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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Digital processing of airborne MSS data for forest cover types classification. Kuo-mu Chiao, Yeong-kuan Chen & Hann-chin Shieh

Document type:: Multivolume work

Structure type:: Chapter

15 ie Sozialwissen- C.H. Hull. Eine ersionen 8 und gart - New York, Satellitenbild aßstab lume "Hohe des österrei- gramms Hohe 1 map enclosed, ag Wagner. 9. Geologie des il. Abhandlun- r Reichsstelle 25, Issue 1, ote sensing ARSS 1982, Anwendungen rrain Model österr. Zeitschrift ation - The manual digi- RTO IV, Nachr. n, Ser . II, !ain (IFAG ) . on im Einzugsge- estlich des : Untersuchungen en Tauern 1974 - rhaushalt. Ver- MaB-Hochgebirgs- me 3, P- 35 - 67, Universitätsver- g und Erprobung rischen Auswer- ktralen Zellen- g/digital-ge- oma Thesis, raz, 228 p. gen zu den Bo- und Umgebung .000. In: MaB- eröffentli- hgebirgspro- 7, p. 23 - 28, Universitäts- ilogische Kenn- Serien längs biet des r (Hohe Tauern, Öffentlichungen ogramms "H . T." . g und Systema- ption. Hercynia derkarten bung" 1 : 5.000 " 1 : 5.000 des gramms Hohe "Hohe Tauern", erreich MaB- ume 7, p. 29 - uck, Universi- Hütter 1986. formation system. Dibag Report, age Processing z Research Center. Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Digital processing of airborne MSS data for forest cover types classification Kuo-mu Chiao, Yeong-kuan Chen & Hann-chin Shieh National Taiwan University, Taipei ABSTRACT: The purpose of this study is to find optimal band combinations of airborne MSS data for forest cover types classification. An eleven-channel airborne multispectral scanner was used to collect data. Processing of the MSS data was achieved through the use of IDIMS. Forest cover types were interpreted with a hybrid super vised/unsupervised approach. Eight original bands of airborne MSS data and enhanced data such as principal component transformations, ratio images, spatial filtering data, resampling data and mixed bands were subjected to standard clustering and classification techniques. Among the numerous band combinations of MSS data, forty- seven best band combinations with highest values of average divergence and minimum divergence were selected, and classified on IDIMS by the maximum likelihood classifier. The optimal band combination for forest cover types classification provided the most accurate and detailed classification results while minimizing computer time and man-hours. Findings of this study are summarized as follows: (1) the best band combination is the combination which contains more bands; (2) resampling and spatial filtering techniques increase 7-10% classifi cation accuracy; and (3) Because of the inherent property, the principal component transformations, ratio images and mixed bands are insufficient to improve the classification accuracy. 1 INTRODUCTION Resource surveys are carried out through the use of an airborne multispectral scanner rather than through the satellite data. The airborne multispectral scanner, which possesses the capabilities of fine resolution, narrow wavelength band and flexible scanning time, is more adequate on land cover types mapping than the Landsat system in an area with many vegetation types to be classified such as in Taiwan. The goal of this study is to find the optimal wave length band combination on land cover type classifi cation in forested area. Experiences have indicated that when working with many wavelength bands, maximum accuracy in classification can be obtained by using all wavelength bands available. However, this requires a marked increase in computer time. It is therefore frequently desirable to reduce computer time by utilizing only limited wavelength band in the classi fication procedure. The problem is which combination of wavenlength bands would be the optimum set to use in the classification. 2 MATERIALS AND METHODS 2.1 Data utilized The airborne multispectral scanner data used in this study were collected by a DS-1260 airborne MSS system with 11 channels in the Chi-tou tract of the Experi mental Forest of National Taiwan University in central Taiwan on November 21, 1982. A computer compatible tape (CCT) containing MSS data in band 4 to band 11 was used in data processing (table 1). In order to produce images adequate for forest cover types classification, image enhancement tech niques were used to create image products which protray spectral pattern representing a variety of surface features and cover types. The enhanced data are principal component transformations, ratio images, spatial filtering data and resampling data. 2.2 Processing techniques The image processing techniques were implemented Table 1. Airborne MSS spectral bands for data processing Channel Wavelength range (pm) 4 0.50-0.55 5 0.55-0.60 6 0.60-0.65 7 0.65-0.69 8 0.70-0.79 9 0.80-0.89 10 0.92-1.10 11 8.50-13.0 through the use of the Interactive Digital Image Manipulation System (IDIMS) at National Central University, Chungli, Taiwan, Republic of China. As a first step in processing the imagery, the MAGNIFY, SCANFIX and REGISTER functions in IDIMS were used to geometrically correct the airborne MSS data. Transformations were completed to correct the syste matic and non-systematic errors such as aspect ratio error, tangential scale distortion, altitude varia tion and attitude variations. The non-systematic distortions are not predictable, 18 widely scattered ground control points were selected to determine the geometric transformations required to correct the image. To generate the enhanced imageries, the principal component transformation was first performed on the 8 original bands data. In accomplishing this proce dure, function KLTRANS in IDIMS was applied. The principal component 1 (PCi) contributes 82.77% of the total variance by its eigenvalue; PC2 contributes 12.24%; and PC3 contributes 4.61%. Together, PCi, PC2, and PC3 already account for 99.62% of the total variance of the 8 bands data. The second enhanced data is ratio images. By the function ADD, DIVIDE, POWER, SCALE, HIST0G and CONVERT in IDIMS, the 8 original airborne MSS .bands can be combined to produce several dozens band ratios. Among them, 8 ratios were selected for data processing experientially. They are MSS10/MSS8, MSS10/MSS6, MSS7/MSS9, MSS9-MSS6/MSS9+MSS6, MSS10/MSS5, MSS9/MSS11, MSS4/MSS7, and MSS5/MSS11. In these band ratios, some are good for vegetation classification; some eliminate

Access restriction

Copyright

fullscreen: Remote sensing for resources development and environmental management (Volume 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation