Estimates of Deforestation Rates in Rural Properties in the Legal Amazon

Leal, Fabrício Assis; Miguel, Éder Pereira; Matricardi, Eraldo Aparecido Trondoli

doi:10.1590/2179-8087.028317

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Deforestation has increased in speed and intensity in the important Amazon rainforest due to numerous factors, including migrant occupation of rural lands, mineral exploration, and timber extraction. The deforestation creates negative impacts on Brazil's biodiversity [60]. AI approaches can help.…”

Section: Target 152 Promote Sustainable Management Forestsmentioning

confidence: 99%

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A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

Isabelle

Westerlund

2022

Sustainability

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The scholarly literature on the links between Artificial Intelligence and the United Nations’ Sustainable Development Goals is burgeoning as climate change and the biotic crisis leading to mass extinction of species are raising concerns across the globe. With a focus on Sustainable Development Goals 14 (Life below Water) and 15 (Life on Land), this paper explores the opportunities of Artificial Intelligence applications in various domains of wildlife, ocean and land conservation. For this purpose, we develop a conceptual framework on the basis of a comprehensive review of the literature and examples of Artificial Intelligence-based approaches to protect endangered species, monitor and predict animal behavior patterns, and track illegal or unsustainable wildlife trade. Our findings provide scholars, governments, environmental organizations, and entrepreneurs with a much-needed taxonomy and real-life examples of Artificial Intelligence opportunities for tackling the grand challenge of rapidly decreasing biological diversity, which has severe implications for global food security, nature, and humanity.

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Section: Target 152 Promote Sustainable Management Forestsmentioning

confidence: 99%

“…Example 11: Leal et al [60] show that AI and, more specifically, artificial neural networks (ANN) can successfully estimate the deforestation rates by rural property, hence showing the high potential of ANN as a tool to estimate deforestation rates.…”

Section: Target 152 Promote Sustainable Management Forestsmentioning

confidence: 99%

A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

Isabelle

Westerlund

2022

Sustainability

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“…Nevertheless, despite the progress in geoscience, the net impact of AI on SDG 15 is still poorly understood. Yu et al [51] [53][54][55][56], managing land degradation [43,56], combating poaching and protecting endangered species [57,58]; halting biodiversity loss and habitat degradation [44], reducing invasive species [59,60], spotting plant diseases and fires or identity seeds [61]. Kolevatova et al [62] claim the relevance of explainable AI (XAI) to support the climate effects of land changes (land cover, deforestation, urbanization) with enhanced computational time and data usage.…”

Section: Group 2: Blockchain As a DI For Food Securitymentioning

confidence: 99%

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Gupta¹,

Zeballos²,

Castro³

et al. 2022

Preprint

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Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is important specifically for Sustainable Development Goals(SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact it creates will help us align it with visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed Digitainability Assessment Framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. We performed the impact assessment of diverse technologies using DAF. This paper summarizes the insights from the Digitainable Spring School 2022 on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the action learning process with diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The operationalization of the DAF was carried out by looking at four different DIs: smart home technologies (SHT) for energy efficiency, blockchain for food security, artificial intelligence for land use cover and changes (LUCC), and big data for international law. Each of the four studies addresses different DIs for digitainability assessment using different techniques for a diverse group of indicators, demonstrating the potential of the DAF but also outlining the existing data gaps that limit a comprehensive analysis.

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“…Com potencial de uso no meio florestal (Almeida et al 2014;Leal et al 2015) a aplicação da inteligência artificial nessa área é recente, porém, tem sido frequente sua utilização para realizar estimativas de variáveis nas diversas linhas de pesquisas, apresentando resultados significativos e até melhores em relação aos modelos tradicionais de regressão. Além disso, diversos estudos confirmam a capacidade das RNA para estimar o volume, (Gorgens et al 2009;Özçelik et al 2010;Binoti et al 2014), altura (Binoti et al 2013aÖzçelik et al 2013;Martins et al 2016), afilamento (Leite et al 2011;Nunes e Gõrgens 2016;Martins et al 2017), crescimento (Castro et al 2013;Lima et al 2013;Binoti et al 2015;Ashraf et al 2015), estimativas de desmatamento (Leal et al 2020), estimativas de variáveis biométricas (Fernandes et al 2019).…”

Section: Introductionunclassified

Redes neurais artificiais e modelos alométricos aplicados para estimativa de volume e altura em Eucalyptus urophylla S.T.Blacke

Leal¹,

Leal

Silva³

2020

ADV. FOR. SCI.

Self Cite

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Redes neurais artificiais (RNA) destacam-se como ferramentas alternativas na modelagem florestal, com resultados muitas vezes superiores em relação aos métodos tradicionais de regressão. Este estudo objetivou avaliar se as estimativas de volume e altura, obtidas por redes neurais artificiais e modelos alométricos, são compatíveis ao método tradicional de cubagem rigorosa. Para obtenção dos dados foram cubadas, pelo método de Hohenadl, 394 árvores de Eucalyptus urophylla. Destas árvores, 70% foram utilizadas nos ajustes dos modelos hipsométricos, volumétricos e para treinamento da RNA e 30% fizeram parte da validação. Para selecionar as melhores equações e RNA observou-se as melhores estatísticas de ajuste e precisão. Estatisticamente, não houve diferença entre os dados da cubagem em campo e as estimativas, ao nível de 5% de significância. Tanto os modelos de regressão quanto as RNA apresentaram bons ajustes, no entanto, as RNA foram superiores em desempenho, pois apresentaram Syx de a 2% no treinamento e 2,5% na validação, para variável altura, e Syx de 6,5% no treinamento e 5,1% na validação, para o volume.

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Estimates of Deforestation Rates in Rural Properties in the Legal Amazon

Cited by 7 publications

References 23 publications

A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Redes neurais artificiais e modelos alométricos aplicados para estimativa de volume e altura em Eucalyptus urophylla S.T.Blacke

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