Conference Papers
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Item A NDVI Based Approach To Detect The Landslides By Using Google Earth Engine(Institute of Electrical and Electronics Engineers Inc., 2023) Vishnu Vardhan, M.; Harish Kumar, S.; Mohan Kumar, S.; Kundapura, S.Detection of landslide-prone areas plays an important role in planning urban connectivity like roads, bridges, etc. Landslides are generally caused by a variety of factors, the most important of which is rainfall. In this paper, the detection is carried out in four taluks of Chikkamagaluru district, namely Koppa, Sringeri, Mudigere, and Narashimarajpur; these four taluks are located in the Western Ghat region. Landslides are primarily caused by heavy rainfall during the monsoon season. For the detection of landslides, Sentinel optical and SAR data are used because of their 10metre resolution and revisiting period of two to five days. The entire methodology for detecting landslides is carried out in Google Earth Engine due to its large collection of data, which aids in multi-temporal studies. This paper attempts to investigate the capabilities of remote sensing and GIS techniques in the detection of landslides. For the detection of landslides, Normalized Difference Vegetation Index (NDVI) is used for Sentinel-2 data and the SAR backscatter change approach is used for Sentinel-1 images, and I thresholding is applied to both methods to detect areas where landslides had occurred. The main thing is that no previous landslide inventory data is used for detection. The previous landslide inventory is used for validation purposes only. Finally, the performance of both approaches was compared using accuracy assessment properties such as overall accuracy and kappa coefficient to determine which approach is superior. © 2023 IEEE.Item AN Integrated Analysis and Forecasting of Wildfires in the Nallamala Hills, India(Institute of Electrical and Electronics Engineers Inc., 2023) Kundapura, S.; Vishnu Vardhan, M.; Apoorva, K.V.Wildfires threaten ecosystems, human lives, and infrastructure, necessitating effective detection and prediction methods. In this study, an in-depth analysis of wildfire detection and forecasting is carried out over the Nallamala hills, which stretch across the states of Telangana and Andhra Pradesh. Our approach comprises three significant steps: Active fire analysis, pre-fire analysis, and post-fire analysis. Pre-fire maps were created using the Normalised Difference Vegetation Index (NDVI) during the pre-fire analysis, which involved time series analysis of significant components. For active fire analysis, the first dataset is created by using satellite imagery and its derived products. A dataset is used to train the five different machine-learning models for prediction. Among these models, the Random Forest classifier outperformed the remaining four models (Support vector Classifier, Gradient Boosting Classifier, Logistic Regression, and K-means algorithms) in accurately detecting and predicting active fires. This step enabled real-Time monitoring and prioritisation of firefighting efforts. The burnt area calculation uses the Normalised Burn Ratio (NBR) in the post-fire analysis. The analysis implemented post-fire rehabilitation and restoration efforts, giving essential information on the scope and severity of fire damage. The comprehensive study of all wildfires will provide a detailed picture of what occurred in the past (Timeseries), present (Prediction models), and future (Pre-fire maps), allowing people and government agencies to take precautions against future wildfires. © 2023 IEEE.Item Feature Elimination and Comparative Assessment of Machine Learning Algorithms for Flood Susceptibility Mapping in Kerala, India(Institute of Electrical and Electronics Engineers Inc., 2023) Kundapura, S.; Aditya, B.; Apoorva, K.V.Floods are a catastrophic phenomenon with far-reaching consequences for infrastructure, the economy, and human lives, profoundly impacting regions globally. This study assesses flood susceptibility in four districts of Kerala: Ernakulam, Idukki, Kottayam, and Alappuzha. For the 2018 storm that caused flooding by Cyclone Ockhi, a flood map for the area was produced using Sentinel 1 satellite data in Google Earth Engine environment. The resulting map served as the foundation for further analysis. Based on the literature review, 16 potential flood causative factors were identified and incorporated into spatial maps in the Geographic Information System (GIS) environment. Analysis of the flood dataset was performed using Machine Learning (ML) algorithms, namely, Random Forest (RF), Decision Tree (DT), Gradient Boosting Machine (GBM), and XG Boost (XGB). Grid search was employed to identify the optimal hyperparameters for each algorithm, ensuring improved performance. Recursive Feature Elimination (RFE) was subsequently applied to select the most influential variables, resulting in a refined dataset. The chosen factors' feature importance scores were obtained, which were used to create the flood susceptibility map using the four ML models in a GIS environment. Evaluation metrics such as F1 score, accuracy, precision, recall, and ROC-AUC score were computed for each model, providing insights into the effectiveness of each algorithm in predicting the flood occurrence. The resulting flood susceptibility map for the best-performing ML model visually represents the varying levels of flood risk in the study area. © 2023 IEEE.