Academic literature on the topic 'Digital repeat imagery phenology'
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Journal articles on the topic "Digital repeat imagery phenology"
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Klosterman, S. T., K. Hufkens, J. M. Gray, E. Melaas, O. Sonnentag, I. Lavine, L. Mitchell, R. Norman, M. A. Friedl, and A. D. Richardson. "Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery." Biogeosciences Discussions 11, no. 2 (February 11, 2014): 2305–42. http://dx.doi.org/10.5194/bgd-11-2305-2014.
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Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of autumn, can be derived from sensor-based time series data at the near-surface and remote scales, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, as well as to perform digital image analysis for time series-based estimates of phenology dates. We then compare these near-surface results to remote sensing metrics of phenology at the landscape scale, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit, using a generalized sigmoid, to estimate phenology dates. We quantify the statistical uncertainty of phenophase transition dates estimated using this method and show that the generalized sigmoid results in less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than remote sensing metrics of phenology, and that dates derived from the remotely-sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time series estimates for the start of spring are found to closely match visual assessment of leaf out, as well as remote sensing-derived estimates of the start of spring. However late spring and autumn phenology exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.
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Moore, Caitlin E., Tim Brown, Trevor F. Keenan, Remko A. Duursma, Albert I. J. M. van Dijk, Jason Beringer, Darius Culvenor, et al. "Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography." Biogeosciences 13, no. 17 (September 13, 2016): 5085–102. http://dx.doi.org/10.5194/bg-13-5085-2016.
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Abstract. Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).
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Klosterman, S. T., K. Hufkens, J. M. Gray, E. Melaas, O. Sonnentag, I. Lavine, L. Mitchell, R. Norman, M. A. Friedl, and A. D. Richardson. "Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery." Biogeosciences 11, no. 16 (August 19, 2014): 4305–20. http://dx.doi.org/10.5194/bg-11-4305-2014.
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Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.
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Fraser, R. H., I. Olthof, M. Maloley, R. Fernandes, C. Prevost, and J. van der Sluijs. "UAV PHOTOGRAMMETRY FOR MAPPING AND MONITORING OF NORTHERN PERMAFROST LANDSCAPES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 27, 2015): 361. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-361-2015.
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Northern environments are changing in response to recent climate warming, resource development, and natural disturbances. The Arctic climate has warmed by 2–3°C since the 1950’s, causing a range of cryospheric changes including declines in sea ice extent, snow cover duration, and glacier mass, and warming permafrost. The terrestrial Arctic has also undergone significant temperature-driven changes in the form of increased thermokarst, larger tundra fires, and enhanced shrub growth. Monitoring these changes to inform land managers and decision makers is challenging due to the vast spatial extents involved and difficult access. <br><br> Environmental monitoring in Canada’s North is often based on local-scale measurements derived from aerial reconnaissance and photography, and ecological, hydrologic, and geologic sampling and surveying. Satellite remote sensing can provide a complementary tool for more spatially comprehensive monitoring but at coarser spatial resolutions. Satellite remote sensing has been used to map Arctic landscape changes related to vegetation productivity, lake expansion and drainage, glacier retreat, thermokarst, and wildfire activity. However, a current limitation with existing satellite-based techniques is the measurement gap between field measurements and high resolution satellite imagery. Bridging this gap is important for scaling up field measurements to landscape levels, and validating and calibrating satellite-based analyses. This gap can be filled to a certain extent using helicopter or fixed-wing aerial surveys, but at a cost that is often prohibitive. <br><br> Unmanned aerial vehicle (UAV) technology has only recently progressed to the point where it can provide an inexpensive and efficient means of capturing imagery at this middle scale of measurement with detail that is adequate to interpret Arctic vegetation (i.e. 1–5 cm) and coverage that can be directly related to satellite imagery (1–10 km<sup>2</sup>). Unlike satellite measurements, UAVs permit frequent surveys (e.g. for monitoring vegetation phenology, fires, and hydrology), are not constrained by repeat cycle or cloud cover, can be rapidly deployed following a significant event, and are better suited than manned aircraft for mapping small areas. UAVs are becoming more common for agriculture, law enforcement, and marketing, but their use in the Arctic is still rare and represents untapped technology for northern mapping, monitoring, and environmental research. <br><br> We are conducting surveys over a range of sensitive or changing northern landscapes using a variety of UAV multicopter platforms and small sensors. Survey targets include retrogressive thaw slumps, tundra shrub vegetation, recently burned vegetation, road infrastructure, and snow. Working with scientific partners involved in northern monitoring programs (NWT CIMP, CHARS, NASA ABOVE, NRCan-GSC) we are investigating the advantages, challenges, and best practices for acquiring high resolution imagery from multicopters to create detailed orthomosaics and co-registered 3D terrain models. Colour and multispectral orthomosaics are being integrated with field measurements and satellite imagery to conduct spatial scaling of environmental parameters. Highly detailed digital terrain models derived using structure from motion (SfM) photogrammetry are being applied to measure thaw slump morphology and change, snow depth, tundra vegetation structure, and surface condition of road infrastructure. <br><br> These surveys and monitoring applications demonstrate that UAV-based photogrammetry is poised to make a rapid contribution to a wide range of northern monitoring and research applications.
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Crimmins, Michael A., and Theresa M. Crimmins. "Monitoring Plant Phenology Using Digital Repeat Photography." Environmental Management 41, no. 6 (February 21, 2008): 949–58. http://dx.doi.org/10.1007/s00267-008-9086-6.
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Songsom, Veeranun, Werapong Koedsin, Raymond J. Ritchie, and Alfredo Huete. "Mangrove Phenology and Water Influences Measured with Digital Repeat Photography." Remote Sensing 13, no. 2 (January 17, 2021): 307. http://dx.doi.org/10.3390/rs13020307.
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The intertidal habitat of mangroves is very complex due to the dynamic roles of land and sea drivers. Knowledge of mangrove phenology can help in understanding mangrove growth cycles and their responses to climate and environmental changes. Studies of phenology based on digital repeat photography, or phenocams, have been successful in many terrestrial forests and other ecosystems, however few phenocam studies in mangrove forests showing the influence and interactions of water color and tidal water levels have been performed in sub-tropical and equatorial environments. In this study, we investigated the diurnal and seasonal patterns of an equatorial mangrove forest area at an Andaman Sea site in Phuket province, Southern Thailand, using two phenocams placed at different elevations and with different view orientations, which continuously monitored vegetation and water dynamics from July 2015 to August 2016. The aims of this study were to investigate fine-resolution, in situ mangrove forest phenology and assess the influence and interactions of water color and tidal water levels on the mangrove–water canopy signal. Diurnal and seasonal patterns of red, green, and blue chromatic coordinate (RCC, GCC, and BCC) indices were analyzed over various mangrove forest and water regions of interest (ROI). GCC signals from the water background were found to positively track diurnal water levels, while RCC signals were negatively related with tidal water levels, hence lower water levels yielded higher RCC values, reflecting brownish water colors and increased soil and mud exposure. At seasonal scales, the GCC profiles of the mangrove forest peaked in the dry season and were negatively related with the water level, however the inclusion of the water background signal dampened this relationship. We also detected a strong lunar tidal water periodicity in seasonal GCC values that was not only present in the water background, but was also detected in the mangrove–water canopy and mangrove forest phenology profiles. This suggests significant interactions between mangrove forests and their water backgrounds (color and depth), which may need to be accounted for in upscaling and coupling with satellite-based mangrove monitoring.
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Atkins, Jeff W., Atticus E. L. Stovall, and Xi Yang. "Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors." Drones 4, no. 3 (September 10, 2020): 56. http://dx.doi.org/10.3390/drones4030056.
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Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.
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Luo, Yunpeng, Tarek S. El-Madany, Gianluca Filippa, Xuanlong Ma, Bernhard Ahrens, Arnaud Carrara, Rosario Gonzalez-Cascon, et al. "Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems." Remote Sensing 10, no. 8 (August 15, 2018): 1293. http://dx.doi.org/10.3390/rs10081293.
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Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.
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ZHOU Lei, 周磊, 何洪林 HE Honglin, 孙晓敏 SUN Xiaomin, 张黎 ZHANG Li, 于贵瑞 YU Guirui, 任小丽 REN Xiaoli, 闵程程 MIN Chengcheng, and 赵凤华 ZHAO Fenghua. "Using digital repeat photography to model winter wheat phenology and photosynthetic CO2uptake." Acta Ecologica Sinica 32, no. 16 (2012): 5146–53. http://dx.doi.org/10.5846/stxb201110271606.
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Scott, Samantha L., Rick Rohde, and Timm Hoffman. "Repeat Landscape Photography, Historical Ecology and the Wonder of Digital Archives in Southern Africa." African Research & Documentation 131 (2017): 35–47. http://dx.doi.org/10.1017/s0305862x00022512.
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Environmental history projects using repeat photography often involve the acquisition of large collections of historical and current images, matching those images for comparative analysis, and then cataloguing and archiving the imagery for long-term storage and later use (Webb et ah, 2010). When used in combination with other techniques, repeat photography is an excellent tool for documenting change (Gruell, 2010) and has been used in a variety of disciplines, including historical ecology, to determine changes in plant populations, soil erosion, climate trends and ecological processes to name a few. Historical photographs often provide greater temporal range to an analysis compared to, for example, satellite imagery and in many cases even aerial photography (Gruell, 2010).
Dissertations / Theses on the topic "Digital repeat imagery phenology"
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GALVAGNO, MARTA RITA. "Carbon dioxide exchange of an alpine grassland: integration of eddy covariance, proximal sensing and models." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/24290.
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The terrestrial biosphere represents a large pool of carbon, whose cycle is governed
by the opposed processes of CO2 uptake (photosynthesis) and release (respiration)
from and to the atmosphere. Considering the role of carbon dioxide in the observed global warming, monitoring, understanding and modeling carbon exchange
of ecosystems is a critical issue in climate change researches. Moreover because
of the multiple implications of vegetation structure dynamics on ecosystem carbon
fluxes, monitoring and modeling plant phenology is also of increasing scientific
interest.
Among terrestrial ecosystem grasslands cover almost 40% of ice-free land surface, nevertheless their role as sources/sinks of atmospheric CO2 is not well clarified.
In this study the eddy covariance method was used to assess CO2 exchange at
an high elevation unmanaged grassland in the North-Western Italian Alps (Aosta
Valley - Torgnon), during three years (2008-2010) of measurements and to evaluate
how environmental factors affect photosynthetic processes.
The seasonal and inter-annual course of net ecosystem CO2 exchange (NEE),
ecosystem respiration (Reco), gross primary production (GPP) and the main meteorological variables was analysed. The three growing seasons had a similar seasonal
dynamic, characterised by a fast rise of photosynthetic activity after snow-melt followed by a gradual autumnal decline. Regarding the meteorological variables, only
precipitation, soil water content and snow depth differed markedly among two of
the studied years (2009-2010) compared to other factors which showed only small
differences in restricted time-periods.
To better interpret how weather variables modulate ecosystem processes at multiple time-scales (day, week, month, year), a quantitative analysis was performed applying wavelet coherence between time-series of GPP and time-series of different
meteorological factors (air and soil temperature, soil water content and photosynthetically active radiation).
Eddy covariance and meteorological data were combined with proximal sensing measurements to identify links between optical indices, canopy development
and fluxes. In particular a colour index derived from continuous digital imagery
(i.e. Greenness Index, (GI), based on RGB channels) and indices derived from an
HyperSpectral System (Hyperspectral Irradiometer, HSI) were used as input to simulate GPP, based on a light use efficiency (LUE) model. Results showed that a LUE
model driven by optical indices and meteorological variables is able to describe the
GPP trend in the two years of study. In particular the use of different model formulations provided insights on the role of the main meteorological factors controlling
grassland photosynthesis. The comprehension of these relationships at stand level
is essential for extrapolating such information at different spatial scales.
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Garnello, Anthony John, and Anthony John Garnello. "Establishing the Role of Digital Repeat Photography in Understanding Phenology and Carbon Cycling in a Subarctic Peatland." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/624140.
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In this thesis, I establish and explore the role of phenology in understanding the rapidly changing environment of a subarctic peatland. First, I demonstrate how digital repeat photography can be used to characterize and differentiate distinct plant communities using two years of images. Each habitat is composed of different plant functional groups, promoting the individualistic approach to characterization that near-earth remote sensing tools can provide. The camera-product Relative Greenness successfully characterized interannual variability in seasonal growth for each habitat type. Across habitats, there was a direct relationship between advancement of spring onset and active season growth though this overall pattern showed habitat-specific variance. The camera images were also useful in characterizing the flowering phenology of an eriophorum-rich fen habitat, for which a metric named Intensity was created. These results suggest that employment of phenology cameras in highly heterogeneous subarctic environments is a robust method to characterize phenology on a habitat to species scale. Next, I explored the role that this phenology product has in modeling Net Ecosystem Exchange (NEE) also measured at the field site. I hypothesized that the explanatory power of the phenology index, which is conceptually tied to a measure of photosynthetic capacity, would be tightly linked to the timescale it was used for: At sub-daily timescales, environmental forces would dominate, though when averaged over days to weekly scales, the biology represented through the camera index would be more influential. I show that at multiple time scales the environmental factors outperform the camera index when modeling NEE. Together, these studies begin to explore the applicability of phenology camera systems in subarctic environments.
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Jorge, Catarina Tonelo. "Phenology analysis in a cork oak woodland through digital photography and spectral vegetation indexes." Master's thesis, ISA, 2019. http://hdl.handle.net/10400.5/19543.
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Mestrado em Engenharia do Ambiente - Instituto Superior de AgronomiaDigital repeat photography is a method to monitor the phenology of vegetation that has gained momentum this past decade. As a result, the need for further case-studies is required. This work aims to prove that it is possible to use digital cameras instead of spectral information to track phenology in a Mediterranean cork oak woodland. The photos will originate the green chromatic coordinates (GCC) index while the normalized difference vegetation index (NDVI) derives from the spectral data collected with a field spectroradiometer. The results were found by employing a regular commercial camera to take monthly pictures along with the spectral measurements. They showed good agreement among methods especially for the herbaceous layer whose GCC had a very good fit with NDVI. The coefficient of determination for the herbaceous layer, the shrub cistus and shrub ulex was 0.89, 0.62 and 0.30, respectively. However, these regressions may be improved upon by grouping the shrub species. The shrubs had a lower correlation between the two indices and all three groups showed a response to water availability. For these reasons, a linear regression between GCC and the normalized water difference index (NDWI) was pursued. This second regression showed better results for shrubs, with coefficients of determination of 0.78 e 0.55, respectively, and a similar value for the herbaceous layer (0.84). The herbaceous layer was found to react quickly to water. Because it only has access to superficial water, its phenology is dependent on precipitation. This group had a good outcome with more long-term observations than shrubs (eight years of data vs. three years). So, it would be the most suitable plant functional type to be tracked using the digital repeat photography method coupled with GCC. Nonetheless, using photos and GCC proves to have the potential to monitor a wide spectrum of vegetation types
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Book chapters on the topic "Digital repeat imagery phenology"
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Horning, Ned, Julie A. Robinson, Eleanor J. Sterling, Woody Turner, and Sacha Spector. "Protected area design and monitoring." In Remote Sensing for Ecology and Conservation. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780199219940.003.0020.
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Researchers interested in remote locations have developed monitoring schemes, sometimes called “Watchful Eye” monitoring, that use a time series of remotely sensed images to assess changes over time to a protected area or habitat. For instance, the European Space Agency (ESA) and UNESCO have set up repeat analyses of satellite imagery for World Heritage sites. The first area for which they developed this technique was the habitat of the critically endangered mountain gorilla (Gorilla berengei berengei) in the Virunga Mountains in Central Africa, including the Bwindi and Mgahinga National Parks in Uganda, the Virunga and Kahuzi-Biega National Parks in the Democratic Republic of Congo, and the trans-boundary Volcanoes Conservation Area. The project developed detailed maps of these inaccessible zones so that protected area managers can monitor the gorilla habitat. Previously, available maps were old and inaccurate (at times handmade), did not completely cover the range of the gorillas, and did not cross national boundaries. Because there was no systematic information from the ground regarding changes over time, researchers also used remotely sensed data to complete change detection analyses over the past two decades. Using both optical (Landsat series) and radar (ENVISAT ASAR) satellite data, researchers were able to quantify rates of deforestation between 1990 and 2003 and relate these rates to human migration rates into the area resulting from regional political instability. Researchers constructed the first digital base maps of the areas, digital elevation models (DEMs), and updated vegetation and land use maps. They faced significant problems in both field and laboratory activities, including lack of existing ground data, dense vegetation cover, and fairly continuous cloud cover. They therefore used a combination of ESA ENVISAT ASAR as well as Landsat and ESA Medium Resolution Imaging Spectrometer (MERIS) optical data. The radar images allowed them to quantify elevation and distances between trees and homes. Landsat and MERIS data helped identify forest cover types, with Landsat providing finer-scale images at less frequent intervals and MERIS serving lower-resolution images more frequently.
Conference papers on the topic "Digital repeat imagery phenology"
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Ganeva, Dessislava, Milen Chanev, Darina Valcheva, Lachezar Filchev, and Georgi Jelev. "MODELLING BARLEY BIOMASS FROM PHENOCAM TIME SERIES WITH MULTI-OUTPUT GAUSSIAN PROCESSES." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/2.1/s08.15.
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Biomass is monitored in many agricultural studies because it is closely related to the growth of the crop. The technique of digital repeat photography that continuously capture images of a given area with an RGB or near-infrared enabled cameras, Phenocams, has been used for more than a decade mainly to estimate phenology. Studies have found a relationship between Phenocam data and above-ground dry biomass. In this context we investigate the modeling of barley fresh above and underground biomass with Green chromatic coordinate (Gcc) colour index, extracted from Phenocam data, and multi-output Gaussian processes (MOGP). We take advantage of the available very high temporal resolution data from the phenocam to predict the biomass. The MOGP models take into account the relationships among output variables learning a cross-domain kernel function able to transfer information between time series. Our results suggest that MOGP model is able to successfully predict the variables simultaneously in regions where no training samples are available by intrinsically exploiting the relationships between the considered output variables.
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Kaye, Alwyn. "Piping and Equipment Dynamics of High Rate HVGO Pumps." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21204.
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Abstract A set of Heavy Vacuum Gas Oil (HVGO) pumps in a 300 kbbl/day operating Upgrader Plant experienced repeated failures; typically less than 7 weeks. The pumps run continuously in a high-pressure, high temperature and corrosive environment and their functional status directly affects the reliability of the plant. Upon research, an experimental strain measurement technique using very high resolution laser digital imagery and optical metrology was found from military and advanced aerospace applications to verify high level dimensional accuracy of critical components [1]. Application to a complex and operating bitumen upgrader was unknown. The objective of this project was to use advanced optical metrology with digital image processing techniques employing multiple laser and high-speed cameras capable of generating pump and pipe component’s real time strain images, displacement and rate of change. Optical metrology can analyze the mechanical properties and behavior of many materials and in various test scenarios [2]. Hot and cold operating service, with variations in flow and temperature all dynamically affect the strain measurements. Three significant advantages of the optical method are: i. Avoids a host of problems of strain gauge application, wiring and setup. ii. The problems of temperature sensitivity and correction are overcome. [3] iii. Gathers much more extensive data than possible with traditional methods. The vibration characteristics of the pumps and related hardware were analyzed using high resolution laser and photogrammetric digital imagery and digital strain mapping analysis to determine the characteristics that would ensure the long-term reliable and safe operation of the HVGO pumps. The stress and deformation analysis were performed on the operating pumps in a variety of normal (1280 m3/hr.) and upset operating conditions including under partial and full load conditions. Dynamic and modal analysis of the pumps was developed and analyzed. The displacement and tensor fields of the hardware including the pumps, bases and piping were measured using high resolution laser cameras and analyzed. From the high-speed data gathering and loading analysis showed the deformation and stress affecting the pump and related hardware. The key variables undermining reliable performance were revealed and from the data the necessary remedial action was determined. The pumps have operated for over 30months to the time of writing without repeat failure. This paper should be read in conjunction with PVP 2020-21203; Investigation and Resolution of the Fluid Structure Interaction of High Rate HVGO Pumps.