Browsing by Author "Kleomenis Kalogeropoulos"

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A novel deep learning change detection approach for estimating spatiotemporal crop field variations from Sentinel-2 imagery
(Elsevier B.V., 2024) Neelam Dahiya; Gurwinder Singh; Dileep Kumar Gupta; Kleomenis Kalogeropoulos; Spyridon E. Detsikas; George P. Petropoulos; Sartajvir Singh; Vishakha Sood
The analysis of crop variation and the ability to quantify it is a critical and challenging task. Remote sensing (RS) has proven to be an effective tool for monitoring crops and detecting seasonal variations worldwide. This opens new opportunities for developing effective crop monitoring models, with deep learning models showing great promise. This study presents a deep learning-based U-Net v5 Change Detection (UCD) model capable of identifying and monitoring the spatio-temporal variations in crop fields. The application of the model is demonstrated using Sentinel-2 imagery over Patiala district in India to monitor the seasonal crop variation (rabi crop) during 2017–2018. The results have shown that the UCD model has achieved better results (95.6–98.4%) in accuracy for classified maps and more than (91.6%–96.6%) in accuracy for change maps. This study will be useful for crop monitoring, precision agriculture and crop yield prediction and can assist in decision and policy making towards a more sustainable environment. © 2024
Long-Term Spatiotemporal Investigation of Various Rainfall Intensities over Central India Using EO Datasets
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Nitesh Awasthi; Jayant Nath Tripathi; George P. Petropoulos; Pradeep Kumar; Abhay Kumar Singh; Kailas Kamaji Dakhore; Kripan Ghosh; Dileep Kumar Gupta; Prashant K. Srivastava; Kleomenis Kalogeropoulos; Sartajvir Singh; Dhiraj Kumar Singh
This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region. © 2024 by the authors.
Quantifying land cover changes in a Mediterranean environment using lands at TM and support vector machines
(MDPI AG, 2020) Sotiria Fragou; Kleomenis Kalogeropoulos; Nikolaos Stathopoulos; Panagiota Louka; Prashant K. Srivastava; Sotiris Karpouzas; Dionissios P. Kalivas; George P. Petropoulos
The rapid advent in geoinformation technologies, such as Earth Observation (EO) and Geographical Information Systems (GIS), has made it possible to observe and monitor the Earth's environment on variable geographical scales and analyze those changes in both time and space. This study explores the synergistic use of Landsat EO imagery and Support Vector Machines (SVMs) in obtaining Land Use/Land Cover (LULC) mapping and quantifying its spatio-temporal changes for the municipality of Mandra-Idyllia, Attica Region, Greece. The study area is representative of typical Mediterranean landscape in terms of physical structure and coverage of species composition. Landsat TM (Thematic Mapper) images from 1993, 2001 and 2010 were acquired, pre-processed and classified using the SVMs classifier. A total of nine basic classes were established. Eight spectral band ratios were created in order to incorporate them in the initial variables of the image. For validating the classification, in-situ data were collected for each LULC type during several field surveys that were conducted in the area. The overall classification accuracy for 1993, 2001 and 2010 Landsat images was reported as 89.85%, 91.01% and 90.24%, respectively, and with a statistical factor (K) of 0.96, 0.89 and 0.99, respectively. The classification results showed that the total extent of forests within the studied period represents the predominant LULC, despite the intense human presence and its impacts. A marginal change happened in the forest cover from 1993 to 2010, although mixed forest decreased significantly during the studied period. This information is very important for future management of the natural resources in the studied area and for understanding the pressures of the anthropogenic activities on the natural environment. All in all, the present study demonstrated the considerable promise towards the support of geoinformation technologies in sustainable environmental development and prudent resource management. © 2020 by the authors.