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:: Sugar beet biomass estimation using spectral data derived from colour infrared slides. Robert R. De Wulf & Roland E. Goossens

Document type:: Multivolume work

Structure type:: Chapter

diversity of the major sugar beet growing areas. The predictive value of the model is rather poor considering the fact that pluviometric data till October 31st are to be included. In the model the leaf yellowing percentage is the only non- meteorological factor and has to be estimated visually at the end of August. Leaf chlorosis can be detected successfully by multispectral remote sensing (Andrieu 1977, Boehnel et al 1983, Reichert 1983). In practice the Royal Belgian Institute for Sugar Beet Amelioration relies on biweekly sampling in the major sugar beet growing areas to assess biomass and formulate trends. 3 MATERIALS AND METHODS 3.1. Collection of agronomic data The experimental results reported herein were obtained in 1984 and 1985 from test fields located in Sint-Laureins, in the north-west of Flanders. Soil type can be characterized as an aquic udifluvent. The area constitutes one of the richest agricul tural regions featuring crops as winter wheat, winter barley, sugar beets and potatoes. Sugar beets cv "Allyx" were sown on 23.04.1984 and on 22.04.1985 respectively. Emergence was recorded on 14.04.1984 and on 05.05,1985. Application of fertilizer and herbicides was similar during both seasons and is not further discussed here. Traces of fungicide sulphur spray could be detected on leaves on 21.08.1984. The use of insecticides on other dates did not leave visible traces on the canopy. Originally a 10 day interval sampling scheme was envisaged. Experiments were to be conducted only when a direct solar path free of clouds was available. Adverse weather conditions occurred frequently during both growing seasons. This resulted in 10 sampling dates in 1984 and 9 sampling dates in 1985. On each sampling date 3 plots were located by throwing a 80cm by 80cm frame onto the canopy from randomly defined coordinates in the test field. All plants inside the frame were extracted. Green leaves were separated from the roots, spread out on a matt- black painted surface and recorded on black-and-white film. All roots (with heads cut off) were weighed immediately to determine fresh weight. Three randomly selected roots per plot were retained for sugar content determination and deep-frozen to avoid sugar loss awaiting analysis. Besides root fresh weight and sugar content (%) green leaf area index (GLAI) was determined. The latter was accomplished by placing a transparent dot grid (4mm spacings) on top of 18cm x 24cm sized photographic paper during enlargement. By counting the dots on the leaves GLAI could be computed as dimensions of black background and sample plot were known. This method proved to be much faster than the use of a polar planimeter. Parallel measurements using both methods on randomly selected samples did not reveal significant differences (Student's T test). 3.2. Extraction of multispectral data Simultaneous recording of the test plots on CIR film assured direct relationship of biomass to multispectral parameters to be extracted from the images. A MIRANDA SENSOREX EE (*) camera fitted with standard 50 mm optics was mounted on a light weight metal boom supported by a ZEISS tripod. The camera shutter could be triggered using an air release.A B&W No Y3 filter, bearing close resemblance to the widely used VRATTEN 12 filter, was used throughout the (*) This and other trademarks are mentioned for information only. No endorsement by the authors is implied. experiments. The selected emulsion was a 35mm KODAK EKTACHROME 2236 film. Pictures were taken at solar noon plus or minus 2 hours to avoid influences by changing solar zenith angle. The plane of the film was kept parallel to the plane of the canopy. Camera distance to ground level was fixed at 2.30 metres. An area of lm by 1.5m filled the 24 x 36 mm frame. A KODAK grey card with known reflectance characteristics was included in every picture. A second reference panel was judged unnecessary as the amount of airlight between camera and target was considered negligible in relation to total reflected energy. Conventional light meters do not indicate proper diaphragm/shutter speed combinations for infrared-sensitive emulsions. Hence series of 4 slightly different combinations were made using an approximate meter setting for 50 ASA, It was experienced that at least one slide was suitable for subsequent processing. Exposed emulsions were developed according to NASA standards. From each development batch a strip of film was exposed through a step wedge in a sensitometer. The step wedge image was used for preparation of characteristic curves. On each slide 50 randomly located points were measured using a MACBETH TD 504 transmission densito meter. The filter assembly consisted of VRATTEN No 94 (blue), No 93 (green) and No 92 (red) filters allowing measurements of integral densities relating to green, red and infrared reflectance respectively. A .5 mm aperture was used. Three measurements were made on the reference panel. Integral densities were converted to analytical densities using unit spectral dye density data for the CIR emulsion. Green, red and infrared reflectance, as well as a number of vegetation indices and transformations were calculated for each slide. Brightness, Greenness and Yellowness were determined using the method outlined by Jackson (1982). Following linear combinations similar to Tasseled Cap transformations for Landsat data were obtained: Brightness= .603*G + .489*R + .629*IR Greenness =-.292tG - .598*R + .746*IR Yellowness=-.794*G + .634*R + .217*IR Eventually only Greenness was retained for further analysis due to its proven relationship to green biomass. In addition to the IR/R ratio (Vegetation Index or VI) and Normalized Difference or ND (SQRT((IR-R)/(IR+R) + .5)), the Perpendicular Vegetation Index (PVI) as the distance to the Soil Line in IR/R space (Richardson and Viegand 1977) was calculated. Figure 1 shows the soil lines for the test fields.Full details on methodology, applied software and hardware configuration used for extraction of reflectance from CIR slides can be found in De Vulf and Goossens (under review). Cover percentage could be calculated by placing a dot grid on projected slides and counting the amount of points falling on vegetation. Figure 1. Soil line for an aquic udifluvent. 4 RESULTS 4,1. Gree Developme both grovi significa featured stronger measured authors f 51 8 ÜÜ Q a if a S “W- Figure 2. GLAI (dai and 1985 Linear re been repc Badhwar e Asymptoti combinati GLAI vali agreement to satur; be est i me This put procedure higher GI Figure 3 sugar bee Best fit and speci The indi predictec and plot (Figure ■ for high powerful should coefficie power.

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