Advancement of Agricultural Activity on Natural Vegetation Areas in National Agribusiness Capital

Giaretta, Jussara; Storck‐Tonon, Danielle; Silva, Joselaine Souto Hall; Filho, Manoel dos Santos; Silva, Dionei José da

doi:10.1590/1809-4422asoc0139r2vu19l4ao

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…These policies were intended to occupy and economically develop the region, but not to understand, study and contribute to human development and ecological conservation ( Giaretta et al ., 2019 ; Silva and Lucas, 2019 ). The policies implemented were top-down ( Sabatier, 1986 ), reverberating in close discrepancy between their application and practice.…”

Section: The Current Brazilian and Amazonian Context For Science And mentioning

confidence: 99%

Emerging complexities and rising omission: Contrasts among socio-ecological contexts of infectious diseases, research and policy in Brazil

et al. 2021

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In this article, we explore elements that highlight the interdependent nature of demands for knowledge production and decision-making related to the appearance of emerging diseases. To this end, we refer to scientific production and current contextual evidence to verify situations mainly related to the Brazilian Amazon, which suffers systematic disturbances and is characterized as a possible source of pathogenic microorganisms. With the acceleration of the Anthropocene's environmental changes, socio-ecological instabilities and the possibility of the emergence of infectious diseases merge into a background of a ´twin insurgency´. Furthermore, there is a tendency to impose economic hegemony in the current Brazilian context, corroborating discourses and pressures to a scientific simplification and denial. With this, we assert that developmental sectoral actions and monoculture of knowledge characterize an agenda of omission, that is, a process of decision making that indirectly reinforces ecological degradation and carelessness in the face of the possibility of the emergence and spreading of new diseases, such as COVID-19. Tackling the socio-ecological complexity inherent in the risk of the emergence of infectious diseases requires robust co-construction of scientific knowledge, eco-social approaches, and corresponding governance and sophisticated decision-making arrangements.

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Section: The Current Brazilian and Amazonian Context For Science And mentioning

confidence: 99%

Emerging complexities and rising omission: Contrasts among socio-ecological contexts of infectious diseases, research and policy in Brazil

et al. 2021

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“…The previous studies in Binh Duong Province reported that vegetation cover change has occurred recently due to the increase in urban areas [13] which causes negative impacts on ecosystems [14][15][16][17]. The study was conducted in Binh Duong Province by Thuy et al showed that some vegetation species are at risk of disappearing due to the reduction of their natural habitat caused by urbanization [17].…”

Section: Introductionmentioning

confidence: 99%

Assessing Vegetation Cover Change Using Remote Sensing: Case Study at Binh Duong Province, Vietnam

et al. 2022

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This study aims to present the application of remote sensing in monitoring vegetation change in Binh Duong Province, Vietnam. The study used Landsat 5 images in the year 2010 and Landsat 8 images in the years 2015 and 2020 to investigate the area of vegetation. The maximum likelihood classification method (MLC) was used to classify land cover and an accuracy matrix was computed to validate the classification results. The references data were collected to support classification and accuracy assessment processes including land use maps in 2010, 2015, and 2020. In addition, collected field points and UAV (unmanned aerial vehicle) in 2020 were used. The overall accuracies are 81.27%, 84.41%, and 83.86%, and Kappa indices were 0.76, 0.80, and 0.80, corresponding to 2010, 2015, and 2020. The results showed that as compared to 2010 and 2015, the area of vegetation in 2020 decreased 10% and 8%, respectively. The average vegetation cover per capita was 740 m2 person-1 in 2020, compared to 1000 m2 person-1 in 2015 and 1200 m2 person-1 in 2010. This reduction was obvious in urban areas in the province, due to the need for construction and development. The study provides meaningful information on vegetation change and green area per capita in Binh Duong Province from 2010 to 2020.

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Mapping Agricultural Intensification in the Brazilian Savanna: A Machine Learning Approach Using Harmonized Data from Landsat Sentinel-2

Bolfe

Parreiras

Silva

et al. 2023

IJGI

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Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021–2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture.

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Advancement of Agricultural Activity on Natural Vegetation Areas in National Agribusiness Capital

Cited by 3 publications

References 21 publications

Emerging complexities and rising omission: Contrasts among socio-ecological contexts of infectious diseases, research and policy in Brazil

Emerging complexities and rising omission: Contrasts among socio-ecological contexts of infectious diseases, research and policy in Brazil

Assessing Vegetation Cover Change Using Remote Sensing: Case Study at Binh Duong Province, Vietnam

Mapping Agricultural Intensification in the Brazilian Savanna: A Machine Learning Approach Using Harmonized Data from Landsat Sentinel-2

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