EVALUATION OF ETM+ DATA APPLICABILITY FOR REMOTE SENSING OF THE SOIL TEXTURE AND VEGETATION EFFECTS ON ACCURACY OF THE PREDICTIONS

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RAHMATI M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Journal: Journal of Water and Soil Conservation Year:2016 | Volume:23 | Issue:1 Page(s): 187-201

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Title

EVALUATION OF ETM+ DATA APPLICABILITY FOR REMOTE SENSING OF THE SOIL TEXTURE AND VEGETATION EFFECTS ON ACCURACY OF THE PREDICTIONS

Author(s)

RAHMATI M. | Issue Writer Certificate

Keywords

SOIL TEXTUREQ2

REMOTE SENSINGQ1

PASSIVE SATELLITEQ1

ARTIFICIAL NEURON NETWORKQ2

Abstract

Background and Objectives: In recent years, several researches have been done for REMOTE SENSING of SOIL TEXTURE using radar data. While there is no report on application of passive and free satellites data including ETM+ and MODIS. The REMOTE SENSING of SOIL TEXTURE also is limited by the presence of vegetation on soil surface. So the current research was aimed to evaluate applicability of ETM+ data for REMOTE SENSING of the SOIL TEXTURE as well as assessment of the vegetation effects on precision of the predictions.Materials and Methods: To achieve the goal of the project, the soil separates were measured in 225 different points within the study area on the northern slopes of Mount Sahand which is located between longitudes of 46 degrees 22 minutes and 23 seconds to 46 degrees 28 minutes and 5 seconds and latitudes of 37 degrees and 43 minutes and 7 seconds to 37 degrees 50 minutes and 8 seconds. Also, the available ETM+ data over the study area were downloaded. Several methods including empirical, statistical and black box (artificial neuron network, ANN) models using Excel, SPSS and Matlab software’s were applied to create different functions for REMOTE SENSING of soil separates. Applied models were evaluated using the statistical criteria including Root Mean Squared Error (RMSE), assessment error (E) and coefficient of determination (R2).Results: The results showed that in the presence of vegetation on the soil surface, prediction accuracy dropped to zero. However, in the bare soils and soils without vegetation, predictions were sufficiently accurate. Although empirical and statistical approaches showed low accuracy (with R2 lower than 0.3) for REMOTE SENSING of the soil separates, black box model using ANN algorithm was accurate enough (with R2 higher than 0.5).Conclusion: The results showed that the use of statistical methods and regressions to remote sense soil separates using ETM+ data had very poor accuracy in whole study area with four different land-uses (poor pastures, drylands, irrigated areas and bare soil) and even in bare soils. However, the results of using artificial neural network algorithm for REMOTE SENSING soil separates in bare soils significantly increased the accuracy of the predictions.

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APA: Copy

RAHMATI, M.. (2016). EVALUATION OF ETM+ DATA APPLICABILITY FOR REMOTE SENSING OF THE SOIL TEXTURE AND VEGETATION EFFECTS ON ACCURACY OF THE PREDICTIONS. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 23(1), 187-201. SID. https://sid.ir/paper/156597/en

Vancouver: Copy

RAHMATI M.. EVALUATION OF ETM+ DATA APPLICABILITY FOR REMOTE SENSING OF THE SOIL TEXTURE AND VEGETATION EFFECTS ON ACCURACY OF THE PREDICTIONS. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2016;23(1):187-201. Available from: https://sid.ir/paper/156597/en

IEEE: Copy

M. RAHMATI, “EVALUATION OF ETM+ DATA APPLICABILITY FOR REMOTE SENSING OF THE SOIL TEXTURE AND VEGETATION EFFECTS ON ACCURACY OF THE PREDICTIONS,” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 23, no. 1, pp. 187–201, 2016, [Online]. Available: https://sid.ir/paper/156597/en

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