Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

A major challenge in ecological restoration is assessing the success of restoration plantings in producing habitats that provide the desired ecosystem functions and services. Forest structural complexity and biomass accumulation are key measures used to monitor restoration success and are important factors determining animal habitat availability and carbon sequestration. Monitoring their development through time using traditional field measurements can be costly and impractical, particularly at the landscape-scale, which is a common requirement in ecological restoration. We explored the application of proximal sensing technology as an alternative to traditional field surveys to capture the development of key forest structural traits in a restoration planting in the Midlands of Tasmania, Australia. We report the use of a hand-held laser scanner (ZEB1) to measure annual changes in structural traits at the tree-level, in a mixed species common-garden experiment from seven- to nine-years after planting. Using very dense point clouds, we derived estimates of multiple structural traits, including above ground biomass, tree height, stem diameter, crown dimensions, and crown properties. We detected annual increases in most LiDAR-derived traits, with individual crowns becoming increasingly interconnected. Time by species interaction were detected, and were associated with differences in productivity between species. We show the potential for remote sensing technology to monitor temporal changes in forest structural traits, as well as to provide base-line measures from which to assess the restoration trajectory towards a desired state.

[1]  Gregory P. Asner,et al.  Cover of tall trees best predicts California spotted owl habitat , 2017 .

[2]  R. Macarthur,et al.  On Bird Species Diversity , 1961 .

[3]  A. Lucieer,et al.  Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests , 2021, Isprs Journal of Photogrammetry and Remote Sensing.

[4]  J. Reif,et al.  Effects of vegetation structure on the diversity of breeding bird communities in forest stands of non-native black pine (Pinus nigra A.) and black locust (Robinia pseudoacacia L.) in the Czech Republic , 2016 .

[5]  C. Silva,et al.  AUTOMATED INDIVIDUAL TREE DETECTION IN AMAZON TROPICAL FOREST FROM AIRBORNE LASER SCANNING DATA , 2019, CERNE.

[6]  Zhongke Feng,et al.  Applicability of personal laser scanning in forestry inventory , 2019, PloS one.

[7]  Douglas M. Bates,et al.  Unconstrained parametrizations for variance-covariance matrices , 1996, Stat. Comput..

[8]  R. Wolfinger.  Heterogeneous Variance-Covariance Structures for Repeated Measures , 1996 .

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

[10]  S. Phinn,et al.  Measuring plot scale woodland structure using terrestrial laser scanning , 2018 .

[11]  Nicholas C. Coops,et al.  Challenges of Multi-Temporal and Multi-Sensor Forest Growth Analyses in a Highly Disturbed Boreal Mixedwood Forests , 2019, Remote. Sens..

[12]  Florian Zellweger,et al.  From field surveys to LiDAR: Shining a light on how bats respond to forest structure , 2016 .

[13]  Phillip B. Gibbons,et al.  Forest and woodland stand structural complexity: Its definition and measurement , 2005 .

[14]  M. Vanhellemont,et al.  Does neighbourhood tree diversity affect the crown arthropod community in saplings? , 2016, Biodiversity and Conservation.

[15]  Can Vatandaşlar,et al.  Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey , 2020, TURKISH JOURNAL OF AGRICULTURE AND FORESTRY.

[16]  D. Cormier,et al.  Potential of Multi-Temporal Uav-Borne LIDAR in Assessing Effectiveness of Silvicultural Treatments , 2017 .

[17]  Alain F. Zuur,et al.  A protocol for conducting and presenting results of regression‐type analyses , 2016 .

[18]  Brad M. Potts,et al.  From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure , 2020, Remote. Sens..

[19]  Mohammad Sadegh Taskhiri,et al.  Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement , 2020, Remote. Sens..

[20]  Gherardo Chirici,et al.  Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning , 2019, Forests.

[21]  Francesca Bovolo,et al.  A Local Projection-Based Approach to Individual Tree Detection and 3-D Crown Delineation in Multistoried Coniferous Forests Using High-Density Airborne LiDAR Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[22]  R. Noss.  Indicators for Monitoring Biodiversity: A Hierarchical Approach , 1990 .

[23]  Roland Brandl,et al.  Composition versus physiognomy of vegetation as predictors of bird assemblages: the role of lidar. , 2010 .

[24]  N. Puletti,et al.  Evaluating the Eccentricities of Poplar Stem Profiles with Terrestrial Laser Scanning , 2019, Forests.

[25]  Wan Shafrina Wan Mohd Jaafar,et al.  ForestGapR: An r Package for forest gap analysis from canopy height models , 2019, Methods in Ecology and Evolution.

[26]  Gregory P Asner,et al.  Canopy structure drives orangutan habitat selection in disturbed Bornean forests , 2017, Proceedings of the National Academy of Sciences.

[27]  Adam J. Leavesley,et al.  Bird's Response to Revegetation of Different Structure and Floristics—Are “Restoration Plantings” Restoring Bird Communities? , 2011 .

[28]  Emily Williams,et al.  Assessing Handheld Mobile Laser Scanners for Forest Surveys , 2015, Remote. Sens..

[29]  Juha Hyyppä,et al.  An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning , 2012, Remote. Sens..

[30]  Albert I. J. M. van Dijk,et al.  Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification , 2016, Environ. Model. Softw..

[31]  Juha Hyyppä,et al.  Autonomous Collection of Forest Field Reference - The Outlook and a First Step with UAV Laser Scanning , 2017, Remote. Sens..

[32]  B. Potts,et al.  Achievements in forest tree improvement in Australia and New Zealand 9. Genetic improvement of Eucalyptus nitens in Australia , 2008 .

[33]  M. Whitlock,et al.  Assisted Gene Flow to Facilitate Local Adaptation to Climate Change , 2013 .

[34]  Mark C. Vanderwel,et al.  Allometric equations for integrating remote sensing imagery into forest monitoring programmes , 2016, Global change biology.

[35]  Michele Dalponte,et al.  How to map forest structure from aircraft, one tree at a time , 2018, Ecology and evolution.

[36]  E. Næsset,et al.  Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser , 2008 .

[37]  F. Bongers,et al.  Biomass partitioning and root morphology of savanna trees across a water gradient , 2012 .

[38]  T. Wardlaw.  A climate analysis of the current and potential future Eucalyptus nitens and E. globulus plantation estate on Tasmanian State forest , 2011 .

[39]  Mark W. Chynoweth,et al.  Remote sensing for restoration planning: how the big picture can inform stakeholders , 2017 .

[40]  Juha Hyyppä,et al.  Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests , 2020, Remote. Sens..

[41]  R. Corkrey,et al.  Establishment of native Eucalyptus pauciflora and exotic Eucalyptus nitens on former grazing land , 2010, New Forests.

[42]  Michele Dalponte,et al.  Tree‐centric mapping of forest carbon density from airborne laser scanning and hyperspectral data , 2016, Methods in ecology and evolution.

[43]  Michael Battaglia,et al.  Managing productivity and drought risk in Eucalyptus globulus plantations in south-western Australia. , 2009 .

[44]  Scott V. Ollinger,et al.  Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest , 2020, Remote. Sens..

[45]  Anne Chao,et al.  Airborne LiDAR reveals context dependence in the effects of canopy architecture on arthropod diversity , 2014 .

[46]  Tanya G. Bailey,et al.  Embedding genetics experiments in restoration to guide plant choice for a degraded landscape with a changing climate , 2021, Ecological Management & Restoration.

[47]  Richard J. Hobbs,et al.  Advances in restoration ecology: rising to the challenges of the coming decades , 2015 .

[48]  Jonathon J. Valente,et al.  Corridors restore animal-mediated pollination in fragmented tropical forest landscapes , 2016, Proceedings of the Royal Society B: Biological Sciences.

[49]  Sébastien Bauwens,et al.  Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning , 2016 .

[50]  R. Chazdon.  Beyond Deforestation: Restoring Forests and Ecosystem Services on Degraded Lands , 2008, Science.

[51]  S. Prober,et al.  Climate-adjusted provenancing: a strategy for climate-resilient ecological restoration , 2015, Front. Ecol. Evol..

[52]  Neil Davidson,et al.  Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches , 2019, New Forests.

[53]  Owen F Price,et al.  The potential for LiDAR technology to map fire fuel hazard over large areas of Australian forest. , 2016, Journal of environmental management.

[54]  D. Whitehead,et al.  Physiological regulation of productivity and water use in Eucalyptus: a review , 2004 .

[55]  Tanya G. Bailey,et al.  Stability of species and provenance performance when translocated into different community assemblages , 2020, Restoration Ecology.

[56]  Benjamin O. Knapp,et al.  Floristic Quality Index for woodland ground flora restoration: Utility and effectiveness in a fire-managed landscape , 2016 .