Hydrogeochemical Mechanism Associated with Land Use Land Cover Indices Using Geospatial, Remote Sensing Techniques, and Health Risks Model

Baloch, Muhammad Yousuf Jat; Zhang, Wenjing; Shoumik, Baig Abdullah Al; Nigar, Anam; Elhassan, Adil A. M.; Elshekh, Ali. E. A.; Bashir, Maaz Osman; Ebrahim, Ahmed Fathi Mohamed Salih; Mohamed, Khalaf alla Adam; Iqbal, Javed

doi:10.3390/su142416768

Cited by 20 publications

(8 citation statements)

References 75 publications

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“…In Pakistan, wheat is a staple food crop that is produced in majority. A wheat-maize cropping pattern has been adopted here under the usage of chemical fertilizers for the last 50 years ( Majeed et al., 2015 ; Jat Baloch et al., 2021 ; Jat Baloch et al., 2022 ). However, it is impossible to achieve the long-lasting targets of highly improved crop production by only relying on the use of chemical fertilizers without any combination ( Sood et al., 2018 ; Khan et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Characterizing indigenous plant growth promoting bacteria and their synergistic effects with organic and chemical fertilizers on wheat (Triticum aestivum)

Asghar,

Ahmed,

Farooq

et al. 2023

Front. Plant Sci.

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The excessive use of chemical fertilizers is deteriorating both the environment and soil, making it a big challenge faced by sustainable agriculture. To assist the efforts for the solution of this burning issue, nine different potential native strains of plant growth-promoting bacteria (PGPB) namely, SA-1(Bacillus subtilis), SA-5 (Stenotrophomonas humi),SA-7(Azospirillum brasilense), BH-1(Azospirillum oryzae), BH-7(Azotobacter armeniacus), BH-8(Rhizobium pusense), BA-3(Azospirillum zeae), BA-6(Rhizobium pusense), and BA-7(Pseudomonas fragi) were isolated that were characterized morphologically, biochemically and molecularly on the basis of 16S rRNA sequencing. Furthermore, the capability of indigenous PGPB in wheat (Triticum aestivum, Chakwal-50) under control, DAP+FYM, SA-1,5,7, BH-1,7,8, BA-3,6,7, DAP+ FYM + SA-1,5,7, DAP+FYM+ BH-1,7,8 and DAP+FYM+ BA-3,6,7 treatments was assessed in a randomized complete block design (RCBD). The results of the study showed that there was a significant increase in plant growth, nutrients, quality parameters, crop yield, and soil nutrients at three depths under SA-1,5,7, BH-1,7,8, and BA-3,6,7 in combination with DAP+FYM. Out of all these treatments, DAP+ FYM + BA-3,6,7 was found to be the most efficient for wheat growth having the highest 1000-grain weight of 55.1 g. The highest values for plant height, no. of grains/spike, spike length, shoot length, root length, shoot dry weight, root dry weight, 1000 grain weight, biological yield, and economic yield were found to be 90.7 cm, 87.7 cm, 7.20 cm, 53.5 cm, 33.5 cm, 4.87 g, 1.32 g, 55.1 g, 8209 kg/h, and 4572 kg/h, respectively, in the DAP+FYM+BA treatment. The DAP+FYM+BA treatment had the highest values of TN (1.68 µg/mL), P (0.38%), and K (1.33%). Likewise, the value of mean protein (10.5%), carbohydrate (75%), lipid (2.5%), and available P (4.68 ppm) was also highest in the DAP+FYM+BA combination. C:P was found to be significantly highest (20.7) in BA alone but was significantly lowest (11.9) in DAP+FYM+BA. Hence, the integration of strains BA-3, BA-5, and BA-7 in fertilizers can be regarded as the most suitable choice for agricultural growth in the sub-mountainous lower region of AJK. This could serve as the best choice for sustainable wheat growth and improved soil fertility with lesser impacts on the environment.

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Section: Introductionmentioning

confidence: 99%

Characterizing indigenous plant growth promoting bacteria and their synergistic effects with organic and chemical fertilizers on wheat (Triticum aestivum)

Asghar,

Ahmed,

Farooq

et al. 2023

Front. Plant Sci.

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“…Notably, changes in LULC can profoundly impact weather conditions and climate patterns, potentially disrupting the delicate water balance in specific geographic regions (Mekonnen et al, 2023). Thus, comprehensive LULC cartography is indispensable for meticulous monitoring, land management, and hazard assessment (Jat Baloch et al, 2022;Acharya et al, 2023). Reducing vegetation cover emerges as a significant driver of elevated land surface temperatures, underscoring the urgent need for proactive land cover management (Moisa et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…When flawlessly integrated into the GEE platform, empowers rigorous LULC research in Pakistan, utilizing Landsat-8 satellite imagery (Panhwar et al, 2024;Safi et al, 2024;Zhao et al, 2024). Various satellite-derived Incorporating indices, such as the Normalized Difference Vegetation Index (NDVI) (DeFries and Townshend, 1994;Jat Baloch et al, 2022) Modified Normalized Difference Water Index (Guo et al, 2017), and Normalized Difference Built Index (NDBI) (Ali et al, 2019), are involved in identifying essential land covers features like Vegetation, Built-up areas, barren land, and water bodies. Integrating machine and deep learning models with Geospatial methods Enhances the accuracy of classification for Land Use and Land Cover (LULC) analysis and robustness (Wang et al, 2022;Zafar et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Nigar,

Li,

Jat Baloch

et al. 2024

Front. Environ. Sci.

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Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation.

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“…LULC models, which combine natural and social aspects of human activity, can analyze these changes over time and space. Remote sensing (RS) and geographic information system (GIS) can be used to analyze LULC, and RS information has helped in spatial studies of urban patterns and planning on different scales, linking local environmental study to international, national, and regional management and protection of LULC (Baloch et al, 2022). Furthermore, groundwater quality is evaluated by measuring chemical, physical, and microbiological characteristics, similar to assessing drinking water quality.…”

Section: Introductionmentioning

confidence: 99%

Assessment of landcover impacts on the groundwater quality using hydrogeochemical and geospatial techniques

Iqbal,

Amin,

et al. 2023

Environ Sci Pollut Res

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Groundwater quality is signi cantly impacted by urbanization and land use land cover (LULC) changes.The current study investigated the impact of LULC on groundwater quality in Quetta city, Baluchistan province, Pakistan. During the years 2015 and 2021, a total of 58 groundwater samples were collected from drinking wells for chemical analysis. The hydrogeochemistry of groundwater was investigated using Gibbs diagrams, Piper diagrams, and spatial distribution maps. The water quality trend was depicted using the Wilcox, USSL diagram, and Water Quality Index (WQI) from 2015 to 2021. The LULC analysis of Quetta was carried out on Google Earth Engine's cloud-computing platform using imagery from the Sentinel-2 satellite with low cloud cover (<10%). The LULC data was later used to calculate the rate of landcover conversion between both years 2015 and 2021 which help to identify the spatial distribution of groundwater and assess its vulnerability to pollution. The ndings indicate an increase in the urban and agricultural classes while a decrease in the barren class. Moreover, according to the Piper diagram, groundwater in Quetta was primarily classi ed as CaMgCl type, CaCl type, and NaCl type. The Gibbs diagrams show water-rock interactions and rock weathering as the dominant evolution of hydrogeochemistry. The majority of the groundwater samples in both years were suitable for irrigation, according to the Wilcox diagram, USSL diagram, and other agricultural indices. The WQI demonstrated that the groundwater sources in the area are safe for human consumption; however, in the northern parts, WQI values are declining due to urbanization over six years.

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Hydrogeochemical Mechanism Associated with Land Use Land Cover Indices Using Geospatial, Remote Sensing Techniques, and Health Risks Model

Cited by 20 publications

References 75 publications

Characterizing indigenous plant growth promoting bacteria and their synergistic effects with organic and chemical fertilizers on wheat (Triticum aestivum)

Characterizing indigenous plant growth promoting bacteria and their synergistic effects with organic and chemical fertilizers on wheat (Triticum aestivum)

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Assessment of landcover impacts on the groundwater quality using hydrogeochemical and geospatial techniques

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