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

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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:: An evaluation of different green vegetation indices for wheat yield forecasting. A. Giovacchini

Document type:: Multivolume work

Structure type:: Chapter

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
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
Remote sensing in the evaluation of natural resources: Forestry in Italy. Eraldo Amadesi & Rodolfo Zecchi, Stefano Bizzi & Roberto Medri, Gilmo Vianello
Visual interpretation of MSS-FCC manual cartographic integration of data. E. Amamoo-Otchere
Optimal Thematic Mapper bands and transformations for discerning metal stress in coniferous tree canopies. C. Banninger
Land use along the Tana River, Kenya - A study with small format aerial photography and microlight aircraft. R. Beck, S. W. Taiti, D. C. P. Thalen
The use of multitemporal Landsat data for improving crop mapping accuracy. Alan S. Belward & John C. Taylor
Aerial photography photointerpretation system. J. Besenicar, A. Bilc
Inventory of decline and mortality in spruce-fir forests of the eastern U.S. with CIR photos. W. M. Ciesla, C. W. Dull, L. R. McCreery & M. E. Mielke
Field experience with different types of remote-sensing data in a small-scale soil and land resource survey in southern Tanzania. T. Christiansen
A remote sensing aided inventory of fuelwood volumes in the Sahel region of west Africa: A case study of five urban zones in the Republic of Niger. Steven J. Daus & Mamane Guero, Lawally Ada
Development of a regional mapping system for the sahelian region of west Africa using medium scale aerial photography. Steven J. Daus, Mamane Guero, Francois Sesso Codjo, Cecilia Polansky & Joseph Tabor
A preliminary study on NOAA images for non-destructive estimation of pasture biomass in semi-arid regions of China. Ding Zhi, Tong Qing-xi, Zheng Lan-fen & Wang Er-he, Xiao Qiang-Uang, Chen Wei-ying & Zhou Ci-song
The application of remote sensing technology to natural resource investigation in semi-arid and arid regions. Ding Zhi
Use of remote sensing for regional mapping of soil organisation data Application in Brittany (France) and French Guiana. M. Dosso, F. Seyler
The use of SPOT simulation data in forestry mapping. S. J. Dury, W. G. Collins & P. D. Hedges
Spruce budworm infestation detection using an airborne pushbroom scanner and Thematic Mapper data. H. Epp, R. Reed
Land use from aerial photographs: A case study in the Nigerian Savannah. N. J. Field, W. G. Collins
The use of aerial photography for assessing soil disturbance caused by logging. J. G. Firth
An integrated study of the Nairobi area - Land-cover map based on FCC 1:1M. F. Grootenhuis & H. Weeda, K. Kalambo
Explorations of the enhanced FCC 1:100.000 for development planning Land-use identification in the Nairobi area. F. Grootenhuis & H. Weeda, K. Kalambo
Contribution of remote sensing to food security and early warning systems in drought affected countries in Africa. Abdishakour A. Gulaid
Double sampling for rice in Bangladesh using Landsat MSS data. Barry N. Haack
Studies on human interference in the Dhaka Sal (Shorea robusta) forest using remote sensing techniques. Md. Jinnahtul Islam
Experiences in application of multispectral scanner-data for forest damage inventory. A. Kadro & S. Kuntz
Landscape methods of air-space data interpretation. D. M. Kirejev
Remote sensing in evaluating land use, land cover and land capability of a part of Cuddapan District, Andhra Preadesh, India. S. V. B. Krishna Bhagavan & K. L. V. Ramana Rao
Farm development using aerial photointerpretation in Ruvu River Valley, Ragamoyo, Tanzania, East Africa. B. P. Mdamu & M. A. Pazi
Application of multispectral scanning remote sensing in agricultural water management problems. G. J. A. Nieuwenhuis, J. M. M. Bouwmans
Mangrove mapping and monitoring. John B. Rehder, Samuel G. Patterson
Photo-interpretation of wetland vegetation in the Lesser Antilles. B. Rollet
Global vegetation monitoring using NOAA GAC data. H. Shimoda, K. Fukue, T. Hosomura & T. Sakata
National land use and land cover mapping: The use of low level sample photography. R. Sinange Kimanga & J. Lumasia Agatsiva
Tropical forest cover classification using Landsat data in north-eastern India. Ashbindu Singh
Classification of the Riverina Forests of south east Australia using co-registered Landsat MSS and SIR-B radar data. A. K. Skidmore, P. W. Woodgate & J. A. Richards
Remote sensing methods of monitoring the anthropogenic activities in the forest. V. I. Sukhikh
Comparison of SPOT-simulated and Landsat 5 TM imagery in vegetation mapping. H. Tommervik
Multi-temporal Landsat for land unit mapping on project scale of the Sudd-floodplain, Southern Sudan. Y. A. Yath, H. A. M. J. van Gils
Assessment of TM thermal infrared band contribution in land cover/land use multispectral classification. José A. Valdes Altamira, Marion F. Baumgardner, Carlos R. Valenzuela
An efficient classification scheme for verifying lack fidelity of existing county level findings to cultivated land cover areas. Yang Kai, Lin Kaiyu, Chen Jun & Lu Jian
The application of remote sensing in Song-nen plain of Heilongjiang province, China. Zhang Xiu-yin, Jin Jing, Cui Da
Cover

Full text

266 
% of variance between field of the index values 
Table 1. Vegetation .indices. 
VEGETATION INDEX 
. ACR0N0M0US 
1 
1 
BANDS AND 
CALCULATION 
l|2 1 
RATIO 
R 
1 
1 
IR/R 
1 1 
1 1 
1 SQUARE RATIO 
1 SOR 
1 
Vir/r 
1 § 1 
LOG RATIO 
Log R 
1 
1 
Log IR/R 
1 1 
NORMALIZED DIFFERENCES ND 
1 
1 
(IR-R)/(IR.R).0.5 
1 1 
iOuj I 
| TRANSF. VEGETATION 
| INDEX 
T.V.I. 
1 
1 
V(IR-R)/(IR+R)+0.5 
| GREENNESS 
1 
1 Greenness 
1 
1 
1 
1 
-aG-bR+cRIR+dIR 
l! 1 
PERPENDICULAR VEGET. 
INDEX 
1 P.V.I. 
1 
1 
1 
V(R -R ) 2 . (IR .1R ) 2 
S V S V 
lisi 
DIFFERENCES 
G 
1 
1 
IR - R 
11 1 
| DIFFERENCES-DIFFEREN 
| CES 
! 00 
(IR-RIR) 2 - (R-G) 2 
1 s 1 
1 s 1 
G ■ green; R « red; 
RIR - red and i 
frar 
ed; IR « near infrared 
% green vegetation cover plotted against calendar 
days . 
vegetation index-values ( ) and % of variance against 
calendar days ( ). 
Table II. Coefficient of correlation between vegeta 
tion index values and final grain yield versus diffe 
rent phenological stages. 
tation indices. 
maximum correlation between spectral vegetation 
values and final grain yield, belong to the 
perpendicular and normalized family indices. From 
flowering to the end of mulky-maturity phase, the 
maximum correlation is related to vegetation indices 
based on the difference concept. Before harvest, 
that is at the physiological maturity, all the 
indices considered in this study show the same 
correlation values. The results obtained point out 
that it is necessary to adopt different vegetation 
indices at specific phenological stages, if the 
maximum correlation values have to be exploited to 
forecast wheat yield by means of remote sensing 
data. This fact can be explained by observing 
Figures 1 and 2. In Fig. 1 three spectral profiles 
and the trend of the % of the soil vegetation cover 
are plotted against seven phenological stages. As 
can be observed, the ratio profile function (R) is 
more sensitive to the percentage of soil vegetation 
cover than the Greenness and Normalized functions 
(Greenness, ND). This fact is more evident in 
proximity of the maximum soil vegetation cover. That 
corresponds approximately to the heading phase. 
Since the soil vegetation cover is correlated with 
the leaf area index of the wheat (at least for soil 
vegetation cover 85%) and the LAI at the heading is 
strongly correlated with the final grain yield (if a 
normal climatic and environmental condition is

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

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