Classification and monitoring of urbanized areas using computer vision techniques

In this paper we propose a computer vision system to classify permeable and impermeable areas of a bounded area for study including the Micro-basin of Segredo and adjacent micro-basins, located in the municipality of Campo Grande/MS, Brazil, in order to evaluate the increase in urban density between the years 2008 and 2016. The proposed system is based on the image segmentation method Simple Linear Iterative Clustering (SLIC) to partition an image into multiple segments and generate superpixels that differentiate the permeable and impermeable areas; and attribute extraction algorithms to describe the visual features such as color, gradient, texture, and shape. The performance of five supervised learning methods was evaluated for the task of permeable and impermeable areas recognition. The proposed approach achieved an accuracy of 94.6% using the Support Vector Machine (SVM) algorithm. In addition, the results showed an increase of 7.2% in the urban occupation rate of the study area between the analyzed years. The results indicate that the proposed approach can support specialists and managers in the monitoring of urban density and its environmental impact.Neste artigo propomos um sistema de visão computacional para classificar áreas permeáveis e impermeáveis de uma região delimitada para estudo compreendendo a Microbacia do Segredo e microbacias adjacentes, localizada no município de Campo Grande/MS, Brasil, a fim de avaliar o aumento do adensamento urbano entre os anos de 2008 e 2016. O sistema proposto baseia-se no método de segmentação de imagens Simple Linear Iterative Clustering (SLIC) para particionar uma imagem em múltiplos segmentos e gerar superpixels que diferenciem as áreas permeáveis e impermeáveis; e algoritmos de extração de atributos para descrever as características visuais, como cor, gradiente, textura e forma. O desempenho de cinco métodos de aprendizado supervisionados foi avaliado para a tarefa de reconhecimento de áreas permeáveis e impermeáveis. A abordagem proposta atingiu uma acurácia de 94,6% usando o algoritmo Support Vector Machine (SVM). Além disso, os resultados mostraram um aumento de 7,2% na taxa de ocupação urbana da área de estudo entre os anos analisados. Os resultados indicam que a abordagem proposta pode apoiar especialistas e gestores no monitoramento do adensamento urbano e o seu impacto ambiental

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost