An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

Du, Haoyang; Li, Manchun; Xu, Yunyun; Zhou, Chen

doi:10.1109/jstars.2023.3247624

Cited by 20 publications

(3 citation statements)

References 81 publications

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“…In situations when data cannot be separated linearly, SVM employs a method known as the kernel trick. This strategy entails mapping the data onto a higher dimensional space, where a hyperplane may effectively separate [66,67]. Figure 3 illustrates the concept of SVM.…”

Section: Support Vector Machine (Svm)mentioning

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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Section: Support Vector Machine (Svm)mentioning

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

View full text Add to dashboard Cite

show abstract

“…Ensemble learning is an essential technique in ML for land use and land cover mapping [86]. To avoid the biases of a single model in terms of performance (e.g., classification accuracy) of the estimated model, the application of an ensemble learning is crucial for land use and land cover classification [87]. It combines fine-tune ML algorithms to develop a robust predictive model [88].…”

Section: Ensemble and Transfer Learningmentioning

confidence: 99%

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Barman,

Mustak,

Kuffer

et al. 2023

Sustainability

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Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

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“…The powerful fitting capabilities of machine learning techniques enable the collaborative use of multi-band remote sensing data for SSM retrieval [26][27][28]. It can solve the complex nonlinear problem between surface parameters, vegetation index, and radar backscattering coefficients [29,30]. Therefore, many scholars have started to combine multi-band (C-, L-, X-, etc.)…”

Section: Introductionmentioning

confidence: 99%

Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data

Xu,

Liu,

Wang

et al. 2024

Remote Sensing

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Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.

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An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

Cited by 20 publications

References 81 publications

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

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

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data

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