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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:
Spectral components analysis: Rationale and results. C. L. Wiegand & A. J. Richardson
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
  • 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

349 
xLanted at 
:ions and 
months late 
lonirrigated 
.5 June to 15 
. the corn 
:ion at 200 
luction. 
conducted at 
k3 long. 
;rtic 
die North 
)°W) on a 
stolls. Both 
Lants/ha in 
lurth was 
Ly planted 
ition of a 
i the rate of 
lead-sized 
300 plant/ha 
if mepiquat 
and 25 g/ha 
and at first 
Lning densely 
the 
<NT) did not 
aintained as 
nations of 
. 1983 at the 
L in rows 
nieved two 
3 197 
), and 
66 m apart. 
s/m 2 . 
D) plots each 
Twelve 
hat the 
significant 
. Rainfall 
y 100 mm all 
each term in 
three 
segments of 
4 x 0.6 m) 
tentative 
of corn were 
lite per 
ted in Table 
d frcm 
1982) 
(Table 1) 
J., 1981) 
he 630 to 690 
! infrared 
igth interval. 
: view and was 
centered over 
>nses plus 
>servations 
r for each of 
luced by the 
¡1). 
the 
'9) and 
Richardson 
these 
based on the 
ndville clay 
PVI = 0.628(RIR) - 0.778(RED) - 2.537 based on the 
soil line 
RED = -3.26 + 0.807(RIR) for the Hidalgo sandy 
clay loam. 
Photosynthetically active radiation (PAR) incident 
on (Io)' transmitted (T) through, reflected (R) 
from the composite canopy-soil backgrounds, and from 
the bare soil (R s ) provided the data for absorbed 
PAR, termed APAR, and defined as (I 0 -'P-R+R S T)/I 0 = 
d-T’-R'+Rs'T') (Hipps et al., 1983; Gallo 
et al., 1985) wherein 
I D = the downward PAR flux density at the top of 
the canopy 
T = the downward PAR flux density at the bottom 
of the canopy 
R = the upward PAR flux density at the top of the 
canopy (the PAR reflectance of the composite 
plant and soil background scene) 
Rg = the reflectance of the soil beneath the 
canopy, and the primes indicate that all 
values are normalized to I Q . 
For the field measurgments T, R, and Rs were 
measured with LI-COR ^ line quantum 
sensors (LI 191SB) and I Q was measured with a 
quantum sensor (LI 190SB). A line quantum sensor 
was inserted below the canopies perpendicular to the 
rows (wheat, cotton) and obliquely middle-to-middle 
(corn) for the T measurements while the sensor for 
measuring R frcm the canopy plus soil was inverted 
30 cm above the canopies and parallel to the sensor 
below the canopies. The Rs term was measured by a 
line quantum sensor inverted 30 cm above a small 
area in the plots where the plants had been removed 
soon after emergence. The LI 190SB sensor was moved 
frcm plot to plot on a 2-m tall stand that was 
leveled at each stop. The sensor outputs for T, R, 
and I D and the time of the observations were all 
electronically logged simultaneously. Care was 
taken to keep all sensors level and to avoid shading 
the sensor measuring T by the one measuring R. 
The data were fit to the ccmmonly used exponential 
relation, ^^2 = (1-Ae“ BIAI /cosZ 2) 
wherein A is an arbitrary coefficient, B is the 
extinction coefficient, and cos Z 2 adjusts LAI for 
the path length through the canopy at the time of 
the APAR observations. 
At the time of each sampling, up to twto days was 
required to determine LAI, one day to make the PAR 
sensor observations, and about 1 hour to make the 
reflectance factor observations. Thus it was 
impossible to make all the necessary observations on 
the same day. Table 1 summarizes the dates on which 
the various measurements were made for each 
experiment. When data frcm the various sensors were 
merged, the sample date was considered to be that of 
the PAR absorption data and they were used as 
measured. For pairing observations, the VI and LAI 
data were plotted versus time and the VI and LAI 
estimates interpolated to the dates of the PAR 
measurements. 
The LAI samples provided information on the 
aboveground biomass by plant parts for those 
sampling dates, while yield canponents were 
determined on the harvest samples. 
Solar zenith angle was calculated frcm latitude 
and longitude of the experimental sites using day 
of year and time of day of the observations in 
ephimeris equations. t 
Equations [1 ] and [2 ] were used in analyzing 
the data. That is, VI and APAR data taken at 
different times of day were accomodated by the solar 
zenith angle adjustment to LAI incorporated into the 
terms of these equations. 
■^Mention of trade names does not infer 
preferential treatment nor endorsement by the U.S. 
Department of Agriculture over similar products 
available frcm other sources. 
6 RESULTS 
Data presented are paired wath the PAR observations 
since they were available on the fewest number of 
dates (Table 1). 
Solar zenith angles were small on all dates for 
corn—because measurements were made near solar noon 
in June and July and our latitude, 26.2°N, is close 
to the Tropic of Cancer—moderate for cotton, and 
wide-ranging for wheat. However, the solar zenith 
angle adjustments in LAI were made for all crops on 
all dates for uniformity of analysis. 
Expressions for each of the three terms of 
equation [1 ] are summarized in Table 3 for each 
of the three crops. Results for two vegetation 
indices, the normalized difference (ND) and the 
perpendicular vegetation index (PVI) are given as 
exponential, power, or linear expressions. For the 
first term on the left, the coefficients of 
determination for the dependence of LAI on PVI are 
0.92 or greater for all three crops, compared with 
0.77 to 0.96 when expressed in terms of ND. The 
results demonstrate that there is a close 
association between LAI and the vegetation indices 
as calculated from reflectance factor measurements 
in the visible and reflective infrared wavelengths, 
for all crops. f 
The second term on the left in equation [1 ] 
estimates absorbed photosynthetically active 
radiation (APAR) frcm leaf area indices adjusted for 
solar zenith angle, LAI/cosZ2* Results are 
presented for both the widely accepted 
(Charles-Edwards, 1982; Pearson, 1984) exponential 
form and a power expression. Again, r 2 ^ 0.93 for 
the exponential form and 0.89 for the power form 
for all crops. The extinction coefficients are 
0.514, 0.540, and 0.347 for cotton, wheat, and corn, 
respectively. Corn had a more open canopy than the 
other crops and consistently transmitted more PAR at 
a given LAI. Pooling the observations for Aim and 
Nadadores cultivars of wheat may have lowered the 
r 2 because we found in unreported analyses that 
their extinction coefficients differ significantly. 
■Jhe results for the right hand side of equation 
[1 ] in Table 3 demonstrate that APAR was 
estimated almost as well for cotton and corn from 
the vegetation indices (r 2 = 0.75 to 0.99) as frcm 
the LAI. For wheat, the r 2 were lower, 0.48 to 
0.83. PVI gave a closer relation in every case than 
did ND. For wheat ND increased rapidly frcm its 
value of 0.2 for bare soil to 0.83 by the time LAI 
was 1. Consequently it was insensitive to increases 
in LAI beyond 1.0 where most of the APAR 
observations were taken and APAR was still 
increasing as LAI increased. In contrast, PVI 
increased at a diminishing rate as LAI increased. 
Figures 1, 2, and 3 display the data by crop for 
each term in equation [1']. In Figure 1 (1st term) 
and Figure 3 (right side term) the data are 
presented in terms of PVI. For all Figures the best 
fit curves for the equations given in Table 2 are 
superimposed on the data displays. The data are 
restricted, as in Table 2 to the dates when the data 
for all three variables LAI, VI, and APAR were 
available for pairing. The data for PVI (Figure 1) 
and APAR (Figure 2) are both asymptotic to the LAI 
axis. The result is that the APAR vs PVI data in 
Figure 3 appear to be nearly linear; coefficients of 
determination for a linear fit in Figure 3 were 
0.924, 0.825, and 0.969 for cotton, wheat, and corn 
compared wath 0.969, .835, and .986 for the power 
form. Asrar, et al. (1984) reported a linear 
relation between APAR and the normalized difference 
vegetation index whereas Gallo et al. (1985) related 
APAR to the RIR/RED ratio, ND, and greenness (GR) 
vegetation indices by quadratic expressions. 
7 SUMMARY 
The plant physiological, agroncmic, and
	        

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