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).
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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).
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Keenan, T. F., B. Darby, E. Felts, O. Sonnentag, M. A. Friedl, K. Hufkens, J. O'Keefe, et al. "Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment." Ecological Applications 24, no. 6 (September 2014): 1478–89. http://dx.doi.org/10.1890/13-0652.1.
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Linkosalmi, Maiju, Mika Aurela, Juha-Pekka Tuovinen, Mikko Peltoniemi, Cemal M. Tanis, Ali N. Arslan, Pasi Kolari, et al. "Digital photography for assessing the link between vegetation phenology and CO<sub>2</sub> exchange in two contrasting northern ecosystems." Geoscientific Instrumentation, Methods and Data Systems 5, no. 2 (September 12, 2016): 417–26. http://dx.doi.org/10.5194/gi-5-417-2016.
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Abstract. Digital repeat photography has become a widely used tool for assessing the annual course of vegetation phenology of different ecosystems. By using the green chromatic coordinate (GCC) as a greenness measure, we examined the feasibility of digital repeat photography for assessing the vegetation phenology in two contrasting high-latitude ecosystems. Ecosystem–atmosphere CO2 fluxes and various meteorological variables were continuously measured at both sites. While the seasonal changes in GCC were more obvious for the ecosystem that is dominated by annual plants (open wetland), clear seasonal patterns were also observed for the evergreen ecosystem (coniferous forest). Daily and seasonal time periods with sufficient solar radiation were determined based on images of a grey reference plate. The variability in cloudiness had only a minor effect on GCC, and GCC did not depend on the sun angle and direction either. The daily GCC of wetland correlated well with the daily photosynthetic capacity estimated from the CO2 flux measurements. At the forest site, the correlation was high in 2015 but there were discernible deviations during the course of the summer of 2014. The year-to-year differences were most likely generated by meteorological conditions, with higher temperatures coinciding with higher GCCs. In addition to depicting the seasonal course of ecosystem functioning, GCC was shown to respond to environmental changes on a timescale of days. Overall, monitoring of phenological variations with digital images provides a powerful tool for linking gross primary production and phenology.
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Jorge, Catarina, João M. N. Silva, Joana Boavida-Portugal, Cristina Soares, and Sofia Cerasoli. "Using Digital Photography to Track Understory Phenology in Mediterranean Cork Oak Woodlands." Remote Sensing 13, no. 4 (February 20, 2021): 776. http://dx.doi.org/10.3390/rs13040776.
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Monitoring vegetation is extremely relevant in the context of climate change, and digital repeat photography is a method that has gained momentum due to a low cost–benefit ratio. This work aims to demonstrate the possibility of using digital cameras instead of field spectroradiometers (FS) to track understory vegetation phenology in Mediterranean cork oak woodlands. A commercial camera was used to take monthly photographs that were processed with the Phenopix package to extract green chromatic coordinates (GCC). GCC showed good agreement with the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) obtained with FS data. The herbaceous layer displayed a very good fit between GCC and NDVI (coefficient of determination, represented by r2 = 0.89). On the contrary, the GCC of shrubs (Cistus salviifolius and Ulex airensis) showed a better fit with NDWI (r2 = 0.78 and 0.55, respectively) than with NDVI (r2 = 0.60 and 0.30). Models show that grouping shrub species together improves the predictive results obtained with ulex but not with cistus. Concerning the relationship with climatic factors, all vegetation types showed a response to rainfall and temperature. Grasses and cistus showed similar responses to meteorological drivers, particularly mean maximum temperature (r = −0.66 and −0.63, respectively). The use of digital repeat photography to track vegetation phenology was found to be very suitable for understory vegetation with the exception of one shrub species. Thus, this method proves to have the potential to monitor a wide spectrum of understory vegetation at a much lower cost than FS.
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Fitchett, Jennifer M., Stefan W. Grab, and Dave I. Thompson. "Plant phenology and climate change." Progress in Physical Geography: Earth and Environment 39, no. 4 (April 26, 2015): 460–82. http://dx.doi.org/10.1177/0309133315578940.
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Phenology, the timing of annually recurrent reproductive biological events, provides a critical signal of climate variability and change effects on plants. Considerable work over the past five decades has quantified the extent to which plant phenophases are responding to local changes in temperature and rainfall. Originally undertaken through the analysis of ground-based phenological observations, the discipline has more recently included phenophase indicators from satellite images and digital repeat photography. With research advances it has become evident that the responses of plant phenology to climate variability and change are both location- and species-specific. The extent to which plants are affected by changes in temperature and rainfall, their intrinsic adaptation capacity, will ultimately determine the potential for sustained ecological stability and food security. We review methodological approaches to plant phenological-climate change over time, analyse the regions and phenophases for which climate variability demonstrates a clear causal role, and finally reflect on the applications of phenological climate change investigations in broader biogeographical contexts.
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Beamish, Alison L., Wiebe Nijland, Marc Edwards, Nicholas C. Coops, and Greg H. R. Henry. "Phenology and vegetation change measurements from true colour digital photography in high Arctic tundra." Arctic Science 2, no. 2 (June 2016): 33–49. http://dx.doi.org/10.1139/as-2014-0003.
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Manual collection of accurate phenology data is time-consuming and expensive. In this study, we investigate whether repeat colour digital photography can be used (1) to identify phenological patterns, (2) to identify differences in vegetation due to experimental warming and site moisture conditions, and (3) as a proxy for biomass. Pixel values (RGB) were extracted from images taken of permanent plots in long-term warming experiments in three tundra communities at a high Arctic site during one growing season. The Greenness Excess Index (GEI) was calculated from image data at the plot scale (1 × 1 m) as well as for two species, Dryas integrifolia and Salix arctica. GEI values were then compared to corresponding field-based phenology observations. GEI and Normalized Difference Vegetation Index (NDVI) values from a paired set of true colour and infrared images were compared with biomass data. The GEI values followed seasonal phenology at the plot and species scale and correlated well with standardized observations. GEI correlated well with biomass and was able to detect quantitative differences between warmed and control plots and the differences between communities due to site-specific moisture conditions. We conclude that true colour images can be used effectively to monitor phenology and biomass in high Arctic tundra. The simplicity and affordability of the photographic method represents an opportunity to expand observations in tundra ecosystems.
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van Iersel, W. K., M. W. Straatsma, E. A. Addink, and H. Middelkoop. "MONITORING PHENOLOGY OF FLOODPLAIN GRASSLAND AND HERBACEOUS VEGETATION WITH UAV IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 569–71. http://dx.doi.org/10.5194/isprs-archives-xli-b7-569-2016.
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River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE = 29-42 cm), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.
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van Iersel, W. K., M. W. Straatsma, E. A. Addink, and H. Middelkoop. "MONITORING PHENOLOGY OF FLOODPLAIN GRASSLAND AND HERBACEOUS VEGETATION WITH UAV IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 569–71. http://dx.doi.org/10.5194/isprsarchives-xli-b7-569-2016.
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River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE = 29-42 cm), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.
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Migliavacca, Mirco, Marta Galvagno, Edoardo Cremonese, Micol Rossini, Michele Meroni, Oliver Sonnentag, Sergio Cogliati, et al. "Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake." Agricultural and Forest Meteorology 151, no. 10 (October 2011): 1325–37. http://dx.doi.org/10.1016/j.agrformet.2011.05.012.
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Goodbody, Tristan, Nicholas Coops, Txomin Hermosilla, Piotr Tompalski, and Gaetan Pelletier. "Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models." Remote Sensing 10, no. 10 (September 27, 2018): 1554. http://dx.doi.org/10.3390/rs10101554.
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Digital aerial photogrammetry (DAP) and unmanned aerial systems (UAS) have emerged as synergistic technologies capable of enhancing forest inventory information. A known limitation of DAP technology is its ability to derive terrain surfaces in areas with moderate to high vegetation coverage. In this study, we sought to investigate the influence of flight acquisition timing on the accuracy and coverage of digital terrain models (DTM) in a low cover forest area in New Brunswick, Canada. To do so, a multi-temporal UAS-acquired DAP data set was used. Acquired imagery was photogrammetrically processed to produce high quality DAP point clouds, from which DTMs were derived. Individual DTMs were evaluated for error using an airborne laser scanning (ALS)-derived DTM as a reference. Unobstructed road areas were used to validate DAP DTM error. Generalized additive mixed models (GAMM) were generated to assess the significance of acquisition timing on mean vegetation cover, DTM error, and proportional DAP coverage. GAMM models for mean vegetation cover and DTM error were found to be significantly influenced by acquisition date. A best available terrain pixel (BATP) compositing exercise was conducted to generate a best possible UAS DAP-derived DTM and outline the importance of flight acquisition timing. The BATP DTM yielded a mean error of −0.01 m. This study helps to show that the timing of DAP acquisitions can influence the accuracy and coverage of DTMs in low cover vegetation areas. These findings provide insight to improve future data set quality and provide a means for managers to cost-effectively derive high accuracy terrain models post-management activity.
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Snyder, Keirith, Justin Huntington, Bryce Wehan, Charles Morton, and Tamzen Stringham. "Comparison of Landsat and Land-Based Phenology Camera Normalized Difference Vegetation Index (NDVI) for Dominant Plant Communities in the Great Basin." Sensors 19, no. 5 (March 6, 2019): 1139. http://dx.doi.org/10.3390/s19051139.
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Phenology of plants is important for ecological interactions. The timing and development of green leaves, plant maturity, and senescence affects biophysical interactions of plants with the environment. In this study we explored the agreement between land-based camera and satellite-based phenology metrics to quantify plant phenology and phenophases dates in five plant community types characteristic of the semi-arid cold desert region of the Great Basin. Three years of data were analyzed. We calculated the Normalized Difference Vegetation Index (NDVI) for both land-based cameras (i.e., phenocams) and Landsat imagery. NDVI from camera images was calculated by taking a standard RGB (red, green, and blue) image and then a near infrared (NIR) plus RGB image. Phenocam NDVI was calculated by extracting the red digital number (DN) and the NIR DN from images taken a few seconds apart. Landsat has a spatial resolution of 30 m2, while phenocam spatial resolution can be analyzed at the single pixel level at the scale of cm2 or area averaged regions can be analyzed with scales up to 1 km2. For this study, phenocam regions of interest were used that approximated the scale of at least one Landsat pixel. In the tall-statured pinyon and juniper woodland sites, there was a lack of agreement in NDVI between phenocam and Landsat NDVI, even after using National Agricultural Imagery Program (NAIP) imagery to account for fractional coverage of pinyon and juniper versus interspace in the phenocam data. Landsat NDVI appeared to be dominated by the signal from the interspace and was insensitive to subtle changes in the pinyon and juniper tree canopy. However, for short-statured sagebrush shrub and meadow communities, there was good agreement between the phenocam and Landsat NDVI as reflected in high Pearson’s correlation coefficients (r > 0.75). Due to greater temporal resolution of the phenocams with images taken daily, versus the 16-day return interval of Landsat, phenocam data provided more utility in determining important phenophase dates: start of season, peak of season, and end of season. More specific species-level information can be obtained with the high temporal resolution of phenocams, but only for a limited number of sites, while Landsat can provide the multi-decadal history and spatial coverage that is unmatched by other platforms. The agreement between Landsat and phenocam NDVI for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.
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Nijland, W., N. C. Coops, S. C. P. Coogan, C. W. Bater, M. A. Wulder, S. E. Nielsen, G. McDermid, and G. B. Stenhouse. "Vegetation phenology can be captured with digital repeat photography and linked to variability of root nutrition inHedysarum alpinum." Applied Vegetation Science 16, no. 2 (September 24, 2012): 317–24. http://dx.doi.org/10.1111/avsc.12000.
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Boyd, Doreen S., Sally Crudge, and Giles Foody. "Towards an Automated Approach for Monitoring Tree Phenology Using Vehicle Dashcams in Urban Environments." Sensors 22, no. 19 (October 10, 2022): 7672. http://dx.doi.org/10.3390/s22197672.
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Trees in urban environments hold significant value in providing ecosystem services, which will become increasingly important as urban populations grow. Tree phenology is highly sensitive to climatic variation, and resultant phenological shifts have significant impact on ecosystem function. Data on urban tree phenology is important to collect. Typical remote methods to monitor tree phenological transitions, such as satellite remote sensing and fixed digital camera networks, are limited by financial costs and coarse resolutions, both spatially and temporally and thus there exists a data gap in urban settings. Here, we report on a pilot study to evaluate the potential to estimate phenological metrics from imagery acquired with a conventional dashcam fitted to a car. Dashcam images were acquired daily in spring 2020, March to May, for a 2000 m stretch of road in Melksham, UK. This pilot study indicates that time series imagery of urban trees, from which meaningful phenological data can be extracted, is obtainable from a car-mounted dashcam. The method based on the YOLOv3 deep learning algorithm demonstrated suitability for automating stages of processing towards deriving a greenness metric from which the date of tree green-up was calculated. These dates of green-up are similar to those obtained by visual analyses, with a maximum of a 4-day difference; and differences in green-up between trees (species-dependent) were evident. Further work is required to fully automate such an approach for other remote sensing capture methods, and to scale-up through authoritative and citizen science agencies.
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Millard, Koreen, Patrick Kirby, Sacha Nandlall, Amir Behnamian, Sarah Banks, and Fabrizio Pacini. "Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series." Remote Sensing 12, no. 15 (July 31, 2020): 2465. http://dx.doi.org/10.3390/rs12152465.
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Differences in topographic structure, vegetation structure, and surface wetness exist between peatland classes, making active remote sensing techniques such as SAR and LiDAR promising for peatland mapping. As the timing of green-up, senescence, and hydrologic conditions vary differently in peatland classes, and in comparison with upland classes, full growing-season time series SAR imagery was expected to produce higher accuracy classification results than using only a few select SAR images. Both interferometric coherence, amplitude and difference in amplitude time series datasets were assessed, as it was hypothesized that these may be able to capture subtle changes in phenology and hydrology, which in turn differentiate classes throughout a growing season. Groups of variables were compared for their effectiveness in Random Forest classification for both Sentinel-1 and Radarsat-2. The Shapley value was used to determine the contribution of each group of variables in thirty scenarios, and Mean Decrease in Accuracy was compared to evaluate its ability to rank variables by relative importance. Despite being dual-pol, the results of classifications using Sentinel-1 coherence (12-day repeat) were significantly better than using fully polarimetric RADARSAT-2 coherence (24-day repeat), likely owing to the difference in baseline and specific acquisition dates of the data in this study. Overall, full growing season Sentinel-1 coherence time series produced higher accuracy results than fully polarimetric quad pol RADARSAT-2 coherence amplitude, difference in amplitude and polarimetric decomposition time series. Using a full growing season of time-series imagery in classification resulted in higher accuracy than using a few dates over a growing season. Using mean decrease in accuracy to rank and reduce variables resulted in a weaker classification than if the entire time series is used.
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Wingate, L., J. Ogée, E. Cremonese, G. Filippa, T. Mizunuma, M. Migliavacca, C. Moisy, et al. "Interpreting canopy development and physiology using a European phenology camera network at flux sites." Biogeosciences 12, no. 20 (October 21, 2015): 5995–6015. http://dx.doi.org/10.5194/bg-12-5995-2015.
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Abstract. Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green-up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations, we found that these phenological events could be detected adequately (RMSE < 8 and 11 days for leaf out and leaf fall, respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring `green hump' observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future.
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Howat, Ian M., Claire Porter, Benjamin E. Smith, Myoung-Jong Noh, and Paul Morin. "The Reference Elevation Model of Antarctica." Cryosphere 13, no. 2 (February 26, 2019): 665–74. http://dx.doi.org/10.5194/tc-13-665-2019.
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Abstract. The Reference Elevation Model of Antarctica (REMA) is the first continental-scale digital elevation model (DEM) at a resolution of less than 10 m. REMA is created from stereophotogrammetry with submeter resolution optical, commercial satellite imagery. The higher spatial and radiometric resolutions of this imagery enable high-quality surface extraction over the low-contrast ice sheet surface. The DEMs are registered to satellite radar and laser altimetry and are mosaicked to provide a continuous surface covering nearly 95 % the entire continent. The mosaic includes an error estimate and a time stamp, enabling change measurement. Typical elevation errors are less than 1 m, as validated by the comparison to airborne laser altimetry. REMA provides a powerful new resource for Antarctic science and provides a proof of concept for generating accurate high-resolution repeat topography at continental scales.
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Nurtyawan, Rian, Asep Saepuloh, Agung Budi Harto, Ketut Wikantika, and Akihiko Kondoh. "Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia." HAYATI Journal of Biosciences 25, no. 3 (October 24, 2018): 126. http://dx.doi.org/10.4308/hjb.25.3.126.
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Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants.
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Moore, Caitlin E., Jason Beringer, Bradley Evans, Lindsay B. Hutley, and Nigel J. Tapper. "Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna." Biogeosciences 14, no. 1 (January 10, 2017): 111–29. http://dx.doi.org/10.5194/bg-14-111-2017.
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Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 = 0.65 to 0.72) but less so for the overstory (r2 = 0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 = 0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.
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Lamping, James E., Harold S. J. Zald, Buddhika D. Madurapperuma, and Jim Graham. "Comparison of Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning across Multiple Forest Types in California, USA." Remote Sensing 13, no. 21 (October 25, 2021): 4292. http://dx.doi.org/10.3390/rs13214292.
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Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74–0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17–0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53–0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions.
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Peichl, Matthias, Oliver Sonnentag, and Mats B. Nilsson. "Bringing Color into the Picture: Using Digital Repeat Photography to Investigate Phenology Controls of the Carbon Dioxide Exchange in a Boreal Mire." Ecosystems 18, no. 1 (November 13, 2014): 115–31. http://dx.doi.org/10.1007/s10021-014-9815-z.
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Wingate, L., J. Ogée, E. Cremonese, G. Filippa, T. Mizunuma, M. Migliavacca, C. Moisy, et al. "Interpreting canopy development and physiology using the EUROPhen camera network at flux sites." Biogeosciences Discussions 12, no. 10 (May 27, 2015): 7979–8034. http://dx.doi.org/10.5194/bgd-12-7979-2015.
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Abstract. Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time-series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations we found that these phenological events could be detected adequately (RMSE < 8 and 11 days for leaf out and leaf fall respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring "green hump" observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future.
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Nagle-McNaughton, Timothy, and Rónadh Cox. "Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits." Remote Sensing 12, no. 1 (December 20, 2019): 42. http://dx.doi.org/10.3390/rs12010042.
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Repeat photogrammetry is increasingly the go-too tool for long-term geomorphic monitoring, but quantifying the differences between structure-from-motion (SfM) models is a developing field. Volumetric differencing software (such as the open-source package CloudCompare) provides an efficient mechanism for quantifying change in landscapes. In this case study, we apply this methodology to coastal boulder deposits on Inishmore, Ireland. Storm waves are known to move these rocks, but boulder transportation and evolution of the deposits are not well documented. We used two disparate SfM data sets for this analysis. The first model was built from imagery captured in 2015 using a GoPro Hero 3+ camera (fisheye lens) and the second used 2017 imagery from a DJI FC300X camera (standard digital single-lens reflex (DSLR) camera); and we used CloudCompare to measure the differences between them. This study produced two noteworthy findings: First, volumetric differencing reveals that short-term changes in boulder deposits can be larger than expected, and that frequent monitoring can reveal not only the scale but the complexities of boulder transport in this setting. This is a valuable addition to our growing understanding of coastal boulder deposits. Second, SfM models generated by different imaging hardware can be successfully compared at sub-decimeter resolution, even when one of the camera systems has substantial lens distortion. This means that older image sets, which might not otherwise be considered of appropriate quality for co-analysis with more recent data, should not be ignored as data sources in long-term monitoring studies.
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Steiner, Jakob F., Philip D. A. Kraaijenbrink, Sergiu G. Jiduc, and Walter W. Immerzeel. "Brief communication: The Khurdopin glacier surge revisited – extreme flow velocities and formation of a dammed lake in 2017." Cryosphere 12, no. 1 (January 11, 2018): 95–101. http://dx.doi.org/10.5194/tc-12-95-2018.
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Abstract. Glacier surges occur regularly in the Karakoram, but the driving mechanisms, their frequency and its relation to a changing climate remain unclear. In this study, we use digital elevation models and Landsat imagery in combination with high-resolution imagery from the Planet satellite constellation to quantify surface elevation changes and flow velocities during a glacier surge of the Khurdopin Glacier in 2017. Results reveal that an accumulation of ice volume above a clearly defined steep section of the glacier tongue since the last surge in 1999 eventually led to a rapid surge in May 2017 peaking with velocities above 5000 m a−1, which were among the fastest rates globally for a mountain glacier. Our data reveal that velocities on the lower tongue increase steadily during a 4-year build-up phase prior to the actual surge only to then rapidly peak and decrease again within a few months, which confirms earlier observations with a higher frequency of available velocity data. The surge return period between the reported surges remains relatively constant at ca. 20 years. We show the potential of a combination of repeat Planet and ASTER imagery to (a) capture peak surge velocities that are easily missed by less frequent Landsat imagery, (b) observe surface changes that indicate potential drivers of a surge and (c) monitor hazards associated with a surge. At Khurdopin specifically, we observe that the surging glacier blocks the river in the valley and causes a lake to form, which may grow in subsequent years and could pose threats to downstream settlements and infrastructure in the case of a sudden breach.
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Barrand, Nicholas E., Tavi Murray, Timothy D. James, Stuart L. Barr, and Jon P. Mills. "Optimizing photogrammetric DEMs for glacier volume change assessment using laser-scanning derived ground-control points." Journal of Glaciology 55, no. 189 (2009): 106–16. http://dx.doi.org/10.3189/002214309788609001.
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AbstractPhotogrammetric processing of archival stereo imagery offers the opportunity to reconstruct glacier volume changes for regions where no such data exist, and to better constrain the contribution to sea-level rise from small glaciers and ice caps. The ability to derive digital elevation model (DEM) measurements of glacier volume from photogrammetry relies on good-quality, well-distributed ground reference data, which may be difficult to acquire. This study shows that ground-control points (GCPs) can be identified and extracted from point-cloud airborne lidar data and used to control photogrammetric glacier models. The technique is applied to midtre Lovénbreen, a small valley glacier in northwest Svalbard. We show that the amount of ground control measured and the elevation accuracy of GCP coordinates (based on known and theoretical error considerations) has a significant effect on photogrammetric model statistics, DEM accuracy and the subsequent geodetic measurement of glacier volume change. Models controlled with fewer than 20 lidar control points or GCPs from sub-optimal areas within the swath footprint overestimated volume change by 14–53% over a 2 year period. DEMs derived from models utilizing 20–25 or more GCPs, however, gave volume change estimates within ∼4% of those from repeat lidar data (−0.51 m a−1 between 2003 and 2005). Our results have important implications for the measurement of glacier volume change from archival stereo-imagery sources.
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Gomarasca, M. A., A. Tornato, D. Spizzichino, E. Valentini, A. Taramelli, G. Satalino, M. Vincini, et al. "SENTINEL FOR APPLICATIONS IN AGRICULTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 91–98. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-91-2019.
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<p><strong>Abstract.</strong> The European Union and the European Space Agency (EU/ESA) have promoted since 1998 (Baveno Manifesto*) the GMES Programme (Global Monitoring for Environment and Security), nowadays called Copernicus (www.copernicus.eu). In the agriculture domain, the use of Copernicus Sentinel imagery and its services are providing several new opportunities. The knowledge of fundamentals of Earth Observation/Geographic Information EO/GI, namely Geomatics, for the development of innovative strategies for professional skills adequacy and capacity building, supporting Copernicus user uptake, becomes mandatory (Gomarasca, 2009). The target is to help bridging gaps between supply and demand of education and training for geospatial sector (www.eo4geo.eu). The innovative and strategical novelties are the complete free access to Sentinel time series imagery and digital image processing software “Sentinel toolboxes” such as SNAP (Sentinel Application Platform) for different environments (Windows, Mac, Unix). The paper introduce topics as crop mapping and monitoring, biophysical parameters, phenology and yield estimations, through several concluded or ongoing international projects such as: ERMES -FP7 (http://www.ermes-fp7space.eu/it/homepage/, Busetto et al. 2017) and SATURNO (https://www.progettosaturno.it/, Nutini et al., 2018) devoted to the regional agricultural monitoring. As conclusion, SNAP software for image processing of Sentinel data was demonstrated and tested together with Earth Engine software for specific vertical agriculture applications. The topics reported in this paper have been part of the Summer School ‘Sentinel for Applications in Agriculture’ supported by the Copernicus programme, several scientific associations (AIT, ASITA, EARSeL - European Association of Remote Sensing Laboratories), the European Erasmus+ project EO4GEO, University Departments and Geo-Information Companies.</p>
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Russell-Smith, Jeremy, Cameron P. Yates, Chris Brock, and Vanessa C. Westcott. "Fire regimes and interval-sensitive vegetation in semiarid Gregory National Park, northern Australia." Australian Journal of Botany 58, no. 4 (2010): 300. http://dx.doi.org/10.1071/bt99210.
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Few data are available concerning contemporary fire regimes and the responses of fire interval-sensitive vegetation types in semiarid woodland savanna landscapes of northern Australia. For a 10 300 km2 semiarid portion of Gregory National Park, in the present paper we describe (1) components of the contemporary fire regime for 1998–2008, on the basis of assessments derived from Landsat and MODIS imagery, (2) for the same period, the population dynamics, and characteristic fine-fuel loads associated with Acacia shirleyi Maiden (lancewood), an obligate seeder tree species occurring in dense monodominant stands, and (3) the fire responses of woody species, and fine-fuel dynamics, sampled in 41 plots comprising shrubby open-woodland over spinifex hummock grassland. While rain-year (July–June) rainfall was consistently reliable over the study period, annual fire extent fluctuated markedly, with an average of 29% being fire affected, mostly in the latter part of the year under relatively harsh fire-climate conditions. Collectively, such conditions facilitated short fire-return intervals, with 30% of the study area experiencing a repeat fire within 1 year, and 80% experiencing a repeat fire within 3 years. Fine fuels associated with the interior of lancewood thickets were characteristically small (<1 t ha–1). Fine fuels dominated by spinifex (Triodia spp.) were found to accumulate at rates equivalent to those observed under higher-rainfall conditions. Stand boundaries of A. shirleyi faired poorly under prevailing fire regimes over the study period; in 16 plots, juvenile density declined 62%, and adult stem density and basal area declined by 53% and 40%, respectively. Although the maturation (primary juvenile) period of A. shirleyi is incompletely known, assembled growth rate and phenology data indicated that it is typically >10 years. Of 133 woody species sampled, all trees (n = 26), with the exception of A. shirleyi, were resprouters, and 58% of all shrub species (n = 105) were obligate seeders, with observed primary juvenile periods <5 years. Assembled data generally supported observations made from other northern Australian studies concerning the responses of fire-sensitive woody taxa in rugged, sandstone-derived landscapes, and illustrated the enormous challenges facing ecologically sustainable fire management in such settings. Contemporary fire regimes of Gregory National Park are not ecologically sustainable.
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Luo, Yunpeng, Tarek El-Madany, Gianluca Filippa, Xuanlong Ma, Bernhard Ahrens, Arnaud Carrara, Rosario Gonzalez-Cascon, et al. "Correction: Luo, Y.P. et al., Using Near-Infrared Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree-Grass Ecosystems. Remote Sens. 2018, 10, 1293." Remote Sensing 11, no. 6 (March 26, 2019): 726. http://dx.doi.org/10.3390/rs11060726.
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Das, Indrani, Regine Hock, Etienne Berthier, and Craig S. Lingle. "21st-century increase in glacier mass loss in the Wrangell Mountains, Alaska, USA, from airborne laser altimetry and satellite stereo imagery." Journal of Glaciology 60, no. 220 (2014): 283–93. http://dx.doi.org/10.3189/2014jog13j119.
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AbstractAlaskan glaciers are among the largest regional contributors to sea-level rise in the latter half of the 20th century. Earlier studies have documented extensive and accelerated ice wastage in most regions of Alaska. Here we study five decades of mass loss on high-elevation, land-terminating glaciers of the Wrangell Mountains (~ 4900 km2) in central Alaska based on airborne center-line laser altimetry data from 2000 and 2007, a digital elevation model (DEM) from ASTER and SPOT5, and US Geological Survey topographic maps from 1957. The regional mass-balance estimates derived from center-line laser altimetry profiles using two regional extrapolation techniques agree well with that from DEM differencing. Repeat altimetry measurements reveal accelerated mass loss over the Wrangell Mountains, with the regional mass-balance rate evolving from –0.07 ± 0.19 m w.e. a–1 during 1957–2000 to –0.24 ± 0.16 m w.e. a–1 during 2000–07. Nabesna, the largest glacier in this region (˜1056 km2), lost mass four times faster during 2000–07 than during 1957–2000. Although accelerated, the mass change over this region is slower than in other glacierized regions of Alaska, particularly those with tidewater glaciers. Together, our laser altimetry and satellite DEM analyses demonstrate increased wastage of these glaciers during the last 50 years.
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Ahlstrøm, Andreas P., Johan J. Mohr, Niels Reeh, Erik Lintz Christensen, and Roger LeB Hooke. "Controls on the basal water pressure in subglacial channels near the margin of the Greenland ice sheet." Journal of Glaciology 51, no. 174 (2005): 443–50. http://dx.doi.org/10.3189/172756505781829214.
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AbstractAssuming a channelized drainage system in steady state, we investigate the influence of enhanced surface melting on the water pressure in subglacial channels, compared to that of changes in conduit geometry, ice rheology and catchment variations. The analysis is carried out for a specific part of the western Greenland ice-sheet margin between 66° N and 66°30′N using new high-resolution digital elevation models of the subglacial topography and the ice-sheet surface, based on an airborne ice-penetrating radar survey in 2003 and satellite repeat-track interferometric synthetic aperture radar analysis of European Remote-sensing Satellite 1 and 2 (ERS-1/-2) imagery, respectively. The water pressure is calculated up-glacier along a likely subglacial channel at distances of 1, 5 and 9 km from the outlet at the ice margin, using a modified version of Röthlisberger’s equation. Our results show that for the margin of the western Greenland ice sheet, the water pressure in subglacial channels is not sensitive to realistic variations in catchment size and mean surface water input compared to small changes in conduit geometry and ice rheology.
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Venkatappa, Manjunatha, Nophea Sasaki, Sutee Anantsuksomsri, and Benjamin Smith. "Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia." Remote Sensing 12, no. 18 (September 22, 2020): 3110. http://dx.doi.org/10.3390/rs12183110.
Full textAbstract:
Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring of Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities. Using the Google Earth Engine (GEE) cloud computing platform, we applied the recently developed phenology-based threshold classification method (PBTC) for detecting and mapping forest cover and carbon stock changes in Siem Reap province, Cambodia, between 1990 and 2018. The obtained PBTC maps were validated using Google Earth high resolution historical imagery and reference land cover maps by creating 3771 systematic 5 × 5 km spatial accuracy points. The overall cumulative accuracy of this study was 92.1% and its cumulative Kappa was 0.9, which are sufficiently high to apply the PBTC method to detect forest land cover change. Accordingly, we estimated the carbon stock changes over a 28-year period in accordance with the Good Practice Guidelines of the Intergovernmental Panel on Climate Change. We found that 322,694 ha of forest cover was lost in Siem Reap, representing an annual deforestation rate of 1.3% between 1990 and 2018. This loss of forest cover was responsible for carbon emissions of 143,729,440 MgCO2 over the same period. If REDD+ activities are implemented during the implementation period of the Paris Climate Agreement between 2020 and 2030, about 8,256,746 MgCO2 of carbon emissions could be reduced, equivalent to about USD 6-115 million annually depending on chosen carbon prices. Our case study demonstrates that the GEE and PBTC method can be used to detect and monitor forest cover change and carbon stock changes in the tropics with high accuracy.
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Melkonian, A. K., M. J. Willis, M. E. Pritchard, A. Rivera, F. Bown, and S. A. Bernstein. "Satellite-Derived Volume Loss Rates and Glacier Speeds for the Cordillera Darwin Icefield, Chile." Cryosphere Discussions 6, no. 4 (August 31, 2012): 3503–38. http://dx.doi.org/10.5194/tcd-6-3503-2012.
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Abstract. We produce the first icefield-wide volume change rate and glacier velocity estimates for the Cordillera Darwin Icefield (CDI), a 2605 km2 temperate icefield in Southern Chile (69.6° W, 54.6° S). Velocities are measured from optical and radar imagery between 2001–2011. Thirty-seven digital elevation models (DEMs) from ASTER and the SRTM are stacked and a weighted linear regression is applied to elevations on a pixel-by-pixel basis to estimate volume change rates. The CDI lost mass at an average rate of 3.9 ± 0.3 Gt yr−1 between 2000 and 2011, equivalent to a sea level rise (SLR) of 0.01 ± 0.001 mm yr−1. Thinning is widespread, with concentrations near the front of two northern glaciers (Marinelli, Darwin) and one western (CDI-08) glacier. Thickening is apparent in the south, most notably over the advancing Garibaldi Glacier. We attribute this thinning pattern to warmer temperatures, particularly in the north, which triggered rapid retreat at Marinelli Glacier (~4 km from 2001–2011). Velocities are obtained over many of the swiftly flowing glaciers for the first time. We provide a repeat speed timeseries at the Marinelli Glacier. Maximum front speeds there accelerated from 7.5 m day−1 in 2001 to 9.5 m day−1 in 2003, to a peak of 10 m day−1 in 2011.
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Caspard, Mathilde, Hervé Yésou, Arnaud Selle, Claire Tinel, Pierre Tessier, Arnaud Durand, Stephen Clandillon, and Paul De Fraipont. "Forest recolonization monitoring based on HR and VHR imagery: the case of the Maido forest fire exploiting Pléiades HR and SPOT Kalideos database." Revue Française de Photogrammétrie et de Télédétection, no. 209 (January 29, 2015): 149–55. http://dx.doi.org/10.52638/rfpt.2015.99.
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End October 2011, in a rapid mapping framework, SERTIT followed during 6 days the increase of the forest fires which hit the Maido slopes, Reunion Island, in the hearth of National Park, a UNESCO world heritage site. SERTIT's Rapid Mapping service followed the evolution of this event in “near real time†in a SAFER activation framework, during one week long, with one satellite image acquisition then one map produced every day. According to the prefecture report, the fire burnt more than 2600ha.After this crisis period, the work turned to the question about the resilience and the monitoring of the vegetation regeneration. In this context, SERTIT is mandated to assess the contribution of very high resolution of Pléiades data acquired theoretically monthly since June 2012 in its thematic commissioning phase framework and of high resolution data from KALIDEOS database (SPOT4 and SPOT 5 images) acquired regularly since 2005. In total, 66 SPOT images and 10 VHR Pléiades or Pléiades-like images have been integrated and exploited.Data exploitation consists of thematic index calculation, which, combined with field information enabled the mapping of vegetation cover/state and to monitor its evolution before and after fire event. Then, thanks to serial data acquired during a one year provision plan, the contribution of very high resolution of Pléiades-HR data is analyzed to monitor phenology changes and forest regeneration at plot and/or massif scales.This study highlights the changes affecting Maido forest since 2005. Indeed, after establishing the vegetation behavior without perturbation (normal seasonal variations), spectral signature analysis derived from indexes shows the forest fire impact on vegetation but also a recolonisation of the massif. In complement, monitoring this “regeneration†with VHR Pléiades data, texture provided by such images is useful to confirm this forest recovery and to qualify it. In reality, the phenomenon observed corresponds as much to recolonization than regeneration because of the different behavior and appearance of the new vegetation. Cooperation with local actors is necessary to check field information. Furthermore, stereo Pléiades data were acquired over the quasi-totality of the Reunion Island. After digital surface model restitution at 50cm would be extract vegetation structure and erosion risk information.
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Sturzenegger, Matthieu, Kris Holm, Carie-Ann Lau, and Matthias Jakob. "Debris-Flow and Debris-Flood Susceptibility Mapping for Geohazard Risk Prioritization." Environmental and Engineering Geoscience 27, no. 2 (March 3, 2021): 179–94. http://dx.doi.org/10.2113/eeg-d-20-00006.
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ABSTRACT Regional-scale assessments for debris-flow and debris-flood propagation and avulsion on fans can be challenging. Geomorphological mapping based on aerial or satellite imagery requires substantial field verification effort. Surface evidence of past events may be obfuscated by development or obscured by repeat erosion or debris inundation, and trenching may be required to record the sedimentary architecture and date past events. This paper evaluates a methodology for debris-flow and debris-flood susceptibility mapping at regional scale based on a combination of digital elevation model (DEM) metrics to identify potential debris source zones and flow propagation modeling using the Flow-R code that is calibrated through comparison to mapped alluvial fans. The DEM metrics enable semi-automated identification and preliminary, process-based classification of streams prone to debris flow and debris flood. Flow-R is a susceptibility mapping tool that models potential flow inundation based on a combination of spreading and runout algorithms considering DEM topography and empirical propagation parameters. The methodology is first evaluated at locations where debris-flow and debris-flood hazards have been previously assessed based on field mapping and detailed numerical modeling. It is then applied over a 125,000 km2 area in southern British Columbia, Canada. The motivation for the application of this methodology is that it represents an objective and repeatable approach to susceptibility mapping, which can be integrated in a debris-flow and debris-flood risk prioritization framework at regional scale to support risk management decisions.
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Gardner, A. S., G. Moholdt, A. Arendt, and B. Wouters. "Long-term contributions of Baffin and Bylot Island Glaciers to sea level rise: an integrated approach using airborne and satellite laser altimetry, stereoscopic imagery and satellite gravimetry." Cryosphere Discussions 6, no. 2 (April 26, 2012): 1563–610. http://dx.doi.org/10.5194/tcd-6-1563-2012.
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Abstract. Canadian Arctic glaciers have recently contributed large volumes of meltwater to the world's oceans. To place recently observed glacier wastage into a historical perspective and to determine the region's longer-term (~50 years) contribution to sea level, we estimate mass and volume changes for the glaciers of Baffin and Bylot Islands using Digital Elevation Models generated from airborne and satellite stereoscopic imagery and elevation postings from repeat airborne and satellite laser altimetry. In addition, we update existing glacier mass change records from GRACE satellite gravimetry to cover the period from 2003 to 2011. Using an integrated approach we find that the rate of mass loss from the region's glaciers increased from 11.1 ± 1.8 Gt a−1 (–270 ± 40 kg m−2 a−1) in 1963–2006 to 23.8 ± 3.1 Gt a−1 (–580 ± 80 kg m2 a−1) in 2003–2011. The doubling of the rate of mass loss is attributed to higher temperatures in summer with little change in annual precipitation. Through both direct and indirect effects, changes in summer temperatures accounted for 68–98 % of the variance in the rate of mass loss to which the Barnes Ice Cap was found to be 1.6 times more sensitive than either the Penny Ice Cap or the regions glaciers as a whole. Between 2003 and 2011 the glaciers of Baffin and Bylot Islands contributed 0.07 ± 0.01 mm a−1 to sea level rise, a rate equivalent to the contribution coming from Patagonian glaciers. Over the 48-year period between 1963 and 2011 the glaciers of Baffin and Bylot Islands contributed 1.7 mm to the world's oceans.
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Sedrati, Mouncef, Juan A. Morales, Abdelmounim El M’rini, Edward J. Anthony, Glen Bulot, Romain Le Gall, and Abdelkarim Tadibaght. "Using UAV and Structure-From-Motion Photogrammetry for the Detection of Boulder Movement by Storms on a Rocky Shore Platform in Laghdira, Northwest Morocco." Remote Sensing 14, no. 16 (August 21, 2022): 4102. http://dx.doi.org/10.3390/rs14164102.
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The detachment and mobilization of boulders from rocky shore platforms by waves involves complex geomorphic and hydrodynamic processes. Understanding these processes requires precise information on the rates and patterns of movement of these megaclasts scaled against the wave conditions that generate boulder mobility. Repeat photogrammetry and structure-from-motion (SfM) models commonly used in geomorphic analyses are an interesting option for monitoring boulder dynamics. In this study, we used unmanned aerial vehicle (UAV)-based digital photogrammetry and SfM differential models to identify recent boulder movements over a rocky shore platform in Laghdira, Morocco. Combining these results with data on storm occurrence in the study area allowed us to identify storm waves as the unique driver of the dislodged and mobilized boulders. The identified storm event had a significant wave height of 5.2 m. The UAV models were built from imagery captured in September and December 2019 using a DJI MAVIC PRO PLATINUM, and we used QGIS to produce 2D and 3D model outputs. The exploitation of the 2D model differentials allowed us to appreciate the response of the boulders to the storm waves and to determine platform volumetric changes and, therefore, boulder mobility. The 3D models were valuable in determining the mode of transport of the boulders. Mobility patterns included sliding, overturning with no further mobility, and rotation and saltation, as well as boulder breakup. Storm waves did not have a preferential impact on any particular boulder shape, size category, or position at the outer edge of the platform. These results highlight the utility of combining UAV surveys with identified storm events, which are much more frequent than tsunamis, in determining observed boulder initiation and mobility.
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Melkonian, A. K., M. J. Willis, M. E. Pritchard, A. Rivera, F. Bown, and S. A. Bernstein. "Satellite-derived volume loss rates and glacier speeds for the Cordillera Darwin Icefield, Chile." Cryosphere 7, no. 3 (May 14, 2013): 823–39. http://dx.doi.org/10.5194/tc-7-823-2013.
Full textAbstract:
Abstract. We produce the first icefield-wide volume change rate and glacier velocity estimates for the Cordillera Darwin Icefield (CDI), a 2605 km2 temperate icefield in southern Chile (69.6° W, 54.6° S). Velocities are measured from optical and radar imagery between 2001–2011. Thirty-six digital elevation models (DEMs) from ASTER and the SRTM DEM are stacked and a weighted linear regression is applied to elevations on a pixel-by-pixel basis to estimate volume change rates. The CDI lost mass at an average rate of −3.9 ± 1.5 Gt yr−1 between 2000 and 2011, equivalent to a sea level rise (SLR) of 0.01 ± 0.004 mm yr−1 and an area-averaged thinning rate of −1.5 ± 0.6 m w.e.(water equivalent) yr−1. Thinning is widespread, with concentrations near the front of two northern glaciers (Marinelli, Darwin) and one western (CDI-08) glacier. Thickening is apparent in the south, most notably over the advancing Garibaldi Glacier. The northeastern part of the CDI has an average thinning rate of −1.9 ± 0.7 m w.e. yr−1, while the southwestern part has an average thinning rate of −1.0 ± 0.4 m w.e. yr−1. Velocities are obtained over many of the CDI glaciers for the first time. We provide a repeat speed time series at the Marinelli Glacier. There we measure maximum front speeds of 7.5 ± 0.2 m day−1 in 2001, 9.5 ± 0.6 m day−1 in 2003 and 10 ± 0.3 m day−1 in 2011. The maintenance of high front speeds from 2001 to 2011 supports the hypothesis that Marinelli is in the retreat phase of the tidewater cycle, with dynamic thinning governed by the fjord bathymetry.
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Palomaki, Ross T., and Eric A. Sproles. "Quantifying the Effect of River Ice Surface Roughness on Sentinel-1 SAR Backscatter." Remote Sensing 14, no. 22 (November 8, 2022): 5644. http://dx.doi.org/10.3390/rs14225644.
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Satellite-based C-band synthetic aperture radar (SAR) imagery is an effective tool to map and monitor river ice on regional scales because the SAR backscatter is affected by various physical properties of the ice, including roughness, thickness, and structure. Validation of SAR-based river ice classification maps is typically performed using expert interpretation of aerial or ground reference images of the river ice surface, using visually apparent changes in surface roughness to delineate different ice classes. Although many studies achieve high classification accuracies using this qualitative technique, it is not possible to determine if the river ice information contained within the SAR backscatter data originates from the changes in surface roughness used to create the validation data, or from some other ice property that may be more relevant for ice jam forecasting. In this study, we present the first systematic, quantitative investigation of the effect of river ice surface roughness on C-band Sentinel-1 backscatter. We use uncrewed aerial vehicle-based Structure from Motion photogrammetry to generate high-resolution (0.03 m) digital elevation models of river ice surfaces, from which we derive measurements of surface roughness. We employ Random Forest models first to repeat previous ice classification studies, and then as regression models to explore quantitative relationships between ice surface roughness and Sentinel-1 backscatter. Classification accuracies are similar to those reported in previous studies (77–96%) but poor regression performance for many surface roughness metrics (5–113% mean absolute percentage errors) indicates a weak relationship between river ice surface roughness and Sentinel-1 backscatter. Additional work is necessary to determine which physical ice properties are strong controls on C-band SAR backscatter.
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Padial-Iglesias, Mario, Pere Serra, Miquel Ninyerola, and Xavier Pons. "A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps." Remote Sensing 13, no. 14 (July 6, 2021): 2662. http://dx.doi.org/10.3390/rs13142662.
Full textAbstract:
Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential.
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Fischer, Andrea, Gabriele Schwaizer, Bernd Seiser, Kay Helfricht, and Martin Stocker-Waldhuber. "High-resolution inventory to capture glacier disintegration in the Austrian Silvretta." Cryosphere 15, no. 10 (October 4, 2021): 4637–54. http://dx.doi.org/10.5194/tc-15-4637-2021.
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Abstract. A new high-resolution glacier inventory captures the rapid decay of the glaciers in the Austrian Silvretta for the years 2017 and 2018. Identifying the glacier outlines offers a wide range of possible interpretations of glaciers that have evolved into small and now totally debris-covered cryogenic structures. In previous inventories, a high proportion of active bare ice allowed a clear delineation of the glacier margins even by optical imagery. In contrast, in the current state of the glacier only the patterns and amounts of volume change allow us to estimate the area of the buried glacier remnants. We mapped the glacier outlines manually based on lidar elevation models and patterns of volume change at 1 to 0.5 m spatial resolution. The vertical accuracy of the digital elevation models (DEMs) generated from six to eight lidar points per square metre is of the order of centimetres. Between 2004/2006 and 2017/2018, the 46 glaciers of the Austrian Silvretta lost −29 ± 4 % of their area and now cover 13.1 ± 0.4 km2. This is only 32 ± 2 % of their Little Ice Age (LIA) extent of 40.9 ± 4.1 km2. The area change rate increased from 0.6 %/yr (1969–2002) to −2.4 %/yr (2004/2006–2017/2018). The Sentinel-2-based glacier inventory of 2018 deviates by just 1 % of the area. The annual geodetic mass balance referring to the area at the beginning of the period showed a loss increasing from −0.2 ± 0.1 m w.e./yr (1969–2002) to −0.8 ± 0.1 m w.e./yr (2004/2006–2017/2018) with an interim peak in 2002–2004/2006 of −1.5 ± 0.7 m w.e./yr. To keep track of the buried ice and its fate and to distinguish increasing debris cover from ice loss, we recommend inventory repeat frequencies of 3 to 5 years and surface elevation data with a spatial resolution of 1 m.
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Gardner, A., G. Moholdt, A. Arendt, and B. Wouters. "Accelerated contributions of Canada's Baffin and Bylot Island glaciers to sea level rise over the past half century." Cryosphere 6, no. 5 (October 12, 2012): 1103–25. http://dx.doi.org/10.5194/tc-6-1103-2012.
Full textAbstract:
Abstract. Canadian Arctic glaciers have recently contributed large volumes of meltwater to the world's oceans. To place recently observed glacier wastage into a historical perspective and to determine the region's longer-term (~50 years) contribution to sea level, we estimate mass and volume changes for the glaciers of Baffin and Bylot Islands using digital elevation models generated from airborne and satellite stereoscopic imagery and elevation postings from repeat airborne and satellite laser altimetry. In addition, we update existing glacier mass change records from GRACE satellite gravimetry to cover the period from 2003 to 2011. Using this integrated approach, we find that the rate of mass loss from the region's glaciers increased from 11.1 ± 3.4 Gt a−1 (271 ± 84 kg m−2 a−1) for the period 1963–2006 to 23.8 ± 6.1 Gt a−1 (581 ± 149 kg m−2 a−1) for the period 2003–2011. The doubling of the rate of mass loss is attributed to higher temperatures in summer with little change in annual precipitation. Through both direct and indirect effects, changes in summer temperatures accounted for 70–98% of the variance in the rate of mass loss, to which the Barnes Ice Cap was found to be 1.7 times more sensitive than either the Penny Ice Cap or the region's glaciers as a whole. This heightened sensitivity is the result of a glacier hypsometry that is skewed to lower elevations, which are shown to have a higher mass change sensitive to temperature compared to glacier surfaces at higher elevations. Between 2003 and 2011 the glaciers of Baffin and Bylot Islands contributed 0.07 ± 0.02 mm a−1 to sea level rise accounting for 16% of the total contribution from glaciers outside of Greenland and Antarctica, a rate much higher than the longer-term average of 0.03 ± 0.01 mm a−1 (1963 to 2006).