Startseite Technik Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
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Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery

  • Israa Hatem ORCID logo EMAIL logo , Imzahim A. Alwan , Abdul Razzak T. Ziboon und Alban Kuriqi ORCID logo
Veröffentlicht/Copyright: 4. März 2024
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Abstract

Climate change poses an urgent global challenge in water resource management, with drought emerging as a pervasive threat worldwide. Over the past two decades, Iraq has faced severe droughts, categorized into meteorological, agricultural, hydrological, and socioeconomic types. Agricultural drought, marked by prolonged soil moisture deficits due to insufficient rainfall, often leads to crop failures. This comprehensive study delves into the impact of drought on Iraq’s vegetation cover from 2000 to 2022, utilizing advanced tools like remote sensing (RS) and geographic information systems. The normalized difference vegetation index (NDVI) facilitated the creation of multitemporal drought maps. Employing Landsat satellite imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, the study revealed 2000, 2008, 2009, 2010, 2012, and 2022 as the most drought-prone years. In specific years such as 2000, 2008, 2010, and 2009, Landsat imagery showcased the lowest vegetation cover percentages (3.356, 4.984, 5.112, and 5.409%, respectively), while MODIS data indicated the lowest percentages in 2008, 2000, 2009, 2012, and 2022 (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively). This study underscores the pivotal role of RS, particularly NDVI, as a valuable tool for agricultural drought early warning systems. The findings highlight the effectiveness of NDVI as a simple and cost-efficient index for monitoring changes in vegetation conditions and assessing the impact of droughts on agriculture.

Keywords: climate change; NDVI; remote sensing; vegetation; water scarcity

1 Introduction

Drought is classified as an environmental hazard and natural disaster that threatens the long-term development of society [1]. Its long-term effects have exacerbated its negative impact on agricultural yields, animal husbandry, the general economy, and the physical environment. Many regions have experienced water crises over the past three decades, severely affecting local economies [2]. Reduced rainfall in Iraq, Syria, Turkey, and Iran has adversely affected agriculture, livelihoods, the economy, and the sustainable quality and quantity of water [3]. There are two phases in the drought development cycle. The first is meteorological drought when rainfall is significantly below average. The second is agricultural drought, caused by the absence of rainfall, resulting in decreased soil moisture, a lack of ideal conditions for plant growth, and a loss of vegetation cover [4]. The agricultural sector in Iraq is the second largest source of revenue after the oil sector. It continues to employ about 20% of the labor force despite a decline in its share of gross domestic product from about 9% in 2002 to about 3.3% in 2008 [5]. Iraq’s arable land has faced soil degradation, causing desertification, economic losses, food shortages, and poor environmental conditions [6]. Between 2003 and 2012, Iraq experienced severe droughts, causing reduced vegetation due to lower river runoff [7]. Remote sensing (RS) techniques have become valuable tools for studying environmental variables using multispectral sensor data. Satellite imagery processing can reveal physical characteristics such as land use, temperature, and surface elevation [8,9,10]. RS is crucial for detecting and monitoring environmental hazards and earth resources [11]. One of the first vegetation indices used for drought monitoring was the normalized difference vegetation index (NDVI), which dates back to 1980 [12,13,14]. It uses global vegetation index data generated by mapping daily 4 km radiance. The NDVI is calculated using radiation intensity measured in visible and near-infrared channels. It assesses the greenness and vigor of vegetation over 7 days to reduce cloud pollution and detect drought-related stress in plants. The strength of NDVI lies in its innovative use of satellite data to monitor the condition of vegetation in the context of drought events [15]. Several studies have been conducted to investigate the spatiotemporal methods of drought; however, the majority focused on investigating the methods of drought detection and analyzing the correlations between agricultural drought, average transpiration, and crop production [12]. Trigo et al. [16] used the NDVI generated from the vegetation instrument to assess the impact of the 2007–2009 drought years on vegetation. Between January and June 2008, negative NDVI anomalies (stressed vegetation) were continuously observed in significant areas of northern Iraq, eastern Syria, southeastern Turkey, and western Iran. Muhaimeed and Al-Hendy [17] used NDVI as a monitoring index to track agricultural drought in three nearby Iraqi governorates (Salah al-Din, Kirkuk, and Mosul) from 2000 to 2010. According to the study, drought was worst in the 2007–2008 harvest season, and NDVI values were lowest. According to an estimate by the united nations educational, scientific and cultural organization (2014), 40% of agricultural fields in Iraq were lost during the severe drought years of 2008–2009. According to the study results, the decrease in vegetation cover, known as desertification, increases with time [7]. Ozyavuz et al. [18] proved the short-term effectiveness of using satellite imagery with the NDVI approach to identify land cover changes, mainly vegetation, in Tekirdag Province, Turkey, Sarkoy (1987, 2002, and 2012) [18,19,20]. Eklund and Seaquist [21] used the Enhanced Vegetation Index (EVI) to monitor agricultural drought in northern Iraq’s Dohuk Governorate between 2000 and 2011. The study concluded that the Dohuk Governorate suffered from agricultural drought between 2007 and 2009, with 2008 being the most severe year. Babu et al. [22] used RS to monitor drought in Anantapur district of India from 2005 to 2010. They extracted normalized difference water index (NDWI) and NDVI indicators from Landsat 4 and 5. They found that the NDVI indicator accurately predicted drought in the region. Al-Quraishi et al. [23] studied drought in Sulaymaniyah Governorate (Iraqi Kurdistan) over 18 years. They used Landsat imagery to determine drought indices such as NDWI, NDVI, and land surface temperature for 1990, 2007, and 2008 [23,24]. Faye assessed the metrological drought using standardized precipitation index and standardized precipitation evapotranspiration index; both indices give high correlation on the same timescales, during the study period [25]. Nama et al. used satellite and field investigations to assess the changes in Tigris River in Iraq, and the study showed how sensitive river shapes are to variations in flow brought about by dam construction, with notable alterations following the Mosul Dam [26]. Jabal et al. [20] used the Moderate Resolution Imaging Spectroradiometer (MODIS) data and NDVI to analyze the agricultural area in long term from 2000 to 2020. The study find a relationship between agricultural areas and the production of essential crops.

Iraq has been suffering from drought for several years due to a lack of rain and water releases from the Tigris and Euphrates rivers, which has led to farmers’ unwillingness to produce, resulting in soil degradation and an increase in dust storms in recent years. This study seeks to delve into the nuanced repercussions of drought on Iraq’s vegetation cover, employing cutting-edge technologies such as RS and geographic information systems (GIS). The overarching goal is to craft comprehensive drought maps utilizing the NDVI, Landsat, and MODIS data across various temporal scales. By scrutinizing the period from 2000 to 2022, the research endeavors to provide a meticulous assessment of agricultural drought dynamics in Iraq.

2 Study area

The Country of Iraq (Figure 1) is located in southwestern Asia and marks the Arab homeland’s eastern boundary. It borders Turkey to the north, Iran to the east, Syria and Jordan to the west, and Saudi Arabia and Kuwait to the south. Iraq has a land area of 438,320 km2 and is located between latitudes (29°00′, 37°15′) N and longitudes (38°45′, 48°25′ E). According to the Food and Agriculture Organization of the United Nations, the topography of the country can be divided into four regions: mountains (21%), sedimentary plain (30%), desert plateau (39%), and hilly terrain (10%). Except for the northeastern region, the climate in Iraq is generally dry or semi-arid. Winters are generally cold (below freezing), and precipitation is low and variable. Precipitation falls from October to May, with the greatest amounts falling between December and February. The summer season in Iraq is dry and hot, with temperatures exceeding 48°C, while spring and fall are short. Climatic conditions such as high temperatures, low rainfall, and high wind speeds cause the evaporation rate in the region to increase extremely. The Tigris and Euphrates rivers flow through the study area, extending from northwest to southeast (Figure 1).

Figure 1 Topography map of Iraq.
Figure 1

Topography map of Iraq.

Wheat and barley contribute to nearly 80% of Iraq’s agricultural production. Two farming methods can be distinguished in Iraq: rainfed agriculture, which is mainly practiced in the north of the country, and irrigated agriculture, which is predominant in the central and southern regions of the country [7].

3 Materials and methods

To achieve the objectives of this study, namely, a spatiotemporal assessment of agricultural drought conditions over Iraqi regions for the study period (2000–2022), the RS datasets were created using three separate Landsat sensors with 30 m spatial resolution: L5 Thematic Mapper (TM), L7 Enhanced Thematic Mapper Plus (ETM+) and L8 Operational Land Imager (OLI), and Terra MODIS vegetation indices (MOD13Q1) version 6 data obtained every 16 days at a spatial resolution of 250 m were used. The methodology of the current study is shown in Figure 2.

Figure 2 Flowchart of NDVI calculation.
Figure 2

Flowchart of NDVI calculation.

The Landsat 5, 7, and 8 TM Collection 1 Tier 1 composites are created from Tier 1 orthorectified scenes using calculated top-of-atmosphere reflectance, thus radiometrically and geometrically matched [27,28,29]. The MOD13Q1 V6.1 product produces a vegetation index value per pixel because there are two basic vegetation layers in this product. The first is the NDVI. EVI is the second vegetation layer that eliminates changes in the background of the tree canopy [30]. For this study, Google Earth Engine is used to collect the Landsat satellite imagery and MOD13Q1 V6.1 products for the study area, apply the NDVI, and calculate the mean NDVI for each year of the study period. The GIS was used in this study to produce maps of the spatial distribution of NDVI classes.

4 NDVI

NDVI is a popular indicator of terrestrial vegetation productivity. It represents the difference in reflectance between the red and near-infrared bands to compute the quantity and condition of vegetation. NDVI has been utilized in environmental research to evaluate the effects of environmental changes on vegetation and foresee productivity changes under various climate scenarios, such as habitat destruction, global warming, and biodiversity loss [31,32,33]. It has also been used to forecast changes in vegetation production under different climatic circumstances by investigating the link between precipitation and temperature [34]. Normally, NDVI is a powerful method for evaluating past and present vegetation conditions, as well as the impacts of climate change on biodiversity and humans [35]. Because it is a rapid and easy method for analyzing the state of vegetation concerning precipitation values, NDVI is used for drought monitoring [36]. It is developed using satellite picture data to determine whether vegetation is stressed [37]. It is a useful method for tracking droughts due to the significant correlation between NDVI and precipitation. NDVI is a tool for analyzing changes in vegetation values over time to determine how drought affects crop production [38,39]. NDVI is defined as follows:

(1) NDVI = ( NIR − RED ) ( NIR + RED ) ,

where RED and NIR in the equation are the RED and NIR spectral reflectance bands in the satellite image, respectively [40].

The red (RED) and near-infrared (NIR) wavelength regions are used to calculate NDVI for several reasons. First, using red and NIR light allows for greater penetration into vegetation, enabling assessment of plant health and density [41]. Also, because the NIR range is less affected by atmospheric scattering, measurements of vegetation characteristics are more accurate [42]. Additionally, the RED and NIR wavelengths are better suited for bioimaging and biosensing applications because they are less prone to interference from background autofluorescence of biomolecules in biological systems [43,44]. Finally, NIR light minimizes phototoxicity and background signals, making it useful for biological imaging and bioanalytical detection [45]. NDVI values between −1 and 1 represent varying plant cover and health degrees. A value of −1 means no vegetation, such as water or infertile soil. NDVI classes used in this study are shown in Table 1 [20].

Table 1

NDVI classification based on the NDVI values

NDVI value Classes
<0 Water
0–0.199 No vegetation area
0.2–0.499 Low vegetation area
0.5–1 High vegetation area

In contrast, a value of 1 indicates the dense and thriving vegetation. Values near 0 indicate the sparse or stressed vegetation [46]. NDVI is determined by the reflectance of NIR and RED light, with higher values indicating higher photosynthetic activity and biomass [47]. NDVI classes refer to different categories or groupings of vegetation that can be categorized based on their NDVI values [48]. Understanding regional and temporal differences in vegetation cover and dynamics is facilitated by classifying NDVI classes [49].

5 NDVI anomaly

The divergence of NDVI data from average or typical values is an anomaly in the NDVI [50]. The NDVI anomaly value can be calculated from comparing current NDVI data to baseline or historical data. The percentage of NDVI anomalies is classified into five drought severity levels, as illustrated in Table 2 [41].

Table 2

Classification of drought severity based on the anomaly of NDVI [41]

NDVI anomalies (%) Class
0 to −10 Slight drought
−10 to −20 Moderately drought
−20 to −30 Severe drought
Above −30 Very severe drought

The anomaly describes the abnormal or unusual vegetation conditions, such as drought or revegetation patterns, and their distribution in the spatial and temporal dimensions. Climate factors such as precipitation, temperature, urban activity, changes in land use, and land cover all impact the anomaly [51,52]. The difference between the average NDVI and the current NDVI can represent the percentage difference of NDVI anomaly. According to the equation, the provided NDVI is deducted from the NDVI mean, and the resulting value is then divided by the NDVI mean. The equation is used to analyze the condition of vegetation and track its deterioration or evolution over time. The NDVI anomaly is a useful index of vegetation conditions that can be used to monitor climate change, vegetation dynamics, and drought [53,54,55]. NDVI anomaly can be calculated as follows [53]:

(2) NDVI average = ∑ i = 1 n NDVI i n ,

where n is the number of years of study period, NDVI i is the NDVI of i year , and NDVI average is the average of NDVI of (n) years.

(3) NDVI anomaly i = NDVI i − NDVI average NDVI average × 100 ,

where NDVI anomaly i is the anomaly of NDVI of i year.

6 Results and discussion

The agricultural drought assessment in the research area hinged on applying the NDVI. Leveraging data from MODIS vegetation indices (MOD13Q1) with a spatial resolution of 250 m and Landsat satellite images (TM, ETM+, OLI) with a higher resolution of 30 m, NDVI results were meticulously generated. This comprehensive approach spanned the entire investigated period (2000–2022), yielding a detailed annual breakdown of NDVI values to meet diverse study objectives.

The discerning NDVI analyses pinpointed 2000, 2008, 2009, 2010, 2012, and 2022 as witnessing Iraq’s most pronounced drought conditions within the specified timeframe (2000–2022) [56]. Table 3 encapsulates the mean, minimum, and maximum NDVI values for each year throughout the study duration, providing a nuanced understanding of the dynamic vegetation response to varying climatic conditions. This multi-faceted examination employing both MODIS and Landsat data underscores the robustness of the methodology in capturing the intricacies of agricultural drought dynamics over the years [57,58].

Table 3

Mean, min., and max. of NDVI values for every year of the study period

Year MODIS Landsat
Mean Min Max Mean Min Max
2000 0.12306 −0.1892 0.6422 0.0717 −0.48322 0.7152
2001 0.14866 −0.2 0.9 0.1011 0.7571 −0.5583
2002 0.1405 −0.1994 0.7017 0.085 −0.6124 0.7278
2003 0.1417 −0.2 0.9423 0.10388 −0.71139 0.8419
2004 0.1527 −0.2 0.9705 0.091 −0.5771 0.7304
2005 0.1365 −0.2 0.975 0.0851 −0.6145 0.7744
2006 0.1413 −0.2 0.9117 0.0926 −0.5019 0.7633
2007 0.138 −0.1989 0.9428 0.09 −0.6374 0.7473
2008 0.1238 −0.1994 0.6168 0.0813 −0.4515 0.7141
2009 0.1279 −0.197 0.6179 0.0809 −0.9741 0.7464
2010 0.1377 −0.2 0.8644 0.08158 −0.6645 0.7138
2011 0.1381 −0.1993 0.9399 0.0863 −0.9802 0.7922
2012 0.1295 −0.01982 0.6424 0.0841 −0.5673 0.754
2013 0.1476 −0.2 0.702 0.0923 −0.53 0.7696
2014 0.1637 −0.2 0.909 0.1007 −0.589 0.7705
2015 0.1517 −0.2 0.665 0.0917 −0.4907 0.7809
2016 0.1539 −0.2 0.931 0.0975 −0.516 0.7297
2017 0.1382 −0.2 0.9782 0.0893 −0.5065 0.7425
2018 0.1481 −0.2 0.9565 0.0899 −0.5452 0.7693
2019 0.2003 −0.2 0.7571 0.1181 −0.5299 0.8204
2020 0.1701 −0.2 0.8823 0.1015 −0.5891 0.7478
2021 0.1476 −0.2 0.9927 0.0948 −0.5041 0.7514
2022 0.1325 −0.2 0.6841 0.0829 −0.6642 0.794

Leveraging the insights derived from the NDVI analysis, the study restricted the study area into four distinctive classes: water, areas devoid of vegetation, regions with low vegetation, and those exhibiting high vegetation, as detailed in Table 1. The meticulous quantification of each class and the computation of their respective percentages and spatial extents is meticulously presented in Tables 4 and 5. This classification facilitates a nuanced understanding of the study area’s landscape dynamics and vegetation distribution.

Table 4

Area and percentage of each class of NDVI classes for MODIS

Year MODIS
Class 1 Class 2 Class 3 Class 4
Area (km2) Area (%) Area (km2) Area (%) Area (km2) Area (%) Area (km2) Area (%)
2000 3585.0 0.8 385372.4 87.9 49180.1 11.2 179.5 0.0
2001 3104.7 0.7 358558.6 81.8 76033.5 17.3 620.3 0.1
2002 3321.4 0.8 358266.7 81.7 76149.4 17.4 579.5 0.1
2003 3588.1 0.8 353241.4 80.6 80994.3 18.5 493.2 0.1
2004 4302.9 1.0 342773.9 78.2 90254.6 20.6 985.7 0.2
2005 4049.7 0.9 363042.1 82.8 68068.1 15.5 3157 0.7
2006 4211.6 1.0 358204.7 81.7 74721.3 17.0 1179.5 0.3
2007 3926.4 0.9 361086.6 82.4 72478.3 16.5 825.7 0.2
2008 3234.9 0.7 386326.3 88.1 48445.0 11.1 310.8 0.1
2009 2915.0 0.7 380257.4 86.8 54969.9 12.5 174.6 0.0
2010 2946.3 0.7 361907.1 82.6 73099.2 16.7 364.5 0.1
2011 3060.0 0.7 374889.2 85.5 59841.9 13.7 525.9 0.1
2012 3114.7 0.7 378101.3 86.3 56790.8 13.0 310.2 0.1
2013 3486.3 0.8 347318.2 79.2 86628.0 19.8 884.5 0.2
2014 3392.2 0.8 325917.5 74.4 107942.4 24.6 1065 0.2
2015 2692.7 0.6 342226.1 78.1 92902.7 21.2 495.5 0.1
2016 2895.7 0.7 336715.1 76.8 97966.5 22.4 739.7 0.2
2017 2604.3 0.6 362314.9 82.7 72518.0 16.5 879.8 0.2
2018 2341.6 0.5 357514.0 81.6 77678.0 17.7 783.5 0.2
2019 3749.6 0.9 270327.0 61.7 162613.9 37.1 1627 0.4
2020 4511.7 1.0 304118.7 69.4 128224.1 29.3 1463 0.3
2021 3390.9 0.8 349241.4 79.7 84827.4 19.4 857.2 0.2
2022 2606.6 0.6 372392.5 85.0 63245.4 14.4 72.5 0.0
Table 5

Area and percentage of each class of NDVI classes for LANDSAT

Year LANDSAT
Class 1 Class 2 Class 3 Class 4
Area (km2) Area (%) Area (km2) Area (%) Area (km2) Area (%) Area (km2) Area (%)
2000 5807.1 1.3 417798.8 95.3 14671.3 3.3 39.8 0.0
2001 6600.4 1.5 392518.8 89.6 38667.2 8.8 530.6 0.1
2002 7685.4 1.8 404151.1 92.2 26114.4 6.0 366.1 0.1
2003 8391.8 1.9 374131.8 85.4 53048.8 12.1 2744.6 0.6
2004 8805.0 2.0 398267.2 90.9 30512.5 7.0 732.2 0.2
2005 8481.5 1.9 402931.5 91.9 26473.1 6.0 430.9 0.1
2006 8838.2 2.0 391136.6 89.2 37664.9 8.6 677.3 0.2
2007 7595.2 1.7 394880.0 90.1 35371.3 8.1 470.5 0.1
2008 6064.3 1.4 410403.3 93.6 21732.2 5.0 117.3 0.0
2009 5036.1 1.1 409569.8 93.4 23581.9 5.4 129.1 0.0
2010 5138.2 1.2 410767.6 93.7 22244.3 5.1 166.9 0.0
2011 5050.1 1.2 406785.8 92.8 26111.9 6.0 369.2 0.1
2012 5158.9 1.2 406612.9 92.8 26257.3 6.0 287.9 0.1
2013 6599.1 1.5 400813.2 91.4 30480.7 7.0 424.1 0.1
2014 6958.4 1.6 397314.4 90.6 33508.6 7.6 535.7 0.1
2015 5749.9 1.3 404285.5 92.2 27925.7 6.4 355.8 0.1
2016 6122.7 1.4 398789.3 91.0 33111.7 7.6 293.3 0.1
2017 5376.8 1.2 404772.0 92.3 27761.6 6.3 406.6 0.1
2018 4709.6 1.1 402269.4 91.8 30823.4 7.0 514.6 0.1
2019 10451.9 2.4 380321.5 86.8 46377.1 10.6 1166.5 0.3
2020 9305.4 2.1 389584.0 88.9 38673.0 8.8 754.6 0.2
2021 7349.6 1.7 393595.7 89.8 36704.5 8.4 667.2 0.2
2022 5799.1 1.3 396447.0 90.4 35431.2 8.1 639.7 0.1

As delineated in Tables 3 and 4, the years 2000, 2008, 2010, and 2009 emerged as critical drought periods, exhibiting the lowest vegetation cover percentages (3.356, 4.984, 5.112, and 5.409%, respectively). Landsat imagery corroborated these findings, indicating the lowest NDVI values during these drought events. Similarly, the years 2008, 2000, 2009, 2012, and 2022 showcased the lowest vegetation cover percentages (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively) along with the lowest NDVI values, as gleaned from MODIS data. For a visual representation of these trends, refer to Figure 3, which illustrates the time series of the area distribution for each vegetation class. This temporal analysis provides a comprehensive perspective on the fluctuations in vegetation cover over the designated years, offering valuable insights into the impact of drought on the study area.

Figure 3 Time series of area for each NDVI class.
Figure 3

Time series of area for each NDVI class.

Figure 4 unveils a dynamic time series encapsulating the ebb and flow of NDVI values, presenting a nuanced chronicle of the vegetation dynamics over time. This graphical representation allows for a comprehensive examination of the temporal variations in NDVI, providing a visual narrative of the evolving vegetation cover. Furthermore, the intricate interplay between NDVI and annual precipitation is elucidated in the same figure. This visual depiction fosters a deeper understanding of the relationship between vegetation health and precipitation patterns, shedding light on how environmental factors, particularly annual precipitation, influence the observed variations in NDVI values over the designated timeframe [59]. This insightful analysis contributes to a holistic comprehension of the intricate ecological dynamics within the study area.

Figure 4 Relationship between NDVI value and precipitation.
Figure 4

Relationship between NDVI value and precipitation.

Namely, Figure 4 shows a close correspondence between NDVI values and precipitation for the study period (2000–2022). The NDVI values decreased rapidly when the rainfall decreased, especially during drought. In addition, the results showed a relatively strong relationship between the lowest NDVI values and the lowest precipitation amounts. The maps of the spatial distribution of NDVI classes for MODIS and Landsat imagery are shown in Figures 5 and 6.

Figure 5 Spatial distribution of NDVI classes for MODIS.
Figure 5

Spatial distribution of NDVI classes for MODIS.

Figure 6 Spatial distribution of NDVI classes for LANDSAT.
Figure 6

Spatial distribution of NDVI classes for LANDSAT.

As illustrated in Figures 5 and 6, a compelling narrative unfolds: the northern region of our study area emerges as the least susceptible to agricultural drought. This resilience is attributed to its robust precipitation patterns, with the area receiving substantial rainfall predominantly from October to May, peaking between December and February [56]. The central regions of Iraq, close to rivers, similarly exhibit a lower vulnerability to drought.

To augment our understanding, an additional layer of analysis is presented in Table 6 through the computation of NDVI anomalies. This crucial metric offers an insightful perspective on deviations from the norm, enabling a deeper comprehension of vegetation responses to varying climatic conditions across the study area. These combined visuals and quantitative measures comprehensively depict the spatial and temporal intricacies of agricultural drought impact in Iraq.

Table 6

NDVI anomaly for every year of the study period (2000–2022)

Year MODIS LANDSAT Year MODIS LANDSAT
Anomaly (%) Anomaly (%) Anomaly (%) Anomaly (%)
2000 −15.48 −22.57 2012 −11.05 −9.17
2001 2.10 9.17 2013 1.37 −0.32
2002 −3.50 −8.20 2014 12.43 8.74
2003 −2.67 12.18 2015 4.18 −0.97
2004 4.87 −1.72 2016 5.70 5.29
2005 −6.25 −8.09 2017 −5.08 −3.56
2006 −2.95 0 2018 1.71 −2.91
2007 −5.21 −2.80 2019 37.56 27.53
2008 −14.97 −12.20 2020 16.82 9.61
2009 −12.15 −12.63 2021 1.37 2.37
2010 −5.42 −11.90 2022 −8.99 −22.57
2011 −5.15 −6.80

Drawing insights from the percentage of NDVI anomalies and the drought severity classification outlined in Table 2, this study categorizes 2008, 2009, 2010, and 2022 as moderate drought periods. In contrast, according to Landsat results, 2000 is designated as a severe drought year. The MODIS anomaly outcomes characterize 2000, 2008, 2009, and 2012 as moderate drought years, while 2022 is mild.

Crucially, the alignment between Landsat’s and MODIS’s results is noteworthy, underscoring a consensus in their findings. However, nuanced disparities exist, attributable to the distinctive spatial resolutions of the imaging technologies. Landsat’s higher spatial resolution (30 m) offers finer details than MODIS (250 m), influencing the precision of drought severity classification. Acknowledging resolution disparities enhances the contextual interpretation of the study’s outcomes [60].

7 Conclusions

Over 23 years (2000–2022), this research harnessed RS, specifically employing the NDVI, to scrutinize the impact of drought on Iraqi agriculture. By elucidating the study’s objective of assessing agricultural drought in Iraq through NDVI and RS, we gain crucial insights into the implications of climate change on agriculture and water resource management. Providing illuminating details on the intensity and spatial distribution of Iraq’s prolonged drought, the study facilitates identifying vulnerable areas and formulating targeted mitigation plans.

The study generated multitemporal drought maps by utilizing MODIS vegetation indices at resolutions of 250 m and Landsat satellite imagery at 30 m. Notably, the findings from Landsat imagery pinpointed the lowest vegetation cover percentages during drought years 2000, 2008, 2010, and 2009 (3.356, 4.984, 5.112, and 5.409%, respectively), while drought years 2008, 2000, 2009, 2012, and 2022 exhibited the highest percentages (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively).

Exploring the relationship between NDVI and precipitation unveiled a robust correlation, with NDVI values mirroring precipitation variations, particularly during drought. A comparative analysis of Landsat and MODIS outcomes, though exhibiting minor differences due to the superior spatial resolution of Landsat, yielded congruent conclusions overall.

However, acknowledging certain limitations, the study relies on NDVI data and satellite imagery, presenting potential spatial and temporal resolution constraints. Notably, it exclusively focuses on agricultural droughts, omitting other drought types. Future research should integrate diverse data sources, encompassing ground-based measures such as soil moisture and socioeconomic data such as agricultural yields and economic losses, to enhance precision. Collaboration with local stakeholders is pivotal for relevance and applicability. Ultimately, this study underscores the efficacy of NDVI coupled with RS as a cost-effective means to monitor drought in Iraqi agriculture swiftly.

Acknowledgement

Alban Kuriqi is grateful for the Foundation for Science and Technology’s support through funding UIDB/04625/2020 from the research unit CERIS.

  1. Funding information: This study did not receive any funding.

  2. Conflict of interest: The authors declare that they have no conflict of interest.

  3. Data availability statement: Upon request.

  4. Consent to participate: Yes.

  5. Consent for publication: Yes.

  6. Ethical approval: Not applicable.

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Received: 2023-10-15
Revised: 2023-12-02
Accepted: 2023-12-18
Published Online: 2024-03-04

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
https://doi.org/10.1515/eng-2022-0583
Schlagwörter für diesen Artikel
climate change; NDVI; remote sensing; vegetation; water scarcity
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Artikel in diesem Heft

  1. Regular Articles
  2. Methodology of automated quality management
  3. Influence of vibratory conveyor design parameters on the trough motion and the self-synchronization of inertial vibrators
  4. Application of finite element method in industrial design, example of an electric motorcycle design project
  5. Correlative evaluation of the corrosion resilience and passivation properties of zinc and aluminum alloys in neutral chloride and acid-chloride solutions
  6. Will COVID “encourage” B2B and data exchange engineering in logistic firms?
  7. Influence of unsupported sleepers on flange climb derailment of two freight wagons
  8. A hybrid detection algorithm for 5G OTFS waveform for 64 and 256 QAM with Rayleigh and Rician channels
  9. Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy
  10. Exploring the potential of ammonia and hydrogen as alternative fuels for transportation
  11. Impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings at various climate zones
  12. Advanced autopilot design with extremum-seeking control for aircraft control
  13. Adaptive multidimensional trust-based recommendation model for peer to peer applications
  14. Effects of CFRP sheets on the flexural behavior of high-strength concrete beam
  15. Enhancing urban sustainability through industrial synergy: A multidisciplinary framework for integrating sustainable industrial practices within urban settings – The case of Hamadan industrial city
  16. Advanced vibrant controller results of an energetic framework structure
  17. Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
  18. Improved correlation of soil modulus with SPT N values
  19. Technologies for high-temperature batch annealing of grain-oriented electrical steel: An overview
  20. Assessing the need for the adoption of digitalization in Indian small and medium enterprises
  21. A non-ideal hybridization issue for vertical TFET-based dielectric-modulated biosensor
  22. Optimizing data retrieval for enhanced data integrity verification in cloud environments
  23. Performance analysis of nonlinear crosstalk of WDM systems using modulation schemes criteria
  24. Nonlinear finite-element analysis of RC beams with various opening near supports
  25. Thermal analysis of Fe3O4–Cu/water over a cone: a fractional Maxwell model
  26. Radial–axial runner blade design using the coordinate slice technique
  27. Theoretical and experimental comparison between straight and curved continuous box girders
  28. Effect of the reinforcement ratio on the mechanical behaviour of textile-reinforced concrete composite: Experiment and numerical modeling
  29. Experimental and numerical investigation on composite beam–column joint connection behavior using different types of connection schemes
  30. Enhanced performance and robustness in anti-lock brake systems using barrier function-based integral sliding mode control
  31. Evaluation of the creep strength of samples produced by fused deposition modeling
  32. A combined feedforward-feedback controller design for nonlinear systems
  33. Effect of adjacent structures on footing settlement for different multi-building arrangements
  34. Analyzing the impact of curved tracks on wheel flange thickness reduction in railway systems
  35. Review Articles
  36. Mechanical and smart properties of cement nanocomposites containing nanomaterials: A brief review
  37. Applications of nanotechnology and nanoproduction techniques
  38. Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review
  39. Communication
  40. Techniques to mitigate the admission of radon inside buildings
  41. Erratum
  42. Erratum to “Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy”
  43. Special Issue: AESMT-3 - Part II
  44. Integrated fuzzy logic and multicriteria decision model methods for selecting suitable sites for wastewater treatment plant: A case study in the center of Basrah, Iraq
  45. Physical and mechanical response of porous metals composites with nano-natural additives
  46. Special Issue: AESMT-4 - Part II
  47. New recycling method of lubricant oil and the effect on the viscosity and viscous shear as an environmentally friendly
  48. Identify the effect of Fe2O3 nanoparticles on mechanical and microstructural characteristics of aluminum matrix composite produced by powder metallurgy technique
  49. Static behavior of piled raft foundation in clay
  50. Ultra-low-power CMOS ring oscillator with minimum power consumption of 2.9 pW using low-voltage biasing technique
  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
  74. Numerical and experimental study of the impact on aerodynamic characteristics of the NACA0012 airfoil
  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
  77. Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study
  78. Flexural behavior of RC beams externally reinforced with CFRP composites using various strategies
  79. Influence of fiber types on the properties of the artificial cold-bonded lightweight aggregates
  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
  82. An experimental and numerical study investigating sediment transport position in the bed of sewer pipes in Karbala
  83. Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach
  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
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