bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Parameter Model for Assessing the Relationship between Money Supply and Economic Growth in Nigeria
Authors: Kazeem Adesanya Kehinde , Yinka Akinbo Rasaki , Yetunde Sebioniga-Lawal
Published: November 2024

The research explores the dynamic relationship between money supply growth (M2) and GDP growth in Nigeria from 2008 to 2023 using a state-space modeling approach. The model effectively captures the relationship between M2 growth and GDP growth, highlighting the importance of M2 as an explanatory factor. The results show a low and statistically significant standard deviation of the observation error, suggesting the model explains GDP variations well. However, the state transition equation indicates a mostly deterministic evolution of unobserved state variables. While the model fits reasonably well according to the log-likelihood value, the study aims to address the limited empirical research on this relationship in Nigeria’s transitional, post-conflict economy. By employing a dynamic parameter state-space model, the researchers seek to capture the potentially changing nature of this relationship over time, moving beyond traditional econometric models that assume fixed relationships. The study draws on various macroeconomic theories, including the quantity theory of money, endogenous growth theory, and state-space models. The log-likelihood value shows that this model fits reasonably well, yet the study notes that other models & more macroeconomic factors should be examined further. Consistent with theoretical predictions, the results highlight the money supply’s vital role in economic growth. To deepen our grasp of how M2 relates to GDP growth, this study suggests that future research might explore more complex dynamics—such as non-linear associations and potential impacts of external shocks. These findings could significantly influence macroeconomic forecasting & monetary policy. The findings support theoretical predictions about the role of money supply in economic growth, but the study suggests future research should explore more complex dynamics, such as non-linear associations and potential impacts of external shocks.
Law Enforcement Against Violations of Space Utilization in River Boundary Areas in Sintang Regency
Authors: Aldi Sofiandi
Published: November 2024

The large number of buildings scattered in the river border area in Sintang Regency indicates that there is a violation of the use of river border area space without permission in Sintang Regency. By using empirical juridical research methods, this research found that there are two regulations governing law enforcement against violations of regional space use and river borders without permits, namely the RTRW Regional Regulation and the Public Order Regional Regulation. However, these two regulations have different provisions in providing administrative sanctions and criminal sanctions against perpetrators of violations. In the RTRW Regional Regulation, criminal sanctions are not specifically mentioned but are more general for spatial planning violations, in contrast to the Public Order Regional Regulation, which more specifically mentions criminal sanctions for these violations. Furthermore, law enforcement factors include the absence of implementing regulations, information regarding the exact boundaries of river borders, and the existence of old traditional village communities that have long lived in river border areas.
Effect of Stakeholders’ Management on Programme Performance: A Case of Crop Intensification Programme in Ngoma District, Rwanda (2018-2023)
Authors: Ruhumuriza Louis , Dushimimana Jean de Dieu
Published: November 2024

The study sought to assess effect of stakeholders’ management on programme performance. The unit of analysis was the crop intensification programme in Ngoma district, Rwanda from 2018 to 2023. The study guided by Stakeholder theory, Goal setting theory and Theory of Change. The study adopted a descriptive survey and correlational research design. The population size was 2123 comprising of smallholder farmers (688), farmers in cooperatives (850), agro-dealers (450), agricultural extension service providers (120), agronomists (14) and Director of Agriculture and Natural Resource in Ngoma district (1). The total sample size was 336 respondents. The sampling procedure was purposive and stratified random sampling. A five-point Likert type scale questionnaire was used to collect quantitative data. The statistical analysis used for quantitative data were frequencies, percentages, arithmetic means and standard deviations, while the statistical tools employed for inferential statistics were Pearson’s Correlation, ANOVA, coefficients test and Multiple Linear Repression. Fisher (F) and P-values were used to test the null hypotheses. The study findings revealed the R value of 0.846 indicating a strong positive relationship between the predictors and the outcome variables, and the R Square value of 0.768 indicates that approximately 76.8% of the variability in the outcome variable can be explained by the predictors in the model. The coefficients of the predictors indicate their individual effects on the CIP performance. Specifically, stakeholders’ identification (β1=0.716, p=0.002), stakeholders’ negotiation (β2= 0.539, p= 0.011), stakeholders’ participation (β3= 0.627, p= 0.006) and stakeholders’ M&E (β4= 0.685, p= 0.014). This indicates that all the coefficients of the predictors are statistically significant, as indicated by their associated Sig. values below 0.05. In short, the four null hypotheses were tested and rejected to accept the alternative hypotheses. The study concluded that all the independent (predictors) variables of the model specification had the greatest effect on the CIP performance in Ngoma district, Rwanda. The study recommends in the form of policy-making and practices that stakeholders’ negotiation and stakeholders’ M&E should be outlined in a pure and concise blueprint to promote stakeholders’ inclusivity in all stages of CIP activities that aims at ensuring food security countrywide and food supply globally.
ASPEK PSIKIATRI TRADISI OMED OMEDAN DI BANJAR KAJA, KELURAHAN SESETAN, KOTA DENPASAR
Authors: KUSUMADEWA BAGUS SURYA , KURNIAWAN I GDE YUDHI , MAHARDIKA I KOMANG ANA
Published: November 2024

The Omed-Omedan tradition is a unique cultural practice originating from Banjar Kaja, Denpasar, Bali. Held annually the day after Nyepi, this tradition involves young men and women in a ritual of mutual pulling, believed to strengthen communal bonds and foster community mental resilience. This study aims to explore the psychiatric aspects of the Omed-Omedan tradition, highlighting the social, emotional, and spiritual impacts of participation in this activity. Beyond fostering closeness among residents, the tradition serves as a means for individuals to develop self-control, identity, and maturity. A holistic cultural psychiatry approach is applied to understand the tradition's influence on participants' mental health.
ABSTRAKTradisi Omed-Omedan merupakan salah satu tradisi unik yang berasal dari Banjar Kaja, Denpasar, Bali. Dilaksanakan sehari setelah Hari Raya Nyepi, tradisi ini melibatkan pemuda-pemudi dalam ritual tarik-menarik yang diyakini dapat mempererat kebersamaan dan menguatkan mentalitas komunitas. Penelitian ini bertujuan untuk mengeksplorasi aspek psikiatri yang terdapat dalam tradisi Omed-Omedan, serta menyoroti dampak sosial, emosional, dan spiritual dari partisipasi dalam kegiatan ini. Tradisi ini tidak hanya menciptakan kedekatan antarwarga tetapi juga melatih pengendalian diri serta menumbuhkan identitas dan kedewasaan pada pesertanya. Pendekatan psikiatri budaya yang holistik diterapkan untuk memahami pengaruh tradisi ini terhadap kesehatan mental peserta.
ANÁLISE DAS DINÂMICAS E CARACTERÍSTICAS DO BULLYING NO AMBIENTE ESCOLAR: IMPLICAÇÕES PARA A INTERVENÇÃO E PREVENÇÃO
Authors: Pinheiro Maria Benedita , Lopes Júnior Hélio Marco Pereira , Silva Luana Guimarães da
Published: November 2024

Este artigo buscou analisar as características do  bullying no contexto escolar. Trata-se de uma revisão de literatura com busca de produções científicas nas bases de dados PUBMED, SCOPUS e WEB OF  SCIENCE, utilizando-se dos descritores: Bullying, escola pública, intervenção e violência escolar que respondesse a questão problema: “Qual é o  impacto do bullying nas crianças e adolescentes no ambiente escolar?” no período de 2019 a 2024. Observa-se que o bullying é uma presença marcante tanto  dentro quanto fora da sala de aula, impactando o ensino e a aprendizagem das  crianças e resultando em problemas psicológicos e distúrbios comportamentais que  as tornam vulneráveis a qualquer tipo de agressão. Conclui-se que o combate ao bullying requer o envolvimento de toda a comunidade escolar, incluindo professores, pais e alunos, para criar um ambiente escolar positivo e livre de violência.
O IMPACTO DA NOVA LEI NOS PROCESSOS DE LICITAÇÕES PÚBLICAS: UMA ANÁLISE DAS PRINCIPAIS INOVAÇÕES TRAZIDAS PELA LEI 14.133/2021
Authors: Souza Luciano Alves de , Andrade Maxwel Mota de
Published: November 2024

A Administração Pública tem como responsabilidade primordial promover o bem-estar geral da população bem como disponibilizar aos seus cidadãos meios para que possam ter seus direitos garantidos. Para isso, é fundamental a aquisição de bens e materiais ou a contratação de serviços básicos necessários para atender às demandas da sociedade. O processo licitatório e as contratações são mecanismos essenciais para evitar o uso inadequado dos recursos públicos arrecadados através de impostos, coibindo práticas antiéticas e criminosas. A Lei 14.133/2021 trouxe alterações significativas que impactarão diretamente os órgãos públicos, visando aumentar a transparência nos processos licitatórios. Isso requer um período de adaptação às mudanças, considerando os desafios que a administração pública poderá enfrentar para atender suas necessidades.
O IMPACTO DO LUTO SEM DESPEDIDA
Authors: Matos Weslei Oliveira , Lopes Júnior Hélio Marco Pereira , Mendonça Francisco Cardoso , Nunes Jenina Ferreira
Published: November 2024

A pandemia de COVID-19 causou mudanças significativas no jeito como as pessoas se sentem em relação ao luto, o que as obriga a lidar com a perda de pessoas queridas sem a chance de realizar despedidas formais. Este estudo examina os efeitos emocionais e psicológicos dessa nova realidade, com ênfase nas consequências da ausência de rituais lutuosos durante a pandemia. O luto, já doloroso por natureza, foi intensificado pela impossibilidade de realizar esses rituais, que são essenciais para a aceitação e superação da perda. A pesquisa faz uma revisão qualitativa de literatura, abrangendo livros e artigos dos últimos 5 a 10 anos, para explorar os efeitos do luto sem despedida e as possíveis estratégias para uma elaboração saudável desse processo. Os resultados mostram que a ausência de rituais de despedida aumentou significativamente os acontecimentos de luto complexo, caracterizado por sentimentos prolongados de negação, culpa e incompletude. Para mais, a ausência de uma despedida formal intensificou o sofrimento emocional, dificultando o processo natural de aceitação. O estudo conclui que é fundamental desenvolver novas formas de suporte emocional, como rituais simbólicos ou virtuais, para atenuar os impactos do luto em tempos de crise. Essas intervenções são essenciais para ajudar os enlutados a processar suas perdas de maneira mais saudável, prevenindo complicações psicológicas em um futuro distante.
FISIOTERAPIA NA PREVENÇÃO DE QUEDAS EM IDOSOS - REVISÃO BIBLIOGRÁFICA
Authors: Castro Maria da Conceição Alvares de , Lima Ronaldo Nunes
Published: November 2024

O estatuto da pessoa idosa considera idoso quem tem idade igual ou superior a 60 anos, fase que aumenta a incidência de queda, a queda é considerada um fenômeno acidental multifatorial, podendo ser fatores intrínsecos ou extrínsecos. O objetivo do trabalho evidencia a importância do profissional fisioterapeuta na prevenção de quedas em idosos, seja em ambiente domiciliar ou hospitalar, por meio da sua atuação e conhecimento aplicado. O artigo trata-se de uma revisão bibliográfica, de ordem qualitativa, descritivo, que busca a integração de informações atualizadas, baseada em publicações das bases de dados: Biblioteca Virtual em Saúde (BVS), Scholar Google e Scientific Eletronic Library Online (SciELO), 24 publicações foram usados para sua elaboração. O aumento de pessoas idosas não deixa de ser um desafio, para os profissionais de saúde, familiares, sociedade e para os próprios idosos, pois há uma incidência maior de risco de queda, e o fisioterapeuta habilitado desenvolve exercícios de prevenção e tratamento de quedas. Conclui-se que as quedas em idosos são um problema de saúde pública, que seus custos geram impacto econômico e na qualidade de vida dos idosos, assim é de suma importância o trabalho do fisioterapeuta na prevenção ou reabilitação desse público.
ESTUDO DA IDENTIFICAÇÃO HUMANA POR MEIO DA IMAGINOLOGIA ODONTOLÓGICA: UMA REVISÃO DE LITERATURA
Authors: Pereira Luis Gustavo dos Santos , Silva Carlos Eduardo de Sousa da , Neves Felipe Lima Castelo Branco , Silva Henrique Mousinho de Santana , Negreiros Iago Braga , Rodrigues João Henrique Miranda , Leal Levi César Teixeira , Silva Samuel Ruben Pereira da , Pereira Thyago de Medeiros , Verde Giselle Maria Ferreira Lima
Published: November 2024

A odontologia forense desempenha um papel crucial na identificação de corpos que são irreconhecíveis visualmente, colaborando com outras áreas, como a medicina. Essa especialidade emprega diversos métodos, como a comparação de dados odontológicos, exames de dentes encontrados e a observação de procedimentos realizados, além do uso de imagens odontológicas. Esses métodos permitem uma identificação precisa e confiável.  Aborda como metodologia uma revisão sistemática de 11 publicações relevantes, com exclusão dos que não abordavam diretamente o tema ou tinham pouca relevância, usando bases de dados como Scielo, PubMed e BVS com auxílio das palavras-chaves. Os resultados destacam a importância das imagens odontológicas como panorâmicas, tomografias computadorizadas, e especialmente radiografias periapicais intraorais, no processo de investigação forense criminal, judicial e social conduzidos por Médicos e Odontologistas Forenses. Os odontologistas enfrentam desafios que podem ser minimizados com prontuários odontológicos atualizados. A colaboração entre odontologistas, peritos policiais e médicos é fundamental para uma identificação precisa através da união de técnicas rápidas, eficazes e claras na resolução desses casos, devido à coleta, exame e comparação de dados realizados por policiais ou investigadores antes e após a morte de um indivíduo. Essa abordagem multidisciplinar melhora a eficiência e a precisão no processo de identificação.
A RELAÇÃO BIDIRECIONAL ENTRE DOENÇA PERIODONTAL E DIABETES MELLITUS: IMPACTOS NA SAÚDE BUCAL E SISTÊMICA
Authors: Barros Jamile Donadia de , Costa Danilo Sales , Pinto Emanuel Vieira
Published: November 2024

O presente estudo propõe discorrer sobre a estreita relação entre doença periodontal e diabetes mellitus. Pesquisas epidemiológicas mostram que o diabetes aumenta o risco e a severidade da doença periodontal, e a mesma quando não tratada, pode interferir no controle glicêmico de pacientes diabéticos. Cujo problema é: Quais os impactos que a relação entre a doença periodontal e o diabetes mellitus tem na saúde bucal e sistêmica? Analisando esse contexto, o presente estudo foi norteado tendo como objetivo geral analisar a relação bidirecional entre doença periodontal e diabetes mellitus e os impactos dessa relação na saúde bucal e sistêmica, os objetivos específicos pautados foram identificar os mecanismos envolvidos na fisiopatologia da doença periodontal associada ao diabetes mellitus; compreender o impacto do diabetes mellitus na saúde periodontal; investigar o impacto da doença periodontal no diabetes mellitus e avaliar a eficácia da terapia periodontal no controle glicêmico de pacientes diabéticos. Essa metodologia consiste em uma pesquisa bibliográfica de cunho exploratório e conduzido pela abordagem qualitativa, como base de dados para consulta utilizou-se a SciELO, LILACS via BVS e PubMed, de artigos científicos, tese de doutorado, livros relevantes e site. Por fim, os resultados desse estudo sugere, seguramente, a relação bidirecional entre a doença periodontal e o diabetes mellitus, onde o diabetes é um fator de risco para a doença periodontal e, por sua vez, a doença periodontal prejudica o controle glicêmico, tendo a terapia periodontal como adjuvante no controle metabólico de pacientes diabéticos.
REABILITAÇÃO ORAL COM PRÓTESE EM PACIENTES IDOSOS EDÊNTULOS TOTAIS: REVISÃO DE LITERATURA
Authors: Carvalho Lidiane Santos de , Pires Lilian Gomes Soares , Brito Matheus Araújo , Verde Gisele Maria Ferreira Lima
Published: November 2024

Introdução: As próteses na odontologia são dispositivos criados com o intuito de repor os elementos e devolver o volume alveolar perdido em uma arcada desdentada, favorecendo a função mastigatória, fonética, e ainda a estética e harmonia facial. Objetivo: Demonstrar a importância da reabilitação de idosos desdentados bem como as técnicas mais indicadas para isto. Metodologia: Utilizou-se uma revisão de literatura, com inclusão de artigos publicados entre 2011 e 2024, em português e inglês. Resultados: As próteses fixas sobre implantes são consideradas pela maioria dos cirurgiões dentista como tratamento padrão ouro na reabilitação de mandíbula e maxila edêntula por sua alta estabilidade e maior resistência às cargas mastigatórias sendo indicadas principalmente em casos de pacientes que perderam pouca estrutura óssea. No entanto, pacientes de baixa renda podem optar pela prótese removível. Conclusão: O uso das próteses sejam elas removíveis ou fixas são essenciais para reestabelecer a fala e função mastigatória.
Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020
Authors: Zhou Jia , Niu Jin , Wu Ning , Lu Tao
Published: November 2024

Abstract. Grazing activities constitute the paramount challenge to grassland conservation over the Qinghai–Tibet Plateau (QTP), underscoring the urgency of obtaining detailed information regarding the extent, patterns, and trends of grazing to enable efficient grassland management and sustainable development. Here, to inform stakeholders about these issues, we provided the first annual Gridded Dataset of Grazing Intensity (GDGI), with a resolution of 100 m, from 1990 to 2020 for the QTP. The five most commonly used machine learning algorithms were leveraged to develop a livestock spatialization model, which spatially disaggregates the livestock census data at the county level into a detailed 100 m × 100 m grid based on seven key predictors from terrain, climate, vegetation, and socio-economic factors. Among these algorithms, the extreme-tree (ET) model performed the best in representing the complex nonlinear relationship between various environmental factors and livestock intensity, with an average absolute error of just 0.081 SU ha−2 (where SU denotes sheep units), a rate outperforming the other models by 21.58 %–414.60 %. By using the ET model, we further generated the GDGI for the QTP to reveal the spatio-temporal heterogeneity and variations in grazing intensities. The GDGI indicates that grazing intensity remained high and largely stable from 1990 to 1997, followed by a sharp decline from 1997 to 2001 and fluctuations thereafter. Encouragingly, compared to other open-access datasets for grazing distribution on the QTP, the GDGI has the highest accuracy, with the determinant coefficient (R2) exceeding 0.8. Given its high resolution, recentness, and robustness, we believe that the GDGI dataset can significantly enhance our understanding of the substantial threats to grasslands emanating from overgrazing activities. Furthermore, the GDGI product holds considerable potential as a foundational source for other research, facilitating the rational utilization of grasslands, refined environmental impact assessments, and the sustainable development of animal husbandry. The GDGI product developed in this study is available at https://doi.org/10.5281/zenodo.10851119 (Zhou et al., 2024).
Multi-attribute decision-making model based on Remote Sensing to enhance the environmental auditing of Sustainable Forest Management in the Brazilian Amazon forest
Authors: de Macêdo Rafael F. , Alves Rita C. M. , Iescheck Andrea L.
Published: November 2024

Abstract. Sustainable Forest Management (SFM) plans are distributed across 2% of the Brazilian Legal Amazon (BLA) territory. Although these enterprises are authorized through the national system (Sinaflor), there is a lack of a computational platform capable of processing, analyzing, and correlating monitoring data from remote sensing techniques with the production data declared in the system. By combining multi-attribute decision-making (MADM) methods with geographic information system (GIS) data and DETEX satellite image Linear Spectral Mixture Model (LSMM) processing on Google Earth Engine (GEE), we developed a semi-automatic system that calculates an Environmental Pressure Index (EPI). This index, composed of categories of cost and benefit indicators, measures the environmental impacts of SFM operations and the surrounding land use and land cover (LULC) dynamics that can externally stress these enterprises. To test this framework, we evaluated 15 SFMs in operation between 2022 and 2023 in the highly environmentally stressed AMACRO region of the BLA. The evaluation was performed in three tiers, proving that cost indicators developed to measure environmental impacts caused by SFMs are more consistent for the selection of enterprises to be audited than LULC dynamics indicators, as they combine planned and declared production data with the classification of intervention shown by DETEX images. The developed semi-automatic system have the potential to be used by environmental agencies as a decision-making tool for selecting SFMs to be audited, as it provides a quantitative approach based on index calculations and can be easily adapted for specific auditing purposes.
Monitoring Vegetation Cover in mining areas in the municipality of Ipixuna do Pará (PA)
Authors: de Oliveira Suelen Melo , Beltrão Norma Ely Santos , Machado Fernanda Ferreira , Lima Ilale Ferreira
Published: November 2024

Abstract. This study focuses on analyzing the spatiotemporal dynamics of mining areas in the municipality of Ipixuna do Pará, using Planet satellite images and the Google Earth Engine (GEE) platform to monitor vegetation cover during the periods of 2016, 2018, 2021, and 2023. Data obtained through remote sensing enabled the generation of products on the Earth's surface, allowing for a detailed analysis of land use and cover changes. Using the Minimum Distance - Euclidean (MMD) algorithm in GEE, it was possible to classify and quantify the delimited areas, creating temporal maps. The analysis revealed a significant decrease in vegetation area from 2016 (41.8%) to 2018 (38.1%), with a substantial increase in 2021 (74.6%) and stability in 2023 (74.8%). The area dedicated to agriculture remained stable from 2016 (54.3%) to 2018 (55.5%) but drastically reduced in 2021 (22.0%) and 2023 (21.9%). Mining areas showed variations with an increase in infrastructure in 2021 and a consistent reduction in exposed soil areas from 2016 to 2023. The analysis emphasizes the importance of continuous monitoring and effective environmental policies to mitigate the negative impacts of mining. The results indicate that using tools like GEE is crucial for understanding land use changes and implementing environmental management strategies.
Pixels relationship analysis for extracting building footprints
Authors: Emelyanov Anton , Knyaz Vladimir , Kniaz Vladimir , Artist Dana
Published: November 2024

Abstract. Currently, remote sensing research is focused on developing an automated algorithm that can compete with empirical methods for mapping the contours of individual buildings. Despite facing numerous challenges related to suboptimal imaging conditions, diverse building architecture, and complex backgrounds, creating such an algorithm is essential for monitoring urban and natural areas, generating 3D city models, managing disasters, and estimating population density. Obtaining the polygonal boundary of a building and extracting a vectorized building mask as output for direct use is one of the current challenges in drawing building outlines. This work provides a comprehensive workflow for building extraction and improves their predicted area by regularization the boundaries of buildings. First, a convolutional neural network is used to train the binary semantic segmentation model and then regularization and vectorization processes are performed. The main difference from existing methods is a new regularization method based on compiling a neighborhood matrix for each point belonging to the “building” class. According to the experimental results, the algorithm shows high efficiency: IOU (intersection over union) = 91.2%, AP (average precision) = 64.1% and AR (average recall) = 75.1%, comparable to leading building boundary extraction methods such as PolyWorld.
Tropical forest robust 3D description using advanced multidimensional SAR imaging: techniques and performance in the context of the upcoming BIOMASS mission
Authors: Ferro-Famil Laurent , Huang Yue , Bou Pierre-Antoine , Tebaldini Stefano
Published: November 2024

Abstract. This paper proposes to quantitatively estimate the potential of 3D SAR imaging for tropical forest characterization using airborne and spaceborne system configuration. A robust forest characterization technique, relying a low-dimensionality model is proposed and is compared with a well-established polarimetric and tomographic approach. The investigated forest descriptors are the underlying ground topography (DTM) and the forest height (CHM). A quantitative performance evaluation reveals that the presented parametric technique achieves high-precision results at both high-resolution (airborne) and low resolution (BIOMASS) modes, whereas existing approaches meet limitations, due the coarse resolution of spaceborne SAR data.
Cloud shadow and uneven illumination detection and correction using the U-net architecture in near-surface images of complex forest canopies
Authors: Goncalves Nathan B. , Nelson Bruce W. , Simonetti Adriana , Stark Scott C.
Published: November 2024

Abstract. In humid tropical regions, irregular illumination and cloud shadows can complicate near-surface optical remote sensing. This could lead to costly and repetitive surveys to maintain geographical and spectral consistency. This could have a significant impact on the regular monitoring of forest ecosystems. A novel correction method using deep learning is presented here to address the issue in high-resolution canopy images. Our method involves training a deep learning model on one or a few well-illuminated/homogeneous reference images augmented with artificially generated cloud shadows. This training enables the model to predict illumination and cloud shadow patterns in any image and ultimately mitigate these effects. Using images captured by multispectral and RGB cameras, we evaluated the method across multiple sensors and conditions. These included nadir-view images from two sensors mounted on a drone and tower-mounted RGB Phenocams. The technique effectively corrects uneven illumination in near-infrared and true-color RGB images, including non-forested areas. This improvement was also evident in more consistent normalized difference vegetation index (NDVI) patterns in areas affected by uneven illumination. By comparing corrected RGB images to the original in a binary classification task, we evaluated the method's accuracy and Kappa values. Our goal was to detect non-photosynthetic vegetation (NPV) in a mosaic. The overall accuracy and Kappa were both significantly improved in corrected images, with a 2.5% and 1.1% increase, respectively. Moreover, the method can be generalized across sensors and conditions. Further work should focus on refining the technique and exploring its applicability to satellite imagery and beyond.
Influence of tree density and terrain slope on ground point density in LiDAR point clouds: a simulation-based study with Helios++
Authors: González-Quiñones Juan José , Polidori Laurent , Ariza-López Francisco Javier , Ureña-Cámara Manuel Antonio , Reinoso-Gordo Juan Francisco
Published: November 2024

Abstract. There is still no reliable solution for obtaining Digital Terrain Models (DTM) under the canopy in areas with difficult access on foot. The difficulty lies in the configuration of the environment. This configuration does not allow access on foot to all areas of the terrain. In addition, the dense leaf litter complicates any photogrammetry technique. Currently, LiDAR is a technique that could help solve this problem. The best solution to this problem requires many samples over hostile environment and LiDAR flight simulation can be a good option to approximate the problem. For this reason, this research carries out a series of LiDAR flight simulations, using Helios++, in different types of scenes under the canopy with the aim of analysing the influence of the density of trees and the mean terrain slope over the number of points that bounce off the terrain. Additionally, this research enables the scientific community to approach the problem and guide future research based on the obtained results.
Assessment of land restoration from optical satellite image time-series
Authors: Ballère Marie , Helen Florian , Mathian Charlotte , Heudes Christophe , Jacquin Anne , Salvat Quentin , Mariappan Muneeswaran
Published: November 2024

Abstract. Ecosystem conservation and restoration is recognized by the international community as a key strategy for human well-being as well as our planet’s health. Assessing the efficiency of actions implemented in this context is essential, as their application on the ground may prove challenging. In the present paper, we present a methodology for characterising vegetation regeneration. This methodology has been developed and tested at the scale of the Sao Paulo state. It is based on two successive steps. Firstly, annual land cover maps are produced with breakpoints detection and Random Forest classification. This process is performed on biophysical variables derived from more than 10 000 optical images (2016 – 2021 included) and 4774 reference data. Secondly, we applied an expert-based rules algorithm to derive land regeneration map from annual land cover time series. The distinction is made between natural and anthropogenic regeneration. The accuracy assessment shows an overall accuracy of more than 80% for both the annual land cover maps and the regeneration map.
A Unified Framework for Analysis-Ready Data generation from Resourcesat-2/2A sensors
Authors: Keerthi V. , Suresh Kumar M. , Radhika T. , Manju Sarma M. , Chauhan Prakash
Published: November 2024

Abstract. More than decadal data is archived by the Indian remote sensing satellite Resourcesat-2 series of sensors. This data holds immense potential for long-term terrestrial monitoring. Harnessing its full potential requires systematic organization of all available observations, necessitating rigorous pre-processing efforts.Analysis Ready Data (ARD) products have streamlined the process of accessing and analyzing satellite imagery, fostering interoperability over time and across various datasets enabling a wide range of applications in environmental monitoring, disaster management, agriculture, urban planning, and scientific research. An optimized frame work is defined for Resourcesat-2/2A images to generate ARD products. The product package contains surface reflectance (SR) and top-of-atmosphere reflectance (TOA) along with pixel quality layer and Metadata file. This paper outlines the procedures undertaken, validation of the techniques and analysis of the obtained results. ARD products are validated for both absolute and relative terms with ground measurements and harmonized Landsat and Sentinel (HLS) data sets and the amount of agreement is around 90% and 95% respectively.
Monitoring mangroves with multi-sensor Earth Observation data sets: from one-shot historical cartography to online and near-real-time monitoring tools
Authors: Blanchard Elodie , Catry Thibault , Marsal Quentin , Delaître Eric , Proisy Christophe , Faure Jean-François
Published: November 2024

Abstract. This study outlines advancements in mangrove monitoring using Earth Observation (EO) technologies, highlighting a transition from historical cartography to modern, near-real-time monitoring systems. Mangroves, critical for biodiversity, climate regulation, and coastal protection, face threats from climate change and human activities. The need for accurate and recurrent mapping tools is emphasized, addressing the limitations of current global datasets like the Global Mangrove Watch (GMW) by incorporating high-resolution data and field measurements. The French National Research Institute for Sustainable Development (IRD) has pioneered mangrove monitoring since the 1990s, with key methodologies including the use of Sentinel-2 and Pleiades satellite imagery for high-resolution mapping, texture-based analysis, and the incorporation of LiDAR data for accurate biomass estimation. Historical cartography is contrasted with contemporary monitoring efforts, focusing on the integration of multi-source data and the importance of localized ground-truthing. This approach enhances the capabilities of earth observation for carbon stock assessments and supports informed decision-making in conservation and climate policy. The work also highlights the need for further integration of local knowledge and advanced EO sensor data to refine carbon sequestration models and improve mangrove management strategies.
Evaluating Forest Disturbance Detection Methods based on Satellite Image Time Series for Amazon Deforestation Alerts
Authors: Mota Flávio Belizário da Silva , Ferreira Karine Reis , Escada Maria Isabel Sobral
Published: November 2024

Abstract. This study explores automated detection methods of forest disturbances using satellite image time series for Amazon deforestation alerts. The research focuses on two municipalities in southern Amazonas, Brazil, known for high numbers of deforestation alerts. Five methods—BFAST Monitor, CCDC, COLD, SCCD, and LSTM—were applied to Landsat image time series from 2017 to 2020 to identify forest disturbances and their effectiveness were evaluated, by comparing their results with alerts from the Brazilian Real-time Deforestation Detection System (DETER). The results demonstrate that the COLD and SCCD methods achieved the highest concordance rates with DETER alerts, at 82% and 85%, respectively, indicating their superior performance in disturbance detection. The LSTM method also performed well, with an 83% concordance rate, showcasing the potential of deep learning techniques in satellite image time series. The CCDC method followed with a 75% concordance rate, and the BFAST method had a concordance rate of 72%. This study highlights the importance of utilizing advanced modeling techniques and multi-spectral analysis for effective forest disturbance detection. The results underscore the need for continued refinement and calibration of these methods to enhance their precision and reliability.
Projections of oil palm area in Malaysia and Indonesia to assess the relationship between plant growth and water consumption
Authors: Naito Chihiro , Takeuchi Wataru
Published: November 2024

Abstract. Palm oil is a widely used agricultural crop in the world, and most of them are produced in Malaysia and Indonesia covering millions of hectares. However, climate change which induces water stress and extreme weather such as El Niño poses a risk to oil palm production. Therefore, it is crucial to identify areas vulnerable to climate change to prioritize adaptation actions for sustainable palm oil production. Several studies have implemented vulnerability assessments for climate change using a limited number of climate variables, despite the complex relationships among vegetation and various water components. Given these situations, this study aims to identify areas vulnerable to climate change for oil palms by analyzing the relationship between vegetation productivity and climate variables, and their changes from 2002 to 2012 and from 2013 to 2022. This analysis utilized satellite-based multiple water components and applied principal component analysis. The identified vulnerable areas were then compared with the biophysical suitability map from another study. Severe El Niño impacts from 2015 to 2016 were also assessed as an indicator of the actual decline in productivity. The results showed that more than half of the study area had experienced water stress recently, particularly in Java, southern Borneo, and the southern New Guinea islands. There was a discrepancy between the biophysical suitability map and the identified vulnerable areas in this study, especially in the southern part of Borneo. These findings highlighted the importance of considering multiple water components in tropical regions and future projections for appropriate palm oil cultivation areas.
Uncovering Patterns: Data Mining the Deforestation Frontier in Brazilian Cerrado
Authors: Pinheiro Taise Farias , Pinto João Felipe Sobrinho Kneipp Cerqueira , de Carvalho Magog Araújo , Sestini Marcelo Francisco , Pettinati Danilo Rocco , Santos Jacqueline Oliveira , dos Santos Sandra Benfica , Soler Luciana de Souza , Scheide Andrea Daleffi , Messias Cassiano Gustavo , Almeida Claudio Aparecido , Amaral Silvana , Moreira Thayse Azevedo , Dousseau Sérgio Lopes , Moraes Diego da Cunha , Costa Jonatas da Silva , Costa Joelma da Silva , Nisimura Thiago de Moraes
Published: November 2024

Abstract. The Brazilian Cerrado, recognized as the world's most diverse tropical savanna, has undergone significant land clearance for agriculture, cattle ranching, and charcoal production, leading to considerable loss of its natural vegetation. This deforestation disrupts major aquifers, affecting ecosystems and human populations. The Cerrado Deforestation Monitoring Project (PRODES), active since 2017, systematically maps new deforestation using satellite imagery. This study used GeoDMA, a geospatial data mining tool for analyzing complex spatial databases such as those provided by the PRODES. The study area is the Alto Parnaíba region within the MATOPIBA agricultural frontier, characterized by high rates of crop expansion and burned areas. Using PRODES deforestation data from 2000 to 2023, the study categorizes deforestation patterns into geometric, multidirectional, and diffuse types, associated with large-scale agriculture, smallholder farming, and subsistence agriculture, respectively. The analysis reveals that approximately 50% of land use trajectories are small-scale subsistence agriculture, predominantly in northern Alto Parnaíba. Unchanged large-scale farms are concentrated in the southern part of the study area, linked to annual crops. The transition from small to large-scale farms is significant in the western region. Protected areas, especially strictly protected conservation units, show minimal changes in native vegetation. This study highlights the expansion of large-scale agriculture in MATOPIBA, emphasizing the need for targeted interventions to manage land use changes and prevent further deforestation. Future research should extend this approach to other Cerrado ecoregions to improve the detection and analysis of deforestation processes.
Advanced Bayesian Method for Timely Small-Scale Forest Loss Detection in the Brazilian Amazon and Cerrado with Sentinel-1 Time-Series
Authors: Bottani Marta , Ferro-Famil Laurent , Doblas Juan , Mermoz Stéphane , Bouvet Alexandre , Koleck Thierry
Published: November 2024

Abstract. The world’s forests are undergoing significant changes due to loss and degradation, emphasizing the need for Near Real-Time (NRT) monitoring to prevent further damage. Traditional monitoring methods using optical imagery are hindered by cloud coverage, while newer Synthetic Aperture Radar (SAR) systems, although operational in all weather conditions, face challenges such as sensitivity to soil moisture and the need for spatial filtering to reduce speckle effects. These limitations affect the detection of small-scale forest loss, especially in seasonally variable regions like dry forests and savannas. This paper presents a SAR-based forest disturbance detection method using Bayesian inference. Unlike traditional methods, this approach maintains the native resolution of the data by avoiding spatial filtering. Forest disturbance is modelled as a change-point detection problem within a non-filtered Sentinel-1 time series, where each new observation updates the probability of forest loss by leveraging prior information and a data model. This sequential adaptation ensures robustness against variations and trends, making it effective in monitoring disturbances across diverse forest types, including areas affected by seasonality. The proposed method was tested against other NRT monitoring systems for the year 2020, using small validation polygons (under 1 hectare) in the Brazilian Amazon and Cerrado savanna. Results demonstrate significant improvements in detecting small-scale disturbances and drastically reduced false alarm rates in both biomes. Notably, in the seasonality-sensitive Cerrado, our solution completely outperforms the leading and only existing optical technology.
Assessment of Urban Heat Islands in an Eastern Amazonian city
Authors: Raiol Lucas Lima , Rocha Yuri Antonio da Silva , Santos Dayla Carolina Rodrigues , de Lima Aline Maria Meiguins , Velastegui-Montoya Andrés
Published: November 2024

Abstract. The increase in heat islands in large cities, especially in tropical areas, has been a significant problem, as the rise in temperature can cause health issues for people. Therefore, this research aimed to analyze changes in land surface temperature (LST) and urban heat islands (UHI) in 1986 and 2023. The methodological procedures were carried out in four steps: I) Calculation of LST and UHI using the Landsat collection; II) Zonal statistics of the mean, the median, the minimum, maximum, and standard deviation of LST and UHI in each neighbourhood; III) Kruskal-Wallis Test applied to the average LST between 1986 and 2023; and IV) Assessment of the spatial autocorrelation of neighbourhoods using Bivariate Global and Local Moran’s I Index between LST and UHI. The results showed that in 2023, there was an increase in heat islands in central city neighbourhoods compared to 1986, as demonstrated by the Kruskal-Wallis test, which showed significant differences in LST. The global Moran’s I Index presented a value of 0.642, indicating a robust spatial autocorrelation in local studies. The neighbourhoods that showed high correlations were Campina, Cidade Velha, Cremação, Condor, Fátima, Jurunas, Nazaré, Pedreira, Reduto, São Braz, and Umarizal. The neighbourhoods that showed low correlations were Águas Lindas, Aurá, Curió-Utinga, Guanabara, and Parque Guajará. These results are directly linked to the intensification of urbanization and low vegetation cover, especially in central areas of the city, which showed a high correlation. These findings can aid decision-makers and support urban planning, focusing on neighbourhoods with higher average temperatures.
Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber)
Authors: Cheriyanda Raveendra Harin Aiyanna , Picco Lorenzo , Pirotti Francesco
Published: November 2024

Abstract. The ability of Earth observation and remote sensing to identify the potential of species to restore ecosystems is a critical element in combating habitat degradation and biodiversity loss. Historical climate data and current species occurrence, coupled with geospatial modelling methods, can significantly aid in decision-making to support ecosystem restoration. We chose Eurasian beavers (spp. Castor fiber) as our case species, which are known ecosystem engineers that create wetland habitats and significantly alter/reinforce the morphology, dynamics, and hydraulics of riverine landscapes. To investigate their ecosystem restoration potential, we applied species distribution modelling (SDM) to determine their current and future distribution scenarios. Our SDMs used current and simulated future environmental data along with different classes of rasters such as elevation, inland swamps, water bodies, natural seasonally or permanently wet grasslands, open mires, riparian mixed forests, riparian swamp broadleaved forests, riparian swamp coniferous forests, watercourses, protected areas, and urban and built-up areas derived from Google Earth Engine to predict areas suitable for beaver habitats. Further, by using the zonation method, we were able to develop a stepping stone model to prioritize habitat conservation efforts. We provide habitat projections according to time periods of "2041–2060", "2061–2080" and "2081–2100", and to different climate change scenarios (SSP126, SSP245, SSP370, and SSP585). Thus, using a potential nature-based solution, we have employed remote sensing data and tools to develop a holistic framework for not only quantifying the ecosystem restoration potential of beavers but will also provide a new direction for understanding species-specific importance in climate change mitigation.
Wide area maps of building differential deformations
Authors: Crosetto Michele , Shahbazi Saeedeh , Barra Anna
Published: November 2024

Abstract. The European Ground Motion Service (EGMS) uses Sentinel-1 data to monitor and measure land displacement across Europe, delivering information on ground motion phenomena. The EGMS service includes three types of products, enabling the analysis of annually updated ground motion data. The authors are working on a project aimed at computing the spatial gradient of land deformation and generating wide-area differential deformation maps. This type of maps can help identifying potentially vulnerable buildings and urban structures. The project involves developing a software pipeline to compute and convert original deformation data and creating a customized web viewer to display the results. Given the large volume of the processed datasets, with several millions of measurement points an accurate algorithm implementation is needed. The resulting information will aid in monitoring anthropogenic phenomena and their impact on building and infrastructures. This will be essential for urban management and risk mitigation planning.
An Analysis of Lexical Cohesion on the Students Thesis at the English Teaching Department of HKBP Nommensen University
Authors: Sihombing Imnesti , Sidabutar Usman , Tampubolon Sahlan
Published: November 2024

This study uses the type of lexical cohesion to analyze the writing of the background of study thesis. The purposes of this study are to find the type of lexical cohesion and what type of lexical is dominant in the background of the study thesis. This study was conducted using a qualitative method. The data collection method used document analysis because the data has taken from the text or document of the background of  study thesis. The object of this research has the writing of the background of  study thesis by students graduating in 2023. The results of this study are data on 10 background of thesis studies of students graduating in 2023. The researcher found 333 data on the use of lexical cohesion in the background of thesis of students graduating in 2023 consisting of 304 repetitions (91.6%), 3 synonyms (0.9%), 7 antonyms (2.1%), 9 hyponyms (2.7%), 10 meronyms (3.0%). From the results of the calculations above, the dominant type of lexical cohesion is repetition (91.6%).