Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

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:: [THP-5 IMAGE INTERPRETATION AND ANALYSIS]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: THE INDIAN REMOTE SENSING SATELLITE (IRS-1A) AND ITS APPLICATION POTENTIALS. MG Chandrasekhar, V Jayaraman, K Radhakrishnan, CBS Dutt, B Manikiam, PP Nageswara Rao

Document type:: Multivolume work

Structure type:: Chapter

for various natural resources applications in India. The following are theme specific potentials of IRS-1A data (DOS, 1988, 1988A). 2.1 Agriculture Crop acreage and production using IRS LISS-I data for paddy and wheat has been estimated covering several districts in India for the States of Punjab, Haryana (for wheat), Orissa and Tamil Nadu (for paddy). For example, the remote sensing based wheat acreage estimation for Punjab for Kharif crop of 1989 gave an estimate of 3.128 + 0.172 M.ha compared well with Bureau of Economic and Statistics (BES) estimate of 3.126 M.Ha. The crop acreage estimates using IRS data have provided better results in comparison to Landsat MSS data. All the estimates have satisifed the 90/90 accuracy criterion. In case of acreage estimation for dryland crops like Sorghum, the results have shown that IRS LISS-I shows improved accuracy over Landsat MSS data due to higher radiometric sensitivity and detectivity determined by their S/N ratios. Digital analysis of IRS-1A data has been found to provide highly accurate estimates of acreage under mulberry and cotton crops. These two plant species provide either directly or indirectly the raw material for natural fibre. The Ministry of Textiles, Govt, of India is making use of the IRS-1A data, on an experimental basis, to forecast the production of silk and cotton so as to determine the exportable surplus. The IRS-1A data has also been tried for locating the illegal cultivation of narcotic plants and in the inventory of commercial agricultural plantations like tea and coffee in the hill areas of southern and north-eastern regions of India. The IRS data is also being applied for crop yield estimation. The yield models are being developed and the operational packages for mono cropped areas of Punjab and Haryana have been transferred for routine operational annual crop acreage estimations. Efforts related to multi-crop estimation using IRS data are presently on. 2.2 Forestry Several case studies have been carried out in the field of forestry using IRS-1A data both by visual and digital interpretation techniques to study forest type descrimiantion and forest cover mapping. The studies indicate that details on forest types on LISS-I data are comparable with that on Landsat MSS data. Interclass boundary discrimination is found to be better on LISS-II data as compared with Landsat Thematic Mapper imagery. Very good discrmination of forest cover species such as Deodar, Pines (old), Pines (young), Oak and Shorea forests have been noted in Himalayan areas specially around Shimla using supervised classification of LISS-II data. The results are also corroborated from other studies (Roy, et.al, 1988) . Feasibility of the use of magnified LISS-II imagery in preparation and updating of forest stock maps within 90% accuracy has also been demonstrated through a case study in Jatga range. Case study carried out in Haliyal and Belgaum forest divisions of Karnataka indicated that differentiation between evergreen, moist deciduous and dry deciduous forest types is better observed on LISS-I data than the Landsat MSS data. (Jadhav, et.al, 1988). The Biennial monitoring for forest cover over India being carried out by Forest Survey of India is planned to be conducted using IRS-1A data. 2.3 Landuse/Land Cover Mapping A large number of Landuse/Land Cover projects using IRS data have been carried out in India and many are in progress. The results of landuse/land cover and urban landuse case studies carried out using IRS LISS-II data for different towns/districts using visual and digital interpretation techniques indicate that Landuse details upto Level-2 are possible using LISS-II data. LISS-I data is useful for Level-I mapping and is comparable in information to Landsat MSS data. Image processing techniques such as stratification approach used for landuse classification, HSI transformation and colour composites, edge enhancement, supervised classification, contract stretching, filtering, principal Component analysis and colour compositing and FCC (Band 2,3,4 and Band 1,3,4) were found very effective in discrimination/classification of landuse categories. Landuse features such as Irrigated and Non-irrigated lands, open and closed forests, open scrub, wastelands, waterbodies, orchards, plantations, vacant lands, grasslands, dense and sparsely built up areas, rail-road network, recreational areas, parks, airports, golf course, industrial areas, city growth directions, settlements, cropland etc. are easily differentiated on LISS-II data. The hard copy of Gaussian stretched IRS LISS-II data offers good discrimination between different landuse features (DOS, 1989) .

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