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Remote sensing for resources development and environmental management (Volume 1)

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Bibliographic data

fullscreen: Remote sensing for resources development and environmental management (Volume 1)

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:
Spectral and botanical classification of grasslands: Auxois example. C. M. Girard
Document type:
Multivolume work
Structure type:
Chapter

Contents

Table of contents

  • Remote sensing for resources development and environmental management
  • Remote sensing for resources development and environmental management (Volume 1)
  • Cover
  • Title page
  • Title page
  • Title page
  • Preface
  • Organization of the Symposium
  • Working Groups
  • Table of contents
  • 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder
  • Structural information of the landscape as ground truth for the interpretation of satellite imagery. M. Antrop
  • Interpretation of classification results of a multiple data set. Helmut Beissmann, Manfred F. Buchroithner
  • Digital processing of airborne MSS data for forest cover types classification. Kuo-mu Chiao, Yeong-kuan Chen & Hann-chin Shieh
  • Methods of contour-line processing of photographs for automated forest mapping. R. I. Elman
  • Detection of subpixel woody features in simulated SPOT imagery. Patricia G. Foschi
  • A GIS-based image processing system for agricultural purposes (GIPS/ALP) - A discussion on its concept. J. Jin King Liu
  • Image optimization versus classification - An application oriented comparison of different methods by use of Thematic Mapper data. Hermann Kaufmann & Berthold Pfeiffer
  • Thematic mapping and data analysis for resource management using the Stereo ZTS VM. Kurt H. Kreckel & George J. Jaynes
  • Comparison of classification results of original and preprocessed satellite data. Barbara Kugler & Rüdiger Tauch
  • Airphoto map control with Landsat - An alternative to the slotted templet method. W. D. Langeraar
  • New approach to semi-automatically generate digital elevation data by using a vidicon camera. C. C. Lin, A. J. Chen & D. C. Chern
  • Man-machine interactive classification technique for land cover mapping with TM imagery. Shunji Murai, Ryuji Matsuoka & Kazuyuli Motohashi
  • Space photomaps - Their compilation and peculiarities of geographical application. B. A. Novakovski
  • Processing of raw digital NOAA-AVHRR data for sea- and land applications. G. J. Prangsma & J. N. Roozekrans
  • Base map production from geocoded imagery. Dennis Ross Rose & Ian Laverty, Mark Sondheim
  • Per-field classification of a segmented SPOT simulated image. J. H. T. Stakenborg
  • Digital classification of forested areas using simulated TM- and SPOT- and Landsat 5/TM-data. H.- J. Stibig, M. Schardt
  • Classification of land features, using Landsat MSS data in a mountainous terrain. H. Taherkia & W. G. Collins
  • Thematic Mapping by Satellite - A new tool for planning and management. J. W. van den Brink & R. Beck, H. Rijks
  • 2 Microwave data. Chairman: N. Lannelongue, Liaison: L. Krul
  • 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik
  • 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
  • Cover

Full text

3.2 Reflectance data 
In order to characterize more fully and to compare 
more easily seasonal(phenological) variations of 
agro-ecologmcal units, we used reflectance data. 
A PCA performed on the ISCO measurements showed 
that the more useful wavelengths were: 550 nm (green) 
675 nm (red), 850 and 1400 nm (near infra-red). 
The first two wavelengths are inversely correlated 
with chlorophyll a, and chlorophyll (a+b)/ carote 
noid respectively (Gates and all 1965, Knipling 1970) 
while the third is correlated with internal struc 
ture of leaves, and the last to water content(Gates 
1970) . 
Variations of reflectance in red and near infra-red 
are linked with phenological and coverage variations 
of plants(Figure 2). 
Red low 
near IR high 
Plants with high chlorophyll content 
Red high 
near IR low 
Plants with less chlorophyll: dying or maturing 
A R% 
Red low 
near IR low 
* * 
R nIR 
Plants with high chlorophyll content but low 
coverage: soil showing 
Red high 
near IR high 
Plants with high chlorophyll and other pigment 
content: flowering. 
Figure 2. Variations of reflectance in red and near 
infra-red in relation with phenological and coverage 
varia tions of plants 
The reflectance values at each period, for each wave 
length of each agro-ecological unit was obtained by 
Calculating the mean of reflectance values of the 
different stations belonging to this agro-ecological 
unit. 
A PCA of the reflectance values in the 4 wavelengths 
at the 4 periods, for the 8 agro-ecological units 
enabled to draw a model of spectral behaviour through 
time (Figure 3). 
unit 1 : heliophilous and calcicolous grasslands 
and grazed rangelands on shallow soils with exces 
sive drainage. 
unit 2:grazed grasslands on steep èlope, shallow soil 
dry. 
unit 3 : grazed grasslands on slope, more or less shal 
low soil, dry. 
unit 4:grazed grasslands on middle or (bottom of slope 
deep, moist soil. 
unit 5:grazed grasslands on middle or bottom of slope 
deep, humid soil. 
unit 6:grazed grasslands on bottom of slope, deep, 
very humid soil. 
unit 7: grazed grasslands on bottom of valleys, or 
slope bogs on deep muddy soils. 
unit 8: grazed grasslands on bottom of valleys, or 
slope bogs on very muddy soils. 
Figure 3.Radiometric behaviour model for the 8 agro- 
ecological units according to red and near infra-red 
from PCA. 
This spectral behaviour of each unit varies accor 
ding to the nature, percent cover and phenological 
stages of the 26 more abundant species. According 
to its agro-ecological characteristics, each unit 
shows a more or less important ! .chlorophvll content 
and a long or short chlorophvllian activitv period. 
For example, chlorophyIlian activity period of helio 
philous and calcicolous grasslands and grazed range- 
lands on shallow soils with excessive drainage is 
short (May, June), while it is long(April till June) 
for grazed grasslands on middle or bottom of slope 
on deep, moist soils. 
This model has been tested with the three stations 
already used to validate the model for phytosociolo- 
gical relevés analysis. 
These stations are clustered respectively to units
	        

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Damen, M. .C. .J. Remote Sensing for Resources Development and Environmental Management. A. A. Balkema, 1986.
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