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Made available in DSpace on 2020-12-10T20:03:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-05-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) One of the challenges in remote phenology studies lies in how to efficiently manage large volumes of data obtained as long-term sequences of high-resolution images. A promising approach is known as image foveation, which is able to reduce the computational resources used (i.e., memory storage) in several applications. In this paper, we propose an image foveation approach towards plant phenology tracking where relevant changes within an image time series guide the creation of foveal models used to resample unseen images. By doing so, images are taken to a space-variant domain where regions vary in resolution according to their contextual relevance for the application. We performed our validation on a dataset of vegetation image sequences previously used in plant phenology studies. Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, Brazil Norwegian Univ Sci & Technol, Dept ICT & Nat Sci, Larsgardsvegen 2, N-6009 Alesund, Norway Sao Paulo State Univ, Inst Biosci, Dept Bot, BR-13506900 Rio Claro, Brazil Univ Stirling, Fac Nat Resources, Biol & Environm Sci, Stirling FK9 4LA, Scotland Sao Paulo State Univ, Inst Biosci, Dept Bot, BR-13506900 Rio Claro, Brazil CNPq: 307560/2016-3 CNPq: 311820/2018-2 CNPq: 310144/2015-9 FAPESP: 2014/12236-1 FAPESP: 2014/00215-0 FAPESP: 2015/24494-8 FAPESP: 2016/50250-1 FAPESP: 2016/01413-5 FAPESP: 2017/20945-0 FAPESP: 2013/50155-0 FAPESP: 2014/50715-9 CAPES: 001

Made available in DSpace on 2018-12-11T16:53:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-08-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) Rupestrian grasslands are biodiverse, evolutionary old vegetation complexes that harbor more than 5000 species of vascular plants and one of the highest levels of plant endemism in the world. Growing on nutrient–impoverished soils and under harsh environmental conditions, these mountaintop ecosystems were once spared from major human interventions of agriculture and intensive cattle ranching. However, in Brazil, rupestrian grasslands have experienced one of the most extreme land use changes among all Brazilian ecosystems, suffering from ill policies leading to intense mining activities, uncontrolled tourism, and unplanned road construction. Indeed, the discovery of large mineral reserves, the adoption of ineffective conservation policies, and, going forward, climate change, are threatening this hyper-diverse ecosystem. Here, we shed light on the severe threats imposed by land-use changes in this ecosystem, modeling its future distribution under different scenarios. We uncover a catastrophic forecast that, if not halted, will lead to the loss of 82% of this unique ecosystem in the future, impacting ecosystem services at regional scales, including water and food security potentially affecting more than 50 million persons. Departamento de Biologia Geral Universidade Federal de Minas Gerais Department of Biology Stanford University Laboratório de Fenologia Departamento de Botânica Instituto de Biociências Universidade Estadual Paulista UNESP Departamento de Demografia Universidade Federal de Minas Gerais Departamento de Zoologia Instituto de Ciências Biológicas Universidade Federal de Minas Gerais, CP 486 Instituto de Geociências Pós-Graduação em Análise e Modelagem de Sistemas Ambientais Universidade Federal de Minas Gerais Departamento de Botânica Instituto de Ciências Biológicas Universidade Federal de Minas Gerais Laboratório de Fenologia Departamento de Botânica Instituto de Biociências Universidade Estadual Paulista UNESP CNPq: 154664/2016-2 CNPq: 441515/2016-9

The application of digital cameras to monitor the environment is becoming global and changing the way of phenological data collection. The technique of repeated digital photographs to monitor plant phenology (phenocams) has increased due to its low-cost investment, reduced size, easy set up installation, and the possibility of handling high-resolution near-remote data. Considering the widespread use of phenocams worldwide, our main goals here are: (i) to provide a step-by-step guide for phenocam set up in the tropics, reinforce its appliance as an efficient tool for monitoring tropical phenology and foster networking, (ii) to discuss phenocam applications for biological conservation, management, and ecological restoration. We provide the concepts and properties for image analysis which allow representing the phenological status of the vegetation. The association of a long-term imagery data with local sensors (e.g., meteorological stations and surface-atmosphere flux towers) allows a wide range of studies, especially linking phenological patterns to climatic drivers; and the impact of climate changes on plant responses. We show phenocams applications for conservation as to document disturbances and changes on vegetation structure, such as deforestation, fire events, and flooding and the vegetation recovery. Networks of phenocams are growing globally and represent an important tool for conservation and restoration, as it provides hourly to daily information of monitored systems spread over several sites, ecosystems, and climatic zones. Moreover, websites enriched by vegetation dynamic imagery data can promote science knowledge by engaging citizen science participation.

The support of time series similarity searches might be crucial in phenology studies, in which long-term time series analysis based on the identification of similar and different phenological patterns shared by individuals belonging to different species is a widely common task. In this paper, we introduce the use of well-established Information Retrieval (IR) technologies in the search of time series. The solution comprises four main steps: extraction of an image-based time series representation; image content description to encode time series properties and patterns; textual signature extraction based on image content descriptions; and textual signature indexing using off-the-shelf IR approaches. In this paper, we demonstrate both the effectiveness and the efficiency of the proposed solution in time series retrieval problems related to the management of phenological data associated with near-surface vegetation images.

Premise Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations. Methods and Results We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool. Conclusions RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long‐term leafing phenology data associated with near‐surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground‐based direct visual observations and camera‐derived phenology observations.

Phenology is among the most trustworthy indicators of climate change effects on plants and animals. The recent application of repeated digital photographs to monitor vegetation phenology has provided accurate measures of plant life cycle changes over time. A fundamental requirement for phenology studies refers to the correct recognition of phenological patterns from plants by taking into account time series associated with their crowns. This paper presents a new similarity measure for identifying plants based on the use of an unsupervised distance learning scheme, instead of using traditional approaches based on pairwise similarities. We experimentally show that its use yields considerable improvements in time-series search tasks. In addition, we also demonstrate how the late fusion of different time series can improve the results on plant species identification.In some cases, significant gains were observed (up to +8.21% and +19.39% for mean average precision and precision at 10 scores, respectively) when compared with the use of time series in isolation.

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground.

Made available in DSpace on 2018-12-11T17:27:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-11-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Nowadays, global warming and its resulting environmental changes is a hot topic in different biology research area. Phenology is one effective way of tracking such environmental changes through the study of plant's periodic events and their relationship to climate. One promising research direction in this area relies on the use of vegetation images to track phenology changes over time. In this scenario, the creation of effective image-based plant identification systems is of paramount importance. In this paper, we propose the use of a new representation of time series to improve plants recognition rates. This representation, called recurrence plot (RP), is a technique for nonlinear data analysis, which represents repeated events on time series into a two-dimensional representation (an image). Therefore, image descriptors can be used to characterize visual properties from this RP images so that these features can be used as input of a classifier. To the best of our knowledge, this is the first work that uses recurrence plot for plant recognition task. Performed experiments show that RP can be a good solution to describe time series. In addition, in a comparison with visual rhythms (VR), another technique used for time series representation, RP shows a better performance to describe texture properties than VR. On the other hand, a correlation analysis and the adoption of a well successful classifier fusion framework show that both representations provide complementary information that is useful for improving classification accuracies. Institute of Science and Technology Federal University of São Paulo – UNIFESP Institute of Computing University of Campinas – UNICAMP Dept. of Botany Sao Paulo State University – UNESP Dept. of Botany Sao Paulo State University – UNESP FAPESP: #2010/52113-5 FAPESP: #2013/50155-0 FAPESP: #2013/50169-1 CNPq: 306580/2012-8 CNPq: 310761/2014-0

Made available in DSpace on 2018-12-11T17:26:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-10-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Global warming and its resulting environmental changes surely are ubiquitous subjects nowadays and undisputedly important research topics. One way of tracking such environmental changes is by means of phenology, which studies natural periodic events and their relationship to climate. Phenology is seen as the simplest and most reliable indicator of the effects of climate change on plants and animals. The search for phenological information and monitoring systems has stimulated many research centers worldwide to pursue the development of effective and innovative solutions in this direction. One fundamental requirement for phenological systems is concerned with achieving fine-grained recognition of plants. In this sense, the present work seeks to understand specific properties of each target plant species and to provide the solutions for gathering specific knowledge of such plants for further levels of recognition and exploration in related tasks. In this work, we address some important questions such as: (i) how species from the same leaf functional group differ from each other; (ii) how different pattern classifiers might be combined to improve the effectiveness results in target species identification; and (iii) whether it is possible to achieve good classification results with fewer classifiers for fine-grained plant species identification. In this sense, we perform different analysis considering RGB color information channels from a digital hemispherical lens camera in different hours of day and plant species. A study about the correlation of classifiers associated with time series extracted from digital images is also performed. We adopt a successful selection and fusion framework to combine the most suitable classifiers and features improving the plant identification decision-making task as it is nearly impossible to develop just a single “silver bullet” image descriptor that would capture all subtle discriminatory features of plants within the same functional group. This adopted framework turns out to be an effective solution in the target task, achieving better results than well-known approaches in the literature. Institute of Science and Technology Federal University of São Paulo – UNIFESP Department of Botany São Paulo State University – UNESP Institute of Computing University of Campinas – UNICAMP Department of Botany São Paulo State University – UNESP FAPESP: #2010/05647-4 FAPESP: #2010/14910-0 FAPESP: #2013/50155-0 CAPES: grant #1260-12-0 FAPESP: grants #2010/52113-5, #2011/51523-8, #201313/20169-1

We extract plant color information from images and correlate with leaf phenological changes.We use time series associated with plants for pattern analysis and knowledge extraction.We present a novel approach for capturing phenological patterns from time series.Our method encodes time series as a visual rhythm, which is characterized by image descriptors.Our method presents high accuracy and computational speed on identifying plant species. Plant phenology, the study of recurrent life cycles events and its relationship to climate, is a key discipline in climate change research. In this context, digital cameras have been effectively used to monitor leaf flushing and senescence on vegetations across the world. A primary condition for the phenological observation refers to the correct identification of plants by taking into account time series associated with their crowns in the digital images. In this paper, we present a novel approach for representing phenological patterns of plant species. The proposed method is based on encoding time series as a visual rhythm. Here, we focus on applications of our approach for plant species identification. In this scenario, visual rhythms are characterized by image description algorithms. A comparative analysis of different descriptors is conducted and discussed. Experimental results show that our approach presents high accuracy on identifying individual plant species from its specific visual rhythm. Additionally, our representation is compact, making it suitable for long-term data series.

International audience; Phenology has achieved a prominent position in current scenarios of global change research given its role in monitoring and predicting the timing of recurrent life cycle events. However, the implications of phenology to environmental conservation and management remain poorly explored. Here, we present the first explicit appraisal of how phenology a multidisciplinary science encompassing biometeorology, ecology, and evolutionary biology can make a key contribution to contemporary conservation biology. We focus on shifts in plant phenology induced by global change, their impacts on species diversity and plant-animal interactions in the tropics, and how conservation efforts could be enhanced in relation to plant resource organization. We identify the effects of phenological changes and mismatches in the maintenance and conservation of mutualistic interactions, and examine how phenological research can contribute to evaluate, manage and mitigate the consequences of land-use change and other natural and anthropogenic disturbances, such as fire, exotic and invasive species. We also identify cutting-edge tools that can improve the spatial and temporal coverage of phonological monitoring, from satellites to drones and digital cameras. We highlight the role of historical information in recovering long-term phenological time series, and track climate-related shifts in tropical systems. Finally, we propose a set of measures to boost the contribution of phonology to conservation science. We advocate the inclusion of phenology into predictive models integrating evolutionary history to identify species groups that are either resilient or sensitive to future climate-change scenarios, and understand how phenological mismatches can affect community dynamics, ecosystem services, and conservation over time. (C) 2015 Elsevier Ltd. All rights reserved.

Plant phenology has been exploited as an important research venue for assessing the impact of climate changes. One common approach for monitoring vegetation relies on the use of digital cameras. The employment of imaging techniques for phenological observation allows the extraction and analysis of visual characteristics based on color and texture information with the objective of determining plant life cycle changes, such as the beginning of the leaf flushing or the senescence period. This paper presents a novel approach for detecting phenological changes by analyzing image temporal series. Our method is based on the use of visual rhythm analysis and the adoption of a dissimilarity measure to detect visual changes in the time line. Experiments were conducted on a three-year data set composed of 3,538 vegetation images and 21 samples of 6 different species of interest. Results demonstrate that the proposed change detection approach is able to effectively identify phenological events.

Phenology is the study of periodic natural phenomena and their relationship to climate. Usually, phenology studies consider the identification of patterns on temporal data. In those studies, several phenological change patterns are often encoded in time series for analysis and knowledge extraction. In this paper, we evaluate the effectiveness of several time series similarity functions in the task of classifying time series related to phonological phenomena characterized by near-surface vegetation indices extracted from images. In addition, we performed a correlation analysis to identify potential candidates for combination.

Plant phenology has gained new importance in the context of global change research, stimulating the development of novel technologies for phenological observations. Regular digital cameras have been effectively used as three-channel imaging sensors, providing measures of leaf color change or phenological shifts in plants. We monitored a species rich Brazilian cerrado savanna to assess the reliability of digital images to detect leaf-changing patterns. Analysis was conducted by extracting color information from selected parts of the image named regions of interest (ROIs). We aimed to answer the following questions: (i) Do digital cameras capture leaf changes in cerrado savanna vegetation? (ii) Can we detect differences in phenological changes among species crowns and the cerrado community? (iii) Is the greening pattern detected for each species by digital camera validated by our on-the-ground leafing phenology (direct observation of tree leaf changes)? We analyzed daily sequences of five images per hour, taken from 6:00 to 18:00 h, recorded during the cerrado main leaf flushing season. We defined 24 ROIs in the original digital image, including total or partial regions and crowns of six plant species. Our results indicated that: (i) for the studied period, single plant species ROIs were more sensitive to changes in relative green values than the community ROIs, (ii) three leaf strategies could be depicted from the species' ROI patterns of green color change, and (iii) the greening patterns and leaf functional groups were validated by our on-the-ground phenology. We concluded that digital cameras are reliable tools to monitor high diverse tropical seasonal vegetation and it is sensitive to inter-species differences of leafing patterns.

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. In this context, digital cameras have been successfully used as multi-channel imaging sensors, providing measures to estimate changes on phenological events, such as leaf flushing and senescence. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. For that, we extract leaf color information and correlated with phenological changes. In this way, time series associated with plant species are obtained, raising the need of using appropriate tools for mining patterns of interest. In this paper, we present a novel approach for representing phenological patterns of plant species. The proposed method is based on encoding time series as a visual rhythm, which is characterized by color description algorithms. A comparative analysis of different descriptors is conducted and discussed. Experimental results show that our approach presents high accuracy on identifying plant species.

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground.