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:: An efficient classification scheme for verifying lack fidelity of existing county level findings to cultivated land cover areas. Yang Kai, Lin Kaiyu, Chen Jun & Lu Jian

Document type:: Multivolume work

Structure type:: Chapter

543 Table 3. confusion matrix of classification results (2) '• s.) •ix of clusters (3) r K persion matrix (4) 1 cluster different the use of the nd feature en- s are shown in expression (1 ) age set is the e basic image cessing. iliary height lassification tion into clas- that the deci se he mem can Cj pure classes mixture classes 0) p ij w± — ' G -F - -c "(c)- -F - c - W - (C) (CJ c F W + + + + + + + + + (F) (G) (F)|(F) (C) (w) (c) (G,F )(S,F ,W ) cultiv. land 72.« il .8 1 (j. 3 60.0 45.7 43.2 £#0 •H CD j Forest ,2.2 72. 1 5.8 24. 1 42.7 6.8 water 5.0 0.3 70.9 3.6 0.7 40.1 grass 3.! 10.0 0 3.4 6.1 0 -p orchards 2. 0 2.3 1.0 6.1 3.7 0.5 3 0 settlem. Ian 3.., » 0 2., 0.4 0.5 43 -P bear land " 0., 0.7 0 ? area 3822 4042 104 6403 3440 220 'S cultiv. land 80.5 8.6 0.9 0 ,2.5 5b. G 50.1 26.0 62.3 29.0 30.2 61 3 •H Forest 7.0 78.0 0 34.7 47.1 26.8 41.9 64.7 6.2 0 27.6 10.9 -p j water 4.3 0.2 90. 1 0 0.2 2.8 0.7 0.5 23.7 66.4 0.8 ine bO l grass 2.8 7.0 ^“5 02.7 32.6 3.2 1.9 2.0 3.4 0.9 29.1 10.3 •H a> i orchards 2.7 2. 1 n 0 4.5 6.4 3.3 2.8 0.6 0 5.5 2.8 43 settlem. Ian 1.7 0 0 0 0 2. 1 0.8 0.2 3.9 3.7 0 0 43 -p bear land 0 3.5 0 2.7 3.1 °-' 1.4 2.9 0 0 0.8 0 area 4075 230, 9, 1275 0739 855 3772 355 107 254 604 &i (x) = + - 2 Inpc^«) (5) Where, the P(W{) represents a priori probability of a feature class (W*), which is usually estimated by the area percentage of class (Wi) in whole study area (see the figures in last line of table 1). However, the a priori probabilities of ground feature in dif ferent hieght range are different in practice (see table 1). So the better classification result can only be got when P (Wt) is estimated in certain ground height range and the classification is per formed within the image area corresponding to the same height ra.nge . In our experiment, the study area was first digitals iy segemented into different height range area by taking- the "density (height)" sliced DTM image as masks. Then the classification was performed se- perately in different height range areas. Finally, the results from them were digitally mosaiced each other and forming- resulting 1 classification image. Table 3 shows the confusion matrix of classification seperately without (upper block) and with (lower block) introducing ground height information. From the table we can find that the classification ac curacy was improved by 8% for cultivated land when height information was introduced. (2) Itsuhito Ohnuki (1981 ): Terrain Effect Nor- marization Method of landsat Data and its Efficiency of Forest Type Glassification, Forestry and Forest Production Institute, P.0. Box 16, Tsukuba Norin- kenkyu-danchi, Ibaraki 305» Japen. (3) J.A. Richards, D .A. landgrebe, P.H. Swain (1982) A Means for Utilizing Auxiliary Information in Mul- tispectral Classification. R.S. of Eavironment, 12, 463 - 477. (4) Yang Kai, Lin Kaiyu, Chen Jun, Lu Jian (1985) s A Classification Scheme of landsat Multitemporal Feature Images with the Use of Auxiliary DTM Data. Acta Geodetica et Cartographies Sinica, Vol. 14» Ho. 3 China. 5* Conclusions Based on above classification processing, the areas of defined ground feature classes in the testing- county was calculated and compared with the existing findings. The results shown that about 30% of cul tivated land area in the county was not taken into account in the existing findings. So that, we can concluded that by elaborate classification scheme, particularly by introducing ground height information into classification procedure, the LANDSAT MSS images, although whose geometric resalution is origionally not high enough, can satisfyingly be used to verify the lack fidelities of existing findings to cultiva ted land in county level. References (1 ) J.T. Tou, Gonzalez (1 974): Pattern Recognition Principles, Addison-Wesley Publishing Company.

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