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:: Global vegetation monitoring using NOAA GAC data. H. Shimoda, K. Fukue, T. Hosomura & T. Sakata

Document type:: Multivolume work

Structure type:: Chapter

505 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Global vegetation monitoring using NOAA GAC data H.Shimoda, K.Fukue, T.Hosomura & T.Sakata Tokai University Research & Information Center, Tokyo, Japan ABSTRACT: In the last decade, the necessity of global monitoring of vegetations or bio-mass has become a more urgent matter. In order to prevent the large disaster which may be caused by vegetation decrease, accurate conditions or stage of the world vegetation should be monitored. The only satellite system and observation data which can be used for the purpose is TIROS/NOAA system and Vegitation Index Data(VID) made of AVHRR, respectively. The purpose of this study is to establish the method to derive a global vegetation map from a VID set. One data set was used in this study. A large shading effects mainly caused by sun angle deviations were first eliminated. The classification was done using a maximum liklihood method with four channels of VID. Training data composed of 67 categories were chosen according to bhe World Vegetation Map made by Preston James et al. After the classification, these 67 categories were unified to 17 categories. Then the classified image were transformed to longitude and latitude coordinates. As a result of this study, NOAA Vegetation Index Data were proved to be a suitable data for world wide vegetation monitorings. 1. INTRODUCTION In the last decade, the necessity of global monitoring of vegetations or bio-mass has become a more urgent matter. Large forest areas in Asia and South America are dissapearing because of cut and burn agricultures as well as soil erosions. In Africa and also in Asia, Sahel areas are penetrated by deserts. The total amount of vegetation dis appearing- areas is estimated to be about 3 00,000Kin for a year, which corresponds to be about the same of the area of Japan. These vegetation decrease in a world scale are causing heavy shortage of foods production, which results in many people starved especially in developing countries in Africa. It may also cause a world scale meteorological change. In order to prevent the large disaster which may be caused by these vegetaion decrease, accurate conditions or stages of the world vegetaion should be monitored. It is obvious that this kind of monitoring could be accompalished only through the use of earth observation satellites. However, past earth observation satellite data were not appropriate for this global monitoring purpose. Landsat MSS data has proved that they are very good tools for vegetation monitorings, but it is almost impossible to use those data in a global scale. There are two other operational satellite systems. They are TIROS/NOAA series satellites and weather satellites in geosynchronous orbits. However, the latter satellites are not appropriate for vegetation monitoring because of their wavelength ranges. They lack the near infra-red channels which are best for vegetaion discriminations. Thus, the only satellite system which can be used for this purpose is TIROS/NOAA system. The mam sensor of these satellites is AVHRR (Advanced Very High Resolution Radiometer) and it has one band in visible and one band in near infra-red regeon. Their repetitive rate is twice a day at least, and the ground resolution is about lKm at the nadir. This ground resolution is stil too high for monitoring purposes, because it means that we need about 500 million pixels to cover the whole earth, and also we must mosaic about 18 paths of each ground coverage. But now, we have more convenient data for our purpose. It is called as a vegetation index data made of these NOAA data. In this data set, each hemisphere is composed of 1024 x 1024 pixels in a Polar Stereo projection, which is a moderate data quantity for data pro cessings and analyses. The purpose of this study is to establish the method to derive a global vegetation map from a NOAA vegetation index data set. 2. NOAA SATELLITES The TIROS/NOAA sereies satellites were first launched in 1960 by NOAA (United States National Oceanic and Atmospheric Administration) for weather and ocean monitoring. Recently these two satellites were unified and called as NOAA satel lites . The orbit of this series satellites is a polar orbit with about 99 inclination and the altitudes are about 850Km. The main sensor is AVHRR and it has 4(5) spectral bands in visible, near and thermal infra-red regeon. The swath width of AVHRR is about 3000Km and one satellite covers the same area twice a day in ascending and descending mode.

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