Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

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

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Sugar beet biomass estimation using spectral data derived from colour infrared slides Robert R.De Wulf & Roland E.Goossens State University Gent, Belgium ABSTRACT: Sugar beet spectral reflectance data were extracted from multitemporal ground - based colour infrared photography. Measured agronomic parameters include green leaf area index (GLAI), cover percentage, fresh root weight and sugar content. Statistical relationships between spectral reflectance and biomass were determined for one growing season and applied for prediction of biomass in a subsequent growing season. 1 INTRODUCTION Crop-weather relationships numerically expressed in prediction models are being applied for yield fore casting in a number of countries. However no crop growth model has been able to perfectly simulate the synergistic effects of environmental conditions, meteorological factors and agro-cultural factors (Colwell 1977 et al). Moreover , a number of abiotic and biotic hazards which cannot be predicted by agromet models or visually assessed in the field, can have conside rable negative effects on crop yield. Remote sensing may serve to complement traditional methods for crop growth monitoring and production fo recasting on local and on global scale. Ground-based radiometry has established empirical relations between spectral and agronomic parameters for a range of economically important crops. Research efforts have established the basics for operational large scale applications as global wheat yield fore casting in the LACIE project (MacDonald and Hall 1978) and biomass estimation in arid zones using multitemporal NOAA AVHRR data (Tucker et al 1985). Crop production can be viewed as a product of acreage and yield (Colwell et al 1977), A colour infrared (CIR) photograph allows extraction of both factors. Using visual interpretation methods or interactive image classification preceded by digitizing, parcels of land covered by a particular crop can be measured fairly precisely. This aspect is not elaborated here. The numerical assessment of the yield factor requires a statistically-based relationship between remotely sensed data and agronomic features. This relationship has been studied for sugar beets during the 1984 and 1985 growing seasons. The choice of a 35mm colour infrared emulsion is tied to its planned use in an automatic SLR camera on board a remotely piloted aircraft (RPA). This delta-winged RPA, custom-built for the Centre for Remote Sensing of Vegetation (CEVA) , made successful test flights in March 1986 and is scheduled to be fully operational for low altitude crop monitoring in the second half of the 1986 growing season. The procedure for extraction of spectral parameters from 35mm CIR slides, in a preparatory phase taken from a tripod, became a priority research issue at CEVA. The selection of sugar beet as crop of interest is determined by its importance in Belgian agriculture. In 1984 sugar beets covered 117000 ha or 8.4 % of the country's agricultural surface (N.I.S. 1985). 2 REMOTE SENSING OF SUGAR BEET BIOMASS : PRINCIPLES According to Thorne (1971) arable crops can be regarded as machines for converting C02 and water into carbohydrate, using the sun's energy. Photosynthate can be transferred to physiological sinks which consitute the harvestable parts of a number of economically important crops : potato tubers, wheat grain kernels and sugar beet roots. White sugar is the most important part of the sugar beet from the economical point of view. However in terms of biomass quantity (15%) it is clearly sur passed by a sizeable amount of processed by-products including pulp and brown molasse, both used as livestock feed. Remote sensing of sugar beet biomass requires efficient and economical data aquisition and know ledge of yield-related crucial moments on the physiological time scale. Sugar beet productivity is strongly favoured by long growing seasons provided that meteorological and soil conditions are not limiting (Analogides 1979). For’ sugar beets (Steven et al 1982), potatoes (Allen and) Scott 1980) as for cereals ( Gossse et al 1986) it has been shown that total dry matter accumulation is proportional to intercepted solar radiation. The amount of photosynthetically active biomass, usually expressed as Green Leaf Area Index (GLAI) and its time of duration (Green Leaf Area Duration,GLAD ) have become critical parameters in the majority of plant growth analysis. The LAD concept is illustrated by Evans (1972), who compared dry matter at harvest with GLAD for potatoes and sugar beets, and found both factors to be 57% higher for sugar beets. From a prediction point of view radiation alone may not be a particular useful variable. In the case of sugar beet, and specifically when its root sugar content is considered meteorological factors are known to play a decisive role which varies with the physiological time scale. Soil moisture deficit at the 4-leaf stage can have disastrous consequences for harvest, whereas too much rains in July and August favour leaf development at the expense of root weight and sugar content (Leblon 1983). Cool days and frosty nights in late September and October check leaf and root growth but increase photosynthetic activity and sugar storage (Whyte 1960). Scammel's general yield prediction model for Belgium is based on sugar beet physiology and meteorological parameters but is said to perform poorly on local basis (Leblon 1983). This is mainly due to varying climatological conditions and soil

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