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

Damen, M. C. J.

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Mehrbändiges Werk

Persistenter Identifier:: 856342815

Titel:: Remote sensing for resources development and environmental management

Untertitel:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Erscheinungsjahr:: 1986

Erscheinungsort des Originals:: Rotterdam; Boston

Verlag des Originals:: A. A. Balkema

Identifier (digital):: 856342815

Sprache:: Englisch

Sonstige Anmerkungen:: Volume 1-3 erschienen von 1986-1988

Herausgeber:: Damen, M. C. J.

Dokumenttyp:: Mehrbändiges Werk

Band

Persistenter Identifier:: 856343064

Titel:: Remote sensing for resources development and environmental management

Untertitel:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Umfang:: XV, 547 Seiten

Erscheinungsjahr:: 1986

Erscheinungsort des Originals:: Rotterdam; Boston

Verlag des Originals:: A. A. Balkema

Identifier (digital):: 856343064

Illustrationsangabe:: Illustrationen, Diagramme

Signatur der Quelle:: ZS 312(26,7,1)

Sprache:: Englisch

Nutzungslizenz:: Attribution 4.0 International (CC BY 4.0)

Herausgeber:: Damen, M. C. J.

Verlag des Digitalisats:: Technische Informationsbibliothek Hannover

Erscheinungsort des Digitalisats:: Hannover

Erscheinungsjahr des Digitalisats:: 2016

Dokumenttyp:: Band

Sammlung:: Geowissenschaften

Kapitel

Titel:: 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

Dokumenttyp:: Mehrbändiges Werk

Strukturtyp:: Kapitel

Kapitel

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

Dokumenttyp:: Mehrbändiges Werk

Strukturtyp:: Kapitel

513 ort No. 116, .1 Development :erns in Azare 1 experimental systematic low »y. Agricul- n and Planning se in Busia Planning and ort No. 118. (1984). Land y of National :hnical report :periments in ising airborne 985-1 pp 9-13. 0). Land use sts. In, New land use and Lications Ltd. (1985). The a disaster. 987-994. Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Tropical forest cover classification using Landsat data in north-eastern India Ashbindu Singh Indian Forest Service, Forest Department, Government of Manipur, Imphal ABSTRACT i Landsat MSS data were used for mapping tropical forest ctver classes in two site conditions of northeastern India. These are areas of rugged mountainous terrain and spec trally complex forest cover types. A supervised classification procedure based on a minimum distance to means algorithm was used in the analysis. Classified images were reclassified using a 3x3 majority filter. A quantitative evaluation of the results were carried out to determine the accuracy of computer-aided forest classification. The computer-aided analysis of Landsat MSS data has shown that 'forest* and 'non-forest* could be classified with a high degree of accuracy, but further break-up of the classes did not yield satisfactory classification accuracy. However, it does provide definitive infor mation about the location of 'closed forest'. The reclassification scheme, modestly improved the classification accuracy in areas of homogeneous cover types but the results were not encouraging for the aras of heterogeneous cover types. 1 INTRODUCTION The tropical forest biome is, biologically and ecologically speaking, the most complex and diverse biome on earth (Richards 1973). However, the location and extent of the area under tropical forests are poorly known. The published statistics are presented with a spuriously high degree of precision but possess a very low level of accuracy and reliability (Persson 1977). Due to inacces sibility of such areas and ruggedness of the terrain in which tropical forests are located the application of satellite data offers only possibility of mapping forest cover types in such regions with any degree of regularity. Since Landsat MSS data are available at regular intervals they may be used to derive a data base for monitoring tropical forest resources. The objective of this study is to assess the suitability of digital Landsat MSS data for tropical forest cover classification and determine the le vels of accuracy of computer aided forest classification. 2 THE STUDY METHODOLOGY 2.1 The study area The study was conducted in the northeastern part of India. Two areas of 256 x 256 Landsat pixels-each labelled here as Area-1 and Area-2 were choosen for investigation. Floristic composition of forests and topo graphic features were different for both the areas. Area-1 has been undergoing rapid change due t© shifting cultivation, wereas, Area-2 is ©nly marginally affected by this practice. Area-1 is situated in the eastern hills of Himalayas; the terrain is extremely rugged. Dense subtropical evergreen forest occur on high hills and moist deciduous fo rest are found in the valleys and at lower altitudes. The majority of moderately steep to gentle slopes (10-30%) have been severely affected by practice of slash and bum agri culture. Area-2 is located in outer parts of the Hi malayas. This is an area with subdued relief. The forests can be classified into tropical semi-evergreen forest and secondary moist bamboo brakes. The typical scrub formation in this area is degraded stages ®f climax high forests resulting from excessive human interference (Champion and Seth 1968). 2.2 Definition of cover classes A suitable definition of cover classes is required in order to assign the ground sur face conditions to specific classes. In thi^ study land cover rather than land use formed the basis ®f classification. Stand density and stand height were considered to be the most important parameters' for defining the classes. The major cover classes occuring in the study areas are given in table 1. 2.3 Reference data set For Area-1 and Area-2 forest types maps pre pared with the help of black and white ae rial photographs after field checking at the scale ©f 1:50,000 and 1: 63,360, res pectively, were used for generation of sam ple sites. A 3 x 3 pixel observation was chosen as a sampling unit. All the pixels in the window were utilised for the sample. 30 sample sites for each category were lo cated using random spatial coordinates. In the classification analysis half of these were used as a training set and the remai ning half as a testing set. 2.4 Landsat data The Landsat-2 CCT of path 145 ROW 042 dated 6-12-1981 collected by the National Remote Sensing Agency ef India at the receiving station in Secunderabad, India was used in the analysis.

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