Classifying Eucalyptus forests with high spatial and spectral resolution imagery: an investigation of individual species and vegetation communities
Nicholas Goodwin A C , Russell Turner B and Ray Merton AA School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
B State Forests of New South Wales, Pennant Hills, NSW 2120, Australia.
C Corresponding author. Email: N.goodwin@student.unsw.edu.au
Australian Journal of Botany 53(4) 337-345 https://doi.org/10.1071/BT04085
Submitted: 17 June 2004 Accepted: 4 April 2005 Published: 24 June 2005
Abstract
Mapping the spatial distribution of individual species is an important ecological and forestry issue that requires continued research to coincide with advances in remote-sensing technologies. In this study, we investigated the application of high spatial resolution (80 cm) Compact Airborne Spectrographic Imager 2 (CASI-2) data for mapping both spectrally complex species and species groups (subgenus grouping) in an Australian eucalypt forest. The relationships between spectral reflectance curves of individual tree species and identified statistical differences among species were analysed with ANOVA. Supervised maximum likelihood classifications were then performed to assess tree species separability in CASI-2 imagery. Results indicated that turpentine (Syncarpia glomulifera Smith), mesic vegetation (primarily rainforest species), and an amalgamated group of eucalypts could be readily distinguished. The discrimination of S. glomulifera was particularly robust, with consistently high classification accuracies. Eucalypt classification as a broader species group, rather than individual species, greatly improved classification performance. However, separating sunlit and shaded aspects of tree crowns did not increase classification accuracy.
Acknowledgments
We thank Murray Webster (State Forest of New South Wales) for assistance in tree identifications as well as Will Cutty and Tom Bourne for field assistance. The critical review by Nicholas Coops (CSIRO) and Christine Stone (State Forest of New South Wales) is most appreciated. The imagery used in this study was supplied by State Forest of New South Wales and we thank Christine Stone for approving the data request.
Cochrane MA
(2000) Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing 21, 2075–2087.
| Crossref | GoogleScholarGoogle Scholar |
Coops NC, Catling PC
(1997) Predicting the complexity of habitat in forests from airborne videography for wildlife management. International Journal of Remote Sensing 21, 2677–2686.
Coops NC,
Stone C,
Culvenor DS,
Chisholm LA, Merton R
(2003) Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high resolution spectral data. Tree Physiology 23, 23–31.
| PubMed |
Coops NC,
Stone C,
Culvenor DS, Chisholm L
(2004) Assessment of crown condition in eucalypt vegetation by remotely sensed optical indices. Journal of Environmental Quality 33, 956–564.
| PubMed |
Curran PJ
(1989) Remote sensing of foliar chemistry. Remote Sensing of Environment 30, 271–278.
| Crossref |
Datt B
(1999) Remote sensing of foliar biochemistry and biophysical properties in Eucalyptus species: application of high spectral resolution reflectance measurements. PhD Thesis
(School of Geography, The University of New South Wales:
Sydney)
Datt B
(1999b) A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves. Journal of Plant Physiology 154, 30–36.
Datt B
(1999c) Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing 20, 2741–2759.
| Crossref | GoogleScholarGoogle Scholar |
Dennison PE, Roberts DA
(2003) The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral. Remote Sensing of Environment 87, 295–309.
| Crossref | GoogleScholarGoogle Scholar |
Ebbers MJH,
Wallis IR,
Dury S,
Floyd R, Foley WJ
(2002) Spectrometric prediction of secondary metabolites and nitrogen in fresh Eucalyptus foliage: towards remote sensing of the nutritional quality of foliage for leaf-eating marsupials. Australian Journal of Ecology 50, 761–768.
Foody GM
(2002) Status of land cover classification accuracy. Remote Sensing of Environment 80, 185–201.
| Crossref | GoogleScholarGoogle Scholar |
Fourty T,
Baret F,
Jacquemoud S,
Schmuck G, Verdebout J
(1996) Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems. Remote Sensing of Environment 56, 104–117.
| Crossref | GoogleScholarGoogle Scholar |
Franklin SE,
Hall RJ,
Moskal LM,
Maudie AJ, Lavigne MB
(2000) Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing 21, 61–79.
| Crossref | GoogleScholarGoogle Scholar |
Gong P,
Ruiliang P, Bin Y
(1997) Conifer species recognition: an exploratory analysis of in situ hyperspectral data. Remote Sensing of Environment 62, 189–200.
| Crossref | GoogleScholarGoogle Scholar |
Gong P,
Pu R, Yu B
(2001) Conifer species recognition: effects on data transformation. International Journal of Remote Sensing 22, 3471–3481.
| Crossref | GoogleScholarGoogle Scholar |
Gougeon FA
(1995) Comparison of possible multi-spectral classification schemes for tree crowns individually delineated on high spatial resolution MEIS images. Canadian Journal of Remote Sensing 21, 1–9.
Greaves BL, Spencer RD
(1993) An evaluation of spectroradiometry and multispectral scanning for differentiating forest communities. Australian Forestry 56, 68–79.
Henry MC, Hope AS
(1998) Monitoring post-burn recovery of chaparral vegetation in southern California using multi-temporal satellite data. International Journal of Remote Sensing 19, 3097–3107.
| Crossref | GoogleScholarGoogle Scholar |
Kumar L
(1998) Modelling forest resources using geographical information systems and hyperspectral remote sensing. PhD Thesis
(School of Geography, The University of New South Wales:
Sydney)
Kumar L, Skidmore AK
(1998) Use of derivative spectroscopy to identify regions of difference between some Australian eucalypt species. In ‘Proceedings of the 9th Australasian remote sensing and photogrammetry conference’ 20–24 July 1998, Sydney. [CD-ROM].
Kushla JD, Ripple WJ
(1998) Assessing wildfire effects with Landsat thematic mapper data. International Journal of Remote Sensing 19, 2493–2507.
| Crossref | GoogleScholarGoogle Scholar |
Leckie DG,
Jay C,
Gougeon FA,
Sturrock RN, Paradine D
(2004) Detection and assessment of trees with Phellinus weirii (laminated root rot) using high resolution multi-spectral imagery. International Journal of Remote Sensing 25, 793–818.
| Crossref | GoogleScholarGoogle Scholar |
Li X, Strahler AH
(1992) Geometric-optical bi-directional reflectance modelling of mutual shadowing effects of crowns in a forest. IEEE Transactions on Geoscience and Remote Sensing 30, 276–292.
| Crossref | GoogleScholarGoogle Scholar |
Martin ME,
Newman SD,
Aber JD, Congalton RG
(1998) Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment 65, 249–254.
| Crossref | GoogleScholarGoogle Scholar |
Nagendra H
(2001) Review article: using remote sensing to assess biodiversity. International Journal of Remote Sensing 22, 2377–2400.
Olthof I, King DJ
(2000) Development of a forest health index using multispectral airborne digital camera imagery. Canadian Journal of Remote Sensing 26, 166–176.
O’Neil AL, Hardy S, Fraser SJ, McCloy KR
(1990) Leaf morphology and the spectral reflectance of some Australian plant species. In ‘Proceedings of the 5th Australasian remote sensing conference’, 8–12 October 1990, Perth. [CD-ROM].
O’Neil RV,
Hunsaker CT,
Timmins SP,
Jackson BL,
Jones KB,
Riitters KH, Wickham JD
(1996) Scale problems in reporting landscape pattern at the regional scale. Landscape Ecology 11, 169–180.
Peterson DL,
Aber JD,
Matson PA,
Card DH,
Swanberg N,
Wessman C, Spanner M
(1988) Remote sensing of forest canopy and leaf biochemical contents. Remote Sensing of Environment 24, 85–108.
| Crossref |
Riano D,
Chuvieco E,
Ustin S,
Zomer R,
Dennison P,
Roberts D, Salas J
(2002) Assessment of vegetation regeneration after fire through multi-temporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sensing of Environment 79, 60–71.
| Crossref | GoogleScholarGoogle Scholar |
Sampson PH,
Zarco-Tejada PJ,
Mohammed GH,
Miller JR, Noland TL
(2003) Hyperspectral remote sensing of forest condition: estimating chlorophyll content in tolerant hardwoods. Forest Science 49, 381–391.
Scarth P, Phinn SR, Held A, Mitchell D
(1999) Mapping koala habitat and eucalypt trees: integration and scaling of field and airborne hyperspectral data. In ‘Proceedings of the 10th Australasian remote sensing and photogrammetry conference’, 21–25 August 1999, Adelaide. [CD-ROM].
StatSoft (2002).
State Forest of New South Wales (1995).
Stone C
(1999) Assessment and monitoring of decline and dieback of forest eucalypts in relation to ecological sustainable forest management: a review with a case study. Australian Forestry 62, 55–63.
Stone C,
Coops N, Culver D
(2000) Conceptual development of a eucalypt canopy condition index using high resolution spatial and spectral remote sensing imagery. Journal of Sustainable Forestry 11, 23–45.
Stone C,
Chisholm L, Coops N
(2001) Spectral reflectance characteristics of eucalypt foliage damaged by insects. Australian Journal of Botany 49, 687–698.
| Crossref | GoogleScholarGoogle Scholar |
Turner W,
Spector S,
Gardiner N,
Fladeland M,
Sterling E, Steininger M
(2003) Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution 18, 306–314.
| Crossref |
Underwood E,
Ustin S, DiPietro D
(2003) Mapping nonnative plants using hyperspectral imagery. Remote Sensing of Environment 86, 150–161.
| Crossref | GoogleScholarGoogle Scholar |
Wessman CA,
Aber JD, Peterson DL
(1989) An evaluation of imaging spectrometry for estimating forest canopy chemistry. International Journal of Remote Sensing 10, 1293–1316.
