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Panorama de la desaparición de personas en Guerrero, 2015-2021.
GABINO SOLANO RAMIREZ (2023, [Artículo])
This work offers a general overview of the disappearance of persons in Guerrero during the period 2015- 2021 seen from the reports published in the local press, and some national media. The objective is to make visible this serious social problem, considered a crime against humanity and of the greatest concern for families and citizens who perceive the feeling of impunity and insecurity. The study follows up on basic indicators of the victims of disappearance, such as the modality (kidnapping, uprising, confrontation, and home invasion), general characteristics of the victims, alleged perpetrators, places and circumstances of the event. Considering the phenomenon as a public issue, the work aims to raise awareness among the population and institutional structures to motivate the generation of strategies to help prevent these disappearances.
Forced Disappearance of Persons Criminal Violence Guerrero pi.evi-UAGro newspaper library CIENCIAS SOCIALES CIENCIA POLÍTICA SOCIOLOGÍA POLÍTICA CONFLICTOS SOCIALES
Mustafa Kamal Timothy Joseph Krupnik (2024, [Artículo])
High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.
Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE