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

Damen, M. C. J.

Bibliographic data

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:: Experiences in application of multispectral scanner-data for forest damage inventory. A. Kadro & S. Kuntz

Document type:: Multivolume work

Structure type:: Chapter

469 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Experiences in application of multispectral scanner-data for forest damage inventory A.Kadro & S.Kuntz Department of Photointerpretation and Remote Sensing, University of Freiburg, FR Germany ABSTRACT: For testing the potential use of multispectral scanner data for the inventory of forest damages in large areas five test sites in South-west Germany were sensed at three flight altitudes with an 11-channel scanner. At the same time, ground truth information in these test sites were obtained and the actual state of the forest stands was documented with color infrared (CIR) aerial photographs. The test sites differ in morphology, forest types and degree of the actual forest damage.The acquired data were evaluated with a com puter aided supervised classification using the maximum-1ikehood method. For verification of the classification results for both single trees and stands, the terrestrial ground truth and the CIR-photographs were used. This paper presents the classification results and discusses the problems of a computer aided forest damage inventory/. 1 INTRODUCTION Since the late 1970's a regional decline affecting many tree species has occured in Europe. The urgent need for detailed information about the actual si tuation of the forests in Germany are of vital in terest for both government and forest departments. So aerial and ground survey methods have been used to get this information. But for large areas these methods are time-consuming and expensive. So in 1983 a project started at the Department of Photointerpretation and Remote Sen sing, University of Freiburg, to evaluate multispec tral scanner data for forest damage inventory. For this purpose data were collected in July 1984 and August 1985 on 5 test areas in South-west Germany with a Bendix-M2S-Scanner flown by the DFVLR Ober pfaffenhofen. This scanner was modified (Table 1 ) by the DFVLR to simulate the Thematic Mapper in Land- sat 5 (Table 2). The data were collected at altitudes of 300 m, 1000 m and 3000 m. A Landsat 5 image from nearly the same time, recording the same areas was also evaluated. Table 1. Spectral channels and wavelengths of the modified Bendix -Scanner median range median range channel Ann* channel A tun 3 515 50 9 720 40 4 560 40 10 1015 90 5 600 40 5TM 1650 200 6 640 40 7TM 2210 270 7 680 40 11 11000 6000 8 720 40 Table 2. Spectral channels and wavelengths of Land- sat 5 (TM) median range median range channel Ann» Alum channel A H»m Alnm 1 485 70 5 1650 200 2 560 80 6 11450 2100 3 660 60 7 2215 270 4 830 140 the different altitudes have the following ground resolution (pixel size): at 300 m altitude at 1000 m " at 3000 m " at 705 km " 0,75 X 0,75 m 2.5 X 2,5 m 7.5 X 7,5 m 30 X 30 m (aircraft MSS) If (Landsat 5) Fran the 300 m altitude pixels numbering up to 100 represent one single tree crown. Fran 1000 m and 3000 m one can evaluate only groups of trees or stands and from Landsat images only large stands can be evaluated. The test site iron which results will be presented is mountainous and contains mostly coniferous trees (spruce mixed with fir) and some smaller stands of deciduous trees (mostly beech). The main interest in this project was focused on coniferous trees because they are of major interest in german forestry al though in a continuing project deciduos species also will be investigated. The computer aided classification of different damage classes is based on the differences in reflection of healthy and damaged vegetation in the spectral re gion of the visible, near infrared and middle infra red part of the electromagnetic spectrum. These spec tral differences are assumed for a computer classi fication to operate, so the first step was the eva luation of the spectral signatures of differently damaged tree species. The results of this evaluation are presented in a special paper at this symposium (Kadro, 1986). For the supervised classification and presentation of results special software was de veloped at the department. The classification algo rithm is a combined box and maximum-1 ikelyhood classifier which can also analyse additional infor mation given by the user. For example: terrain model, masks, modification of the covariance matrix, a pri ori probabilities and permanent or temporary condi tions for including or excluding pixels during the classification process. 2 RESULTS OF THE COMPUTER AIDED CLASSIFICATION! 2.1 Frcm 300 m altitude The test sites differ in morphology, forest types and degree of the actual forest damage to simulate all possible inventory problems. The data collected frcm For checking the classification from 300 m altitude a crown map drawn with a Bausch and Lomb Zocm-Trans- ferscope was digitized and used as an overlay (photo 1).

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