Tracking Land Use and Land Cover Changes in the Volta Grande Do Xingu(PARÁ - Brazil) Between 2000 and 2017 Through Three Pixel-Based Classification Methods

Affonso, Alynne Almeida; Mandai, Silvia Sayuri; Portella, Tatiana Pineda; Quintanilha, José Alberto; Grohmann, Carlos Henrique

doi:10.1109/igarss46834.2022.9884422

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…Another important aspect that might influence the classification in a complex landscape such as tropical forests is the reference data. In a first attempt to classify the two Landsat images using only parametric algorithms, the training stage was based on the user's expertise of the area and on previously collected ground information data based on a 2008 raster image elaborated for the BMHC Environmental Impact Study, resulting in a poor performance of the classification algorithms [41]. Existing data are rarely acceptable for accuracy assessment, since the data might not represent the classes being mapped.…”

Section: Accuracy Assessment Of Classification Methodsmentioning

confidence: 99%

“…Based on the Amazon landscape, in a first attempt of class selection, primary and secondary forest, crops, exposed soils, agricultural lands, pasture, river, rocks and clouds were chosen based on a thematic vegetation map from the area [41]. It was not possible to completely remove clouds and their shadows from the 2000-year Landsat image using a mask, so it was determined that clouds/shadows would be considered a single class, one that was easier to disregard on the post-classification analysis and other quantitative approaches.…”

Section: Class Selection and Spectral Signaturesmentioning

confidence: 99%

“…sen based on a thematic vegetation map from the area [41]. It was not possible to comple remove clouds and their shadows from the 2000-year Landsat image using a mask, so it w determined that clouds/shadows would be considered a single class, one that was easie disregard on the post-classification analysis and other quantitative approaches.…”

Section: Class Selection and Spectral Signaturesmentioning

confidence: 99%

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A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

et al. 2023

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The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing.

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Section: Accuracy Assessment Of Classification Methodsmentioning

confidence: 99%

Section: Class Selection and Spectral Signaturesmentioning

confidence: 99%

Section: Class Selection and Spectral Signaturesmentioning

confidence: 99%

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A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

et al. 2023

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“…The construction had significant social-ecological impacts, notably a substantial reduction in water flow in a specific stretch of about 100 km of the river known as Volta Grande do Xingu. In this area, the river flow decreased by approximately 80% due to the redirection of the natural water flow to the diversion channel leading to the main powerhouse [34,45,46].…”

Section: Datamentioning

confidence: 99%

Poverty–Food Insecurity Nexus in the Post-Construction Context of a Large Hydropower Dam in the Brazilian Amazon

Johansen,

Calvi,

Luz

et al. 2024

IJERPH

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Within the 2030 Sustainable Development Agenda, large hydropower dams are positioned as a sustainable energy source, notwithstanding their adverse impacts on societies and ecosystems. This study contributed to ongoing discussions about the persistence of critical social issues, even after the investments of large amounts of resources in areas impacted by the construction of large hydropower dams. Our study focused on food insecurity and evaluated this issue in the city of Altamira in the Brazilian Amazon, which has been profoundly socially and economically impacted by the construction, between 2011 and 2015, of Brazil’s second-largest dam, namely, Belo Monte. A survey in Altamira city featured a 500-household random sample. Structural equation modeling showed conditioning factors of 60% of the population experiencing varying food insecurity degrees. Poverty, female-led households, lower education, youth, and unemployment were strongly linked to higher food insecurity. Crowded, officially impacted, and resettled households also faced heightened food insecurity. Our findings underscore the food insecurity conditions in the region impacted by the Belo Monte dam, emphasizing the need to take into account this crucial issue while planning and implementing hydropower dams.

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Tracking Land Use and Land Cover Changes in the Volta Grande Do Xingu(PARÁ - Brazil) Between 2000 and 2017 Through Three Pixel-Based Classification Methods

Cited by 2 publications

References 2 publications

A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

Poverty–Food Insecurity Nexus in the Post-Construction Context of a Large Hydropower Dam in the Brazilian Amazon

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