Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Classification of the Riverina Forests of south east Australia using co-registered Landsat MSS and SIR-B radar data. A. K. Skidmore, P. W. Woodgate & J. A. Richards

Document type:: Multivolume work

Structure type:: Chapter

yed many prominent i to known surface wed differences in j Gum site quality 1 n flooded) appearinc r (i.e. vegetation on ¡d the colour lighter, adar appeared dark, tigation of bispectral ind Landsat images, le range of radar ses was developed e cover classes i >Dark ¡3 Swamp Agriculture water ark as they acted as jiving antennae. The so the density of the te quality (Forestry e forest type (i.e. the the radar response. 5), who found that increasing age (or (during the needle iund that for L-Band correlated with tree Des of pine forest in id higher backscatter phenomena may be i subject to an 80% iis aspect is to be verall classification -B data classification ng accuracy was nol r the supervised [¡cation accuracy Table 5 - Mapping accuracies for the supervised classification of the combined Landsat and SIR-B data. Class A VV F I II III cr “T - TM Agriculture (AJ 27 27 0 0 o o Swamp (S) 3 11 1 7 1 6 29 18 62 38 Water (W) 1 28 1 30 2 7 93 Box (B) 23 1 1 2 2 29 6 21 79 Red Gum SQ1 (I) 4 1 19 6 1 31 12 39 61 Red Gum SQ2 (II) 2 2 2 21 1 2 30 9 30 30 Red Gum SQ3 (III) 2 12 1 7 7 1 30 23 77 23 Total No. of Pixels (T)30 20 30 44 24 36 10 12 209 No. Comissions 3 9 2 21 5 15 3 12 % Comissions 10 45 7 48 21 42 30 100 U = Unclassified pixels OM = number of omissions P = percentage of omissions CA = class mapping accuracy statistical testing of thematic map accuracy. Rem. Sens, of the „ Environ. 7:3-14. Wu S. (1984). Analysis of synthetic aperture radar data acquired ovei a variety of land covers. IEEE Trans, on Geosci. and Rem. Sens. GE-22(6):550-5578. There was some qualitative’evidence to suggest that the remote sensing data was more accurate than some, sections of the site quality and vegetation maps used for ground truthing and mapping accuracy assesment. A more detailed ground truthing exercise is needed to evaluate whether some misclassified pixels ar< actually correctly classified, and in fact it is the ground truth data which is inaccurate. Some research has been undertaken to determine the optimal combination of wavelength, polarization, resolution and look angle for agricultural applications (De Loor, 1974; Ulaby, 1975; Brakke el al., 1981; Dobson et al., 1983), though much still needs to be done in forestry. Reliable models to describe radar backscatter from forests also need to be developed. 4. Conclusions The highest overall classification accuracy of 65% was obtained with co-registered Landsat MSS and SIR-B radar data. SIR-B provides additional information for delineation of forest types and site quality classes for the Riverina forests of Australia, though the amount of extra information is limited. Stand structure appeared the main factor affecting radar backscatter from forests. Acknowledgements Mr T. Lee provided assistance in running the static average filter which he developed on the Dipix system, at the Centre tor Remote Sensing, Unversity of New South Wales. Ms L. Bischof provided valuable advice on the operation (and peculiarities) of the Dipix system. We are also grateful to The Forestry Commission of N.S.W. and the Department of Conservation, Forests and Lands, Victoria, who made available ground truth maps and reports of the study area. Literature cited 55.1% 502% 56.4% best overall result, on which increased itributed information SS. The Landsat and ¡dual class mapping sky and Sherk, 1975) ) site quality classes data, so these result! ever, Benning et al. >tic forest types into ''Jew Zealand., with i 58%; a poor result , (1980) used a four and estimated the l was 6 m. Guidon et >l airborne MSS and a ;t terrain, and showed vith the MSS imagery results. The airborne sat MSS, due to the anner. st three principal ie Landsat MSS data e matrix produced by it 84.2% of the total was due to the radar mponents, generally Benning V.M., Ching N.P., Bennetts R.L., Ellis P.J., Beach D.W. (1981jNew Zealand land use cover and forestry mapping from a satellite. Proc. Second Aust. Rem. Sens. Conference, Canberra. p2.1.1-2.1.5. Brakke T.W., Kanemasu E.T., Steiner J.L., Ulaby F.T., Wilson E. (1981). Microwave radar response to canopy moisture, leaf area index and dry weight of wheat corn. Rem. Sens of Environ. 11:207-220. De Loor G.P., Jurriens A.A., and Gravesteign H. (1974). The radar backscatter from selected agricultural crops. IEEE Trans, on Geoscience Workshops GE-12(2):70-77. Dobson M., Ulaby F.T., Moezzel S. (1983). A simulation study of the effects of land cover and crop type on sensing soil moisture from an orbital C-band radar. Int. Geoscience and Rem. Sens. Symp., Vol 1., TA-1-3. Forestry Commision of N.S.W. (unpub). Murray Management Plan (Draft). Forestry Commission of N.S.W., P.O. Box 2667, G.P.O., Sydney, N.S.W. 2001, Australia. Fung A.K. and Ulaby F.T. (1983). Matter-energy interaction in the microwave region. In Manual of Remote Sensing, Vol. 1, Chapt. 4:115-164. Guidon B., Gentle M.R., O'Callaghan J.F., Briggs I.C., Dreiven G. (1980). Integration of MSS and SAR data of forested regions in mountainous terrain. Proc. 14th Int. Symp. on Rem. Sens, of Environ. Vol. 3, pp. 1673-1690. Hoekman D.H. (1985). Radar backscattering of forest stands. Int. J. Rem. Sens. 6(2):325-343 Inkster D.R., Lowry R.T., and Thompson M.D. (1980). Optimal radar resolution studies for land use and forestry applications. Proc. 14th. Int. Symp. on Rem. Sens, of Environ. Vol. 2, pp. 865-882. Kalensky z. and Sherk L.R. (1975). Accuracy of forest mapping from Landsat CCTs. Proc. 10th. Int. Symp. on Rem. Sens, of the Environ. Vol. II pp. 1159. Ulaby F.T. (1975). Radar response to vegetation. IEEE Trans. Antenna. Propagation., AP-23(1):36-45 Van Genderen J.L., Lock B.F., Vass P.A. (1978). Remote sensing 519

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