Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain

The study of soil degradation phenomena requires the characterization of surface properties. This characterization is based on the integration of the descriptive variables of the local environment: physical, biological and anthropic. In Morocco, the spectacular expansion of erosive processes shows i...

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Main Author: Chikhaoui, Mohamed
Other Authors: Bonn, F.
Language:French
Published: Université de Sherbrooke 2005
Online Access:http://savoirs.usherbrooke.ca/handle/11143/2747
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spelling ndltd-usherbrooke.ca-oai-savoirs.usherbrooke.ca-11143-27472017-08-24T05:17:46Z Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain Chikhaoui, Mohamed Bonn, F. Merzouk, Abdelaziz The study of soil degradation phenomena requires the characterization of surface properties. This characterization is based on the integration of the descriptive variables of the local environment: physical, biological and anthropic. In Morocco, the spectacular expansion of erosive processes shows increasingly alarming aspects. Faced with this situation, the country has an urgent need for evaluating the effects of erosion on soil productivity to measure its amplitude and to foster the implementation of better conservation strategies. The overall objective of our research project is to develop a geomatics approach for the mapping and determination of the surface states in a small Mediterranean watershed that can be used for modeling the soil degradation parameters. In the present research, the class definitions of the different levels of degradation are based on the GLASOD ( Global Assessment of Soil Deterioration ) method. To reach the objectives set for our study, we adopted two approaches. The first is a spectral approach and is based on spectral indices, the SAM (Spectral Angle Mapping ) method, and on spectral unmixing. This procedure gave us a better comprehension of the synergistic relationship between the spectral properties of soils and their state of degradation. Also, we elaborated a new spectral index referred to as the LDI (Land Degradation Index ). This index is interesting and provides quantitative results. We were also able to establish an interesting correlation (R[superscript 2] =0.67) between a spectral index derived from ASTER data and the clayey fraction of the soil surface horizon in our study area. The second approach focuses on the modeling of multisource data through the use of a supervised neural network based on a back-propagation algorithm. The quality of results of this approach depends on the determination of the intrinsic parameters of RN. In the present research, we provide some answers concerning issues relating to these parameters in the hope of better defining them. The comparison of the results obtained using the different approaches outlined the existence of a global correspondence between these parameters, in the sense that they represent ground reality. Moreover, the neural approach stands out with a higher accuracy rate (Kappa =0.91) due to the integration of multisource data and spectral information. The Kappa coefficient permitted the evaluation of the accuracy of the classification and its interpretation was carried out based on the calculation of the Z statistics test. The validation of results is based on photointerpretation, ground truth and the analysis of the spatial variability of the data using the geostatistics approach."--Résumé abrégé par UMI. 2005 Thèse 9780494148495 http://savoirs.usherbrooke.ca/handle/11143/2747 fre © Mohamed Chikhaoui Université de Sherbrooke
collection NDLTD
language French
sources NDLTD
description The study of soil degradation phenomena requires the characterization of surface properties. This characterization is based on the integration of the descriptive variables of the local environment: physical, biological and anthropic. In Morocco, the spectacular expansion of erosive processes shows increasingly alarming aspects. Faced with this situation, the country has an urgent need for evaluating the effects of erosion on soil productivity to measure its amplitude and to foster the implementation of better conservation strategies. The overall objective of our research project is to develop a geomatics approach for the mapping and determination of the surface states in a small Mediterranean watershed that can be used for modeling the soil degradation parameters. In the present research, the class definitions of the different levels of degradation are based on the GLASOD ( Global Assessment of Soil Deterioration ) method. To reach the objectives set for our study, we adopted two approaches. The first is a spectral approach and is based on spectral indices, the SAM (Spectral Angle Mapping ) method, and on spectral unmixing. This procedure gave us a better comprehension of the synergistic relationship between the spectral properties of soils and their state of degradation. Also, we elaborated a new spectral index referred to as the LDI (Land Degradation Index ). This index is interesting and provides quantitative results. We were also able to establish an interesting correlation (R[superscript 2] =0.67) between a spectral index derived from ASTER data and the clayey fraction of the soil surface horizon in our study area. The second approach focuses on the modeling of multisource data through the use of a supervised neural network based on a back-propagation algorithm. The quality of results of this approach depends on the determination of the intrinsic parameters of RN. In the present research, we provide some answers concerning issues relating to these parameters in the hope of better defining them. The comparison of the results obtained using the different approaches outlined the existence of a global correspondence between these parameters, in the sense that they represent ground reality. Moreover, the neural approach stands out with a higher accuracy rate (Kappa =0.91) due to the integration of multisource data and spectral information. The Kappa coefficient permitted the evaluation of the accuracy of the classification and its interpretation was carried out based on the calculation of the Z statistics test. The validation of results is based on photointerpretation, ground truth and the analysis of the spatial variability of the data using the geostatistics approach."--Résumé abrégé par UMI.
author2 Bonn, F.
author_facet Bonn, F.
Chikhaoui, Mohamed
author Chikhaoui, Mohamed
spellingShingle Chikhaoui, Mohamed
Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain
author_sort Chikhaoui, Mohamed
title Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain
title_short Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain
title_full Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain
title_fullStr Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain
title_full_unstemmed Apport des données ASTER et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du Rif marocain
title_sort apport des données aster et d'un réseau de neurones à rétropropagation à la modélisation de la dégradation du sol d'un bassin marneux du rif marocain
publisher Université de Sherbrooke
publishDate 2005
url http://savoirs.usherbrooke.ca/handle/11143/2747
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