Land suitability classification using different sources of information: Soil maps and predicted soil attributes in Jordan

Ziadat, Feras

doi:10.1016/j.geoderma.2007.03.004

Cited by 43 publications

(33 citation statements)

References 31 publications

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“…Soil units, which could be called soil-mapping units (Ziadat 2007), were generated by overlaying maps of soil type and topography at a scale of 1:50 000. There is the same soil type and the same slope gradient in a soil unit.…”

Section: Study Area and Datamentioning

confidence: 99%

A relative weight model for soil productivity assessment

Zhang

2008

Can. J. Soil. Sci.

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Zhang, J. 2008. A relative weight model for soil productivity assessment. Can J. Soil Sci. 88: 827Á832. Soil productivity is a function of inherent factors such as topography, parent material, physical and chemical properties of the soil, and the infrastructure for irrigation and drainage. As multi-criteria evaluation methods in soil productivity assessment, the leastfactor and weight methods, while popular, have limitations. The least-factor method is not accurate enough in the absence of a vital constraint factor, and the weight method leads to an inaccurate, and even incorrect result when there is a vital constraint factor. In order to overcome these limitations a new concept, relative weight, was introduced and a prototype model developed. In this prototype model, every factor has different relative weights in different soil units, thus allowing it to overcome shortcomings of the Weight method where the weights of a given factor are assumed to be equal in all soil units. This prototype model was then applied in a case study on the Loess Plateau in Northwest China. Results from the case study indicated this prototype model was more precise than either the least-factor or weight methods, and was able to avoid the invalid results of the Weight method.Key words: Soil productivity index, relative weight, multi-criteria evaluation, Loess Plateau Zhang, J. 2008. Mode`le a`ponde´ration relative pour l'e´valuation de la productivite´du sol. Can. J. Soil Sci. 88: 827Á832. La productivite´du sol de´pend de facteurs inhe´rents comme la topographie, le mate´riau d'origine, les proprie´te´s physiques et chimiques ainsi que l'infrastructure d'irrigation et de drainage. Aussi populaire qu'elles soient pour e´valuer la productivited u sol a`partir de crite`res multiples, la me´thode du plus petit facteur et la me´thode de ponde´ration ont leur limites. La premie`re n'est pas assez pre´cise sans facteur de contrainte vital alors que la seconde aboutit a`des re´sultats impre´cis, sinon errone´s, en pre´sence d'un tel facteur. Pour surmonter ces obstacles, l'auteur propose un nouveau concept, la ponde´ration relative, et a mis au point un mode`le expe´rimental. Dans ce mode`le, chaque facteur posse`de une ponde´ration relative diffe´rente selon l'unite´de sol, ce qui permet de surmonter les carences de la me´thode de ponde´ration qui pre´sume l'application d'un meˆme facteur de ponde´ration, peu importe l'unite´de sol. Le mode`le expe´rimental a e´te´applique´a`une e´tude de cas sur le plateau Loess, dans le nord-ouest de la Chine. Les re´sultats re´ve`lent que ce mode`le est plus pre´cis que les deux pre´ce´dents et permet d'e´viter les re´sultats errone´s de la me´thode de ponde´ration.

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Section: Study Area and Datamentioning

confidence: 99%

A relative weight model for soil productivity assessment

Zhang

2008

Can. J. Soil. Sci.

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“…In many cases, the soil mappers faces complex problems and must make empirical decisions based on their knowledge to decide soil limits or classification. Some investigations indicated that in conventional soil mapping, the purity of soil units is frequently low [10,11]. Dobos et al [12] estimated a taxonomic purity of 49.1% for the soil and terrain information in Hungary.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Demattê¹,

López

Rizzo

et al. 2016

Remote Sensing

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There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in São Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400-2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R 2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R 2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since...

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“…The precision of the generated maps ranges from 50 to 88 % (Lagacherie & Holmes, 1997;Dobos et al, 2001;Moran & Bui, 2002;Hengl & Rossiter, 2003;Peng et al, 2003;Qi & Zhu, 2003;Schmidt & Hewitt, 2004;Scull et al, 2005;Giasson et al, 2006;Smith et al, 2006;Qi et al, 2006;Ziadat, 2007;Figueiredo et al, 2008;Schmidt et al, 2008;Hansen et al, 2009;Ballabio, 2010;Behrens et al, 2010). These maps produced by DSM are based on scientific data by the extrapolation of soil properties, but do not take local soil knowledge into consideration.…”

Section: Introductionmentioning

confidence: 99%

Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

Cruz-Cárdenas

Solorio

Trejo

et al. 2011

Rev. Bras. Ciênc. Solo

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SUMMARYMap units directly related to properties of soil-landscape are generated by local soil classes. Therefore to take into consideration the knowledge of farmers is essential to automate the procedure. The aim of this study was to map local soil classes by computer-assisted cartography (CAC), using several combinations of topographic properties produced by GIS (digital elevation model, aspect, slope, and profile curvature). A decision tree was used to find the number of topographic properties required for digital cartography of the local soil classes. The maps produced were evaluated based on the attributes of map quality defined as precision and accuracy of the CAC-based maps. The evaluation was carried out in Central Mexico using three maps of local soil classes with contrasting landscape and climatic conditions (desert, temperate, and tropical). In the three areas the precision (56 %) of the CAC maps based on elevation as topographical feature was higher than when based on slope, aspect and profile curvature. The accuracy of the maps (boundary locations) was however low (33 %), in other words, further research is required to improve this indicator.Index terms: decision tree, digital elevation model, map quality.(1) Received for publication in March 2010 and approved in January 2011.

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Land suitability classification using different sources of information: Soil maps and predicted soil attributes in Jordan

Cited by 43 publications

References 31 publications

A relative weight model for soil productivity assessment

A relative weight model for soil productivity assessment

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

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