The Importance of Correcting Land Indices in Determination of Land Suitability Classes

Land evaluation plays a decisive role in determination of land suitability for the intended uses. For this purpose, various approaches have been proposed, among which the parametric approach is an important one. In this approach, the land index (LI) is calculated using the Khidir (the square root) and/or the Storrie methods, and then the land suitability classes were determined based on the LI. Unfortunately, in many land suitability studies, the LI has been used without any correction, called uncorrected land index (ULI) that led to different results in evaluation of land suitability approaches. The current study shows the importance of the corrected land index (CLI) and its effect on land suitability classes.

In this study land suitability classes were determined by four methods including 1-simple limitation, 2- number and intensity of limitations, 3- Kiddir (square root) and 4- Storrie, using two cases i.e. the CLI and ULI. Properties and criteria for determining land suitability classes are shown in Table1. Simple limitation method is based on the Liebig’s law or the law of the minimum. Land classes are defined according to the lowest class level of the land characteristics. Number and intensity of limitation method has been described in Table 1. In parametric approach, the ULI is calculated using Kiddir and Storrie methods as shown in equations 1 and 2, respectively. The relationships between ULI and CLI are presented in Table 2. (1) (2) Then, a simulation process was done for the eight characteristics involved in calculating the land suitability index. For this purpose, one million random values were considered for each of the S1 to N2 classes; so that the minimum rating (Rmin) was a random number for each class in own defined range (Rating in Table 1) and the other seven characteristics were random numbers between Rmin and 100. For example, in the S2 class, a minimum random number is in the range of 60 to 85 and seven other characteristics were between this Rmin and 100. Finally, a total of five million random simulations were considered.

Table 3 shows the results of five million simulations for S1 to N2 classes. Based on the minimum, maximum and mean values obtained, it can be seen that the simulation process is acceptable. These numbers show that the simulations have simulated almost all the cases that may occur in reality, from the best to the worst. Based on the results, it is clear that the mean values of the ULIs or the Storrie method are much lower than the Khiddir ones (Table 3), but there was no significant difference between mean values both in Storrie and Khiddir methods using CLIs. These results are sufficient to conclude the importance of using CLIs and to show the difference between the results obtained from the CLIs and ULIs. Tables 4 to 8 show the results of one million simulations for each suitability class. The present study revealed that the results of the four employed methods using the CLIs are much closer, especially for the Storrie and Khiddir methods. All together, the simple limitation method was more consistent with the Khiddir method. On the other hand, the employed methods differed greatly when the ULIs were used. The analysis of five million simulations has shown that the contradictory results of land evaluation methods in various studies can be quite mathematically logical, but with a different probability.

According to the findings of the current study, it can be illustrated that it is very important and necessary to use the CLIs to determine the land suitability class. The study showed that using the CLIs leads to the closeness of the results of different methods, so that there was no significant difference between Storrie and Khiddir methods. In general, the results of the Khidir method are closer to the simple constraint method compared to Storrie. There was a significant difference between the Khiddir and Storrie methods using the ULIs, but the difference was too small and insignificant using the CLIs. Totally, the results of the ULIs may be largely inaccurate, misleading and unrealistic. Therefore, it is strongly suggested that the CLIs be used in determining the suitability classes, and then the results be compared with the observations in the reality.