Skip to main content

Advertisement

Log in

Mapping Vernal Pools Using LiDAR Data and Multitemporal Satellite Imagery

  • Wetlands Conservation
  • Published:
Wetlands Aims and scope Submit manuscript

Abstract

Seasonal depressional wetlands, or vernal pools, offer critical breeding habitats for diverse species (primarily amphibians and invertebrates) in northeastern America. Their seasonal aspect contributes to their uniqueness. Degradation and loss of these habitats are attributed to human activities such as urban development and some forestry activities. Thus, it is important to map vernal pools to plan conservation measures. We studied topographic depressions derived from airborne LiDAR Digital Elevation Models (3 m) combined with optical multitemporal satellite imagery provided by Pléiades (50 cm) to detect depressional wetlands with the objective of discriminating vernal pools from other small wetlands. We first set a hierarchical identification approach with five criteria to plan the field campaigns, but after using statistical analysis, we were able to reduce these to only one criterion, the temporal difference (May to September) of the Normalized Difference Water Index (NDWI). Our results suggest that vernal pool occurrence is highly correlated with the temporal difference of NDWI. The higher the difference, the higher the observed occurrence. Considering 81 field-validated vernal pools, the user’s accuracy reached 83% and the producer’s accuracy, 59%. Our study presents a simple approach to map vernal pools on large territories while considering their temporary status.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

Not applicable.

References

  • Amani M, Brisco B (2018) Spectral analysis of wetlands using multi-source optical satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 144:119–136. https://doi.org/10.1016/j.isprsjprs.2018.07.005

    Article  Google Scholar 

  • Barnes R, Callaghan KL, Wickert AD (2019) Computing water flow through complex landscapes, part 2: finding hierarchies in depressions and morphological segmentations. Earth Surface Dynamics Discussions 2019:1–19. https://doi.org/10.5194/esurf-2019-34

    Article  Google Scholar 

  • Bertacchi W, Maisonneuve C (2011) Les étangs vernaux et l’aménagement durable des forêts, application concrète d’une approche écosystémique. Ministère des Ressources Naturelles et de la Faune, 7 p

  • Berven KA (1990) Factors affecting population fluctuations in larval and adult stages of the wood frog (Rana sylvatica). Ecology 71:1599–1608. https://doi.org/10.2307/1938295

    Article  Google Scholar 

  • Bourgeau-Chavez LL, Lee MY, Battaglia M et al (2016) Identification of woodland vernal pools with seasonal change PALSAR data for habitat conservation. Remote Sensing 8:490. https://doi.org/10.3390/rs8060490

    Article  Google Scholar 

  • Bournival P, Fink J (2017) Critère de sélection et mesures de protection pour les étangs vernaux. Centre d’enseignement et de recherche en foresterie de Sainte-Foy inc. (CERFO), Technical report 2017–01, 27 p

  • Bournival P, Lessard G, Blouin D, Khaldoune J (2013) Validation de l’ouverture du couvert après une coupe progressive irrégulière (UAF 071-51, secteur Cloak). Centre d’enseignement et de recherche en foresterie de Sainte-Foy inc. (CERFO), Technical report 2013-23, 50 p

  • Bournival P, Varin M, Fink J (2017) Validation d’une méthode semi- automatisée de détection des milieux humides à partir du lidar aéroporté. Centre d’enseignement et de recherche en foresterie de Sainte-Foy inc. (CERFO). Technical report 2017-01, 44 p

  • Brooks RT (2004) Weather-related effects on woodland vernal pool hydrology and hydroperiod. Wetlands 24:104–114. https://doi.org/10.1672/0277-5212(2004)024[0104:WEOWVP]2.0.CO;2

    Article  Google Scholar 

  • Brooks RT, Hayashi M (2002) Depth-area-volume and hydroperiod relationships of ephemeral (vernal) forest pools in southern New England. The Society of Wetland Scientists 22:247–255. https://doi.org/10.1672/0277-5212(2002)022[0247:DAVAHR]2.0.CO;2

    Article  Google Scholar 

  • Calhoun AJK, de Maynadier P (2017) Forestry habitat management guidelines for vernal Pool wildlife. MCA technical paper no. 6, metropolitan conservation Alliance, Wildlife Conservation Society, Bronx, New York, 38 p

  • Calhoun AJK, Miller NA, Klemens MW (2005) Conserving pool-breeding amphibians in human-dominated landscapes through local implementation of best development practices. Wetlands Ecology and Management 13:291–304. https://doi.org/10.1007/s11273-004-7523-8

    Article  Google Scholar 

  • Calhoun AJK, Arrigoni J, Brooks RP, Hunter ML, Richter SC (2014) Creating successful vernal pools: a literature review and advice for practitioners. Wetlands 34:1027–1038. https://doi.org/10.1007/s13157-014-0556-8

    Article  Google Scholar 

  • Calhoun AJK, Mushet DM, Bell KP, Boix D, Fitzsimons JA, Isselin-Nondedeu F (2017) Temporary wetlands: challenges and solutions to conserving a “disappearing” ecosystem. Biological Conservation 211:3–11. https://doi.org/10.1016/j.biocon.2016.11.024

    Article  Google Scholar 

  • Catanzaro P, Fish J, Kittredge D (2013) Massachussetts forestry best management practices manual. University of Massachusetts, Massassuchetts department of conservation and recreation service. Forestry Program, Second Edition, 52 p

  • Chust G, Ducrot D, Pretus JL (2004) Land cover mapping with patch-derived landscape indices. Landscape and Urban Planning 69:437–449. https://doi.org/10.1016/j.landurbplan.2003.12.002

    Article  Google Scholar 

  • Colburn EA (2004) Vernal pools: natural history and conservation. The McDonald and Woodward Publishing Company, 426 p

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B

  • Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for automated Geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development 8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015

    Article  Google Scholar 

  • de Maynadier P (2011) Vernal Pools: Milestones and Misconceptions. Maine Department of Inland Fisheries and Wildlife. Special Legislative Edition of the IF&W Insider February 2011. Augusta, ME 04333

  • de Maynadier P, Hunter ML Jr (1995) The relationship between forest management and amphibian ecology: a review of the north American literature. Environmental Reviews 3:230–261. https://doi.org/10.1139/a95-012

    Article  Google Scholar 

  • Doctor D, Young J (2017) An evaluation of automated GIS tools for delineating karst sinkholes and closed depressions from 1-meter LiDAR-derived digital elevation data. 449–458. https://doi.org/10.5038/9780979542275.1156

  • Dronova I (2015) Object-based image analysis in wetland research: a review. Remote Sensing 7:6380–6413. https://doi.org/10.3390/rs70506380

    Article  Google Scholar 

  • Faccio Lewe-Smith, M. et Worthley, A. SD (2012) Vermont vernal Pool mapping project 2009–2012. Vermont center for ecostudies, Final Report to the Natural Heritage Information Project of the Vermont Department of Fish and Wildlife, 40 p

  • Fournier RA, Grenier M, Lavoie A, Helie R (2007) Towards a strategy to implement the Canadian wetland inventory using satellite remote sensing. Canadian Journal of Remote Sensing 33:S1–S16. https://doi.org/10.5589/m07-051

    Article  Google Scholar 

  • Franklin S, Ahmed O (2017) Object-based Wetland Characterization Using Radarsat-2 Quad-Polarimetric SAR Data, Landsat-8 OLI Imagery, and Airborne Lidar-Derived Geomorphometric Variables. Photogrammetric Engineering & Remote Sensing 83:27–36. https://doi.org/10.14358/pers.83.1.27

    Article  Google Scholar 

  • Gabrielsen CG, Murphy MA, Evans JS (2016) Using a multiscale, probabilistic approach to identify spatial-temporal wetland gradients. Remote Sensing of Environment 184:522–538. https://doi.org/10.1016/j.rse.2016.07.034

    Article  Google Scholar 

  • Gamble LR, McGarigal K, Compton BW (2007) Fidelity and dispersal in the pond-breeding amphibian, Ambystoma opacum: implications for spatio-temporal population dynamics and conservation. Biological Conservation 139:247–257. https://doi.org/10.1016/j.biocon.2007.07.001

    Article  Google Scholar 

  • Gao P, Trettin CC, Ghoshal S (2012) Object-oriented segmentation and classification of wetlands within the Khalong-la-Lithuny a catchment, Lesotho, Africa. Proceedings - 2012 20th International Conference on Geoinformatics, Geoinformatics 2012 1–6. https://doi.org/10.1109/Geoinformatics.2012.6270319

  • Goulden T, Hopkinson C, Jamieson R, Sterling S (2014) Sensitivity of watershed attributes to spatial resolution and interpolation method of LiDAR DEMs in three distinct landscapes. Journal of the American Water Resources Association 50:1908–1927. https://doi.org/10.1111/j.1752-1688.1969.tb04897.x

    Article  Google Scholar 

  • Halabisky M, Babcock C, Moskal LM (2018) Harnessing the temporal dimension to improve object-based image analysis classification of wetlands. Remote Sensing 10. https://doi.org/10.3390/rs10091467

  • Hidayat S, Matsuoka M, Baja S, Rampisela DA (2018) Object-based image analysis for sago palm classification: the most important features from high-resolution satellite imagery. Remote Sensing 10. https://doi.org/10.3390/RS10081319

  • Higginbottom TP, Field CD, Symeonakis E et al (2018) High-resolution wetness index mapping: a useful tool for regional scale wetland management. Ecological Informatics 48:89–96. https://doi.org/10.1016/j.ecoinf.2018.08.003

    Article  Google Scholar 

  • Hjerdt KN, McDonnell JJ, Seibert J, Rodhe A (2004) A new topographic index to quantify downslope controls on local drainage. Water Resources Research 40:1–6. https://doi.org/10.1029/2004WR003130

    Article  Google Scholar 

  • Hogg AR, Holland J (2008) An evaluation of DEMs derived from LiDAR and photogrammetry for wetland mapping. The Forestry Chronicle 84:840–849. https://doi.org/10.5558/tfc84840-6

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S, (Sons JW&, Service) WI (Online (2000) Applied logistic regression, 2nd ed. New York: Wiley

  • Hossain MD, Chen D (2019) Segmentation for object-based image analysis (OBIA): a review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing 150:115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009

    Article  Google Scholar 

  • Immitzer M, Atzberger C, Koukal T (2012) Tree species classification with random forest using very high spatial resolution 8-band worldView-2 satellite data. Remote Sensing 4:2661–2693. https://doi.org/10.3390/rs4092661

    Article  Google Scholar 

  • Jahncke R, Leblon B, Bush P, LaRocque A (2018) Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data. International Journal of Applied Earth Observation and Geoinformation 68:139–156. https://doi.org/10.1016/j.jag.2018.01.012

    Article  Google Scholar 

  • Jenson SK, Domingue JO (1988) Extracting topographic structure from digital elevation data for geographic information system analysis. Photogrammetric Engineering and Remote Sensing 54:1593–1600

    Google Scholar 

  • Julian JT, Young JA, Jones JW, Snyder CD, Wright CW (2009) The use of local indicators of spatial association to improve LiDAR-derived predictions of potential amphibian breeding ponds. Journal of Geographical Systems 11:89–106. https://doi.org/10.1007/s10109-008-0074-4

    Article  Google Scholar 

  • Kaplan G, Avdan U (2019) Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification. Catena 178:109–119. https://doi.org/10.1016/j.catena.2019.03.011

    Article  Google Scholar 

  • Karlson M, Gålfalk M, Crill P, Bousquet P, Saunois M, Bastviken D (2019) Remote sensing of environment delineating northern peatlands using Sentinel-1 time series and terrain indices from local and regional digital elevation models. Remote Sensing of Environment 231:111252. https://doi.org/10.1016/j.rse.2019.111252

    Article  Google Scholar 

  • Kirby J, Beaulieu J (2006) Rapport méthodologique de la cartographie des milieux humides du territoire de la Communauté métropolitaine de Québec. Canards illimités Canada, 40 p

  • Lang MW, McCarty GW (2009) Lidar intensity for improved detection of inundation below the forest canopy. Wetlands 29:1166–1178. https://doi.org/10.1672/08-197.1

    Article  Google Scholar 

  • Lathrop RG, Montesano P, Tesauro J, Zarate B (2005) Statewide mapping and assessment of vernal pools: a New Jersey case study. Journal of Environmental Management 76:230–238. https://doi.org/10.1016/j.jenvman.2005.02.006

    Article  PubMed  Google Scholar 

  • Leonard PB, Baldwin RF, Homyack JA, Wigley TB (2012) Remote detection of small wetlands in the Atlantic coastal plain of North America: local relief models, ground validation, and high-throughput computing. Forest Ecology and Management 284:107–115. https://doi.org/10.1016/j.foreco.2012.07.034

    Article  Google Scholar 

  • Li S, MacMillan RA, Lobb DA et al (2011) Lidar DEM error analyses and topographic depression identification in a hummocky landscape in the prairie region of Canada. Geomorphology 129:263–275. https://doi.org/10.1016/j.geomorph.2011.02.020

    Article  Google Scholar 

  • Lichvar RW, Finnegan DC, Newman S, Ochs W (2006) Delineating and evaluating vegetation conditions of vernal pools using spaceborne and airborne remote sensing techniaues. Cold Regions Research and Engineering Laboratory, U.S. Army Engineer Research and Development Center, 25 p

  • Lin C, Wu C-C, Tsogt K, Ouyang YC, Chang CI (2015) Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery. Information Processing in Agriculture 2:25–36. https://doi.org/10.1016/j.inpa.2015.01.003

    Article  Google Scholar 

  • Lindsay JB (2016) Efficient hybrid breaching-filling sink removal methods for flow path enforcement in digital elevation models. Hydrological Processes 30:846–857. https://doi.org/10.1002/hyp.10648

    Article  Google Scholar 

  • Lindsay JB, Dhun K (2015) Modelling surface drainage patterns in altered landscapes using LiDAR. International Journal of Geographical Information Science 29:397–411. https://doi.org/10.1080/13658816.2014.975715

    Article  Google Scholar 

  • Liu T, Abd-Elrahman A, Morton J, Wilhelm VL (2018) Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience & Remote Sensing 55:243–264. https://doi.org/10.1080/15481603.2018.1426091

    Article  Google Scholar 

  • Ludwig C, Walli A, Schleicher C, Weichselbaum J, Riffler M (2019) A highly automated algorithm for wetland detection using multi-temporal optical satellite data. Remote Sensing of Environment 224:333–351. https://doi.org/10.1016/j.rse.2019.01.017

    Article  Google Scholar 

  • Machala M, Zejdová L (2014) Forest mapping through object-based image analysis of multispectral and LiDAR aerial data. European Journal of Remote Sensing 47:117–131. https://doi.org/10.5721/EuJRS20144708

    Article  Google Scholar 

  • Mahdavi S, Salehi B, Granger J, Amani M, Brisco B, Huang W (2018) Remote sensing for wetland classification: a comprehensive review. GIScience and Remote Sensing 55:623–658. https://doi.org/10.1080/15481603.2017.1419602

    Article  Google Scholar 

  • Mahdianpari M, Salehi B, Mohammadimanesh F, Motagh M (2017) Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS Journal of Photogrammetry and Remote Sensing 130:13–31. https://doi.org/10.1016/j.isprsjprs.2017.05.010

    Article  Google Scholar 

  • Mahdianpari M, Salehi B, Mohammadimanesh F, Homayouni S, Gill E (2019) The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the Google earth engine cloud computing platform. Remote Sensing 11:1–27. https://doi.org/10.3390/rs11010043

    Article  Google Scholar 

  • Maine Forest Service (2006) Forest Management and Vernal Pools. Information Sheet #15, Department of Agriculture, Conservation & Forestry, Department of Inland Fisheries and Wildlife, Augusta, ME, 2 p

  • Maltamo M, Vauhkonen J, Næsset E (2014) Forestry applications of airborne laser scanning - concepts and case studies. Managing Forest Ecosystems, 464 p. https://doi.org/10.1007/978-94-017-8663-8

  • Marchand M (2016) Identifying and documenting vernal pools in New Hampshire. New Hampshire Fish and Game Department. Nongame and Endangered Wildlife Program. Third edition, 87 p

  • Maxa M, Bolstad P (2009) Mapping northern wetlands with high resolution satellite images and Lidar. Wetlands 29:248–260. https://doi.org/10.1672/08-91.1

    Article  Google Scholar 

  • McCarthy MJ, Radabaugh KR, Moyer RP, Muller-Karger FE (2018) Enabling efficient, large-scale high-spatial resolution wetland mapping using satellites. Remote Sensing of Environment 208:189–201. https://doi.org/10.1016/j.rse.2018.02.021

    Article  Google Scholar 

  • McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17:1425–1432. https://doi.org/10.1080/01431169608948714

    Article  Google Scholar 

  • McGreavy B, Webler T, Calhoun AJK (2012) Science communication and vernal pool conservation: a study of local decision maker attitudes in a knowledge-action system. Journal of Environmental Management 95:1–8. https://doi.org/10.1016/j.jenvman.2011.09.020

    Article  PubMed  Google Scholar 

  • Mohammadimanesh F, Salehi B, Mahdianpari M, Brisco B, Motagh M (2018) Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands. ISPRS Journal of Photogrammetry and Remote Sensing 142:78–93. https://doi.org/10.1016/j.isprsjprs.2018.05.009

    Article  Google Scholar 

  • Montgomery J, Brisco B, Chasmer L, Devito K, Cobbaert D, Hopkinson C (2019) SAR and lidar temporal data fusion approaches to boreal wetland ecosystem monitoring. Remote Sensing 11. https://doi.org/10.3390/rs11020161

  • Mui A, He Y, Weng Q (2015) An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 109:30–46. https://doi.org/10.1016/j.isprsjprs.2015.08.005

    Article  Google Scholar 

  • Palik B, Streblow D, Egeland L, Buech R (2007) Landscape variation of seasonal pool plant communities in forests of northern Minnesota, USA. Wetlands 27:12–23. https://doi.org/10.1672/0277-5212(2007)27[12:LVOSPP]2.0.CO;2

    Article  Google Scholar 

  • Pashaei M, Kamangir H, Starek MJ, Tissot P (2020) Review and evaluation of deep learning architectures for efficient land cover mapping with UAS hyper-spatial imagery: a case study over a wetland. Remote Sensing 12. https://doi.org/10.3390/rs12060959

  • Planchon O, Darboux F (2002) A fast, simple and versatile algorithm to fill the depressions of digital elevation models. CATENA 46:159–176. https://doi.org/10.1016/S0341-8162(01)00164-3

    Article  Google Scholar 

  • Poppenga SK, Worstell BB, Stoker JM, et al (2010) Using selective drainage methods to extract continuous surface flow from 1-meter Lidar-derived digital elevation data. Scientific Investigations Report, 12 p. https://doi.org/10.3133/sir20105059

  • Preisser EL, Kefer JY, Lawrence JD, Clark TW (2000) Vernal Pool conservation in Connecticut: an assessment and recommendations. Environmental Management 26:503–513. https://doi.org/10.1007/s002670010108

    Article  CAS  PubMed  Google Scholar 

  • Previant WJ, Nagel LM (2014) Forest diversity and structure surrounding vernal pools in pictured rocks National Lakeshore, Michigan, USA. Wetlands 34:1073–1083. https://doi.org/10.1007/s13157-014-0567-5

    Article  Google Scholar 

  • Reif M, Frohn RC, Lane CR, Autrey B (2009) Mapping isolated wetlands in a karst landscape: GIS and remote sensing methods. GIScience & Remote Sensing 46:187–211. https://doi.org/10.2747/1548-1603.46.2.187

    Article  Google Scholar 

  • Riley JW, Calhoun DL, Barichivich WJ, Walls SC (2017) Identifying small Depressional wetlands and using a topographic position index to infer Hydroperiod regimes for pond-breeding amphibians. Wetlands 37:325–338. https://doi.org/10.1007/s13157-016-0872-2

    Article  Google Scholar 

  • Roux M (2019) Dynamique hydrique de milieux humides temporaires dans la forêt du Québec méridional. Master thesis. Université du Québec à Montréal, Québec, Canada, 154 p

  • Roy M-È, Nolet P (2013) Suivi de dispositif de récolte de la biomasse forestière sur la biodiversité animale et végétale. Institut des Sciences de la Forêt tempérée. Ripon, Québec. Technical report. 44 p

  • Saint-Germain M, Drapeau P, Hebert C (2004) Landscape-scale habitat selection patterns of Monochamus scutellatus (Coleoptera: Cerambycidae) in a recently burned black spruce forest. Environmental Entomology 33:1703–1710

    Article  Google Scholar 

  • SAS Institute Inc (2013) SAS-STAT User’s Guide: Release 9.4 Edition. SAS Institute Inc, Cary

    Google Scholar 

  • Scheffers BR, Furman BLS, Evans JP (2013) SalamanderS Continue to Breed in ephemeral pondS Following the removal oF Surrounding terreStrial haBitat. Herpetological Conservation and Biology 8(3):715−723

  • Schwanghart W, Scherler D (2017) Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques. Earth Surface Dynamics 5:821–839. https://doi.org/10.5194/esurf-5-821-2017

    Article  Google Scholar 

  • Semlitsch RD, Bodie JR (2003) Biological criteria for buffer zones around wetlands and riparian habitats for amphibians and ReptilesCriterios Biológicos Para Zonas de Amortiguamiento Alrededor de Hábitats de Humedales y Riparios Para Anfibios y reptiles. Conservation Biology 17:1219–1228. https://doi.org/10.1046/j.1523-1739.2003.02177.x

    Article  Google Scholar 

  • Snyder GI, Lang M (2012) Significance of a 3D elevation program to wetland mapping. National Wetlands Newsletter 34:11–15

    Google Scholar 

  • Stuart SN, Chanson JS, Cox NA, Young BE, Rodrigues ASL, Fischman DL, Waller RW (2004) Status and trends in Science 1783–1786. https://doi.org/10.1126/science.1103538

  • Swartz TM, Stuart E, Foster DK, Lindquist ED (2016) Testing rapid-assessment models for the conservation of woodland vernal pools in south-Central Pennsylvania. Northeastern Naturalist 23:339–351

    Article  Google Scholar 

  • Tarboton DG, Bras RL, Rodriguez-Iturbe I (1991) Tarboton_et_al-1991-Hydrological_Processes. Hydrological Processes 5:81–100. https://doi.org/10.1002/hyp.3360050107

    Article  Google Scholar 

  • The Pennsylvania Natural Heritage Program (2015) Vernal pool conservation and management: a landowner’s guide to vernal pool stewardship. Western Pennsylvania Conservancy, Harrisburg, PA, 21 p

  • Townshend JRG, Justice CO (1986) Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing 7:1435–1445. https://doi.org/10.1080/01431168608948946

    Article  Google Scholar 

  • Töyrä J, Pietroniro A (2005) Towards operational monitoring of a northern wetland using geomatics-based techniques. Remote Sensing of Environment 97:174–191. https://doi.org/10.1016/j.rse.2005.03.012

    Article  Google Scholar 

  • Unger Holtz TS (2007) Introductory digital image processing: a remote sensing perspective, third edition, 3nd edn. Environmental and Engineering Geoscience, 656 p. https://doi.org/10.2113/gseegeosci.13.1.89, 13, 89, 90

  • Valtera M, Schaetzl RJ (2017) Pit-mound microrelief in forest soils: review of implications for water retention and hydrologic modelling. Forest Ecology and Management 393:40–51. https://doi.org/10.1016/j.foreco.2017.02.048

    Article  Google Scholar 

  • Vanderhoof MK, Distler HE, Mendiola DATG, Lang M (2017) Integrating Radarsat-2, Lidar, and Worldview-3 imagery to maximize detection of forested inundation extent in the Delmarva Peninsula, USA. Remote Sensing 9:1–26. https://doi.org/10.3390/rs9020105

    Article  Google Scholar 

  • Varin M, Bournival P, Duclos I, Fink J (2014) Identification d’étangs vernaux à l’aide du LiDAR et de la photo-interprétation. Centre d’enseignement et de recherche en foresterie de Sainte-Foy inc. (CERFO), Technical report 2014-02, 18 p

  • Varin M, Bournival P, Dupuis M, Fink J (2016) Développement d’une méthode de cartographie d’étangs vernaux à l’aide du lidar et d’images multispectrales. Centre d’enseignement et de recherche en foresterie de Sainte-Foy inc. (CERFO), Technical report 2016-12, 35 p

  • Varin M, Bournival P, Blanchot C, Boulfroy E (2017) Identification du réseau hydrologique potentiel à partir du lidar aéroporté. Centre d’enseignement et de recherche en foresterie de Sainte-Foy inc. (CERFO), Technical note 2017-02, 9 p

  • Varin M, Théau J, Fournier RA (2019) Mapping ecosystem services provided by wetlands at multiple spatiotemporal scales: a case study in Quebec, Canada. Journal of Environmental Management 246:334–344. https://doi.org/10.1016/j.jenvman.2019.05.115

    Article  PubMed  Google Scholar 

  • Varin M, Chalghaf B, Joanisse G (2020) Object-based approach using very high spatial resolution 16-band WorldView-3 and LiDAR data for tree species classification in a broadleaf Forest in Quebec, Canada. Remote Sensing. https://doi.org/10.3390/rs12183092

  • Wake D (1991) Declining amphibian populations. Science 253:860

    Article  CAS  Google Scholar 

  • Wake DB, Vredenburg VT (2008) Are we in the midst of the sixth mass extinction? A view from the world of the amphibians. Proceedings of the National Academy of Sciences 105:11466–11473. https://doi.org/10.1073/pnas.0801921105

    Article  Google Scholar 

  • Wang L, Liu H (2006) An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science 20:193–213. https://doi.org/10.1080/13658810500433453

    Article  CAS  Google Scholar 

  • Waser LT, Küchler M, Jütte K, Stampfer T (2014) Evaluating the potential of worldview-2 data to classify tree species and different levels of ash mortality. Remote Sensing 6:4515–4545. https://doi.org/10.3390/rs6054515

    Article  Google Scholar 

  • Weih RC, Riggan ND, El-Hattab MM (2016) Object-based classification vs. pixel-based classification: comparative importance of multi-resolution imagery. Egyptian Journal of Remote Sensing and Space Science 19:1–6. https://doi.org/10.1016/j.ejrs.2016.02.002

    Article  Google Scholar 

  • Whyte A, Ferentinos KP, Petropoulos GP (2018) A new synergistic approach for monitoring wetlands using sentinels −1 and 2 data with object-based machine learning algorithms. Environmental Modelling and Software 104:40–54. https://doi.org/10.1016/j.envsoft.2018.01.023

    Article  Google Scholar 

  • Woodrow K, Lindsay JB, Berg AA (2016) Evaluating DEM conditioning techniques, elevation source data, and grid resolution for field-scale hydrological parameter extraction. Journal of Hydrology 540:1022–1029. https://doi.org/10.1016/j.jhydrol.2016.07.018

    Article  Google Scholar 

  • Wu Q, Lane C, Liu H (2014) An effective method for detecting potential woodland vernal pools using high-resolution LiDAR data and aerial imagery. Remote Sensing 6:11444–11467

    Article  Google Scholar 

  • Wu Q, Lane CR (2017) Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. Hydrol Earth Syst Sci 21(7):3579–3595. https://doi.org/10.5194/hess-21-3579-2017

  • Wu Q, Lane CR, Wang L, Vanderhoof MK, Christensen JR, Liu H (2019) Efficient delineation of nested depression hierarchy in digital elevation models for hydrological analysis using level-set method. Journal of the American Water Resources Association 55:354–368. https://doi.org/10.1111/1752-1688.12689

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu H, Hodgson ME, Piovan SE, Tufford DL (2018) The potential of using LiDAR and color-infrared aerial imagery for palustrine wetland typology and change. GIScience and Remote Sensing 55:477–501. https://doi.org/10.1080/15481603.2017.1412145

    Article  Google Scholar 

  • Zedler PH (2003) Vernal pools and the concept of “Isolated wetlands”. Wetlands 23:597–607. https://doi.org/10.1672/0277-5212(2003)023[0597:VPATCO]2.0.CO;2

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge Martin Dupuis for his expertise in photo-interpretation and Liane Nowell from the Kenauk Institute for on-site assistance.

Funding

Financial support for this study was provided by the Nature Conservancy of Canada [33232], Ducks Unlimited Canada [33232], Kenauk Canada ULC [33232] and the Ministry of Economic Development, Innovation and Export Trade of Québec (Passeport innovation program) [33232–33528].

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization, M.V., P.B. and J.F.; methodology, M.V., P.B. and J.F.; software, M.V. and P.B.; validation, M.V. and P.B.; formal analysis, M.V. and P.B.; writing—original draft preparation, M.V. and P.B.; writing—review and editing, M.V., P.B., J.F. and B.C.; supervision, P.B.; project administration, P.B. and J.F.; funding acquisition, M.V., P.B. and J.F.

Corresponding author

Correspondence to Mathieu Varin.

Ethics declarations

Conflict of Interest

The authors declare no conflict of interest.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

All authors have read and agreed to the published version of the manuscript.

Code Availability

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Varin, M., Bournival, P., Fink, J. et al. Mapping Vernal Pools Using LiDAR Data and Multitemporal Satellite Imagery. Wetlands 41, 34 (2021). https://doi.org/10.1007/s13157-021-01422-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13157-021-01422-9

Keywords

Navigation