DEVELOPMENT OF METHODOLOGY FOR PLANT PHENOLOGY MONITORING BY GROUND-BASED OBSERVATION USING DIGITAL CAMERA

When monitoring phenology at ground level, it would be more important to continue observations in long terms and to detect the timing of various phenological events such as leafing, flowering and autumn senescence. In this study, to develop the methodology for plant phenology monitoring by using digital camera, we examined how multiple image indices, which are derived from multi-temporal visible images, respond to the changes of colors of leaves and flowers for several target species of plants, and tried to detect various phenology events by tracing time series changes of the coordinate in the feature spaces of two indices. As a result, we found out that it was possible to understand the characteristics of the phenological events for different species from each image index. Also, it was identified that the utility of combination with two indices would be effective to detect the timing of phenology events in the feature space of two indices. In the actual phenology monitoring, it would be effective to use a single index for understanding the seasonal characteristics and to use the combination of two indices for detection of the timing of phenology events by tracing the time series changes in the feature space

Using digital repeat photography for monitoring the regrowth of a clear-cut area

Sammanfattning Digitala övervakningskameror har tidigare använts inom fenologisk forskning för att kunna mäta grönskan i trädkronor. Man har under en längre tid kunnat koppla lövträdets årscykel till klimatförändringar och på så sätt haft ett starkt incitament att studera sambandet. I denna uppsats utforskas möjligheten att applicera metoden med digitalkameror på ett nyligen kalhugget område för att se om man kan mäta återväxten under tre års tid (2011-2013). Genom att extrahera pixelvärden och beräkna grönskan kan man under de tre växtsäsongerna studera en signifikant ökning i markvegetation. Under arbetet har digitalkamerorna jämförts med sensordata, så kallad NDVI, för att studera huruvida metoden kan mäta sig med mer precis och konventionell teknik. Detta har den visat sig kunna där de digitala kamerorna, i vissa fall, visat sig vara mer känsliga för hastiga variationer i grönska än de parallella sensormätningarna. Forskning har bevisat att det största läckaget av markbundet kol sker under de inledande åren efter skogsfällning. Detta kol har skogens jordar tidigare förvarat och konserverat, men efter fällning läcker det ut i atmosfären som koldioxid. Läckaget har också visat sig ha ett starkt samband med den initiala återväxten av markvegetation på kalhygget. Digitalkamerorna skulle således kunna användas för att studera relationen. I denna uppsats diskuteras därför vidare användning av metoden inom skogsforskning och industri. En annan applicering, i form av vegetationsövervakning, undersöks också i denna uppsats där digitalkamerorna skulle kunna vara till stor hjälp inom skogsindustri och skogsvård. Målet med denna uppsats har alltså varit att utforska användningen av digitala kameror för att studera möjligheten att övervaka återväxten av ett kalhygge.Abstract The use of inexpensive digital cameras in phenological research has been acclaimed since results in previous research have shown that they are reliable and precise in measuring greenness of vegetation. The work of this thesis aims to broaden the applicability by studying how well the method performs when measuring the regrowth of a clear-cut area. This is needed to complement existing research that primarily uses the technique to study phenology. Data acquisition from the digital images was carried out with the use of chromatic coordinates in comparison to parallel measurements of sensor-based NDVI. These time series were analyzed through correlations, measuring the linear dependency and covariance. The results show significant similarities between the two measured time series and the increase in vegetation denseness during the studied period (2011-2013). It is also shown that the chromatic coordinates are more sensitive to variations in chlorophyll greenness than NDVI. These results, together with previous research, show that the digital camera is a valuable tool that is possible to apply to forest research and industry. Studies of clear-cut areas have shown that soil carbon release is strongly dependent on initial vegetation, a relationship that is possible to study following the results of this thesis. However, more research is needed to calibrate the method for measurements in different forest types and climate zones, before this is reality. The use of ground-level reference panels are also analyzed but fails to provide the reliable information needed. Too many sources of error are detected where the brightness calibration does more harm than good. Instead, chromatic coordinates and smoothing of the time series are used for suppressing the diurnal and seasonal variations in scene illumination

A System for Acquisition, Processing and Visualization of Image Time Series from Multiple Camera Networks

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Eficácia de medidas de similaridade para a classificação de séries temporais associadas ao comportamento fenológico de plantas

Orientadores: Luiz Camolesi Júnior, Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: Fenologia é o estudo de fenômenos naturais periódicos e sua relação com o clima. Nos últimos anos, tem se apresentado relevante como o indicador mais simples e confiável dos efeitos das mudanças climáticas em plantas e animais. É nesse contexto que se destaca o e-phenology, um projeto multidisciplinar envolvendo pesquisas na área de computação e fenologia. Suas principais características são: o uso de novas tecnologias de monitoramento ambiental, o fornecimento de modelos, métodos e algoritmos para apoiar o gerenciamento, a integração e a análise remota de dados de fenologia, além da criação de um protocolo para um programa de monitoramento de fenologia. Do ponto de vista da computação, as pesquisas científicas buscam modelos, ferramentas e técnicas baseadas em processamento de imagem, extraindo e indexando características de imagens associadas a diferentes tipos de vegetação, além de se concentrar no gerenciamento e mineração de dados e no processamento de séries temporais. Diante desse cenário, esse trabalho especificamente, tem como objetivo investigar a eficácia de medidas de similaridade para a classificação de séries temporais sobre fenômenos fenológicos caracterizados por vetores de características extraídos de imagens de vegetação. Os cálculos foram realizados considerando regiões de imagens de vegetação e foram considerados diferentes critérios de avaliação: espécies de planta, hora do dia e canais de cor. Os resultados obtidos oferecem algumas possibilidades de análise, porém na visão geral, a medida de distância Edit Distance with Real Penalty (ERP) apresentou o índice de acerto mais alto com 29,90%. Adicionalmente, resultados obtidos mostram que as primeiras horas do dia e no final da tarde, provavelmente devido à luminosidade, apresentam os índices de acerto mais altos para todas as visões de análiseAbstract: Phenology is the study of periodic natural phenomena and their relationship to climate. In recent years, it has gained importance as the more simple and reliable indicator of effects of climate changes on plants and animals. In this context, we emphasizes the e-phenology, a multidisciplinary research project in computer science and phenology. Its main characteristics are: The use of new technologies for environmental monitoring, providing models, methods and algorithms to support management, integration and remote analysis of data on phenology, and the creation a protocol for a program to monitoring phenology. From the computer science point of view, the e-phenology project has been dedicated to creating models, tools and techniques based on image processing algorithms, extracting and indexing image features associated with different types of vegetation, and implementing data mining algorithms for processing time series. This project has as main goal to investigate the effectiveness of similarity measures for the classification of time series associated with phenological phenomena characterized by feature vectors extracted from images. Conducted experiments considered different regions containing individuals of different species and considering different criteria such as: plant species, time of day and color channels. Obtained results show that the Edit Distance with Real Penalty (ERP) distance measure yields the highest accuracy. Additionally, the analyzes show that in the early morning and late afternoon, probably due to light conditions, it can be observed the highest accuracy rates for all views analysisMestradoTecnologia e InovaçãoMestre em Tecnologi

ENHANCING CONSERVATION WITH HIGH RESOLUTION PRODUCTIVITY DATASETS FOR THE CONTERMINOUS UNITED STATES

Human driven alteration of the earth’s terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earth’s terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS). In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production across the CONUS domain. The main results of this work are three publically available datasets: 1) 30 m Landsat NDVI; 2) 250 m MODIS based GPP and NPP; and 3) 30 m Landsat based GPP and NPP. My goal is that these products prove useful for the wider scientific, conservation, and land management communities as we continue to strive for better conservation and management practices

Applications of Time-lapse Imagery for Monitoring and Illustrating Ecological Dynamics in a Water-stressed System

Understanding and perceiving the natural world is a key part of management, policy, conservation, and inevitably for our future. Increased demand on natural resources has heightened the importance of documenting ecosystem changes, and knowledge-sharing to foster awareness. The advancement of digital technologies has improved the efficiency of passive monitoring, connectivity among systems, and expanded the potential for innovative and communicative approaches. From technological progression, time-lapse imagery has emerged a valuable tool to capture and depict natural systems. I sought to enhance our understanding of a water-stressed system by analyzing imagery, in addition to integrating images with data visualization to illustrate the complexity of a river basin in central Nebraska. Image analysis was used to quantify wetland water inundation and vegetation phenology. These measurements from visible changes were combined with less visible data from additional passive monitoring to examine the relationship between vegetation phenology and bat activity, as well as wetland inundation and water quality. Moreover, time-lapse data sequences were constructed by integrating time-lapse imagery with data visualization in an interactive digital framework to examine the applications for communicating social-ecological dynamics. Findings suggest vegetation phenology was differentially associated with seasonal bat activity, possibly related to migratory versus resident life history strategies. In regards to examining wetland hydrology, water inundation was found to be correlated with nitrate, dissolved oxygen, and DEA, and negatively correlated with water temperature, indicating the importance of understanding water levels. AEM-RDA analysis identified several significant temporal patterns occurring with the wetland as well as the river site. Similarities between river and wetland patterns were suggestive of regional conditions driving fluctuations, while discrepancies were indicative of structural, biological, and local differences within individual sites. In examining communicative applications, time-lapse data sequences depicted a range of ecological dynamics while linking visible and invisible occurrences. The framework shows potential to offer a tangible context with explanatory content to aid in understanding environmental changes that are often too subtle to see or beyond the temporal scale of unaided human observation. Overall, cumulative findings suggest time-lapse imagery is of dual utility and has high potential for collecting data and illustrating ecological dynamics. Advisor: Craig R. Alle

Applications of Time-lapse Imagery for Monitoring and Illustrating Ecological Dynamics in a Water-stressed System

Understanding and perceiving the natural world is a key part of management, policy, conservation, and inevitably for our future. Increased demand on natural resources has heightened the importance of documenting ecosystem changes, and knowledge-sharing to foster awareness. The advancement of digital technologies has improved the efficiency of passive monitoring, connectivity among systems, and expanded the potential for innovative and communicative approaches. From technological progression, time-lapse imagery has emerged a valuable tool to capture and depict natural systems. I sought to enhance our understanding of a water-stressed system by analyzing imagery, in addition to integrating images with data visualization to illustrate the complexity of a river basin in central Nebraska. Image analysis was used to quantify wetland water inundation and vegetation phenology. These measurements from visible changes were combined with less visible data from additional passive monitoring to examine the relationship between vegetation phenology and bat activity, as well as wetland inundation and water quality. Moreover, time-lapse data sequences were constructed by integrating time-lapse imagery with data visualization in an interactive digital framework to examine the applications for communicating social-ecological dynamics. Findings suggest vegetation phenology was differentially associated with seasonal bat activity, possibly related to migratory versus resident life history strategies. In regards to examining wetland hydrology, water inundation was found to be correlated with nitrate, dissolved oxygen, and DEA, and negatively correlated with water temperature, indicating the importance of understanding water levels. AEM-RDA analysis identified several significant temporal patterns occurring with the wetland as well as the river site. Similarities between river and wetland patterns were suggestive of regional conditions driving fluctuations, while discrepancies were indicative of structural, biological, and local differences within individual sites. In examining communicative applications, time-lapse data sequences depicted a range of ecological dynamics while linking visible and invisible occurrences. The framework shows potential to offer a tangible context with explanatory content to aid in understanding environmental changes that are often too subtle to see or beyond the temporal scale of unaided human observation. Overall, cumulative findings suggest time-lapse imagery is of dual utility and has high potential for collecting data and illustrating ecological dynamics. Advisor: Craig R. Alle

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)

Deriving Vegetation Indices For Phenology Analysis Using Genetic Programming

Plant phenology studies recurrent plant life cycle events and is a key component for understanding the impact of climate change. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful strategies relies on the use of digital cameras, which are used as multi-channel imaging sensors to estimate color changes that are related to phenological events. We monitor leaf-changing patterns of a cerrado-savanna vegetation by taking daily digital images. We extract individual plant color information and correlate with leaf phenological changes. For that, several vegetation indices associated with plant species are exploited for both pattern analysis and knowledge extraction. In this paper, we present a novel approach for deriving appropriate vegetation indices from vegetation digital images. The proposed method is based on learning phenological patterns from plant species through a genetic programming framework. A comparative analysis of different vegetation indices is conducted and discussed. Experimental results show that our approach presents higher accuracy on characterizing plant species phenology.26P36169Ahrends, H., Etzold, S., Kutsch, W., Stoeckli, R., Bruegger, R., Jeanneret, F., Wanner, H., Eugster, W., Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation at the ecosystem scale (2009) Clim. Res., 39, pp. 261-274Alberton, B., Almeida, J., Henneken, R., da S.Torres, R., Menzel, A., Morellato, L.P.C., Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation (2014) Ecol. Inform., 19, pp. 62-70Almeida, J., dos Santos, J.A., Alberton, B., da S.Torres, R., Morellato, L.P.C., Remote phenology: applying machine learning to detect phenological patterns in a cerrado savanna (2012) IEEE International Conference on eScience (eScience'12), pp. 1-8Almeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., da S.Torres, R., Plant species identification with phenological visual rhythms (2013) IEEE International Conference on eScience (eScience'13), pp. 148-154Almeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., da S.Torres, R., Visual rhythm-based time series analysis for phenology studies (2013) IEEE International Conference on Image Processing (ICIP'13), pp. 4412-4416Almeida, J., dos Santos, J.A., Alberton, B., da S.Torres, R., Morellato, L.P.C., Applying machine learning based on multiscale classifiers to detect remote phenology patterns in cerrado savanna trees (2014) Ecol. 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