Journal of Hyperspectral Remote Sensing最新文献_第8页

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling 利用机器学习和地理加权回归模型，甲烷和孟加拉国空气污染之间的可能联系

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-20 DOI: 10.29150/2237-2202.2021.251959

Md. Shareful Hassan, M. Islam, M. Bhuiyan

{"title":"Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling","authors":"Md. Shareful Hassan, M. Islam, M. Bhuiyan","doi":"10.29150/2237-2202.2021.251959","DOIUrl":"https://doi.org/10.29150/2237-2202.2021.251959","url":null,"abstract":"This paper investigates the probable nexus between methane (CH4) and air pollutants, a public health hazard in Bangladesh. The hypothesis considers that the concentration of CH4 is dependent on the ten air pollutants found in the five districts in Dhaka Division, a major urban and industrial area in Bangladesh. These pollutants are: Particular matters (PM2.5), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Aerosol optical thickness (AOT), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Black carbon (BC), Formaldehyde (HCHO) and Dust. The study applies Machine Learning (ML) technique and Geographically Weighted Regression (GWR) Modeling. Temporal CH4 datasets from the Sentinel-5P sensor are classified to estimate the annual CH4 concentration during 2019-2021.Seven supervised classifiers of ML coupled with the GWR model are used to predict the statistical and spatial relationships. CH4 increases gradually during 2018-2021 in Dhaka, Gazipur, and Munshiganj Districts. It relates differently with various air pollutants, e.g., positively with BC, Dust, NO2, PM2.5, O3, and AOT, and negatively with NOx, CO, HCHO, and SO2.This study results that Rational quadratic (RMSE-0.001, MAE-0.001, R2-0.96), Random Forest (RMSE-0.004, MAE-0.003, R2-0.91), and Stepwise regression (RMSE-0.002, MAE-0.002, R2-0.87) are the suitable method in ML. The highest goodness-of-fit (R2) of 82%-96% is found in Dhaka and Narshingdi Districts. The key findings may help formulate the appropriate action plan to mitigate ongoing and future air pollution in Bangladesh. In addition, the methodology of the research may be applicable elsewhere nationally and internationally for air pollution research.","PeriodicalId":332244,"journal":{"name":"Journal of Hyperspectral Remote Sensing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134640144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Variação diária da evapotranspiração em Bom Jesus, Piauí Bom Jesus的日蒸散变化，piaui

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-20 DOI: 10.29150/2237-2202.2021.252181

Arão De Moura Neto, Ana Caroline Guimarães Miranda, Angelo da Silva Gonçalves Júnior, Gabriel Siqueira Tavares Fernandes, Laila Lucia Sousa E Silva, Raiany de Oliveira Silva, E. A. Lima

引用次数: 0

Mapping as instrument to support public authorities in risk analysis and promotion of climate justice 测绘是支持公共当局进行风险分析和促进气候正义的工具

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-20 DOI: 10.29150/2237-2202.2021.252401

Tiago Henrique De Oliveira, Leta Vieira de Sousa, Marina Jardim dos Santos Lopes, Marcelo Olímpio dos Santos, Aurélio Lúcio De Melo e Silva Júnior, Luiz Gustavo Pinto

{"title":"Mapping as instrument to support public authorities in risk analysis and promotion of climate justice","authors":"Tiago Henrique De Oliveira, Leta Vieira de Sousa, Marina Jardim dos Santos Lopes, Marcelo Olímpio dos Santos, Aurélio Lúcio De Melo e Silva Júnior, Luiz Gustavo Pinto","doi":"10.29150/2237-2202.2021.252401","DOIUrl":"https://doi.org/10.29150/2237-2202.2021.252401","url":null,"abstract":"Urbanization is one of the most powerful anthropogenic causes of Earth change. The report by the Intergovernmental Panel on Climate Change claims that human influence on the warming of the soil, ocean and atmosphere is unequivocal. People who are socially, economically, culturally, politically, institutionally or otherwise marginalized are especially vulnerable to climate change and also to adaptation and mitigation responses. Climate injustice refers to the fact that groups in a greater situation of vulnerability present challenges that accumulate as a result of less political power and great social inequality and, therefore, are disproportionately harmed by the impacts of climate change. Thus, this article seeks to address, from indices such as the NDBI, NDVI and census data from the IBGE Census, for the year 2010 and data from the analysis of Climate Risks and Vulnerabilities in Recife, the climate (in)justice in the city of Recife. For this purpose, 3 images from the TM sensor and one from the OLI sensor aboard the Landsat satellites were used. Results show that NDBI values above 0.21 were extended to various areas of the city of Recife over the years, while the NDVI showed a decrease in vegetation cover, indicating greater soil sealing and construction of buildings. These results, in accordance with the mapping of vulnerabilities to climate change, corroborate that a public policy focused on reducing climate injustices is essential, especially when it is concluded that the areas with the greatest social vulnerability are those with the greatest climate vulnerability.","PeriodicalId":332244,"journal":{"name":"Journal of Hyperspectral Remote Sensing","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133794069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANÁLISE DE MÉTODOS DE INTERPOLAÇÃO ESPACIAL PARA CURVAS IDF DA BACIA HIDROGRÁFICA DO RIO MADEIRA 马德拉河流域IDF曲线空间插值方法分析

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-20 DOI: 10.29150/2237-2202.2021.251829

F. D. Silva, Adria Lorena de Moraes Cordeiro, C. Costa, Gabriela Rousi Abdon da Silva

引用次数: 0

Land use and cover change caused by the expansion of soybean production in Sorriso - MT, Brazil (1990-2020) 巴西Sorriso - MT大豆生产扩张引起的土地利用和覆被变化(1990-2020年)

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-20 DOI: 10.29150/2237-2202.2021.252394

Rodrigo Rodrigues, R. Miranda

引用次数: 0

Estudo da variabilidade de índices de vegetação utilizando análise de agrupamentos 利用聚类分析研究植被指数的变异性

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-20 DOI: 10.29150/2237-2202.2021.250962

A. C. V. L. Gusmão, Diogo Francisco Borba Rodrigues, Diego Santos de Araújo, Jussara Freire de Souza Viana, S. Montenegro

引用次数: 0

Análise espaço-temporal da evapotranspiração e do sequestro de carbono na bacia do Rio Pajeú-PE utilizando a computação em nuvem 利用云计算对pajeu -PE河流域蒸散和碳固存进行时空分析

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-17 DOI: 10.29150/2237-2202.2021.250958

Diogo Francisco Borba Rodrigues, S. Montenegro, A. C. V. L. Gusmão, Diego Santos de Araújo, Bruno e Silva Ursulino, José Aparecido Alves de Oliveira

引用次数: 0

Índices espectrais na detecção de corpo hídrico utilizando imagens do sensor MSI - Sentinel 2 利用MSI - Sentinel 2传感器图像检测水体的光谱指数

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-17 DOI: 10.29150/2237-2202.2021.252362

Déborah Priscilla Oliveira Almeida, C. O. D. B. Salgueiro, J. V. Chaves, S. Santos, L. M. M. D. Oliveira

引用次数: 1

APLICAÇÃO DE DADOS AEROGEOFÍSICOS NO MAPEAMENTO GEOLÓGICO DIGITAL: O EXEMPLO DA FOLHA SOBRAL/CE. 航空地球物理数据在数字地质测绘中的应用:以SOBRAL/CE表为例。

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-17 DOI: 10.29150/2237-2202.2021.251442

Marcia Valadares Dos Santos, Camila Nascimento Alves, F. Abreu

引用次数: 0

THE INFLUENCE OF MINING IN THE QUADRILÁTERO FERRÍFERO: LANDSCAPE SPATIAL-TEMPORAL DYNAMICS 采矿对quadrilÁtero ferrÍfero景观时空动态的影响

Journal of Hyperspectral Remote Sensing Pub Date : 2021-12-17 DOI: 10.29150/2237-2202.2021.252158

Cássia Ribeiro Macedo, Reginaldo Arthur Gloria Marcelino, Arthur Amaral e Silva, J. F. Lorentz, Letícia Rodrigues de Assis, Vitor Juste dos Santos, M. Calijuri

{"title":"THE INFLUENCE OF MINING IN THE QUADRILÁTERO FERRÍFERO: LANDSCAPE SPATIAL-TEMPORAL DYNAMICS","authors":"Cássia Ribeiro Macedo, Reginaldo Arthur Gloria Marcelino, Arthur Amaral e Silva, J. F. Lorentz, Letícia Rodrigues de Assis, Vitor Juste dos Santos, M. Calijuri","doi":"10.29150/2237-2202.2021.252158","DOIUrl":"https://doi.org/10.29150/2237-2202.2021.252158","url":null,"abstract":"The Quadrilátero Ferrífero – QF is the main producer/explorer of iron ore in Brazil, occupying the worldwide leadership group in Mineral Exploration. Considering all the environmental impacts associated with this activity, it is necessary to assess the landscape patterns to support the sustainable planning of mining companies. So, this paper aimed to define the temporal changes in the QF landscape's configuration and composition from 1985 to 2018 and predict 2053. The research was carried out in the QF region, located in Minas Gerais, Brazil. We used classified images of land use/cover from 1985 to 2018 to calculate the landscape metrics and perform the Land Change Modeler tool. Thus, we obtained the landscape patterns over the years and a prediction for 2053. To that content, we used class and landscape metric levels, especially to describe the spatial distribution of land use/cover, and to identify how its composition has changed over the years. The results show that the Forest class contributed the most to Mining, with +0.09% in the area. In addition, the Farming class decreased 12%, with its total area converted among the others land use/cover. Thus, the Forest and Mining patches’ areas raised by 4% and 0.2%, with a tendency for a continuous increase until 2053. However, the forest fragments tended to disaggregate, while the Mining areas tended to become more connected. These results converge to a worrying scenario from an ecological point of view. Therefore, it is necessary to have/search for better supervision related to the compliance of environmental laws to avoid biodiversity losses in mining areas.","PeriodicalId":332244,"journal":{"name":"Journal of Hyperspectral Remote Sensing","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126426018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0