Data extraction from digital repeat photography using xROI: An interactive framework to facilitate the process

Abstract Digital repeat photography and near-surface remote sensing have been used by environmental scientists to study environmental change for nearly a decade. However, a user-friendly, reliable, and robust platform to extract color-based statistics and time series from a large stack of images is still lacking. Here, we present an interactive open-source toolkit, called xROI, that facilitates the process of time series extraction and improves the quality of the final data. xROI provides a responsive environment for scientists to interactively (a) delineate regions of interest (ROI), (b) handle field of view (FOV) shifts, and (c) extract and export time series data characterizing color-based metrics. The software gives user the opportunity to adjust mask files or draw new masks, every time an FOV shift occurs. Utilizing xROI can significantly facilitate data extraction from digital repeat photography and enhance the accuracy and continuity of extracted data.

[1]  Margaret Kosmala,et al.  Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery , 2018, Scientific Data.

[2]  S. Barr,et al.  Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations , 2019, Remote Sensing of Environment.

[3]  Bijan Seyednasrollah,et al.  Leaf phenology paradox: Why warming matters most where it is already warm , 2018 .

[4]  D. Roberts,et al.  Design of an image analysis website for phenological and meteorological monitoring , 2010, Environ. Model. Softw..

[5]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[6]  Michael A. Crimmins,et al.  Monitoring Plant Phenology Using Digital Repeat Photography , 2008, Environmental management.

[7]  Larry Aronson HTML 3 Manual of Style , 1995 .

[8]  N. Coops,et al.  Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras , 2014 .

[9]  Olivier Merlin,et al.  An operational method for the disaggregation of land surface temperature to estimate actual evapotranspiration in the arid region of Chile , 2017 .

[10]  Joel A. Granados,et al.  Using phenocams to monitor our changing Earth: toward a global phenocam network , 2016 .

[11]  Roger Bivand,et al.  Bindings for the Geospatial Data Abstraction Library , 2015 .

[12]  Elizabeth Pattey,et al.  Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops , 2010 .

[13]  Jessica O'Connell,et al.  A smart classifier for extracting environmental data from digital image time-series: Applications for PhenoCam data in a tidal salt marsh , 2016, Environ. Model. Softw..

[14]  Linlin Lu,et al.  A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[15]  Elle Banaszak,et al.  Tracing the Sand Dunes: Using a Combination of Panoramic Photography and Dune Pins to Track Changes in Michigan's Sand Dunes Over Time , 2016 .

[16]  Mark A. Friedl,et al.  Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography , 2018 .

[17]  D. Baldocchi,et al.  Using digital camera and Landsat imagery with eddy covariance data to model gross primary production in restored wetlands , 2017 .

[18]  Camillo Ressl,et al.  Potential of time‐lapse photography for identifying saturation area dynamics on agricultural hillslopes , 2017 .

[19]  Mark A. Friedl,et al.  Digital repeat photography for phenological research in forest ecosystems , 2012 .

[20]  Uthai Phommasak,et al.  Detecting Foggy Images and Estimating the Haze Degree Factor , 2014 .

[21]  Ben Baumer,et al.  R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics , 2014, 1402.1894.

[22]  M. Rossini,et al.  Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake , 2011 .

[23]  M. Migliavacca,et al.  Phenopix: A R package for image-based vegetation phenology , 2016 .

[24]  A. Huete,et al.  Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography , 2016 .

[25]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[26]  Margaret Kosmala,et al.  An interactive toolkit to extract phenological time series data from digital repeat photography , 2017 .

[27]  Jim Aton In The Footsteps of John Wesley Powell: An Album of Comparative Photographs of the Green and Colorado Rivers, 1871–72 and 1968 by Hal G. Stephens and Eugene M. Shoemaker (review) , 1988 .

[28]  Cibele Hummel do Amaral,et al.  Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations , 2017 .

[29]  C. Igathinathane,et al.  Color calibration of digital images for agriculture and other applications , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[30]  Lei Zhou,et al.  Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data , 2013, Ecol. Informatics.

[31]  A. Huete,et al.  Multi-Scale Phenology of Temperate Grasslands: Improving Monitoring and Management With Near-Surface Phenocams , 2019, Front. Environ. Sci..

[32]  M F Sanner,et al.  Python: a programming language for software integration and development. , 1999, Journal of molecular graphics & modelling.

[33]  Margaret Kosmala,et al.  PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015 , 2017 .

[34]  Andrew D Richardson,et al.  Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery. , 2018, The New phytologist.

[35]  Brian E. Mapes,et al.  Bimodality in tropical water vapour , 2003 .

[36]  Jan Magnusson,et al.  Snow accumulation distribution inferred from time‐lapse photography and simple modelling , 2010 .

[37]  David Joseph Moore,et al.  Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data , 2019, Remote Sensing of Environment.

[38]  Tommi Mikkonen,et al.  Using JavaScript as a real programming language , 2007 .

[39]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

[40]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[41]  Bijan Seyednasrollah,et al.  Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures , 2018, Nature.

[42]  Thomas A. Powell HTML & CSS : the complete reference , 2010 .

[43]  Bruno D. Borges,et al.  Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation , 2017 .

[44]  James E. Smith,et al.  Comparing Program Phase Detection Techniques , 2003, MICRO.

[45]  Tom Milliman,et al.  Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing , 2018, Scientific Reports.

[46]  Raymond M. Turner,et al.  The Changing Mile: An Ecological Study of Vegetation Change with Time in the Lower Mile of An Arid and Semiarid Region , 1966 .

[47]  G. Brent Hall,et al.  Open Source Approaches in Spatial Data Handling , 2008 .