Leafing patterns and drivers across seasonally dry tropical communities

Investigating the timing of key phenological events across environments with variable seasonality is crucial to understand the drivers of ecosystem dynamics. Leaf production in the tropics is mainly constrained by water and light availability. Identifying the factors regulating leaf phenology patterns allows efficiently forecasting of climate change impacts. We conducted a novel phenological monitoring study across four Neotropical vegetation sites using leaf phenology time series obtained from digital repeated photographs (phenocameras). Seasonality differed among sites, from very seasonally dry climate in the caatinga dry scrubland with an eight-month long dry season to the less restrictive Cerrado vegetation with a six-month dry season. To unravel the main drivers of leaf phenology and understand how they influence seasonal dynamics (represented by the green color channel (Gcc) vegetation index), we applied Generalized Additive Mixed Models (GAMMs) to estimate the growing seasons, using water deficit and day length as covariates. Our results indicated that plant-water relationships are more important in the caatinga, while light (measured as day-length) was more relevant in explaining leafing patterns in Cerrado communities. Leafing behaviors and predictor-response relationships (distinct smooth functions) were more variable at the less seasonal Cerrado sites, suggesting that different life-forms (grasses, herbs, shrubs, and trees) are capable of overcoming drought through specific phenological strategies and associated functional traits, such as deep root systems in trees

Climate and the Myracrodruon urundeuva Allemão seed production.

The seed physiological quality is related with climate variation during development. Thus, the aim of this study was to determinate the relation among climatic factors and germination of M. urundeuva seeds in different growing seasons and to predict the germination according to the climatic scenarios. Seeds from 14 crop seasons (2005 to 2018) and climatic data from the ?Bebedouro? weather station (Embrapa Semiarid) were used to determine the influence of climatic conditions on the vegetative, female and male flowers and the fruiting of M. urundeuva. The simple linear correlation and the multiple linear regression model were applied to determine the influence of climatic elements on the production of M. urundeuva seeds. The multivariate calibration model was developed using the previous selection of variables by the algorithm of successive projections. From the mathematical model, the germination of M. urundeuva seeds was predicted according to climate change, as provided by the IPCC. Seed germination showed a significant difference between harvests. Through the correlation it was observed that the temperature correlated negatively with all phenological phases of M. urundeuva. The quality of M. urundeuva seeds is related to the maximum, average and minimum temperature, average and minimum humidity, and precipitation. These climate variables during the different phenological phases of M. urundeuva affect the physiological quality of the seeds, and, in climate change scenarios, there will be a reduction in the seed production of this species

Avaliação do índice de área foliar e índice de área da planta em floresta seca utilizando modelos simplificados em imagens de alta resolução com o uso de VANT.

O sensoriamento remoto tem possibilitado a aplicação de modelos para estimar variáveis ambientais, dentre eles o índice de área foliar (LAI) e o índice de área da planta (PAI), importantes para avaliação da sazonalidade da vegetação, principalmente em florestas secas. Assim, objetivou-se avaliar o LAI e PAI na caatinga usando imagens aéreas de alta resolução obtidas com um veículo aéreo não tripulado (VANT). Em área de caatinga preservada foram realizados voos com o VANT acoplado com c âmeras RGB e RGN. Utilizou-se modelos para estimativa do LAI e PAI tendo como parâmetro de entrada o NDVI. Dados de LAI e PAI a partir do satélite Landsat-8 foram usados para comparação entre os produtos obtidos pelo VANT. A avaliação do NDVI ocorreu por regressão linear (R2=0,993), obtendo NDVI médio da Caatinga de 0,14 e 0,38 com os dados Landsat -8 nos períodos seco e chuvoso; 0,12 e 0,07 com a câmera RGB e RGN do VANT nos períodos seco e 0,65 e 0,27 para período chuvoso. Os dados LAI e o PAI (m 2 m-2) representaram bem a área em estudo, obtendo R2=0,992 e R2=0,993 para LAI e PAI, respectivamente. O LAI médio da Caatinga foi 0,19 (período seco) e 0,80 (período chuvoso) pelo Landsat-8; 0,26 e 0,14 com a câmera RGB e RGN do VANT nos períodos secos e 2,18 e 0,48 para o período chuvoso. Já o PAI, os valores médios foram 1,39 e 2,02 com os dados Landsat -8 nos períodos seco e chuvoso; 1,46 e 1,34 com a câmera RGB e RGN do VANT nos períodos seco e 3,42 e 1,69 para o período chuvoso. Desse modo, os modelos calculados com imagens VANT para estimativa do LAI e do PAI da caatinga podem ser aplicados em imagens de alta resolução espacial obtidas em câmeras multiespectrais acopladas em VANT, o btendo resultados satisfatórios

Phenological dynamics of four populations of Handroanthus spongiosus in seasonally dry tropical forest in Brazil.

The scarcity of phenological studies based on different populations of tropical forest trees limits seed management and collection for reforestation efforts. Precipitation is the primary factor driving tropical plant phenology in seasonal environments, although other environmental variables and plant traits may be associated. We examined the seasonality, synchrony, and intensities of the vegetative and reproductive phenophases of four populations of Handroanthus spongiosus, an endangered species, under similar climate regimes in a seasonally dry tropical forest, in northeastern Brazil. We expected to observe some divergence in the phenologies of the populations related to distinct functional traits selected for by differences in rainfall and soil properties. Mature trees (n = 87) were monitored during a three-year period. Seasonality was examined using circular statistics, and the influences of environmental variables on phenophases were investigated using generalized additive models. Variations in intensities and activity indices were identified among the different populations. Vegetative phenophases were seasonal, driven by precipitation and photoperiod, with leaf longevity of up to 7 months; budding peaked in February-March, while leaf fall peaked in April and October. The reproductive phenophases were found to be seasonal, during the rainy season (November to April), influenced by temperature and photoperiod. The slight divergences noted among the phenological behaviors of the populations were related to distinct functional traits (e.g., tree height, stem diameter) selected for by differences in certain environmental variables (rainfall volumes and soil properties). Given ongoing global climate changes, increases in leaf fall and reductions of flowering intensity, as verified here, will likely be observed

Multiscale phenology of seasonally dry tropical forests in an aridity gradient

The leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing of new leaves triggered by the first rains of the wet season. Such phenological transitions may not be accurately detected by remote sensing vegetation indices and derived transition dates (TDs) due to the coarse spatial and temporal resolutions of satellite data. The aim of this study was to compared TDs from PhenoCams and satellite remote sensing (RS) and used the TDs calculated from PhenoCams to select the best thresholds for RS time series and calculate TDs. For this purpose, we assembled cameras in seven sites along an aridity gradient in the Brazilian Caatinga, a region dominated by SDTFs. The leafing patterns were registered during one to three growing seasons from 2017 to 2020. We drew a region of interest (ROI) in the images to calculate the normalized green chromatic coordinate index. We compared the camera data with the NDVI time series (2000–2019) derived from near-infrared (NIR) and red bands from MODIS product data. Using calibrated PhenoCam thresholds reduced the mean absolute error by 5 days for SOS and 34 days for EOS, compared to common thresholds in land surface phenology studies. On average, growing season length (LOS) did not differ significantly among vegetation types, but the driest sites showed the highest interannual variation. This pattern was applied to leaf flushing (SOS) and leaf fall (EOS) as well. We found a positive relationship between the accumulated precipitation and the LOS and between the accumulated precipitation and maximum and minimum temperatures and the vegetation productivity (peak and accumulated NDVI). Our results demonstrated that (A) the fine temporal resolution of phenocamera phenology time series improved the definitions of TDs and thresholds for RS landscape phenology; (b) long-term RS greening responded to the variability in rainfall, adjusting their timing of green-up and green-down, and (C) the amount of rainfall, although not determinant for the length of the growing season, is related to the estimates of vegetation productivity

Ecologie comparative des écosystèmes tropicaux (en Afrique)

After a short presentation of my career trajectory in academia, and of the PhD students and post-doctoral fellows I had the chance to supervise, I present a synthesis of the research work I conducted over the last decade on the comparative ecology of tropical ecosystems in Africa. This work is anchored into applied forest sciences and the data that were accumulated to answer practical questions also helped answer more fundamental questions in ecology. In my work, trees are used as the starting point in the understanding of tropical ecosystems, mostly moist forests but also drier formations, such as woodlands and savannas. With a background in community and functional ecology and a position in Gembloux Agro-Bio Tech, University of Liège, targeting tropical tree allometry and forest carbon, I derived two types of comparative approaches in my research activities, I compare either sites (trees or stands) or lineages (species or genera, mostly). For the site comparison, I used either the angle of tree architecture and stand structure or that of diversity and composition, at different spatial and temporal scales, from tree allometry and biomass estimates, up to the landscape scale for the structural approach, and from diversity recovery after logging, the delineation of forest types for management and up to biogeography studies, including cross-taxonomic and cross-continent comparisons for the diversity approach. For the lineage comparison, the concept of functional traits has been central and transversal since it allowed relating the structure and diversity approaches. It was however first adapted to tropical trees for which leaves are difficult to access, and size can vary tremendously over the tree life span and among tree life histories. Allometric or size-controlled traits were notably derived from tree measurements in the field and computed at a certain diameter to compare species of contrasted morphologies. Wood anatomical traits were also investigated and notably related to tree hydraulics. In this line, I finally propose a research project on tree and forest seasonal functioning, and response to drought. Tropical forests of central Africa are indeed found under drier and more seasonal climates than their south-eastern Asian and south American counterparts, and their resilience to climate (change) is a timely topic. These research perspectives will complement ongoing work on (i) the biogeography of Africa using species occurrence derived from herbarium records instead of checklists, (ii) carbon and biodiversity changes over the last decade by re-census a set of existing plots in the Congo basin, and on (iii) the seasonality in tree and forest functioning, using tree dendrometers and phenological cameras (PhenoCams) to monitor in depth how trees cope with the dry season. This project entitled CANOPI has been accepted for founding and offer the opportunity to collect unique ground-based measurements of tree and forest functioning in central Africa.15. Life on lan

Image-based time series representations for satellite images classification

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A classificação de imagens de sensoriamento remoto por pixel com base no perfil temporal desempenha um papel importante em várias aplicações, tais como: reconhecimento de culturas, estudos fenológicos e monitoramento de mudanças na cobertura do solo. Avanços sensores captura de imagem aumentaram a necessidade de criação de metodologias para analisar o perfil temporal das informações coletadas. Nós investigamos dados coletados em dois tipos de sensores: (i) sensores em plataformas orbitais, esse tipo de imagem sofre interferências de nuvens e fatores atmosféricos; e (ii) sensores fixados em campo, mais especificamente, uma câmera digital no alto de uma torre, cujas imagens capturadas podem conter dezenas de espécies, dificultando a identificação de padrões de interesse. Devido às particularidades dos dados detectados remotamente, torna-se custoso enviar a imagem capturada pelo sensor diretamente para métodos de aprendizado de máquina sem realizar um pré-processamento. Para algumas aplicações de sensoriamento remoto, comumente não se utiliza as imagens brutas oriundas dos sensores, mas os índices de vegetação extraídos das regiões de interesse ao longo do tempo. Assim, o perfil temporal pode ser caracterizado como uma série de observações dos índices de vegetação dos pixels de interesse. Métodos baseados em aprendizado profundo obtiveram bons resultados em aplicações de sensoriamento remoto relacionadas à classificação de imagens. Contudo, em consequência da natureza dos dados, nem sempre é possível realizar o treinamento adequado das redes de aprendizado profundo pela limitação causada por dados faltantes. Entretanto, podemos nos beneficiar de redes previamente treinadas para detecção de objetos para extrair características e padrões de imagens. O problema alvo deste trabalho é classificar séries temporais extraídas de imagens de sensoriamento remoto representando as características temporais como imagens 2D. Este trabalho investiga abordagens que codificam séries temporais como representação de imagem para propor metodologias de classificação binária e multiclasse no contexto de sensoriamento remoto, se beneficiando de redes extratoras de características profundas. Os experimentos conduzidos para classificação binária foram realizados em dados de satélite para identificar plantações de eucalipto. Os resultados superaram métodos baseline propostos recentemente. Os experimentos realizados para classificação multiclasse focaram em imagens capturadas com câmera digital para detectar o padrão fenológico de regiões de interesse. Os resultados mostram que a acurácia aumenta se consideramos conjuntos de pixelsAbstract: Pixelwise remote sensing image classification based on temporal profile plays an important role in several applications, such as crop recognition, phenological studies, and land cover change monitoring. Advances in image capture sensors have increased the need for methodologies to analyze the temporal profile of collected information. We investigate data collected by two types of sensors: (i) sensors on orbital platforms, this type of image suffers from cloud interference and atmospheric factors; and (ii) field-mounted sensors, in particular, a digital camera on top of a tower, where captured images may contain dozens of species, making it difficult to identify patterns of interest. Due to the particularities of remotely detected data, it is prohibitive to send sensor captured images directly to machine learning methods without preprocessing. In some remote sensing applications, it is not commonly used the raw images from the sensors, but the vegetation indices extracted from regions of interest over time. Thus, the temporal profile can be characterized as a series of observations of vegetative indices of pixels of interest. Deep learning methods have been successfully in remote sensing applications related to image classification. However, due to the nature of the data, it is not always possible to properly train deep learning networks because of the lack of enough labeled data. However, we can benefit from previously trained 2D object detection networks to extract features and patterns from images. The target problem of this work is to classify remote sensing images, based on pixel time series represented as 2D representations. This work investigates approaches that encode time series into image representations to propose binary and multiclass classification methodologies in the context of remote sensing, taking advantage of data-driven feature extractor approaches. The experiments conducted for binary classification were performed on satellite data to identify eucalyptus plantations. The results surpassed the ones of recently proposed baseline methods. The experiments performed for multiclass classification focused on detecting regions of interest within images captured by a digital camera. The results show that the accuracy increases if we consider a set of pixelsDoutoradoCiência da ComputaçãoDoutora em Ciência da ComputaçãoCAPE

Beyond carbon: The contributions of South American tropical humid and subhumid forests to ecosystem services

Tropical forests are recognized for their role in providing diverse ecosystem services (ESs), with carbon uptake the best recognized. The capacity of tropical forests to provide ESs is strongly linked to their enormous biodiversity. However, causal relationships between biodiversity and ESs are poorly understood. This may be because biodiversity is often translated into species richness. Here we argue that focusing on multiple attributes of biodiversity—structure, composition, and function—will make relationships between biodiversity and ESs clearer. In this review, we discuss the ecological processes behind ESs from tropical humid and subhumid forests of South America. Our main goal is to understand the links between the ESs and those three biodiversity attributes. While supporting and regulating services relate more closely to forest structure and function, provisioning services relate more closely to forest composition and function, and cultural services are more related to structure and composition attributes. In this sense, ESs from subhumid forests (savannas) differ from those provided by the Amazon Forest, although both ecosystems are recognized as harboring tremendous biodiversity. Given this, if anthropogenic drivers of change promote a shift in the Amazon Forest toward savanna—the savannization hypothesis—the types of services provided will change, especially climate regulating services. This review emphasizes the importance of deeply understanding ecosystem structure, composition, and function to better understand the services ecosystems provide. Understanding that anthropogenic impacts on biodiversity occur through these three main attributes, it becomes easier to anticipate how humans will impact ESs