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The rate of global urbanization is exponentially increasing and reducing areas of natural vegetation. Remote sensing can determine spatiotemporal changes in vegetation and urban land cover. The aim of this work is to assess spatiotemporal variations of two vegetation indices (VI), the Normalized Difference Vegetation Index (NDVI) and Enhanced Veget...
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... the pixel representation must be more than 2/3 of the area, therefore the 2009 data for both March and De- cember contain a very high portion of bad pixels. Sev- eral years of data captured in December (2000-2002, 2006-2009 and 2011) also contain very high portions of bad pixels (Table 1). MODIS land covers (MCD12Q1) the quality con- trol of the pixels was verified and all were excellent. ...
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... The most common point of these studies is the utilization of a diverse range of vegetation indices derived from satellite data, such as the NDVI [10]. The NDVI is the most sensitive indicator for chlorophyll in vegetation considering its density and dynamics [11]. Another new study investigated vegetation cover in the Middle East with vegetation cover dynamics, and this research identified a notable rise in NDVI coverage across numerous countries in the Middle East. ...
This research paper is made to investigate the vegetation land cover and no vegetation land cover that changes in Iraq during the period 2000-2023 Utilizing the 1-month/1000 m composites of the Normalized Difference Vegetation Index (NDVI) derived from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS; product code MOD13Q1). The period was divided into five years (2004, 2009, 2014, 2019, 2023) and compared with the start year 2000; to calculate changes (increase and decrease) in vegetation cover. The results of the spatial analysis using the ArcGIS program showed that by comparing April (the growing month in Iraq) during 2000 as the start year with each selected year through the interval, the highest value of lands covered by vegetation observed in 2019, where vegetation land reached 223,780.17 km ² (51.2%) of the total area, while the start year 2000 was 63,462.53 km ² (14.5%) of the total area. In contrast, the lowest value of vegetation was in 2009, during this year vegetation land cover value was approximately 61,696.33 km ² (14%) of the total area. Maps also demonstrated that the highest increase in vegetation cover was concentrated in the northern part, moderate in the eastern part of the alluvial plain, and sparse in the southern regions. Overall, the research provides valuable insights into long-term vegetation trends in Iraq, crucial for understanding ecosystem dynamics and informing environmental management strategies.
... It boasts frequent data acquisition, serves as an accessible and standardized information source across diverse spectral regions, and furnishes a global perspective on phenomena [21][22][23][24][25]. Spectral models like the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI) have demonstrated their efficacy in various studies [26][27][28][29][30][31][32][33][34]. These images have enabled the identification of substantial changes in vegetation, given that variations in vegetation indices exhibit significant correlations with green biomass content and plant water stress [35,36]. ...
Utilizing path analysis, we examined the interconnectedness among six meteorological variables. Among these, three pertain to energy conditions—air temperature, net solar radiation, and reference evapotranspiration (ET0)—while the others are associated with hydrological conditions: precipitation, relative humidity, and water deficiency. These variables were assessed across five distinct temporal delay levels to understand their influences on the normalized difference vegetation Index (NDVI) and enhanced vegetation index (EVI) within grassland areas situated in the state of Espírito Santo, southeastern Brazil. The images underwent processing using analytical algorithms and a geographic information system (GIS). The direct and indirect impacts of these variables on the NDVI and EVI exhibited remarkable similarity across varying temporal delays and geographic regions. Meteorological variables explained over 50% of the observed variation in both indices, occasionally even reaching levels of 70%. Temperature and relative humidity primarily exerted direct effects on the indices. Conversely, precipitation exhibited indirect effects on the indices, often in conjunction with other hydrological variables. ET0 demonstrated a direct effect on the vegetation indices, particularly after a delay of 32 days. Solar radiation and water deficiency displayed direct effects up to the 32-day mark, implying that vegetation responds more promptly to these variables. The proposed methodology enabled a consistent and stable assessment of the direct and indirect effects of meteorological variables on vegetation indices.
... The results in Table ( In general, the vegetation cover in 2022 increased compare to 1988 was due to the increases in growth intensity with the increasing age of trees, especially those planted at the main entrance area of the campus and other roads, the cultivation of shrubs that increased in growth, planting of herbaceous annual plants (winter season) and the increase in the number of football sports fields that were characterized by green spaces that have good growth and green color in Winter season, the farms and fields belonging to the College of Agriculture and the creation of landscape in separate places of the campus, while the lowest NDVI values were in 1988, due to the greater percentage of barren lands, the lack of buildings and facilities, as well as the small size and number of plants planted during that period, since the university campus was in the stage of establishment and emergence, as well as the vegetation cover is subject to the influence of climatic elements such as rainfall, and marked temperature variations [25]. (Figure 4). ...
This study aims to investigate changes in vegetation cover of the University of Baghdad campus by Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) as a tool to determine the changes in vegetation cover on the University landscape were used for the period 1988 and 2022 for two seasons (Winter and Summer). Land cover was divided into four categories: Barren & buildings, Low, Medium, and Dense vegetation. The results showed an increase in NDVI values in winter (-0.2-0.5), and (-0.1-0.9) in 1988, and 2022 respectively, and recorded in summer (-0.3-0.5) and (-0.04-0.4) in 1988, and 2022 respectively. The highest value was recorded in winter of 2022. Barren & buildings recorded the highest value 1470630 Km ² with 43% in summer of 1988 while in 2022 recorded 1411156 Km ² with 40%, for both seasons. The Low vegetation recorded the highest value 130031 Km ² with 377% in winter 1988 and the lowest was recorded at 941671 Km ² with 28% in summer 2022. Medium vegetation was recorded the highest 787580 Km ² with 22% in winter 2022 and lowest 667730 Km ² with 19 %. Dense vegetation was the highest 413614 Km ² with 12 % in summer 2022 while the lowest was recorded 211763 Km ² with 6 % in winter 1988. SAVI values were recorded in winter (-0.3-0.7), (-0.1-0.4) in 1988 and 2022 respectively and in summer (-0.4 - 0.7)(-0.06 - 0.6) in 1988 and 2022 respectively. The results showed slight differences from NDVI values of vegetation cover (Dense). The integration of remote sensing and geographic information systems is an effective and sustainable tool for studying land cover changes in order to obtain reliable and accurate rapid and economical results.
... Its value ranges from −1 to 1, with a typical range of 0.2 to 0.9 for green vegetation areas. It is generally considered that when the NDVI705 value is less than 0.2, it indicates that the plant is under stress [27,30]. In this study, the NDVI705 decreased first and then increased with increasing cadmium concentrations during the seedling stage, but its value was lower than that of the CK group. ...
In order to rapidly and accurately monitor cadmium contamination in lettuce and understand the growth conditions of lettuce under cadmium pollution, lettuce is used as the test material. Under different concentrations of cadmium stress and at different growth stages, relative chlorophyll content of lettuce leaves, the cadmium content in the leaves, and the visible-near infrared reflectance spectra are detected and analyzed. An inversion model of the cadmium content and relative chlorophyll content in the lettuce leaves is established. The results indicate that cadmium concentrations of 1 mg/kg and 5 mg/kg promote relative chlorophyll content, while concentrations of 10 mg/kg and 20 mg/kg inhibit relative chlorophyll content. The cadmium content in the leaves increases with increasing cadmium concentrations. Cadmium stress caused a “blue shift” in the red edge position only during the mature period, while the red valley position underwent a “blue shift” during the seedling and growth periods and a “red shift” during the mature period. The green peak position exhibited a “blue shift”. After model validation, it was found that the model constructed using the ratio of red edge area to yellow edge area and the normalized values of red edge area and yellow edge area effectively estimated the cadmium content in lettuce leaves. The model established using the normalized vegetation index of the red edge and the ratio of the peak green value to red shoulder amplitude can effectively estimate the relative chlorophyll content in lettuce leaves. This study demonstrates that the visible-near infrared spectroscopy technique holds great potential for monitoring cadmium contamination and estimating chlorophyll content in lettuce.
... Summers are hot and dry, and winters are cold and rainy. The average annual rainfall between October and November amounts to 543 mm (Hussein et al., 2017). ...
This study authorizes processes and approaches using optical and microwave data to determine the availability of water in the study area at any given moment. This will aid in identifying the optimal time and location for irrigation to enhance crop growth. For this purpose, a set of spectral vegetation parameters (from Sentinel-2), soil moisture (from Sentinel-1), evapotranspiration, and surface temperature (from Landsat-8) were used, along with field data on water content and irrigation timing. The results showed that both NDVI and NDMI are highly sensitive to moisture, making them the best indices for determining the timing and location of irrigation. This research contributes to sustainable agricultural development. It has implications for farmers, policymakers, and researchers in optimizing irrigation schedules, developing policies for sustainable agriculture, and enhancing crop productivity while conserving water resources. This approach can be particularly useful in regions facing water scarcity, where the efficient use of water resources is crucial for sustainable agricultural development
... Lower NDVI and EVI were observed in cropland than in the other classes, which addresses the sensitivity of indices towards the type of plant species/crops cultivated. These results were corroborated by Hussein et al. (2017), where NDVI and EVI showed sensitivity towards the dense canopies in North and South America and also in the South Western parts of Gujarat by Dhanapriya et al. (2018). The NDVI showed no varia-tion within the years during the study period except for 2011 and 2012. ...
Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period.
... Since the country's food security depends mainly on its agricultural production, MD monitoring and prediction are of paramount importance for their sustainable development. Despite numerous studies on drought monitoring across Iraq (e.g., [27][28][29][30][31]), developing a drought prediction tool has rarely been studied in the relevant literature [32]. This is mainly due to the lack of long-term observatory data needed to identify the underlying complex pattern. ...
Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However, various earth science and environmental databases are available that can be used for hydrological studies, even at a catchment scale. In this study, the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then, the variational mode decomposition (VMD), in conjunction with state-of-the-art genetic programming, is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil, Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks.
... The climate in the region is semi-arid, similar to that of the Mediterranean region, with hot and dry summers and cold and rainy winters, according to Köppen classification (Koeppe & Ridgley, 1958). The annual average rainfall is 543 mm, with inadequate precipitation during October and November (Hussein et al., 2017). The study area consists of four types of soil, which are a mixture of lithology with limestone, brown soils with medium thickness, and deeply covered with lucky dust stone, according to the soil classification (Buringh, 1960). ...
Crop classification plays a crucial role in ensuring food security, agricultural policy development, and effective land management. Remote sensing data, particularly Sentinel-1 and Sentinel-2 data, has been widely used for crop mapping and classification in cloudy regions due to their high temporal and spatial resolution. This study aimed to enhance the classification accuracy of grain crops, specifically barley and wheat, by integrating Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral instrument (MSI) data. The study utilized two classification models, random forest (RF) and classification and regression trees (CART), to classify the grain crops based on the integrated data. The results showed an overall accuracy (OA) of 93%, and a Kappa coefficient (K) of 0.896 for RF, and an OA of 89.15% and K of 0.84 for the CART classifier. The integration of both radar and optical data has the potential to improve the accuracy of crop classification compared to using a single-sensor classification technique. The significance of this study is that it demonstrates the effectiveness of integrating radar and optical data to improve crop classification accuracy. These findings can be used to support crop management, environmental monitoring, and policy development, particularly in areas with cloud cover or limited optical data. The study’s implications are particularly relevant in the context of global food security, where accurate crop classification is essential for monitoring crop health and yield estimation. Concisely, this study provides a useful approach for crop classification using Sentinel-1 and Sentinel-2 data integration, which can be employed to support sustainable agriculture and food security initiatives.
... Numerous studies have shown the importance of remote sensing, information technology, and automatic monitoring systems to sustainable watershed development [30][31][32]. These approaches have already been applied to assess drought and vegetation coverage across Iraq [33][34][35][36]. For example, [34] used the SPI and the NDVI to develop drought risk maps for Iraq. ...
... The authors showed that approximately 24% of Iraq's territory suffers from severe or extremely severe drought risk. In another study, [36] investigated urban growth and the vegetation coverage extent within and around the city of Erbil using MODIS imagery. The study showed that most of the green area was replaced with urban areas over the period of 2000-2015. ...
... Regarding the Erbil governorates, the correlation analysis showed that the relationship between the SPEI and the MODIS-NDVI is positive but insignificant. This finding is in line with the results of [36], which demonstrated no evidence of a correlation between any climate variable compared to the vegetation indices over the city of Erbil and its surroundings. A similar conclusion was reported by [28], in which a correlation analysis between the SPI and NDVI was carried out. ...
In this article, meteorological and agricultural droughts across the Erbil province, Iraq, were assessed using remote sensing data and satellite products. To this end, the long-term (2000–2022) Standardized Precipitation Evapotranspiration index (SPEI) at 1- and 3-month accumulation periods (SPEI-1 and SPEI-3) as well as the Normalized Difference Vegetation Index (NDVI) across Erbil were utilized. While the former was retrieved from the global SPEI data repository, the latter was derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The spatiotemporal variations in the SPEI indices indicated that two to nine extreme drought events occurred in the province with an increasing northward pattern. An increasing trend in the long-term NDVI series was also detected, having more diversity in vegetation coverage in the northern part of the province. The relationship between the SPEI and MODIS-NDVI was found to be positive but insignificant. Thus, we concluded that short-term meteorological droughts were not the only reason for the agricultural droughts in Erbil. Furthermore, the climate characteristics related to the cumulative water balance over a previous season is not an important trigger for the spatial variation in vegetation coverage across the province.
... In assessing exposure to greenness, many studies use various data and technical methods. Hussein and colleagues analyzed green exposure using two satellitedsserived vegetation indices from a Moderate Resolution Imaging Spectroradiometer (MODIS), a Normalized Difference Vegetation Index (NDVI) and an Enhanced Vegetation Index (EVI) [1]. An NDVI is widely used to estimate greenness because this index is sensitive to the presence of vegetation and its dynamics [2]. ...
Remote sensing technology is used practically in epidemiological studies. This chapter demonstrates how green exposure that is related to health burdens is analyzed. Using satellite-derived data, green exposure is represented as a Normalized Difference Vegetation Index (NDVI). Population and individual studies are used, and several green estimation methods are implemented, wherein the spatial data for the study object is linked to the NDVI. The variables that influence the health burden and the effectiveness of the effect of green exposure on health are determined. This study uses adjusted spatial-statistical methods to determine the relationship between green exposure and health burdens. The overall findings show a significant relationship between green exposure and health burden in both regional and global analyses. This study increases knowledge of remote sensing applications to preserve the environment and health.KeywordsGreen exposureHuman healthRegional-global analysisSatellite-derived vegetation indices
