Skip to main content
SpringerLink
Log in

Towards Automated Forest Mapping

  • Chapter
  • First Online:
Mapping Forest Landscape Patterns

  • 1232 Accesses

Abstract

The need for up-to-date and accurate information on forest resources has rapidly increased in recent years. Forest mapping is an important source of information for the assessment of woodland resources and a key issue for any National Forest Inventory (NFI). Nowadays, new perspectives for automated forest mapping are emerging through the latest developments in remote sensing data and techniques. In this chapter, an overview of current remote sensing data and techniques for mapping woodland and forests, the challenges and requirements for optimization and automation, and the need for validating final products are presented. Special attention is paid to land use—a crucial criterion for forest mapping which, in contrast to land cover, cannot be easily derived from remotely sensed data. Three different approaches for extracting woodland areas (i.e., patches of trees and shrubs) are presented, all of which involve a high degree of automation. Two additional approaches, which are based on NFI forest definitions, are presented. These require the subdivision of woodlands into the classes “used for forestry” and “other use” and implement the criteria “height,” “minimum crown coverage,” “minimum area,” “minimum width,” and “land use”. Special attention is paid to connecting patches using distance criteria from national forest definitions. The main points of this chapter are as follows: (1) forest needs an exact definition which may differ depending on the country, (2) mapping woodland can be highly automated and is indispensable prior to mapping forests, and (3) forest mapping is now feasible using remote sensing data and techniques; however, it is less automated due to the implementation of a forest definition.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ALS:

Airborne laser scanning

CHM:

Canopy height model

CIR:

Color-infrared

DSM:

Digital surface model

DTM:

Digital terrain model

GLM:

Generalized linear model

GSD:

Ground sample distance

IGBP:

International geosphere biosphere program

IP:

Interpretation plot

ITC:

Individual tree crowns

k-NN:

K-nearest neighbor

LiDAR:

Light detection and ranging

NDVI:

Normalized difference vegetation index

NFI:

National forest inventory

REDD:

Reducing emissions from deforestation and degradation

SAR:

Synthetic aperture radar

TOF:

Trees outside forest

VHM:

Vegetation height model

VHR:

Very high resolution

References

  • Alford J (1993) Towards a new public management model: beyond “managerialism” and its critics. Aust J Publ Admin 52(2):135–148

    Article  Google Scholar 

  • APEX (2011) Airborne Prism Experiment. http://www.apex-esa.org. Accessed 16 Mar 2017

  • Baatz M, Schäpe A (2000) Multiresolution segmentation—an optimization approach for high quality multi-scale image segmentation. In: Strobl J, Blaschke T, Griesebner G (eds) Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT Symposium, Wichmann, Heidelberg, pp 12–23

    Google Scholar 

  • Barrett F, McRoberts RE, Tomppo E, Cienciala E, Waser LT (2016) A questionnaire-based review of the operational use of remotely sensed data by national forest inventories. Remote Sens Environ 174:279–289

    Article  Google Scholar 

  • Bartholomé E, Belward AS (2005) GLC2000: a new approach to global land cover mapping from Earth observation data. Int J Remote Sens 26:1959–1977

    Article  Google Scholar 

  • Brandtberg T (2002) Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets Syst 132(3):371–387

    Article  Google Scholar 

  • Chen JM, Leblanc S (1997) A four-scale bidirectional reflectance model based on canopy architecture. IEEE Trans Geosci Remote 35:1316–1337

    Article  Google Scholar 

  • Cheng YZ (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal 17:790–799

    Article  Google Scholar 

  • Chubey M, Stehle K, Albricht R, Gougeon F, Leckie D, Gray S, Woods M, Courville P (2009) Semi-automated species classification in Ontario Great Lakes—St. Lawrence forest conditions. Final report: Great Lakes—St. Lawrence ITC Project (2005/2008). Ontario Ministry of Natural Resources, January 2009, p 71

    Google Scholar 

  • Dalponte M, Orka HO, Ene LT, Gobakken T, Naesset E (2014) Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens Environ 140:306–317

    Article  Google Scholar 

  • Darvishsefat A, Kellenberger T, Itten K (2002) Application of hyperspectral data for forest stand mapping. Int Arch Photogram Rem Sens Spatial Inform Sci. 34(4)

    Google Scholar 

  • Dees M, Koch B, Pelz D (1998) Integrating satellite based forest mapping with Landsat TM in a concept of a large scale forest information system. PFG 4:209–220

    Google Scholar 

  • Dong J, Xiao X, Sheldon S, Biradar C, Zhang G, Dinh Duong N et al (2014) A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment. PLoS One 9(1):e85801. doi:10.1371/journal.pone.0085801

    Article  PubMed  PubMed Central  Google Scholar 

  • Edelsbrunner H (1995) Smooth surfaces for multi-scale shape representation. Lect Notes Comput Sci 1026:391–412

    Article  Google Scholar 

  • Eysn L, Hollaus M, Schadauer K, Pfeifer N (2012) Forest delineation based on airborne LiDAR data. Remote Sens 4:762–783

    Article  Google Scholar 

  • Eysn L, Hollaus M, Lindberg E, Berger F, Monnet J, Dalponte M, Kobal M, Pellegrini M, Lingua E, Mongus D, Pfeifer N (2015) A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the alpine space. Forests 6:1721–1747

    Article  Google Scholar 

  • FAO (1997) Food and Agriculture Organization. State of the World’s Forests. Food and Agriculture Organization, Rome, Italy, 200 pp

    Google Scholar 

  • FAO (1999) Food and Agriculture Organization. UNEP terminology for integrated resources planning and management. Food and Agriculture Organization/United nations Environmental Program, Rome, Italy and Nairobi, Kenia

    Google Scholar 

  • FAO (2000) Food and Agriculture Organization. FRA 2000: on definitions of forest and forest change. Working paper 33, Forest Resource Assessment, Rome, 14 p

    Google Scholar 

  • FAO (2001) Food and Agriculture Organization. Global Forest Resources Assessment 2000 Main Report. FAO Forestry Paper 140. FAO, Rome, Italy

    Google Scholar 

  • FAO (2010) Food and Agriculture Organization. Global Forest Resources Assessment 2010 Main Report. FAO Forestry Paper163, Rome

    Google Scholar 

  • FAO (2012) Food and Agriculture Organization. Global forest land-use change 1990–2005. FAO Forestry Paper 2012, Rome

    Google Scholar 

  • Fisher P, Comber A, Wadsworth R (2005) Land use and land cover: contradiction or complement. In: Fisher P, Unwin DJ (eds) Re-presenting GIS. Wiley, Hoboken, NJ, pp 199–209

    Google Scholar 

  • Förster M, Kleinschmit B (2008) Object-based classification of QuickBird data using ancillary information for the detection of forest types and NATURA 2000 habitats. In: Blaschke T, Lang S, Hay G (eds) Object-based image analysis. Springer, Berlin, Heidelberg, pp 275–290

    Chapter  Google Scholar 

  • Förster M, Kleinschmit B, Walentwoski H (2005) Monitoring NATURA 2000 forest habitats in Bavaria by the use of ASTER, SPOT5 and GIS data—an integrated approach. In: Proceedings of the ForestSAT 2005 conference, 31th May–2nd June 2005, Borås, Sweden

    Google Scholar 

  • Fransson JES, Magnusson M, Folkesson K, Hallberg B, Sandberg G, Smith-Jonforsen G, Gustavsson A, Ulander LMH (2007) Mapping of wind-thrown forests using VHF/UHF SAR images. In: Proceedings of the Geoscience and Remote Sensing Symposium, IGARSS 2007, 23rd–28th July 2007, Barcelona, Spain, pp 2350–2353

    Google Scholar 

  • Fukunaga K, Hostetler LD (1975) Estimation of gradient of a density-function, with applications in pattern-recognition. IEEE Trans Inform Theory 21:32–40

    Article  Google Scholar 

  • Garcia-Haro FJ, Sommer S (2002) A fast canopy reflectance model to simulate realistic remote sensing scenarios. Remote Sens Environ 81(2–3):205–227

    Article  Google Scholar 

  • Gebhardt S, Wehrmann T, Ruiz M, Maeda P, Bishop J, Schramm M, Kopeinig R, Cartus O, Kellndorfer J, Ressl R et al (2014) MAD-MEX: automatic wall-to-wall land cover monitoring for the Mexican REDD-MRV program using all Landsat data. Remote Sens 6:3923–3943

    Article  Google Scholar 

  • Gerard F (2003) Single angle, dual angle and multi-temporal viewing: assessing through modelling the implications for forest structure variable extraction. Int J Remote Sens 24(6):1317–1334

    Article  Google Scholar 

  • Gerard FF, North PRJ (1997) Analyzing the effect of structural variability and canopy gaps on forest BRDF using a geometric-optical model. Remote Sens Environ 62(1):42–62

    Article  Google Scholar 

  • GFW (2016) The global forest watch. http://www.globalforestwatch.org/map/3/15.00/27.00/ALL/grayscale/loss,forestgain?begin=2001-01-01&end=2015-01-01&threshold=30. Accessed 23 July 2017

  • Gillis M, Leckie D (1996) Forest inventory update in Canada. For Chron 72(2):138–156

    Article  Google Scholar 

  • Ginzler C, Hobi M (2015) Countrywide stereo-image matching for updating digital surface models in the framework of the Swiss national forest inventory. Remote Sens 7:4343–4370

    Article  Google Scholar 

  • Hajnsek I, Kugler F, Lee SK, Papathanassiou KP (2009) Tropical-forest-parameter estimation by means of Pol-InSAR: the INDREX II campaign. IEEE Trans Geosci Remote 47(2):81–493

    Google Scholar 

  • Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853

    Article  CAS  PubMed  Google Scholar 

  • Hirschmugl M, Weninger B, Raggam H, Schardt M (2007) Single tree detection in very high resolution remote sensing data. Remote Sens Environ 110(4):533–544

    Article  Google Scholar 

  • Hirschmüller H (2008) Stereo processing by semi-global matching and mutual information. IEEE Trans Pattern Anal 30(2):328–341

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York

    Book  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 Sens 4(9):2661–2693

    Article  Google Scholar 

  • JAXA (2016) World’s first high-resolution global forest/non-forest map. http://global.jaxa.jp/article/special/geo/shimada_e.html. Accessed 23 July 2017

  • Jensen JR (2005) Introductory digital image processing: a remote sensing perspective. Prentice Hall, Upper Saddle River, NY

    Google Scholar 

  • JRC (2016) European Joint Research Center JRC. http://forest.jrc.ec.europa.eu/activities/forest-mapping/forest-cover-map-2006/. Accessed 23 July 2017

  • Kaartinen H, Hyyppä J (2008) EuroSDR/ISPRS Project, Commission II “Tree Extraction” Final Report. EuroSDR (European Spatial Data Research): Dublin, Ireland, 2008

    Google Scholar 

  • Kaartinen H, Hyyppä J, Yu X, Vastaranta M, Hyyppä H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F, Næsset E, Pitkänen J, Popescu S, Solberg S, Wolf BM, J-C W (2012) An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens 4(4):950–974

    Article  Google Scholar 

  • Keil M, Schardt M, Schurek A, Winter R (1990) Untersuchung und Kartierung von Waldschäden mit Methoden der Fernerkundung. DLR-Abschlussdokumentation, Kapitel Auswertung von Satellitendaten, pp 71–131

    Google Scholar 

  • Kennedy P, Bertolo F (2002) Mapping sub-pixel forest cover in Europe using AVHRR data and national and regional statistics. Can J Remote Sens 28(2):302–321

    Article  Google Scholar 

  • Koch B, Dees M, van Brusselen J, Eriksson L, Fransson J, Gallaun H, Leblon B, McRoberts RE, Nilsson M, Schardt M, Seitz R, Waser LT (2008) Forestry applications. In: Li Z, Chen J, Baltsavias E (eds) Advances in photogrammetry, remote sensing and spatial information—ISPRS 2008 Congress Book. Taylor & Francis Group, London, pp 439–468

    Chapter  Google Scholar 

  • Koch B, Straub C, Dees M, Wang Y, Weinacker H (2009) Airborne laser data for stand delineation and information extraction. Int J Remote Sens 30(4):935–963

    Article  Google Scholar 

  • Kuusk A, Nilson T (2000) A directional multispectral forest reflectance model. Remote Sens Environ 72(2):244–252

    Article  Google Scholar 

  • Laliberte AS, Rango A, Havstad KM, Paris JF, Beck RF, McNeely R (2004) Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sens Environ 93(1–2):198−210

    Google Scholar 

  • Lamonaca A, Corona P, Barbati A (2008) Exploring forest structural complexity by multi-scale segmentation of VHR imagery. Remote Sens Environ 112(6):2839–2849

    Article  Google Scholar 

  • Leckie DG, Gougeon FA, Walsworth N, Paradine D (2003) Stand delineation and composition estimation using semi-automated individual tree crown analysis. Remote Sens Environ 85(3):355–369

    Article  Google Scholar 

  • Leckie DG, Tinis S, Nelson T, Burnett C, Gougeon FA, Cloney E, Paradine D (2005) Issues in species classification of trees in old growth conifer stands. Can J Remote Sens 31(2):175–190

    Article  Google Scholar 

  • Li XW, Strahler AH (1992) Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy—effect of crown shape and mutual shadowing. IEEE Trans Geosci Remote 30(2):276–292

    Article  Google Scholar 

  • Li XW, Strahler AH, Woodcock CE (1995) A hybrid geometric optical–radiative transfer approach for modeling albedo and directional reflectance of discontinuous canopies. IEEE Trans Geosci Remote 33:466–480

    Article  CAS  Google Scholar 

  • Lindberg E, Hollaus M (2012) Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest. Remote Sens 4(4):1004–1023

    Article  Google Scholar 

  • Lund HG (2016) rev*. Definitions of forest, deforestation, afforestation, and reforestation. Gainesville, V.A.: Forest Information Services. [Online] Note this paper has been continuously updated since 1998. DOI: 10.13140/RG.2.1.2364.9760

  • Magdon P, Fischer C, Fuchs H, Kleinn C (2014) Translating criteria of international forest definitions into remote sensing image analysis. Remote Sens Environ 149:252–262

    Article  Google Scholar 

  • McRoberts RE (2012) Satellite image-based maps: scientific inference or pretty pictures? Remote Sens Environ 115(2):715–724

    Article  Google Scholar 

  • McRoberts RE, Tomppo EO (2007) Remote sensing support for national forest inventories. Remote Sens Environ 110(4):412–419

    Article  Google Scholar 

  • McRoberts RE, Liknes GC, Domke GM (2014) Using a remote sensing-based, percent tree cover map to enhance forest inventory estimation. For Ecol Manag 331:12–18

    Article  Google Scholar 

  • Mottus M, Sulev M, Lang M (2006) Estimation of crown volume for a geometric radiation model from detailed measurements of tree structure. Ecol Model 198:506–514

    Article  Google Scholar 

  • 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(5):433–442

    Article  Google Scholar 

  • Næsset E, Gobakken T (2005) Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sens Environ 96(3–4):453–465

    Article  Google Scholar 

  • Nilson T, Peterson U (1991) A forest canopy reflectance model and a test case. Remote Sens Environ 37:131–142

    Article  Google Scholar 

  • Pekkarinen A, Reithmaier L, Strobl P (2009) Pan-European forest/non-forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data. ISPRS J Photogramm 64(2):171–183

    Article  Google Scholar 

  • Petrie G, Walker AS (2007) Airborne digital imaging technology: a new overview. Photogramm Rec 22(119):203–225

    Article  Google Scholar 

  • Rahman M, Sumantyo J (2010) Mapping tropical forest cover and deforestation using synthetic aperture radar (SAR) images. Appl Geomat 2:113–121

    Article  Google Scholar 

  • Reitberger J, Krzystek P, Heurich M (2006) Full-waveform analysis of small footprint airborne laser scanning data in the Bavarian forest national park for tree species classification. In: Koukal T, Schneider W (eds) Proceedings of the International Workshop 3D Remote Sensing in Forestry, 14-15th February 2006. University of Natural Resources and Applied Life Sciences, Vienna, Austria, pp 218–228

    Google Scholar 

  • Rosenquist Å, Shimada M, Ito N, Watanabe M (2007) ALOS PALSAR: a pathfinder mission for global-scale monitoring of the environment. IEEE Trans Geosci Remote 45(11):3307–3316

    Article  Google Scholar 

  • Rutzinger M, Pratihast AK, Elberink SO, Vosselman G (2010) Detection And Modelling Of 3D Trees From Mobile Laser Scanning Data. Int Arch Photogram Rem Sens Spatial. Inf Sci 38(5):520–525

    Google Scholar 

  • Schepaschenko D, See L, Lesiv M, McCallum I, Fritz S, Salk C, Moltchanova E, Perger C, Schepashchenko M, Shvidenko A et al (2015) Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sens Environ 162:208–220

    Article  Google Scholar 

  • Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa R, Lucas R (2014) New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens Environ 155:13–31

    Article  Google Scholar 

  • Solberg S, Naesset E, Bollandsas OM (2006) Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogramm Eng Remote Sens 72:1369–1378

    Article  Google Scholar 

  • Spurr SH (1960) Photogrammetry and photo-interpretation, 2nd edn. Ronald Press, New York

    Google Scholar 

  • Stibig HJ, Achard F, Fritz S (2004) A new forest cover map of continental southeast Asia derived from SPOT-VEGETATION satellite imagery. Appl Veg Sci 7(2):153–162

    Article  Google Scholar 

  • Stoffels J, Mader S, Hill J, Werner W, Ontrup G (2012) Satellite-based standwise forest cover type mapping using a spatially adaptive classification approach. Eur J For Res 131(4):1071–1089

    Article  Google Scholar 

  • Straub BM, Heipke, C (2001) Automatic extraction of trees for 3D city models from images and height data. Automatic extraction of man-made objects from aerial and space images, vol 3, pp 267–277

    Google Scholar 

  • Straub C, Weinacker H, Koch B (2008) A fully automated procedure for delineation and classification of forest and non-forest vegetation based on full-waveform laser scanner data. Int Arch Photogram Rem Sens Spatial Inform Sci 37(8):1013–1019

    Google Scholar 

  • Straub C, Stepper C, Seitz R, Waser LT (2013) Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests. Can J For Res 43(8):731–741

    Article  Google Scholar 

  • Swisstopo (2016) The topographic landscape model TML. http://www.swisstopo.admin.ch/internet/swisstopo/en/home/topics/geodata/tlm.html. Accessed 23 July 2017

  • Thiel C, Drezet P, Weise C, Quegan S, Schmullius C (2006) Radar remote sensing for the delineation of forest cover maps and the detection of deforestation. Forestry 79(5):589–597

    Article  Google Scholar 

  • Tomppo E, Halme M (2004) Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach. Remote Sens Environ 92(1):1–20

    Article  Google Scholar 

  • Tomppo E, Haakana M, Katila M, Peräsaari J (2008) Multi-source national forest inventory-methods and applications, Managing Forest Ecosystems, vol 18. Springer, Berlin

    Google Scholar 

  • Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (eds) (2010) National forest inventories—pathways for common reporting, 1st edn. Berlin, Springer

    Google Scholar 

  • Vauhkonen J, Seppänen A, Packalén P, Tokola T (2012) Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data. Remote Sens Environ 124:534–541

    Article  Google Scholar 

  • Vosselman G (2003) 3D reconstruction of roads and trees for city modeling. Int Arch Photogram Rem Sens Spatial Inform Sci. 34(3), W13, Dresden, Germany, pp 231–236

    Google Scholar 

  • Wagner W, Luckman A, Vietmeier J, Tansey K, Balzter H, Schmullius C, Davidson M, Gaveau D, Gluck M, Le Toan T, Quegan S, Shvidenko A, Wiesmann A, JJ Y (2003) Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data. Remote Sens Environ 85(2):125–144

    Article  Google Scholar 

  • Wang Z, Boesch R, Ginzler C (2007) Color and LIDAR data fusion: application to automated forest boundary delineation in aerial images. Int Arch Photogram Rem Sens Spatial Inform Sci 36(1)

    Google Scholar 

  • Wang Z, Ginzler C, Waser LT (2015) A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds. Forestry 88:429–440

    Article  Google Scholar 

  • Waser LT (2012) Airborne remote sensing data for semi-automated extraction of tree area and classification of tree species. Dissertation, ETH Zürich, 153 p. http://e-collection.library.ethz.ch/view/eth:6087

  • Waser LT, Baltsavias E, Ecker K, Eisenbeiss H, Feldmeyer-Christe E, Ginzler C, Küchler M, Thee P, Zhang L (2008a) Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR-aerial images. Remote Sens Environ 112(5):1956–1968

    Article  Google Scholar 

  • Waser LT, Baltsavias E, Ecker K, Eisenbeiss H, Ginzler C, Küchler M, Thee P, Zhang L (2008b) High-resolution digital surface models (DSM) for modeling fractional shrub/tree cover in a mire environment. Int J Remote Sens 29(5):1261–1276

    Article  Google Scholar 

  • Waser LT, Klonus S, Ehlers M, Küchler M, Jung A (2010) Potential of digital sensors for land cover and tree species classifications—a case study in the framework of the DGPF-Project. PFG 2:141–156

    Google Scholar 

  • Waser LT, Ginzler C, Kuechler M, Baltsavias E, Hurni L (2011) Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sens Environ 115(1):76–85

    Article  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 Sens 6:4515–4545

    Article  Google Scholar 

  • Waser LT, Fischer C, Wang Z, Ginzler C (2015) Wall-to-wall forest mapping based on digital surface models from image-based point clouds and a NFI forest definition. Forests 6:4510–4528

    Article  Google Scholar 

  • Zeverbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surf Processes 12:47–56

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Swiss Federal Institute for Forest, Snow and Landscape Research, Zuercherstrasse 111, Birmensdorf, CH-8903, Switzerland

    Lars T. Waser, Ruedi Boesch, Zuyuan Wang & Christian Ginzler

Authors
  1. Lars T. Waser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruedi Boesch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zuyuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Ginzler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lars T. Waser .

Editor information

Editors and Affiliations

  1. Department of Geography, York University, Toronto, Ontario, Canada

    Tarmo K. Remmel

  2. Ministry of Natural Resources and Forestry, Ontario Forest Research Institute, Sault Ste. Marie, Ontario, Canada

    Ajith H. Perera

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this chapter

Cite this chapter

Waser, L.T., Boesch, R., Wang, Z., Ginzler, C. (2017). Towards Automated Forest Mapping. In: Remmel, T., Perera, A. (eds) Mapping Forest Landscape Patterns. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7331-6_7

Download citation

Publish with us

Policies and ethics

Search

Navigation