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:: 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Relation between spectral reflectance and vegetation index. S. M. Singh

Document type:: Multivolume work

Structure type:: Chapter

317 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Relation between spectral reflectance and vegetation index S.M.Singh NERC Unit for Thematic Information Systems, Department of Geography, University of Reading, UK ABSTRACT: Atmospheric corrections are applied to the Advanced Very High Resolution Radiometer (AVHRR) channel-1 and channel-2 data. Both raw and atmospherically corrected Normalized Difference Vegetation Indices (NDVIs) are calculated. A comparison between them shows a contrast enhancement by a factor of at least two when atmospheric corrections are applied. Spectral reflectances and atmospherically corrected NDVI are partially correlated indicating a possibility of improving surface cover classification using NDVI and spectral reflectances instead of NDVI values alone. Raw NDVI and atmospherically corrected NDVI do not have a unique relationship but are highly correlated. This indicates that atmospheric corrections be applied to each scene of interest. 1 INTRODUCTION The visible channel (0.58-0.68 pm; hereafter referred to as channel-1) and near infrared channel (0.725-1.1 Pm; hereafter referred to as channel-2) data from the Advanced Very High Resolution Radio meter (AVHRR) instrument flown on the Tiros-N/NOAA meteorological satellites have been found to be useful for monitoring health and vigour of photo- synthetically active vegetation canopy. These data have been used for mapping and monitoring vegetation cover on local and continental scale (for example, see Tucker et al., 1983, 1984 and Hayes and Cracknell, 1984) as well as on a global scale (Justice et al., 1985). There is a daily coverage at higher latitudes but around the equator complete coverage requires three days. This means that the global coverage data could be obtained in three days if there were no cloud covers. There is an absorption band of chlorophyll within channel-1 wavelength range whereas wavelengths within channel-2 spectral band width are strongly reflected by green pigments. In principle, the data from these two spectral channels should be correlated to the abundance of vegetation. Many relations exist in the literature for calculating vegetation index, for example, see Hayes (1985). Most popular of all relations is the so-called Normalized Difference Vegetation Index (NDVI) which is defined as NDVI DN2 - DN1 DN2 + DN1 (1) where DN1 and DN2 are channel-1 and channel-2 pixel values, respectively. There are several advantages of using equation (1) rather than channel-2 data only; because of optical properties of photo- synthetically active chlorophyll as noted above, equation (1) results in enhanced values of NDVI, which could be useful particularly for low vegetation; the relation (1) partially compensates for atmospheric interference, solar elevation, changing solar irradiance on the surface and topo graphic effects (see, Justice et al., 1985). Ideally, one would have liked to remove atmos pheric effects from these data first and then calculated vegetation indices because band ratioing does not remove atmospheric effects completely (Holben and Justice, 1981). The reason is that atmospheric contaminations in channel-1 and channel-2 are not proportional to each other. The larger the view angle of the sensor the larger the atmospheric contribution is expected to be. Therefore, even if there were no topographic effects and if surfaces were Lambertian in nature, the NDVI values calculated from equation (1) have strong view angle dependence (Duggin et al., 1982), a significant fraction of which is expected to be due to atmos pheric effects. Within the framework of vegetation mapping and monitoring, the same surface area is viewed from various viewing angles (from different orbits) and it is evident from the work of Duggin et al. (1982) and many others that the NDVI values do depend on the view angle. However, it has not yet been possible to come up with a perfect atmos pheric correction algorithm because of the diffi culties in estimating atmospheric contamination due to aerosols. Nevertheless, an approximate estimate of atmospheric contribution to remotely sensed data can be made. One finds the same NDVI value for several surface cover types (Townshend and Tucker, 1985) and, therefore, vegetation type classification using NDVI values alone has limited success. Ideally, one would like to have several spectral band data which are partially or poorly correlated among themselves so that each spectral channel data carries information about the nature of surface cover which supplements information carried in other spectral channels. Using Landsat Thematic Mapper (TM) data Toll (1985) demonstrates that the land cover classification accuracy does not improve by adding spectral channel data which are highly correlated to other channel(s) data which are already used for classi fication. Also, when photosynthetically active chlorophyll amount increases (say, in dense forests) the NDVI values calculated from equation (1) tend to saturate thereby limiting the range of applicability of equation (1). Under such circumstances it would be interesting to see how channel-1 reflectivity changes and whether or not this reflectivity is still a sensitive function of vegetation abundance. It is in this spirit that a brief summary of atmos pheric correction technique will be presented, channel-1 and channel-2 reflectivities will be calculated, raw and atmospherically corrected NDVI will be calculated and relationship betweèn reflectivities and NDVI values will be examined in order to find some uncorrelated or partially correlated parameters which may prove to be valuable for land cover classification.

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