Informatie voor professionals in voedsel en groen

Informatie voor professionals in voedsel en groen

  • externe gebruiker (Let opwarning)
  • Log in as
  • Over Groenekennis


    Groenekennis - Informatie voor professionals in voedsel en groen

    Groenekennis bevat artikelen uit vaktijdschriften, rapporten, video’s, presentaties, posters en websites op het gebied van landbouw, visserij, groene ruimte en voeding. Groenekennis wordt dagelijks bijgewerkt en bevat ongeveer 500.000 bronnen.

    Groenekennis is de globale view over diverse deelbestanden. Groenekennis is gevuld met alle informatie uit Groen Kennisnet, de Hydrotheek, Tuinpad, IB archief, ARTIK, bioKennis, Kennisbank Plantaardige bronnen, Kennisbank Zeldzame landbouwhuisdieren en afgesloten documentatiebestanden zoals Land Bodem Water en Consumenten- en huishoudstudies.

    Groen Kennisnet is een zeer belangrijk onderdeel van Groenekennis. De doelstelling van Groen Kennisnet is kennis delen op het gebied van Voedsel en Groen te bevorderen en te faciliteren voor een breed publiek.

    Bronnen in Groenekennis kunnen direct opgevraagd worden via een geavanceerde zoekmachine met een 'google-achtige' interface. Met filters kan ingezoomd worden op diverse aspecten, zoals Trefwoord, Collectie, Jaar en Auteur. Bovendien biedt Groenekennis gebruikers de mogelijkheid om via de E-mail geattendeerd te worden op aanvullingen in specifieke vakgebieden.
    De Tijdschriftenlijst biedt een overzicht van tijdschriften waaruit de artikelen voor Groenekennis worden geselecteerd. Door te klikken op een titel krijgt u alle artikelen uit dat tijdschrift in de Groenekennis database getoond.
    Zoeken op kaart biedt een geografische ingang op de beschikbare publicaties over de binnen dit bestand onderscheiden gebieden.

    Groenekennis is onderdeel van het bibliotheeksysteem van WUR. Praktijkgerichte publicaties en rapporten van WUR komen daardoor automatisch beschikbaar. Daarnaast wordt de database doorlopend gevuld met voor het groen onderwijs bruikbare bronnen en artikelen, video’s en websites. Het percentage online is de laatste jaren gegroeid tot tweederde van de totale aanwas per jaar. Dit percentage groeit nog steeds.

    Over
Record nummer 964643
Titel Constrained optimisation of spatial sampling : a geostatistical approach
toon extra info.
Jan-Willem van Groenigen
Auteur(s) Groenigen, J.W. van
Uitgever [S.l.] : [s.n.]
Jaar van uitgave 1999
Pagina's XII, 148 p
Pagina's 1 online resource (XII, 148 p)
Titel van reeks ITC publication series (65)
Annotatie(s) Met lit. opg. - Met samenvatting in het Nederlands  toon alle annotatie(s)
Proefschrift Wageningen
ISBN 906164156X
Tutor(s) Bouma, Dr. ir. J. ; Stein, Dr. ir. A.
Promotiedatum 1999-03-17
Proefschrift nr. 2589
Samenvatting door auteur toon abstract

Aims

This thesis aims at the development of optimal sampling strategies for geostatistical studies. Special emphasis is on the optimal use of ancillary data, such as co-related imagery, preliminary observations and historic knowledge. Although the object of all studies is the soil, the developed methodology can be used in any scientific field dealing with geostatistics.

In summary, the objectives of this study were:

  • Formulation of a range of optimisation criteria that honour a wide variety of aims in soil-related surveys.
  • Development of an optimisation algorithm for spatial sampling that is able to handle these different optimisation criteria.
  • Incorporation of ancillary data such as co-related imagery, historic knowledge and expert knowledge in the sampling strategy.
  • Comparison of the performances of the developed optimisation algorithms with established sampling strategies.
  • Application of developed optimisation techniques in practical soil sampling studies.

Outline of major tools

Chapter 2 shows how a phased sampling procedure can optimise environmental risk assessment. Using indicator kriging, probability maps of exceeding environmental threshold levels are used to direct subsequent sampling. The method is applied in a lead-pollution study in the city of Schoonhoven, The Netherlands. It is tested using stochastic simulations, and results are compared to conventional sampling schemes in terms of type-I and type-II errors. The phased sampling schemes have much lower type-I errors than the conventional sampling schemes with comparable type-II errors. They predict almost 70% of the area correctly (polluted or not-polluted), as compared to 55% by conventional schemes.

Chapter 3 introduces the spatial simulated annealing (SSA) algorithm as a general, flexible optimisation method for spatial sampling. Sampling schemes are optimised at the point level, taking into account sampling constraints and preliminary observations. Different optimisation criteria can be handled. SSA is demonstrated using two optimisation criteria from the literature. The first (the MMSD criterion) aims at even spreading of points over the area. The second (WM criterion) optimises the realised point pair distribution for variogram estimation. For several examples it is shown that SSA is superior to conventional sampling strategies. Improvements up to 30% occur for the first criterion, while an almost complete solution is found for the second criterion. SSA is flexible in adding extra criteria.

Optimising sampling for spatial interpolation

Chapter 4 introduces the MEAN_OK algorithm in SSA, which aims at minimisation of the mean ordinary kriging variance over the research area. It is applied on texture and phosphate content on a river terrace in Thailand. First, sampling is conducted for estimation of the variogram. The variograms thus obtained are used to optimise additional sampling for minimal kriging variance using SSA. This reduces kriging variance of sand percentage from 28.2 to 23.7 (%) 2. The variograms are used subsequently in a geomorphologically similar area. Optimised sampling schemes for anisotropic variables differ considerably from isotropic ones. Size of kriging neighbourhood has a small but distinct effect on the schemes. The schemes are especially efficient in reducing high kriging variances near boundaries of the area.

Chapter 5 further explores the possibilities of minimising kriging variance using SSA. Next to the MEAN_OK criterion, the MAX_OK criterion is introduced, which minimises maximum kriging variance. Both criteria are compared to a regular grid. Using SSA, the mean kriging variance reduces from 40.64 [unit] 2to 39.99 [unit] 2. The maximum kriging variance reduces from 68.83 [unit] 2to 53.36 [unit] 2. An additional sampling scheme of 10 observations is optimised for an irregularly scattered data set of 100 observations. This reduces the mean kriging variance from 21.62 [unit] 2to 15.83 [unit] 2, and maximum kriging variance from 70.22 [unit] 2to 34.60 [unit] 2. The influence of variogram parameters on the optimised sampling schemes is investigated. A Gaussian variogram produces a very different sampling scheme than an exponential variogram with similar nugget, sill and (effective) range. A very short range results in random sampling schemes, with observations separated by distances larger than twice the range. For a spherical variogram, magnitude of the relative nugget effect does not effect the sampling schemes, except for high values.

Chapter 6 introduces the WMSD criterion into SSA, which optimises sampling using a spatial weight function. This allows distinguishing between different areas of priority. A multivariate contamination study in the Rotterdam harbour with five contaminants at two depths shows two subsequent sampling stages with two spatial weight functions. The first stage combines earlier observations and historic knowledge, with emphasis on areas with high priority. The resulting scheme shows a contamination at 17.4% of the samples, with 1.5% heavily contaminated. The second stage uses probability maps of exceeding intermediate threshold values to guide additional sampling to possible hot spots. This yields 26.7% contaminated samples, with 16.7% heavily contaminated. These include new locations that were not detected during the first stage. The WMSD criterion can be used as a valuable tool in decision making processes.

Optimising sampling for model estimation

Chapter 7 focuses on the use of ancillary data to optimise sampling for precision farming research. Using a cheap, low-tech scoring technique yield maps were predicted for millet in an on-farm study in Niger. Yield varied from 0 to 2500 kg ha -1. Subsequently, SSA was used to optimise three different sampling schemes. Scheme 1 optimised coverage of the whole area. Scheme 2 covered the whole yield range, and scheme 3 covered the low producing areas. Using correlation coefficients, scheme 2 found significant correlations between 5 variables and yield. Scheme 1 found only one significant correlation. Using multivariate regression of yield on soil variables, scheme 2 explained 70% of the yield variation. For scheme 1 this was only 37%. Differences between scheme 3 and scheme 1 proved to be significant for distance to shrubs, micro-relief, pH-H2O and CEC. From this study we concluded that shrubs are the main factor influencing yield by catching eroded particles and improving soil fertility. In general, we concluded that the sampling strategy of scheme 2 should be recommended for establishing yield/soil relations. Variograms of micro-relief and yield suggested that spatial correlation is largely confined to distances of 3 to 5 m.

Chapter 8 evaluates a number of sampling strategies for variogram estimation. In the first part, a regular grid is compared to a sampling scheme that optimises the point pair distribution for variogram estimation. This yields unbiased experimental variograms. However, the fluctuation of the experimental variograms is much lower with a regular grid. We concluded from this that the point pair distribution alone is not a useful optimisation criterion for variogram estimation. In the second part, additional observations selected for optimal point pair distribution are compared with randomly drawn additional observations. The random observations result in much higher standard deviations at shorter distances. We concluded from this that for additional short distance observations the point pair distribution is a very useful optimisation criterion. The third part focusses on optimal variogram use. A sampling grid of 81observations is completed, after preliminary estimation of the variogram, with 19 additional observations for minimal kriging variance.

The scheme is compared to a regular grid of 100 observations. For an exponential field without nugget effect, the use of the phased sampling scheme reduces the mean squared kriging error from 0.39 [unit] 2to 0.31 [unit] 2, and the maximum squared kriging error from 6.05 [unit] 2to 4.24 [unit] 2. For a spherical field with a nugget effect of 33%, mean squared kriging error does not change and maximum squared kriging error decreases from 15.98 [unit] 2to 11.52 [unit] 2. We concluded that minimisation of the squared kriging error is often more relevant than accurate estimation of the variogram. Taking samples just outside the area improved the quality of the prediction in terms of both kriging variance and squared kriging error.

Online full text
Op papier Haal het document, vind aanverwante informatie of gebruik andere SFX-diensten
Trefwoorden (cab) kriging / statistische analyse / bemonsteren / bodem / geostatistiek
Rubrieken Statistische analyse / Bodemkunde (algemeen)
Publicatie type Proefschrift
Taal Engels
Reacties
Er zijn nog geen reacties. U kunt de eerste schrijven!
Schrijf een reactie
 

To support researchers to publish their research Open Access, deals have been negotiated with various publishers. Depending on the deal, a discount is provided for the author on the Article Processing Charges that need to be paid by the author to publish an article Open Access. A discount of 100% means that (after approval) the author does not have to pay Article Processing Charges.

For the approval of an Open Access deal for an article, the corresponding author of this article must be affiliated with Wageningen University & Research.

U moet eerst inloggen om gebruik te maken van deze service. Login als Wageningen University & Research user of guest user rechtsboven op deze pagina.