Species and habitat mapping in two dimensions and beyond. Structure-from-Motion Multi-View Stereo photogrammetry for the Conservation Community

Structure-from-Motion Multi View Stereo (SfM-MVS) photogrammetry is a technique by which volumetric data can be derived from overlapping image sets, using changes of an objects position between images to determine its height and spatial structure. Whilst SfM-MVS has fast become a powerful tool for scientific research, its potential lies beyond the scientific setting, since it can aid in delivering information about habitat structure, biomass, landscape topography, spatial distribution of species in both two and three dimensions, and aid in mapping change over time – both actual and predicted. All of which are of strong relevance for the conservation community, whether from a practical management perspective or understanding and presenting data in new and novel ways from a policy perspective. For practitioners outside of academia wanting to use SfM-MVS there are technical barriers to its application. For example, there are many SfM-MVS software options, but knowing which to choose, or how to get the best results from the software can be difficult for the uninitiated. There are also free and open source software options (FOSS) for processing data through a SfM-MVS pipeline that could benefit those in conservation management and policy, especially in instances where there is limited funding (i.e. commonly within grassroots or community-based projects). This paper signposts the way for the conservation community to understand the choices and options for SfM-MVS implementation, its limitations, current best practice guidelines and introduces applicable FOSS options such as OpenDroneMap, MicMac, CloudCompare, QGIS and speciesgeocodeR. It will also highlight why and where this technology has the potential to become an asset for spatial, temporal and volumetric studies of landscape and conservation ecology.

[1]  Marco Palma,et al.  High Resolution Orthomosaics of African Coral Reefs: A Tool for Wide-Scale Benthic Monitoring , 2017, Remote. Sens..

[2]  D. Baker,et al.  Reefs of tomorrow: eutrophication reduces coral biodiversity in an urbanized seascape , 2016, Global change biology.

[3]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

[4]  Brendan F. Kohrn,et al.  Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology , 2016, Applications in Plant Sciences.

[5]  Alexandre Antonelli,et al.  Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? , 2015, Global ecology and biogeography : a journal of macroecology.

[6]  Heiko Balzter,et al.  Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments , 2005 .

[7]  Adam Mosbrucker,et al.  Camera system considerations for geomorphic applications of SfM photogrammetry , 2017 .

[8]  Gregory M. Crutsinger,et al.  The future of UAVs in ecology: an insider perspective from the Silicon Valley drone industry , 2016 .

[9]  Heiko Balzter,et al.  Modelling relationships between organisms and vegetation structure using airborne LiDAR data , 2005 .

[10]  Greg Brown,et al.  Stakeholder analysis for marine conservation planning using public participation GIS , 2016 .

[11]  Anders Knudby,et al.  Mapping Wild Leek through the Forest Canopy Using a UAV , 2018, Remote. Sens..

[12]  Geoffrey J. Hay,et al.  Free and open source geographic information tools for landscape ecology , 2009, Ecol. Informatics.

[13]  Karen Anderson,et al.  Tracking Fine-Scale Structural Changes in Coastal Dune Morphology Using Kite Aerial Photography and Uncertainty-Assessed Structure-from-Motion Photogrammetry , 2018, Remote. Sens..

[14]  Stefan Dech,et al.  Remote Sensing and GIS for Ecologists: Using Open Source Software , 2016 .

[15]  Marco Minghini,et al.  Free and open source software for geospatial applications (FOSS4G) to support Future Earth , 2017, Int. J. Digit. Earth.

[16]  R. Fraser,et al.  UAV photogrammetry for mapping vegetation in the low-Arctic , 2016 .

[17]  Gregory P Asner,et al.  Advances in animal ecology from 3D-LiDAR ecosystem mapping. , 2014, Trends in ecology & evolution.

[18]  Miriah D. Meyer,et al.  Information visualisation for science and policy: engaging users and avoiding bias. , 2014, Trends in ecology & evolution.

[19]  Robert J. Smith,et al.  The CLUZ plugin for QGIS: designing conservation area systems and other ecological networks , 2019, Research Ideas and Outcomes.

[20]  David Gutiérrez,et al.  Habitat‐based statistical models for predicting the spatial distribution of butterflies and day‐flying moths in a fragmented landscape , 2000 .

[21]  Christophe Delacourt,et al.  Suggestions to Limit Geometric Distortions in the Reconstruction of Linear Coastal Landforms by SfM Photogrammetry with PhotoScan® and MicMac® for UAV Surveys with Restricted GCPs Pattern , 2018, Drones.

[22]  M. Sebastià,et al.  Vegetation dynamics patterns, biodiversity conservation and structure of forest ecosystems in the wildlife reserve of Togodo in Togo, West Africa , 2017 .

[23]  Markus Neteler,et al.  Free and Open Source Geospatial Tools for Environmental Modeling and Management , 2006 .

[24]  Julien Michel,et al.  Orfeo ToolBox Applications , 2018 .

[25]  D. Silvestro,et al.  SpeciesGeoCoder: Fast Categorization of Species Occurrences for Analyses of Biodiversity, Biogeography, Ecology, and Evolution , 2016, Systematic biology.

[26]  B. Kløve,et al.  Evaluation of erosion and surface roughness in peatland forest ditches using pin meter measurements and terrestrial laser scanning , 2016 .

[27]  Daniele Oxoli,et al.  Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS , 2016 .

[28]  R. Salomão,et al.  Amazonian tree species threatened by deforestation and climate change , 2019, Nature Climate Change.

[29]  Alexander Brenning,et al.  RQGIS: Integrating R with QGIS for Statistical Geocomputing , 2017, R J..

[30]  Alexander Zizka Big data suggest migration and bioregion connectivity as crucial for the evolution of Neotropical biodiversity , 2019, Frontiers of Biogeography.

[31]  Christophe Delacourt,et al.  Assessing the Accuracy of High Resolution Digital Surface Models Computed by PhotoScan® and MicMac® in Sub-Optimal Survey Conditions , 2016, Remote. Sens..

[32]  S. Robson,et al.  3‐D uncertainty‐based topographic change detection with structure‐from‐motion photogrammetry: precision maps for ground control and directly georeferenced surveys , 2017 .

[33]  E. Rupnik,et al.  MicMac – a free, open-source solution for photogrammetry , 2017, Open Geospatial Data, Software and Standards.

[34]  A. Pollard,et al.  Limb proportions show developmental plasticity in response to embryo movement , 2017, Scientific Reports.

[35]  Jean-Baptiste Féret,et al.  A generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning , 2018, Methods in Ecology and Evolution.

[36]  N. Pettorelli,et al.  A new platform to support research at the interface of remote sensing, ecology and conservation , 2015 .

[37]  M. Westoby,et al.  Assessing climate change associated sea‐level rise impacts on sea turtle nesting beaches using drones, photogrammetry and a novel GPS system , 2018, Global change biology.

[38]  Mark Wilkinson,et al.  Drone‐based structure‐from‐motion photogrammetry captures grassland sward height variability , 2018 .

[39]  R. Pelorosso,et al.  PANDORA 3.0 plugin: A new biodiversity ecosystem service assessment tool for urban green infrastructure connectivity planning , 2017 .

[40]  Andreas Fichtner,et al.  A high‐resolution approach for the spatiotemporal analysis of forest canopy space using terrestrial laser scanning data , 2018, Ecology and evolution.

[41]  Jason J. Mercer,et al.  Ultrahigh‐resolution mapping of peatland microform using ground‐based structure from motion with multiview stereo , 2016 .

[42]  Abdullah Abdullah,et al.  Locating emergent trees in a tropical rainforest using data from an Unmanned Aerial Vehicle (UAV) , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[43]  Sarah E. Gergel,et al.  Mapping for Coral Reef Conservation: Comparing the Value of Participatory and Remote Sensing Approaches , 2016 .

[44]  T. Krueger,et al.  Effectiveness of conservation areas for protecting biodiversity and ecosystem services: a multi-criteria approach , 2017 .

[45]  Alexandre Antonelli,et al.  speciesgeocodeR: An R package for linking species occurrences, user-defined regions and phylogenetic trees for biogeography, ecology and evolution , 2015, bioRxiv.

[46]  Martin Jung,et al.  LecoS - A python plugin for automated landscape ecology analysis , 2016, Ecol. Informatics.

[47]  D. Lague,et al.  Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) , 2013, 1302.1183.

[48]  Arif Oguz Altunel,et al.  Accuracy assessment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain , 2019, Measurement.

[49]  Bernard Lacaze,et al.  GRASS GIS Software with QGIS , 2018 .

[50]  M. Strager,et al.  Incorporating stakeholder preferences for land conservation: Weights and measures in spatial MCA , 2006 .

[51]  Kenji Ose,et al.  Introduction to GDAL Tools in QGIS , 2018 .

[52]  Paul Ryan Nesbit,et al.  Enhancing UAV-SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images , 2019, Remote. Sens..

[53]  T. Quine,et al.  Testing the utility of structure‐from‐motion photogrammetry reconstructions using small unmanned aerial vehicles and ground photography to estimate the extent of upland soil erosion , 2017 .

[54]  Andrew M. Cunliffe,et al.  Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry , 2016 .

[55]  Andreas Kääb,et al.  Terrain changes from images acquired on opportunistic flights by SfM photogrammetry , 2016 .

[56]  Katleen Robert,et al.  New approaches to high-resolution mapping of marine vertical structures , 2017, Scientific Reports.

[57]  H. Steege,et al.  Finding needles in the haystack: where to look for rare species in the American tropics , 2018 .

[58]  Marc Olano,et al.  Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure , 2015, Remote. Sens..

[59]  S. Hancock,et al.  A Grassroots Remote Sensing Toolkit Using Live Coding, Smartphones, Kites and Lightweight Drones , 2016, PloS one.

[60]  S. Robson,et al.  Mitigating systematic error in topographic models derived from UAV and ground‐based image networks , 2014 .

[61]  Francesca Cagnacci,et al.  Managing wildlife: A spatial information system for GPS collars data , 2008, Environ. Model. Softw..

[62]  Margarita Mulero-Pázmány,et al.  Drones for Conservation in Protected Areas: Present and Future , 2019, Drones.

[63]  Armin Gruen,et al.  MONITORING CORAL GROWTH – THE DICHOTOMY BETWEEN UNDERWATER PHOTOGRAMMETRY AND GEODETIC CONTROL NETWORK , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[64]  J. Lorite,et al.  Hotspots within hotspots: Endemic plant richness, environmental drivers, and implications for conservation , 2014 .

[65]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[66]  Daniele Oxoli,et al.  Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS , 2016, PeerJ Prepr..

[67]  Mohamed Rached Boussema,et al.  Using kites for 3-D mapping of gullies at decimetre-resolution over several square kilometres: a case study on the Kamech catchment, Tunisia , 2018, Natural Hazards and Earth System Sciences.

[68]  R. Perroy,et al.  High resolution habitat suitability modelling for an endemic restricted-range Hawaiian insect (Nysius wekiuicola, Hemiptera: Lygaeidae) , 2017, Journal of Insect Conservation.

[69]  I. Woodhouse,et al.  Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries , 2017 .

[70]  H. Lynch,et al.  Ultra-Fine Scale Spatially-Integrated Mapping of Habitat and Occupancy Using Structure-From-Motion , 2017, PloS one.

[71]  Paul Passy,et al.  The Use of SAGA GIS Modules in QGIS , 2018 .

[72]  Fabio Menna,et al.  A CRITICAL REVIEW OF AUTOMATED PHOTOGRAMMETRICPROCESSING OF LARGE DATASETS , 2017 .

[73]  R. Green,et al.  Agricultural intensification and the collapse of Europe's farmland bird populations , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[74]  P J Franks Generic tools. , 1996, Journal of wound care.