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

selected from 8 , principal component ing data, resampling Is of above mentioned is composed of selec- 8 bands. n=l,2,...,8. ands or 2 or more bands symbol C6B represents mposed of 6 bands out 28 possible combina- and 5, 7, 8, 9, 10, 11 it has the highest e total band combina- ination of CnB. is composed of selec- omponent transforma- ination of PmB. is composed of selec- usly stated 8 ratio 3 RESULTS 3.1 Classification results The imageries selected from 60 training areas were approximately grouped into 3 categories, cryptomeria plantations, conifers and mixed conifers plantations and miscellaneous group (hardwoods, bamboo etc.). According to their statistical and spatial related ness, 29 cover type classes were obtained in the study area. The data of the 29 classes were put into the ISOCLA function in IDIMS, cluster classes from the same cover type whose interclass statistical distance were less than the chosen threshold, i.e., a transformed divergence less then 1800, were grouped together. 29 cover types were combined into 23. (table 4). The parameters used in ISOCLA function were STDMAX:4.5; DLMIN:3.2; MAXCLS:35; NMIN:30; and ISTOP:10. Among the 23 classes, there were several similar classes. Then the MAP, LPMAP and TRANSFER functions in IDIMS were used to combine the 512x312 pixels study area into 12 classes. ination of RnB. which is selected from nB; 3 principal com- st band combinations PCi, PC 2 , PC 3 , 10/8, ination of AnB. d combination of CnB. aination of AVnB. ction, average diver- e used as the ranking ergences, 47 best band above mentioned riances and covariance ions were input to a tiich was used to ypes. t the accuracy of a i is in the form of k is a square array columns which express 3 a particular land- al land cover as aterpreted aerial f represent the a rows indicate the asses. a generated, both auracy are computed. c correctly In a class , nn aumber of a that class : pixels that were articular surface aation. But it is ression of the error a error must combine me summary measure a in class pattern a well as additions 3 (Kalensky et al., ised the mapping 100 ed pixels in n class I ¡sions) 3.2 Best band combination determination As previously stated, the best band combination is Table 4. 23 cover type classes Class No. Code Species Age class Slope class Aspect 1 A Cryptomeria plantation 4 4 North 2 C Cryptomeria plantation 2 6 Northwest 3 D China-fir plantation 2 7 West 4 E Cryptomeria plantation 3 7 North 5 G Natural hardwoods 4 Southeast 6 H Moso bamboo 2 North 7 I Natural hardwoods 6 Northwest 8 J Taiwania plantation 1 3 East 9 К Moso bamboo 4 East 10 M Taiwan red cypress 5 4 West 11 N Taiwan red cypress 5 5 Northwest 12 0 Moso bamboo 2 Southeast 13 P Ma bamboo 3 Northwest 14 Q Natural hardwoods 6 Northwest 15 R Nursery 3 Northwest 16 S Parking lot 3 Northwest 17 T Mixed conifer plantation 2 7 Southeast 18 и China-fir plantation 2 2 Northeast 19 V Taiwan incense- cedar plantation 3 3 North 20 w Conifers & hard woods plantation 2 4 East 21 X Mixed conifers 1 4 North 22 Y Conifers & hard woods plantation 1 3 North 23 Z Mixed conifers 2 3 North determined via divergences. All the band combinations, i.e., CnB, PmB, RnB, AnB, AVnB and their resampling data were introduced to the divergence module and a request was made for the best band combination to be ranked according to average divergence and minimum divergence. The average divergence is the average interclass divergence for all pairwise combinations of cover type classes. The band combination offering the highest average divergence is then ranked first. On the other hand, minimum divergence determines the lowest interclass divergence for each and every band combination. That combination possessing the highest minimum interclass divergence is then ranked first. The 47 best band combinations are shown in table 5. Table 5. 47 best band combinations Code Band combination Remarks ClB C2B C3B C4B C5B C6B C7B C8B 6 5,9 6,8,9 5,8,9,11 5,7,8,9,11 5,7,8,9,10,11 5,6,7,8,9,10,11 4,5,6,7,8,9,10,11 original band ClB-R C2B-R C3B-R C4B-R C5B-R C6B-R C7B-R C3B-R 5 5,9 6,8,9 5,8,9,11 5,7,8,9,11 5,7,8,9,10,11 5,6,7,8,9,10,11 4,5,6,7,8,9,10,11 resampling original band PlB P2B P3B PCl PCi,PC 2 PC, ,PC?,PC 3 principal component transformation P3B-R PC,,PC?,PC, resampling PC RIB R2B R3B R4B R5B R6B R7B R8B 10/8,7/9 10/8,7/9,5/11 10/8,7/9,9/11,4/7 10/8,7/9,9/11,4/7,5/11 10/8,7/9,10/5,9/11,4/7,5/11 10/8,10/6,9-6/9+6,10/5,9/11,4/7,5/11 10/8.10/6,7/9,9-6/946.10/5,9/11,4/7,5/11 ratio image R8B-R 10/8,10/6,7/9,9-6/946,10/5,9/11,4/7,5/11 resampling ratio image A1B A2B A3B A4B A5B A6B A7B A8B PC! PCi,7/9 PCi,PC 2 ,PC 3 PC 2 ,PC 3 ,7/9,9 PC 2 ,PC 3 ,10/8,7/9,9 PC 2 ,10/8,7/9,5/11,5,9 PC 2 ,PC 3 ,10/8,7/9,5/11,5,9 рсьРСр.рс,, 10/8,7/9,5/11,5,9 mixed band A8B-R PCi,PC 2 ,PC 3 ,10/8,7/9,5/11,5,9 resampling ' mixed band AV1B AV2B AV3B AV4B AV5B AV6B AV7B AV8B 6 5,9 6,8,9 5,8,9,11 5,7,8,9,11 5,7,8,9,10,11 5,6,7,8,9,10,11 4,5,6,7,8,9,10,11 spatial filtering band AV8B-R 4,5,6,7,8,9,10,11 resampling filtering band Table 6. Divergences of C2B and C5B Code Average divergence Minimum divergence Rank Band combination C2B 1790 146 1 5,9 1789 114 2 5,10 1784 329 3 5,8 C5B 1965 1111 1 5,7,8,9,11 1965 1018 2 5,7,8,10,11 1963 1115 3 5,6,8,9,11 1963 1089 4 5,8,9,10,11

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