https://cloudjl.com/index.php/RemoteSensing <p><em>International Journal of Advanced Remote Sensing and GIS </em>(IJARSG, ISSN 2320 – 0243) is an open-access peer-reviewed scholarly journal publishes original research papers, reviews, case study, case reports, and methodology articles in all aspects of Remote Sensing and GIS including associated fields. This Journal commits to working for quality and transparency in its publishing by following standard Publication Ethics and Policies. </p> Cloud Publications en-US International Journal of Advanced Remote Sensing and GIS 2320-0243 https://cloudjl.com/index.php/RemoteSensing/article/view/70 <p>This study aims to use the potential of satellite imagery for monitoring and characterization of drought conditions based on the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) in the Côte d'Ivoire from 2010-2015 during the season from November to March. These indexes were obtained from two types of data, namely rainfall data from the Tropical Rainfall Measuring Mission (TRMM) and NDVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Earth Observation System (EOS) Terra satellite and the EOS-Aqua platform. They were combined to perform a spatio-temporal analysis of the Drought Index (DI) to determine the driest months. Analyzes and digital processing of the data using the ArgGis 10.2.1 software showed that the months of December and January are the most critical months according to the SPI analysis. On the other hand, according to the NDVI, the critical months are January and February. As for the integrated Drought Index (DI), it indicates that the moderately humid months observed in November, December and March are respectively the mark of the end and the beginning of the rainy season in Côte d'Ivoire. Clearly, the months of January and February are the least rained and therefore the driest and the month most affected by drought is that of January (-0.46 ≤ DI ≤ -0.18) in Côte d'Ivoire during the study period.</p> <p><strong>Keywords</strong> <em>DI; Drought; Ivory Coast; NDVI; SPI </em></p> Konan Anicet DJE Jacques André TIEMELE Cisse VASSIRIKI Eric Valere DJAGOUA Kouakou Bernard DJE Kouadio Ernest M'BRA Copyright (c) 2024 Konan Anicet DJE, Jacques André TIEMELE, Cisse VASSIRIKI, Eric Valere DJAGOUA, Kouakou Bernard DJE, Kouadio Ernest M'BRA https://creativecommons.org/licenses/by/4.0 2024-07-19 2024-07-19 12 1 3545 3556 https://cloudjl.com/index.php/RemoteSensing/article/view/67 <p>The segmentation of sky cloud images is a complex task essential for applications like weather analysis. Compared to all-sky imagers, horizon-oriented cameras provide a more detailed view of clouds near the horizon. In our study, we evaluated three semantic segmentation models: HRNet48, PPLite, and SegFormerB3, utilizing a variety of loss functions on a novel dataset of horizon cloud images. Throughout our experiments, we consistently observed segmentation leakage issues. To address this, we introduced machine learning-based post-processing methods, including random forest and xgboost, that leverage region-specific features to refine the segmentation. Our results showed notable improvements, with the Cumuliform class dice score increasing from 0.552 to 0.583, and Stratiform class accuracy improving from 0.49 to 0.511 when applying xgboost on SegFormerB3's output. The study revealed the relative contributions of the loss functions and post-processing steps.</p> <p><strong>Keywords</strong> <em>Remote Sensing; Segmentation; Sky Clouds; Deep Learning</em></p> Allan Cerentini Bruno Juncklaus Martins Juliana Marian Arrais Sylvio Luiz Mantelli Neto Gilberto Perello Ricci Neto Aldo von Wangenheim Copyright (c) 2024 Allan Cerentini, Bruno Juncklaus Martins, Juliana Marian Arrais, Sylvio Luiz Mantelli Neto, Gilberto Perello Ricci Neto, Aldo von Wangenheim https://creativecommons.org/licenses/by/4.0 2024-06-03 2024-06-03 12 1 3531 3544