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:: Tropical forest cover classification using Landsat data in north-eastern India. Ashbindu Singh

Document type:: Multivolume work

Structure type:: Chapter

Table 1. Definitisn of cover classes 2.6 Preprocessing 514 Cover classes Description Area-1 Area-2 Forest Land covered by trees (woody plant with single stem and more than 5m. high), minimum area 2 ha. Closed forest Mixed broad-lea- V' ved forest, ca nopy closure>50% V Open forest Canopy closure, V 20%-50% V Dense mixed bamboo forest Broad-leaved spe cies mixed with bamboos, canopy closure 750% V Scrub Vegetation type, the main woody elements of which are scrubs of more that 50cm. and less than 5m. height. They are woody plants with multiple stems of branching near the ground. V" Grassland Land covered by ч/ grass and her baceous plants, maximum height lm. Shifting Area which is v" cultivation under active cultivation, freshly burned or abandoned but regrowth is not more than two years old. Regrowth Areas left fallow long enough after shifting cultiva tion for vegeta tion to regene rate . Bare soil Land surface devoid of vege tation cover<20%. It includes built areas. Vindicates cover class belonging to either Area-1 or Area-2. 2.5 Digital image processing Digital image processing was done on a microcomputer based interactive image proce ssing system housed in the Department of Geography, University ©f Reading,England with indigeneously developed software. Preprocessing of Landsat-2 MSS data was un dertaken in order to improve the image qua lity. 'Destriying' of the images was carried out by a histogram normalisation technique. Corrections for 'bit-slips' were also applied, 3 CLASSIFICATION ANALYSIS 3.1 Supervised classification A supervised multispectral image classifi cation procedure based on the minimum dis tance to means (Euclidean distance) algori thm was used in the study. In this classi fier a distance is computed for each pixel vector from the class means and the pixel is assigned to the class with the nearer means. Since this classifier is a special case of a more general maximum likelihood classifier and computationally can be pro grammed effecièntly it was thought ta be most suitable for implementing on the micro computer system. 3.2 Reclassification In the classified image, usually, there are many isolated pixels whose classification is different from that of their neighbours. However, one would expect some degree of spatial dependence in land cover from pixel to pixel, if this spatial information can be incorporated in the classification pro cedure it would have the potential benefit of improving classification by removal of isolated inliers within homogeneous areas (Justice and Townshend 1982). One classifi cation smoothing algorithm is the majority filter which is illustrated in Fig.l. A spatial window of specified size (3x3, 5x5) is passed through the classified image and the center pixels classification is changed to the majority class of the surrounding pixels in the window. In this study the cla ssified images were reclassified using a 3x3 majority filter. (a) Central pixel changed Original classification Reclassification Class Pixels AAA A 6 AAA ACB B 1 A A B CAA C 2 CAA (b) Central pixel unchanged Original classification Reclassification Class Pixels AAB A 3 A A B CCB B 3 CCB C B A c 3 C B A Figure 1. The 3-by -3 majority filter (Source: Schowengerdt 1983) 4 ACCURACY ASSESSMENT After the classification, the results were evaluated to get an expression of its accu racy. In this study the accuracy assessment was conducted using 'test sets' and confu sion matrices. A confusion matrix is a square V" V \/

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