI is calculated using the normalized difference between six Landsat-8 bands (B, R, G, NIR, SWIR-1, SWIR-2). The linear SVM algorithm was implemented using library (LibLINEAR) in the R software to calculate a new linear equation. Two versions of the index have been proposed, the first is the complete version (DSI-C), and the second is the reduced version (DSI-R). The suggested indices results were compared to four previously proposed spectral indices (NDSI-1, NDSI-2, CI, and NDSLI). The acquired results demonstrated that the DSI-C and DSI-R had a high ability to distinguish between sand and different land covers, such as vegetation, water bodies, and various soil types. The average overall accuracy for all levels of the DSI-R, DSI-C, NDSI-1, NDSI-2, CI, and NDSLI was 87.455%,83.4333%, 78.030%, 68.528%, 65.986%, and 56.490%, respectively. The average Kappa Coefficient for DSI-R, DSI-C NDSI-1, NDSI-2, CI, and NDSLI was 74.915%, 66.873%, 56.076%, 37.073%, 31.978%, and 13.011%, respectively.

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Section: Validation and Change Detectionmentioning

confidence: 99%

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Al-Zubaidi

Al-Sulttani

Rabee

2022

IGJ

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“…Position and topography are the main circumstances that manage the aggressive climate of Iraq (Beg and Al-Sulttani 2020). The weather in Iraq is witnessing many variations from the north or northeast due to the Mediterranean and from the south due to the arid or semi-arid continents.…”

Section: Study Areamentioning

confidence: 99%

Surface roughness distribution map for Iraq using satellite data and GIS techniques

et al. 2020

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This study shows the potential of using the satellite data techniques that are supported by the GIS and remote sensing (RS) approaches to produce the roughness map for the surface of Iraq and any area in a very efficient manner. Normalized difference vegetation index (NDVI) was generated using MODIS data acquired on 23 April 2015. Thirty-six points were used to build and verify the proposed model of surface roughness coefficient (n), which was correlated with NDVI layer according to the types of soil cover and land use to create the predicted distributed roughness map. The results revealed that there has been a great match between the proposed surface roughness map produced in this study with the topographical map of Iraq. The results presented also that the minimum and maximum values of surface roughness n were located in the far south and far north of about 0.029 and o.16, respectively, whereas the concentrated value of n was scattered and ranged from 0.035 to 0.16 in north, central, and south parts of the study area. The statistical analysis resulted mean, R, and COV values of 1.02, 0989, and 15.553% consecutively, which exposed the good accuracy and consistency of the predicted roughness map. Verification was conducted which revealed a good accuracy and consistency of the predicted roughness map.

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“…Coarse particles cover the maximum area during the summer and the minimum area during the winter [10]. Iraq is an interesting site for aerosol studies, due to the contribution of particles from the surrounding and local desert regions, such as the Western Sahara and Mesopotamian floodplains [11], as well as climate change represented by decreased precipitation, drying of the marshes, and urbanization [12]. The pressure systems that are formed in neighboring countries cause the transfer of different amounts of aerosols to region [13].…”

Section: Introductionmentioning

confidence: 99%

Classification of Aerosols Types Over Iraq, using MODIS Date

Zeki,

Al-Salihi,

Al-Lami

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The optical properties of aerosols are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the period 2003–2021 at seventeen regions in Iraq, which were used to determine the different types of aerosols using threshold values based on Aerosols Optical Depth (AOD) and Angstrom Exponent (AE), seasonal aerosol classification was performed. This approach was verified and used in the detection process. Six aerosols categories: maritime, dust, mixture, urban, dust desert, and biomass burning were examined in this study. The result indicates that dust is most frequently detected in the summer and spring seasons, while the maritime type is most common in the winter and autumn, with wide variability in mixture type. The relative contribution during the winter months revealed a clear predominance of Maritime-type aerosols in the northern part of Iraq. Maximum contributions recorded in Erbil (93%), and Sulaymaniyah (90%). The maximum relative contributions of dust are found in the range of 71%–77% over Qaim and Haditha, while the southern regions of Iraq have a percentage ranging from 54%–65% in the summer season. The results of the monthly classification of aerosol type indicate that dust type are overhead maritime aerosols between March and September in the west, northwest, and south regions of Iraq. The highest contribution of desert dust was recorded in Basra, 60% during July and August. The maximum urban type was recorded at 50% in Baghdad in June. Biomass-burning type contribution was very little.

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Spatial Assessment of Drought Conditions Over Iraq Using the Standardized Precipitation Index (SPI) and GIS Techniques

Cited by 8 publications

References 13 publications

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Surface roughness distribution map for Iraq using satellite data and GIS techniques

Classification of Aerosols Types Over Iraq, using MODIS Date

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