Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Yousefi, Saleh; Mirzaee, Somayeh; Almohamad, Hussein; Dughairi, Ahmed Abdullah Al; Gomez, Christopher; Siamian, Narges; Alrasheedi, Mona; Abdo, Hazem Ghassan

doi:10.3390/land11070993

Cited by 22 publications

(7 citation statements)

References 67 publications

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“…The data was analysed using ArcGIS version 10.8 to detect land use/land cover changes. Standard image classification procedures were used in the analysis of the land use/land cover types (Khatami et al, 2016;Ma et al, 2017;Costa et al, 2018;Yousefi et al, 2022). Pre-classification involved band stacking and image cleaning (brightness, transparency, and contrast) (Kazemi et al, 2011;Saing et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Interrogating the Impact of Land Use/Land Cover Dynamics in Mbarara City in Southwestern Uganda

Nuwagira

Yasin

Nasasira

2023

East Afr. j. environ. nat. resour.

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The urbanization in Mbarara City Southwestern Uganda has increased the number of built-up and small-scale agricultural areas thus escalating pressure on the grassland and woodlot areas hence influencing land fragmentation and slum development. In this study, we focused on land use/land cover dynamics. This helped us to identify land uses that reduce grassland areas, which increases land fragmentation, and slum development. Presumably, the land uses in the area included built-up areas, grassland, small-scale agriculture, and woodlots. Built-up, grassland, and small-scale agriculture were vital for our analysis. We employed remote sensing techniques (Supervised-Maximum Likelihood Classifier) with two cloud-free high-resolution images of 2010 and 2022 in mapping spatial-temporal patterns in built-up, grassland, and small-scale agriculture. Our results evidenced that built-up area increased by (5.14%), grassland declined by (7.1%), small-scale agriculture increased by (2.62%), and woodlots reduced by (0.73%) between 2010 to 2022 and the accuracy assessment for the image classification was (83%). Our study found that an increase in built-up areas and small-scale agriculture led to the decline of the grassland and woodlot areas between 2010 to 2022 with a statistically significant difference (p = 0.03). There was also a strong positive correlation (r = 0.97) between land use/land cover types in 2010 and 2022. Our study recommends that Mbarara City Authority should put strict laws and regulations governing the minimum size of land required during sub-division to avoid land fragmentation which results in increased slum development. Increased land fragmentation may have an intense impact on important ecosystems such as grasslands.

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Section: Discussionmentioning

confidence: 99%

Interrogating the Impact of Land Use/Land Cover Dynamics in Mbarara City in Southwestern Uganda

Nuwagira

Yasin

Nasasira

2023

East Afr. j. environ. nat. resour.

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“…SVM incorporates parameters such as Cost C, a regularization parameter that balances the need to model the training data accurately and keep the model simple for good performance on unseen data [50]. The sigma hyperparameter determines the kernel coefficients and influences the impact of a single training example on defining the decision boundary [59]. The number of support vectors indicates the examples used to define the decision boundary [60].…”

Section: Workflow Scenariomentioning

confidence: 99%

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Utama,

Indriani,

Hermana

et al. 2024

J. Hum. Earth Future

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The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization of water resources, as well as for striking a balance between the production of renewable energy and the preservation of the environment. This study primarily compared Support Vector Machine (SVM) and Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 and Sentinel 2 between 2021 and 2023, to monitor land use in the Patuha geothermal area. The objective is to improve sustainable water management practices by accurately categorizing different land cover types. This comparative analysis assessed the efficacy of these techniques in upholding water sustainability in geothermal regions. This study examined the application of SVM and RF machine learning techniques, with particular emphasis on parameter refinement and model assessment, to enhance land use classification accuracy. By employing Kernlab and e1071 for algorithm comparison, the research sought to produce a precise Land Use Model Map, which underscores the significance of advanced analytical techniques in environmental management. This approach was of utmost importance in improving land use monitoring and reinforcing sustainable practices. The comparative evaluation of SVM and RF methods for land use classification demonstrates the superiority of RF in terms of accuracy, stability, and precision, particularly in intricate urban settings, hence establishing it as the preferred model for tasks demanding high reliability. The application of SVM and RF for monitoring land use in geothermal areas is in alignment with Sustainable Development Goals (SDGs) 6 and 15, as it fosters sustainable water management and the conservation of ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

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“…Sentinel-2 satellite imagery has been widely used for LULC studies [18][19][20][21]. As of 2015, the Sentinel-2A Multispectral Imager (MSI) offers 13 optical bands: four bands at 10 m spatial resolution, six bands at 20 m spatial resolution, and three bands at 60 m spatial resolution and the 10 m spatial resolution can be used to improve the spatial resolution of other 20 or 60 m bands [22,23].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Tempa,

Ilunga,

Agarwal

et al. 2024

Applied Sciences

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Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

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Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Cited by 22 publications

References 67 publications

Interrogating the Impact of Land Use/Land Cover Dynamics in Mbarara City in Southwestern Uganda

Interrogating the Impact of Land Use/Land Cover Dynamics in Mbarara City in Southwestern Uganda

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

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