Abstract
With the demand for, and scale of, ecological restoration increasing globally, effectiveness monitoring remains a significant challenge. For forest restoration, structural complexity is a recognised indicator of ecosystem biodiversity and in turn a surrogate for restoration effectiveness. Structural complexity captures the diversity in vegetation elements, from tree height to species composition, and the layering of these elements is critical for dependent organisms which rely upon them for their survival. Traditional methods of measuring structural complexity are costly and time-consuming, resulting in a discrepancy between the scales of ‘available’ versus ‘needed’ information. With advancements in both sensors and platforms, there exists an unprecedented opportunity for landscape-level effectiveness monitoring using remote sensing. We here review the key literature on passive (e.g., optical) and active (e.g., LiDAR) sensors and their available platforms (spaceborne to unmanned aerial vehicles) used to capture structural attributes at the tree- and stand-level relevant for effectiveness monitoring. Good cross-validation between remotely sensed and ground truthed data has been shown for many traditional attributes, but remote sensing offers opportunities for assessment of novel or difficult to measure attributes. While there are examples of the application of such technologies in forestry and conservation ecology, there are few reports of remote sensing for monitoring the effectiveness of ecological restoration actions in reversing land degradation. Such monitoring requires baseline data for the restoration site as well as benchmarking the trajectory of remediation against the structural complexity of a reference system.
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References
Acevedo MA (2007) Bird feeding behavior as a measure of restoration success in a Caribbean forest wetland. Ornitol Neotrop 18:305–310
Aerts R, Honnay O (2011) Forest restoration, biodiversity and ecosystem functioning. BMC Ecol 11:29. https://doi.org/10.1186/1472-6785-11-29
Andersen AN, Sparling GP (1997) Ants as indicators of restoration success: relationship with soil microbial biomass in the Australian seasonal tropics. Restor Ecol 5:109–114. https://doi.org/10.1046/j.1526-100X.1997.09713.x
Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11:138–146. https://doi.org/10.1890/120150
Andre F, Jonard M, Lambot S (2014) Full-wave InverSIon of ground-penetrating radar data for forest litter characterization. In: Proceedings of the 2014 15th international conference on ground penetrating radar (GPR 2014), pp 196–201
Atkins JW, Fahey RT, Hardiman BH, Gough CM (2018) Forest canopy structural complexity and light absorption relationships at the subcontinental scale. J Geophys Res Biogeosci 123:1387–1405. https://doi.org/10.1002/2017JG004256
Bailey T, Davidson N, Potts B et al (2013) Plantings for carbon, biodiversity and restoration in dry rural landscapes. Aust For Grow 35:39–41
Blanchard SD, Jakubowski MK, Kelly M (2011) Object-based image analysis of downed logs in disturbed forested landscapes using LiDAR. Remote Sens 3:2420
Block WM, Franklin AB, Ward JP et al (2001) Design and implementation of monitoring studies to evaluate the success of ecological restoration on wildlife. Restor Ecol 9:293–303. https://doi.org/10.1046/j.1526-100x.2001.009003293.x
Camarretta N, Puletti N, Chiavetta U, Corona P (2017) Quantitative changes of forest landscapes over the last century across Italy. Plant Biosyst 3504:1–9. https://doi.org/10.1080/11263504.2017.1407374
Cavada N, Ciolli M, Rocchini D et al (2017) Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates. Ecol Appl 27:235–243. https://doi.org/10.1002/eap.1438
Chazdon RL (2008) Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science (80-) 320:1458–1460. https://doi.org/10.1126/science.1155365
Chianucci F, Disperati L, Guzzi D et al (2016) Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. Int J Appl Earth Obs Geoinf 47:60–68. https://doi.org/10.1016/j.jag.2015.12.005
Chirici G, Bottalico F, Giannetti F et al (2018) Assessing forest windthrow damage using single-date, post-event airborne laser scanning data. Forestry 91:27–37. https://doi.org/10.1093/forestry/cpx029
Christensen M, Hahn K, Mountford EP et al (2005) Dead wood in European beech (Fagus sylvatica) forest reserves. For Ecol Manag 210:267–282. https://doi.org/10.1016/j.foreco.2005.02.032
Cordell S, Questad EJ, Asner GP et al (2017) Remote sensing for restoration planning: how the big picture can inform stakeholders. Restor Ecol 25:S147–S154. https://doi.org/10.1111/rec.12448
d’Oliveira MVN, Reutebuch SE, McGaughey RJ, Andersen H-E (2012) Estimating forest biomass and identifying low-intensity logging areas using airborne scanning LiDAR in Antimary State Forest, Acre State, Western Brazilian Amazon. Remote Sens Environ 124:479–491. https://doi.org/10.1016/j.rse.2012.05.014
Da Ponte E, Mack B, Wohlfart C et al (2017) Assessing forest cover dynamics and forest perception in the Atlantic Forest of Paraguay, combining remote sensing and household level data. Forests 8:1–21. https://doi.org/10.3390/f8100389
Daliakopoulos IN, Grillakis EG, Koutroulis AG, Tsanis IK (2009) Tree crown detection on multispectral VHR satellite imagery. Photogramm Eng Remote Sens 75:1201–1211. https://doi.org/10.14358/PERS.75.10.1201
Dash JP, Watt MS, Pearse GD et al (2017) Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS J Photogramm Remote Sens 131:1–14. https://doi.org/10.1016/j.isprsjprs.2017.07.007
Dickinson Y, Pelz K, Giles E, Howie J (2016) Have we been successful? Monitoring horizontal forest complexity for forest restoration projects. Restor Ecol 24:8–17. https://doi.org/10.1111/rec.12291
Dungey HS, Dash JP, Pont D et al (2018) Phenotyping whole forests will help to track genetic performance. Trends Plant Sci 23:854–864. https://doi.org/10.1016/j.tplants.2018.08.005
Ehbrecht M, Schall P, Ammer C, Seidel D (2017) Quantifying stand structural complexity and its relationship with forest management, tree species diversity and microclimate. Agric For Meteorol 242:1–9. https://doi.org/10.1016/j.agrformet.2017.04.012
El-Sheimy N (2009) Emerging MEMS IMU and its impact on mapping applications. In: Photogrammetric week ’09. Institute for Photogrammetry, Stuttgart, pp 203–216
Ene LT, Næsset E, Gobakken T et al (2016) Large-scale estimation of aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data. Remote Sens Environ 186:626–636. https://doi.org/10.1016/j.rse.2016.09.006
Estes LD, Reillo PR, Mwangi AG et al (2010) Remote sensing of structural complexity indices for habitat and species distribution modeling. Remote Sens Environ 114:792–804. https://doi.org/10.1016/j.rse.2009.11.016
Fankhauser KE, Strigul NS, Gatziolis D (2018) Augmentation of traditional forest inventory and Airborne laser scanning with unmanned aerial systems and photogrammetry for forest monitoring. Remote Sens 10:1–17. https://doi.org/10.3390/rs10101562
FAO (2011) The state of the world’s land and water resources for food and agriculture (SOLAW): managing systems at risk. Food and Agriculture Organization of the United Nations, Rome
Fassnacht FE, Latifi H, Stereńczak K et al (2016) Review of studies on tree species classification from remotely sensed data. Remote Sens Environ 186:64–87. https://doi.org/10.1016/j.rse.2016.08.013
Fava F, Pulighe G, Monteiro AT (2016) Mapping changes in land cover composition and pattern for comparing mediterranean rangeland restoration alternatives. Land Degrad Dev 27:671–681. https://doi.org/10.1002/ldr.2456
Ferraz A, Goncalves G, Soares P et al (2012) Comparing small-footprint lidar and forest inventory data for single strata biomass estimation: a case study over a multi-layered mediterranean forest. In: 2012 IEEE international geoscience and remote sensing symposium. IEEE, pp 6384–6387
Fotis AT, Morin TH, Fahey RT et al (2018) Forest structure in space and time: biotic and abiotic determinants of canopy complexity and their effects on net primary productivity. Agric For Meteorol 250–251:181–191. https://doi.org/10.1016/j.agrformet.2017.12.251
Franklin JF, Spies TA, Van Pelt R et al (2002) Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For Ecol Manag 155:399–423. https://doi.org/10.1016/S0378-1127(01)00575-8
Garabedian JE, McGaughey RJ, Reutebuch SE et al (2014) Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition. Remote Sens Environ 145:68–80. https://doi.org/10.1016/j.rse.2014.01.022
Garden JG, Mcalpine CA, Possingham HP, Jones DN (2007) Habitat structure is more important than vegetation composition for local-level management of native terrestrial reptile and small mammal species living in urban remnants: a case study from Brisbane, Australia. Austral Ecol 32:669–685. https://doi.org/10.1111/j.1442-9993.2007.01750.x
Gatica-Saavedra P, Echeverría C, Nelson CR (2017) Ecological indicators for assessing ecological success of forest restoration: a world review. Restor Ecol 25:850–857. https://doi.org/10.1111/rec.12586
Getzin S, Nuske RS, Wiegand K (2014) Using unmanned aerial vehicles (UAV) to quantify spatial gap patterns in forests. Remote Sens 6:6988–7004. https://doi.org/10.3390/rs6086988
Gibbons P, Freudenberger D (2006) An overview of methods used to assess vegetation condition at the scale of the site. Ecol Manag Restor 7:S10–S17. https://doi.org/10.1111/j.1442-8903.2006.00286.x
Gibbons P, Zerger A, Jones S, Ryan P (2006) Mapping vegetation condition in the context of biodiversity conservation. Ecol Manag Restor 7:S1–S2. https://doi.org/10.1111/j.1442-8903.2006.00282.x
Gibbs HK, Salmon JM (2015) Mapping the world’s degraded lands. Appl Geogr 57:12–21. https://doi.org/10.1016/j.apgeog.2014.11.024
Gleason CJ, Im J (2011) A review of remote sensing of forest biomass and biofuel: options for small scale forests. GIScience Remote Sens 48:141–170
Gleason CJ, Im J (2012) Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sens Environ 125:80–91. https://doi.org/10.1016/j.rse.2012.07.006
Gobakken T, Næsset E, Nelson R et al (2012) Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning. Remote Sens Environ 123:443–456. https://doi.org/10.1016/j.rse.2012.01.025
Gonsamo A, D’Odorico P, Pellikka P (2013) Measuring fractional forest canopy element cover and openness: definitions and methodologies revisited. Oikos 122:1289–1291. https://doi.org/10.1111/j.1600-0706.2013.00369.x
Hansen MC, Potapov PV, Goetz SJ et al (2016) Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sens Environ 185:221–232. https://doi.org/10.1016/j.rse.2016.02.023
Hardiman BS, Bohrer G, Gough CM, Curtis PS (2013) Canopy structural changes following widespread mortality of canopy dominant trees. Forests 4:537–552. https://doi.org/10.3390/f4030537
Harris JA (2003) Measurements of the soil microbial community for estimating the success of restoration. Eur J Soil Sci 54:801–808. https://doi.org/10.1046/j.1351-0754.2003.0559.x
Hobbs RJ, Harris JA (2001) Restoration ecology: repairing the Earth’s ecosystems in the new Millennium. Restor Ecol 9:239–246. https://doi.org/10.1046/j.1526-100x.2001.009002239.x
Houghton RA (2005) Aboveground forest biomass and the global carbon balance. Glob Change Biol 11:945–958. https://doi.org/10.1111/j.1365-2486.2005.00955.x
Hung C, Bryson M, Sukkarieh S (2012) Multi-class predictive template for tree crown detection. ISPRS J Photogramm Remote Sens 68:170–183. https://doi.org/10.1016/j.isprsjprs.2012.01.009
Hunt MAMA, Beadle CLCL, Cherry MLML (1999) Allometric relationships between stem variables and leaf area in planted Eucalyptus nitens and naturally regenerating Acacia dealbata. Tree Physiol 29:289–300
Hyyppä J, Hyyppä H, Inkinen M et al (2000) Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. For Ecol Manag 128:109–120. https://doi.org/10.1016/S0378-1127(99)00278-9
Ingram JC, Dawson TP, Whittaker RJ (2005) Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens Environ 94:491–507. https://doi.org/10.1016/j.rse.2004.12.001
Itakura K, Hosoi F (2018) Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images obtained from ground-based lidar. J Agric Meteorol 74:109–113. https://doi.org/10.2480/agrmet.D-18-00012
Jaakkola A, Hyyppä J, Kukko A et al (2010) A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J Photogramm Remote Sens 65:514–522. https://doi.org/10.1016/j.isprsjprs.2010.08.002
Jaakkola A, Hyyppä J, Yu X et al (2017) Autonomous collection of forest field reference: the outlook and a first step with UAV laser scanning. Remote Sens 9:1–12. https://doi.org/10.3390/rs9080785
Jones ME, Davidson N (2016) Applying an animal-centric approach to improve ecological restoration. Restor Ecol 24:836–842. https://doi.org/10.1111/rec.12447
Jucker T, Caspersen J, Chave J et al (2017) Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Change Biol 23:177–190. https://doi.org/10.1111/gcb.13388
Kamińska A, Lisiewicz M, Stereńczak K et al (2018) Species-related single dead tree detection using multi-temporal ALS data and CIR imagery. Remote Sens Environ 219:31–43. https://doi.org/10.1016/j.rse.2018.10.005
Karl JW, Gillan JK, Barger NN et al (2014) Interpretation of high-resolution imagery for detecting vegetation cover composition change after fuels reduction treatments in woodlands. Ecol Indic 45:570–578. https://doi.org/10.1016/j.ecolind.2014.05.017
Kormann U, Scherber C, Tscharntke T et al (2016) Corridors restore animal-mediated pollination in fragmented tropical forest landscapes. Proc R Soc B Biol Sci 283:20152347. https://doi.org/10.1098/rspb.2015.2347
Kovács B, Tinya F, Ódor P (2017) Stand structural drivers of microclimate in mature temperate mixed forests. Agric For Meteorol 234–235:11–21. https://doi.org/10.1016/j.agrformet.2016.11.268
Kwak D-A, Lee W-K, Lee J-H et al (2007) Detection of individual trees and estimation of tree height using LiDAR data. J For Res 12:425–434. https://doi.org/10.1007/s10310-007-0041-9
Lecigne B, Delagrange S, Messier C (2018) Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Ann Bot 121:589–601. https://doi.org/10.1093/aob/mcx095
Leeuwen M, Nieuwenhuis M (2010) Retrieval of forest structural parameters using LiDAR remote sensing. Eur J For Res 129:749–770. https://doi.org/10.1007/s10342-010-0381-4
Leiterer R, Furrer R, Schaepman ME, Morsdorf F (2015) Forest canopy-structure characterization: a data-driven approach. For Ecol Manage 358:48–61. https://doi.org/10.1016/j.foreco.2015.09.003
Li GQ, Li XB, Zhou T et al (2016) A model for simulating the soil organic carbon pool of steppe ecosystems. Environ Model Assess 21:339–355. https://doi.org/10.1007/s10666-015-9488-9
Lim K, Treitz P, Woods M, Etheridge D (2010) Operationalizing the use of LiDAR in forest resource inventories: what is the optimal point density? In: ASPRS 2010 annual conference
Löf M, Madsen P, Metslaid M et al (2019) Restoring forests: regeneration and ecosystem function for the future. New For 50:139–151. https://doi.org/10.1007/s11056-019-09713-0
Luo S, Wang C, Xi X et al (2017) Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation. Ecol Indic 73:378–387. https://doi.org/10.1016/j.ecolind.2016.10.001
MacArthur RH, MacArthur JW (1961) On bird species diversity. Ecology 42:594–598. https://doi.org/10.2307/1932254
Macfarlane C, Arndt SK, Livesley SJ et al (2007) Estimation of leaf area index in eucalypt forest with vertical foliage, using cover and fullframe fisheye photography. For Ecol Manag 242:756–763. https://doi.org/10.1016/j.foreco.2007.02.021
Maginel CJ, Knapp BO, Kabrick JM et al (2016) Floristic quality index for woodland ground flora restoration: utility and effectiveness in a fire-managed landscape. Ecol Indic 67:58–67. https://doi.org/10.1016/j.ecolind.2016.02.035
Mandelbrot BB (1977) The fractal geometry of nature. W.H. Freeman and Company, New York
Marlene M, Christoph S (2002) Effectiveness monitoring guidelines for ecosystem restoration. Pandion Ecological Research Ltd., Nelson
Marselis SM, Yebra M, Jovanovic T, van Dijk A (2016) Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification. Environ Model Softw 82:142–151. https://doi.org/10.1016/j.envsoft.2016.04.025
Maurer KD, Hardiman BS, Vogel CS, Bohrer G (2013) Canopy-structure effects on surface roughness parameters: observations in a Great Lakes mixed-deciduous forest. Agric For Meteorol 177:24–34. https://doi.org/10.1016/j.agrformet.2013.04.002
McElhinny C, Gibbons P, Brack C, Bauhus J (2005) Forest and woodland stand structural complexity: its definition and measurement. For Ecol Manag 218:1–24. https://doi.org/10.1016/j.foreco.2005.08.034
McElhinny C, Gibbons P, Brack C (2006) An objective and quantitative methodology for constructing an index of stand structural complexity. For Ecol Manag 235:54–71. https://doi.org/10.1016/j.foreco.2006.07.024
Melin M, Hinsley SA, Broughton RK et al (2018) Living on the edge: utilising lidar data to assess the importance of vegetation structure for avian diversity in fragmented woodlands and their edges. Landsc Ecol 33:895–910. https://doi.org/10.1007/s10980-018-0639-7
Muir J, Phinn S, Eyre T, Scarth P (2018) Measuring plot scale woodland structure using terrestrial laser scanning. Remote Sens Ecol Conserv 4:320–338. https://doi.org/10.1002/rse2.82
Næsset E (2007) Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scand J For Res 22:433–442. https://doi.org/10.1080/02827580701672147
Nagendra H, Lucas R, Honrado JP et al (2013) Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol Indic 33:45–59. https://doi.org/10.1016/j.ecolind.2012.09.014
Nave LE, Walters BF, Hofmeister KL et al (2019) The role of reforestation in carbon sequestration. New For 50:115–137. https://doi.org/10.1007/s11056-018-9655-3
Noss RF (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conserv Biol 4:355–364. https://doi.org/10.1111/j.1523-1739.1990.tb00309.x
Nyström M, Holmgren J, Fransson JES, Olsson H (2014) Detection of windthrown trees using airborne laser scanning. Int J Appl Earth Obs Geoinf 30:21–29. https://doi.org/10.1016/j.jag.2014.01.012
Ørka HO, Næsset E, Bollandsås OM (2009) Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens Environ 113:1163–1174. https://doi.org/10.1016/j.rse.2009.02.002
Paris C, Valduga D, Bruzzone L (2016) A hierarchical approach to three-dimensional segmentation of LiDAR data at single-tree level in a multilayered forest. IEEE Trans Geosci Remote Sens 54:4190–4203. https://doi.org/10.1109/TGRS.2016.2538203
Pasher J, King DJ (2009) Mapping dead wood distribution in a temperate hardwood forest using high resolution airborne imagery. For Ecol Manag 258:1536–1548. https://doi.org/10.1016/j.foreco.2009.07.009
Paul KI, Roxburgh SH, Chave J et al (2016) Testing the generality of above-ground biomass allometry across plant functional types at the continent scale. Glob Change Biol 22:2106–2124. https://doi.org/10.1111/gcb.13201
Pearse GD, Dash JP, Persson HJ, Watt MS (2018) Comparison of high-density LiDAR and satellite photogrammetry for forest inventory. ISPRS J Photogramm Remote Sens 142:257–267. https://doi.org/10.1016/j.isprsjprs.2018.06.006
Pereira HM, Ferrier S, Walters M et al (2013) Essential biodiversity variables. Science (80-) 339:277–278. https://doi.org/10.1126/science.1229931
Pérez DR, Pilustrelli C, Farinaccio FM et al (2019) Evaluating success of various restorative interventions through drone- and field-collected data, using six putative framework species in Argentinian Patagonia. Restor Ecol. https://doi.org/10.1111/rec.13025
Perring MP, Standish RJ, Hulvey KB et al (2012) The ridgefield multiple ecosystem services experiment: can restoration of former agricultural land achieve multiple outcomes? Agric Ecosyst Environ 163:14–27. https://doi.org/10.1016/j.agee.2012.02.016
Perring MP, Standish RJ, Price JN et al (2015) Advances in restoration ecology: rising to the challenges of the coming decades. Ecosphere 6:131. https://doi.org/10.1890/ES15-00121.1
Petropoulos GP, Arvanitis K, Sigrimis N (2012) Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping. Expert Syst Appl 39:3800–3809. https://doi.org/10.1016/j.eswa.2011.09.083
Pickett STA, Parker TV (1994) Avoiding the old pitfalls: opportunities in a new discipline. Restor Ecol 2:75–79
Piermattei L, Karel W, Wang D et al (2019) Terrestrial structure from motion photogrammetry for deriving forest inventory data. Remote Sens 11:950
Polewski P, Yao W, Heurich M et al (2015) Detection of fallen trees in ALS point clouds using a normalized cut approach trained by simulation. ISPRS J Photogramm Remote Sens 105:252–271. https://doi.org/10.1016/j.isprsjprs.2015.01.010
Price OF, Gordon CE (2016) The potential for LiDAR technology to map fire fuel hazard over large areas of Australian forest. J Environ Manag 181:663–673. https://doi.org/10.1016/j.jenvman.2016.08.042
Pueschel P, Newnham G, Rock G et al (2013) The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans. ISPRS J Photogramm Remote Sens 77:44–56. https://doi.org/10.1016/j.isprsjprs.2012.12.001
Puletti N, Camarretta N, Corona P (2016) Evaluating EO1-hyperion capability for mapping conifer and broadleaved forests. Eur J Remote Sens 49:157–169. https://doi.org/10.5721/EuJRS20164909
Questad EJ, Kellner JR, Kinney K et al (2014) Mapping habitat suitability for at-risk plant species and its implications for restoration and reintroduction. Ecol Appl 24:385–395. https://doi.org/10.1890/13-0775.1
Rance SJ, Mendham DS, Cameron DM (2017) Assessment of crown woody biomass in Eucalyptus grandis and E. globulus plantations. New For 48:381–396. https://doi.org/10.1007/s11056-016-9563-3
Rapinel S, Clément B, Magnanon S et al (2014) Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image. J Environ Manag 144:236–246. https://doi.org/10.1016/j.jenvman.2014.05.027
Reif MK, Theel HJ (2017) Remote sensing for restoration ecology: application for restoring degraded, damaged, transformed, or destroyed ecosystems. Integr Environ Assess Manag 13:614–630. https://doi.org/10.1002/ieam.1847
Reitberger J, Schnörr C, Krzystek P, Stilla U (2009) 3D segmentation of single trees exploiting full waveform LiDAR data. ISPRS J Photogramm Remote Sens 64:561–574. https://doi.org/10.1016/j.isprsjprs.2009.04.002
Ren Y, Lü Y, Fu B, Zhang K (2017) Biodiversity and ecosystem functional enhancement by forest restoration: a meta-analysis in China. Land Degrad Dev 28:2062–2073. https://doi.org/10.1002/ldr.2728
Rich PM (1990) Characterizing plant canopies with hemispherical photographs. Remote Sens Rev 5:13–29. https://doi.org/10.1080/02757259009532119
Rose RA, Byler D, Eastman JR et al (2015) Ten ways remote sensing can contribute to conservation. Conserv Biol 29:350–359. https://doi.org/10.1111/cobi.12397
Ruiz-Jaén MC, Aide TM (2005) Vegetation structure, species diversity, and ecosystem processes as measures of restoration success. For Ecol Manag 218:159–173. https://doi.org/10.1016/j.foreco.2005.07.008
Sankey TT, McVay J, Swetnam TL et al (2018) UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sens Ecol Conserv 4:20–33. https://doi.org/10.1002/rse2.44
Sasaki T, Ishii H, Morimoto Y (2018) Evaluating restoration success of a 40-year-old urban forest in reference to mature natural forest. Urban For Urban Green 32:123–132. https://doi.org/10.1016/j.ufug.2018.04.008
Schiegg K (2000) Effects of dead wood volume and connectivity on saproxylic insect species diversity. Écoscience 7:290–298. https://doi.org/10.1080/11956860.2000.11682598
Schmeller DS, Weatherdon LV, Loyau A et al (2018) A suite of essential biodiversity variables for detecting critical biodiversity change. Biol Rev 93:55–71. https://doi.org/10.1111/brv.12332
Schweiger AK, Cavender-Bares J, Townsend PA et al (2018) Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat Ecol Evol 2:976–982. https://doi.org/10.1038/s41559-018-0551-1
Seidel D (2018) A holistic approach to determine tree structural complexity based on laser scanning data and fractal analysis. Ecol Evol 8:128–134. https://doi.org/10.1002/ece3.3661
Seidel D, Ehbrecht M, Annighöfer P, Ammer C (2019) From tree to stand-level structural complexity: which properties make a forest stand complex? Agric For Meteorol 278:107699. https://doi.org/10.1016/j.agrformet.2019.107699
SER (2004) The SER international primer on ecological restoration. Version 2. https://www.ser.org/resources/resources-detail-view/ser-internationalprimer-on-ecological-restoration. Accessed 4 July 2016
Shang X, Chisholm LA (2014) Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2481–2489. https://doi.org/10.1109/JSTARS.2013.2282166
Snavely N, Seitz SM, Szeliski R (2008) Modeling the world from Internet photo collections. Int J Comput Vis 80:189–210. https://doi.org/10.1007/s11263-007-0107-3
Sousa AMO, Gonçalves AC, Mesquita P, Marques da Silva JR (2015) Biomass estimation with high resolution satellite images: a case study of Quercus rotundifolia. ISPRS J Photogramm Remote Sens 101:69–79. https://doi.org/10.1016/J.ISPRSJPRS.2014.12.004
Sukma HT, Di Stefano J, Swan M, Sitters H (2019) Mammal functional diversity increases with vegetation structural complexity in two forest types. For Ecol Manag 433:85–92. https://doi.org/10.1016/j.foreco.2018.10.035
Sverdrup-Thygeson A, Ørka HO, Gobakken T, Næsset E (2016) Can airborne laser scanning assist in mapping and monitoring natural forests? For Ecol Manage 369:116–125. https://doi.org/10.1016/j.foreco.2016.03.035
Thomson ER, Malhi Y, Bartholomeus H et al (2018) Mapping the leaf economic spectrum across West African tropical forests using UAV-Acquired hyperspectral imagery. Remote Sens. https://doi.org/10.3390/rs10101532
Torontow V, King D (2011) Forest complexity modelling and mapping with remote sensing and topographic data: a comparison of three methods. Can J Remote Sens 37:387–402
Toth C, Jóźków G (2016) Remote sensing platforms and sensors: a survey. ISPRS J Photogramm Remote Sens 115:22–36. https://doi.org/10.1016/j.isprsjprs.2015.10.004
Vepakomma U, Cormier D (2017) Potential of multi-temporal UAV-borne lidar in assessing effectiveness of silvicultural treatments. Int Arch Photogramm Remote Sens Spat Inf Sci: ISPRS Arch 42:393–397. https://doi.org/10.5194/isprs-archives-XLII-2-W6-393-2017
Verdone M, Seidl A (2017) Time, space, place, and the Bonn Challenge global forest restoration target. Restor Ecol 25:903–911. https://doi.org/10.1111/rec.12512
Wallace L, Musk R, Lucieer A (2014) An assessment of the repeatability of automatic forest inventory metrics derived from UAV-borne laser scanning data. IEEE Trans Geosci Remote Sens 52:7160–7169. https://doi.org/10.1109/TGRS.2014.2308208
Wallace L, Lucieer A, Malenovský Z et al (2016) Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forest. https://doi.org/10.3390/f7030062
Wang X, Huang H, Gong P et al (2016) Quantifying multi-decadal change of planted forest cover using airborne LiDAR and Landsat imagery. Remote Sens. https://doi.org/10.3390/rs8010062
Wang Y, Lehtomäki M, Liang X et al (2019) Is field-measured tree height as reliable as believed: a comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest. ISPRS J Photogramm Remote Sens 147:132–145. https://doi.org/10.1016/j.isprsjprs.2018.11.008
Wilkins S, Keith DA, Adam P (2003) Measuring success: evaluating the restoration of a grassy eucalypt woodland on the Cumberland plain, Sydney, Australia. Restor Ecol 11:489–503. https://doi.org/10.1046/j.1526-100X.2003.rec0244.x
Wood EM, Pidgeon AM, Radeloff VC, Keuler NS (2012) Image texture as a remotely sensed measure of vegetation structure. Remote Sens Environ 121:516–526. https://doi.org/10.1016/j.rse.2012.01.003
Wu B, Yu B, Wu Q et al (2016) Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests. Int J Appl Earth Obs Geoinf 52:82–94. https://doi.org/10.1016/j.jag.2016.06.003
Zahawi RA, Dandois JP, Holl KD et al (2015) Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biol Conserv 186:287–295. https://doi.org/10.1016/j.biocon.2015.03.031
Zenner EK, Hibbs DE (2000) A new method for modeling the heterogeneity of forest structure. For Ecol Manag 129:75–87. https://doi.org/10.1016/S0378-1127(99)00140-1
Zhang D (2019) Costs of delayed reforestation and failure to reforest. New For 50:57–70. https://doi.org/10.1007/s11056-018-9676-y
Zhao K, Popescu S, Nelson R (2009) LiDAR remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sens Environ 113:182–196. https://doi.org/10.1016/j.rse.2008.09.009
Zolkos SG, Goetz SJ, Dubayah R (2013) A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens Environ 128:289–298. https://doi.org/10.1016/j.rse.2012.10.017
Acknowledgements
Nicolò Camarretta was supported by a scholarship from the ARC Industrial Transformation Training Centre for Forest Value, which is funded by the Australian Research Council’s Industrial Transformation Research Program (Project Number IC150100004).
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NC conceived and designed the review; NC, PAH, TB, MH, BP, AL, ND wrote and edited the manuscript.
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Camarretta, N., Harrison, P.A., Bailey, T. et al. Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches. New Forests 51, 573–596 (2020). https://doi.org/10.1007/s11056-019-09754-5
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DOI: https://doi.org/10.1007/s11056-019-09754-5