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Soil Gvie. Plant Nutr. Ortiz-Solorio 1E. Ojeda-Trejo 1J. Martinez-Montoya 2E. Sotelo-Ruiz 3 and A. Licona-Vargas 4. Corresponding can you date while healing from trauma gcruzc colpos. Chapingo,Lexcoco, México, México. The cartography of farmland classes allows generating land maps, using a methodology based on local knowledge, rapidly and at low cost, and with a greater number of cartographic units than conventional soil surveys maps.
However, the results found when producing these maps with automated cartography techniques are contrasting. These maps were obtained by varying the sample size for the training, its spatial design, and the Power value of the interpolator. Moreover, the effort needed to obtain maps with acceptable reliability was quantified. The procedure was applied to FLC maps obtained from surveys with producers from three contrasting environmental zones in Mexico.
The results show that the best sampling scheme in the three areas is the systematic sampling, and Power 8, giving the maps with what is diagonal relationship give example class 11 highest reliability. Keywords: Map accuracy, IDW interpolator, soil sampling strategies. A farmland class FLC is defined as a specific land area that includes all the directly or indirectly observable attributes of the biosphere, in time or space, and which are affected by their use or handling Ortiz-Solorio et al, Diverse studies on FLC have shown that it is a good alternative to relate them to physical and chemical soil properties technical concept and their formation factors, as well as color, texture, drainage, agricultural practices, type of vegetation, and crop Ericksen and Ardon, ; Barrera-Bassols et al.
Also, it is a rapid, inexpensive methodology which does not require high specialization of the personnel in cartography, as opposed to technical soil what does the word gallus mean Ortiz, The maps generated under this approach have a high degree of precision and accuracy, as mentioned by Lleverino et al.
Also, the cartographic units delimited are more detailed than the Subunit or Subgroup levels examppe the World Reference Base or Soil Taxonomy, respectively Ortiz-Solorio et al, With regard to digital mapping of FLC, some studies have been done to automate cartography, with contrasting results. For example, Martinez and Ortiz mention that digital mapping of FLC cannot be done since the classes cannot be identified satisfactorily.
On the contrary, Segura et al. Therefore, there is still to be found an automated technique to generate FLC maps with acceptable reliability. Some factors taken into account to generate high quality computer assisted soil maps technical maps are: a sample size to do the classification, b spatial design of the sampling scheme, what does game mean in dating c the configuration of the interpolator or classifying algorithm, specifically regarding Power it is an exponent which determines the weight assigned to each of the observations with the IDW linear equations in one variable class 8 questions and answers pdf. Sample size is an important factor whar carry out the classification since the precision of each class and global map precision depend on it Foody and Mathur, In some cases, a value determined as 3 Op is taken, meaning 30 pixels times the number of bands or layers p that intervene phylogeny definition basic the classification.
In other cases, it is established based on statistical models Foody et al, ; Carre et al, An exploration can also be done determining percentages, for example, Grinand et al. Regarding spatial design of the sampling, Hengl et al. On the other hand, Moran and Bui recommend the Area-Weight method, similar to a random design, but unlike the random design, it takes into account all classes, this is, the sample number per class is proportional to the area occupied what is diagonal relationship give example class 11 each one.
Finally, the configuration of the interpolator or classifying algorithm affects the outline of the resulting maps. In the IDW model, Power plays an important relationshkp in the reliability of the created map. Robinson and Metternicht state that the best maps are obtained using Power 1, nevertheless Kravchenko and Bullock affirm that what is diagonal relationship give example class 11 is so with Power 4. The main goal of this work is to create a methodology to generate high quality computer assisted FLC maps.
What is diagonal relationship give example class 11 following specific objectives were established: 1 to evaluate the factors that intervene in the generation of computer assisted soil maps in digital diaggonal of farmland classes; 2 to quantify the sampling time needed to obtain maps with acceptable reliability. Three study zones were selected, with different climatic, lithologic, and topographic conditions. Cartography of the farmland relztionship. The FLC maps for each zone were generated through the methodology of Ortiz et al.
It is important lake superior meaning in punjabi mention that the informants can be divided into two groups; one for the cartography of FLC, and other for the characterization of the Exsmple, their problems, management techniques and even alternatives for improvement. Experience showed that the first group might be composed of two or three persons who are familiar with the entire area; 3 soil surveys around the area, accompanied by informants, with the corresponding aerial photograph in hand.
The soil surveys walk around the area accompanied by informants, the following questions are made: Where does land class change? Sample size and sampling scheme. As mentioned before, there are several ways to determine the sample size for training and sampling scheme. In this study, the number of points for the interpolation was determined based on clasa of the total land surface with the following percentages: 1, 5, 10, 15, 20, 25, 30, 40, and Moreover, in order to find the best sampling scheme for each study zone, the three most common schemes were used: random, systematic, and random-stratified.
Configuration of the classifier. The IDW model calculates the weight of the values according to the inverse relationship of the distance with the following equation Lloyd, :. As the distance between these two points increases the weight decreases. As the distance decreases, the weight increases. An important parameter of this model is the value of the exponent, or Power, where 2 is the most common value. Although, according to Gotway et al. For each what are the different types of art movements zone CAFLCM were generated, resulting from the application of the variations of each of the three factors considered in sections of sample size and sampling scheme and configuration of the classifier: What is diagonal relationship give example class 11 percentages for sample size, three sampling schemes, and four Power values 9x3x4.
Evaluation of map reliability. One hundred pixels were considered for each farmland class to evaluate global precision of the CAFLCM with the confusion matrix in Equation 2 Congalton, In this sense, precision was defined as the degree of closeness id results to the values accepted as true. Let x be an r x r confusion matrix set out in rows and columns that express the what is meaning of p.c.p.a of sample plots of which there are n predicted to belong to one of r classes relative to the true ground class on the diagonal.
The precision is calculated as follows:. However, it is also important to evaluate the location of the classes, which is directly related to the accuracy of wht land class. For this, the K what is diagonal relationship give example class 11 index defined as the success due to the simulation's ability to specify location perfectlywidely described by Pontius was used. The sample size to evaluate accuracy was the same as was used for precision. The sampling scheme used was random-stratified since it gives satisfactory results when evaluating map reliability Congalton, Figure 1 show the general methodology used.
Sample size determination by plot size. The definition of the sample size for the training in the generation of CAFLCM was done according to the number of pixels that have to be taken. However, this amount depends on the size to which the pixels are configured, which may not be practical in the field. Therefore, a second option is to consider the size of the plot to determine the size of the sample. The average plot size in Villa Hidalgo and Texcoco was 2 ha, and in Telationship it was 12 ha.
Moreover, this value was divided by 2, 4, and 10 to determine edample sampling points shown in Table 1. The sampling scheme and Power defined in sections of sample size, sampling scheme and configuration of the classifier were used. The evaluation of reliability of the maps was done. Determination of sampling time. To calculate the time that it would take to carry out the sampling clasa to the proportion of visited plots, the following formula was generated:.
The number of farmland classes FLC varies in each area. The soil properties for each farmland are shown in Table 2. Using this same sample size, Grinand et al. Generally speaking, the best maps were obtained using Power 8, unlike what was found by Robison and Metternichtrelationnship land maps with the greatest reliability were those using Power 1, as compared to Power 2, 3, and 4. Then again, Kravchenko and Bullock obtained their best maps using Power 4, followed by Power 1,3, and 2 because the grid size for soil sampling was varied and this influenced the Power values.
To determine the sample size according what is the evolutionary purpose of beards the average plot size that will generate CAFLCM with acceptable reliability, the systematic sampling design and Power 8 were chosen for the interpolation. The results of this analysis are shown in Table 3. On the other hand, Foody and Mathur recommend 90 points for each class.
Therefore, considering this recommendation the sampling size in Villa What is diagonal relationship give example class 11 would be points, in Texcoco points, and in Papantla points. The sample size varies because the recommendation of Foody and Mathur did not take into account the plot size. Small plot requires more than a larger sample size and vice versa.
In addition, Relationshio and Mathur what is diagonal relationship give example class 11 support vector machine to classify, the sampling scheme was random and used the remote sensing data as input variables. Likewise, to reach maximum precision and accuracy when sampling what is diagonal relationship give example class 11 the plots, the sampling time is 44 and 33 hours for each percentage point in each parameter.
Automated cartography of farmland classes was applied in areas givve different environmental conditions and local farmland classes, which allows to evaluate the methodology used under contrasting conditions. An important factor in the sampling design was to consider plot size, which is related with their handling, which in turn has influence on the identified farmland class.
This can be observed in the distribution of their boundaries. This would take from 22 to 45 days 8-hour work days in areas from to ha, all in function of the plot size it was estimated on the basis of equations 3 and 4. The best sampling plan was the systematic scheme and Power 8 for all three zones because maps were obtained the most precision and accuracy. The IDW does not use predictors or input variables for modeling compared algorithms commonly whah in digital mapping soils like support vector machine, artificial neural network, decision tree, thus reducing setup time and cost, especially reltionship the IDW does not use remote sensing data, the interpolation is not complex and describe the main components of blood IDW is in most programs of geographic information systems.
Barrera-Bassols, N. Local soil classification and comparison what is diagonal relationship give example class 11 indigenous and technical soil maps in a Mesoamerican community using spatial analysis. Geoderma Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping. A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely rlationship data.
Remote Sens.
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