Tesis doctoral de María Del Carmen González Sanpedro
The global population growth, as well as the social and economic importance that the agricultural sector has in many regions of the world, makes it very important to develop methods to monitor the status of crops, to improve their management, as well as to be able to make early estimates of the agricultural production. One of the main causes of uncertainty in the production of crops is due to the weather, for example, in arid and semiarid regions of the world, periods of drought can generate big losses in agricultural production, which may result in famine. Thus, fao, during their summit in june 2008, stressed the need to increase agricultural production as a measure to strengthen food security and reduce malnutrition in the world. concern for increasing crop production, has generated, during the last decades, significant changes in agricultural techniques. For example, there has been a widespread use of pesticides, genetically modified crops, as well as an increase in intensive farming. In turn, the market influences crop rotations, and as a consequence, changes in the spatial distribution of crops are very common. Therefore, in order to make estimates of agricultural production, it is also necessary to map regularly the crop fields, as well as their state of development. the aim of this thesis is to develop methods based on remote sensing data, in the radar and optical spectral regions, in order to monitor crops, as well as a to map them. The results of this thesis can be combined with other techniques, especially with models of crop growth, to improve the prediction of crops. the optical remote sensing methods for classifying and for the cartography of crops are well established and can be considered almost operational. The disadvantage of the methods based on optical data is that they are not applicable to regions of the world where cloud coverage is frequent. In such cases, the use of radar data is more advisable. However, the classification methods using radar data are not as well established as the optical ones, therefore, there is a need for more scientific studies in this field. As a consequence, this thesis focuses on the classification of crops using radar data, particularly using airsar airborne data and asar satellite data. the monitoring of crops by remote sensing is based on the estimation of biophysical parameters and their evolution over time. These parameters are, among others, lai (leaf area index), chlorophyll and biomass. In this thesis, satellite data from lansat-tm are used for the inversion of lai, and envisat-meris data for estimating lai and chlorophyll. Finally, envisat-asar radar data are used to investigate its potential in the estimation of the biomass of cereals. chapter 1 of the thesis introduces the context of this study and its scientific objectives. chapter 2 presents the theoretical basis of optical remote sensing. chapter 3 is dedicated to the inversion of lai in the region of barrax (castilla-la mancha, spain) using 12 landsat-tm images acquired during the same agricultural season. The lai is calculated using luts (look up tables) to invert the radiative transfer model sail, which is coupled to the model of leaf reflectance prospect. The results are validated with experimental measurements acquired during the field campaign esa sparc-2003, showing a good correlation. chapter 4 proposes a method to invert, at the same time, lai and chlorophyll data from envisat-meris. This method involves an inversion of the same model, prospect + sail, which was used in chapter 3, but with the special addition of a temporal constraint. Thus, instead of inverting a single value of lai and chlorophyll for each date, a curve for the entire crop cycle is inverted. This method seeks to take as much information as possible from the temporal dimension of the data. The results show that the multitemporal method works better than the inversions on a single date. However, the inversion of chlorophyll still requires further study. chapter 5 introduces the concepts related to the radar remote sensing, which will be used along the second part of this thesis. in chapter 6 a method of hierarchical classification of crops is developed. It uses polarimetric data in c band, from the airborne instrument airsar. The method is applied to images in flevoland (netherland) and is validated with field observations. chapter 7 investigates the use of envisat-asar data for agricultural applications in the region of toulouse. The first part discusses the possibilities for classification of crops. The second part investigates the potential of the polarization ratio hh / vv to estimate the biomass of wheat. It is confirmed that there is a clear link between this ratio and the biomass of wheat, however, this relationship depends on many other factors and seems to be dependent on the experimental site. Therefore, more studies needs to be conducted. the findings of this study, as well as their prospects are outlined in greater detail in chapter 8. to sum up, this thesis investigates the use of optical and radar remote sensing to the monitoring of agricultural areas. Four different instruments, three on board satellites (landsat-tm, envisat-meris, and envisat-asar) and 1 airborne instrument (airsar) are used, in three areas of study in europe (barrax, toulouse and flevoland), as well as an important number of field measurements. This study highlights the importance of the multi-temporal aspect in agricultural studies using remote sensing.
Datos académicos de la tesis doctoral «Optical and radar remote sensing applied to agricultural areas in europe«
- Título de la tesis: Optical and radar remote sensing applied to agricultural areas in europe
- Autor: María Del Carmen González Sanpedro
- Universidad: Universitat de valéncia (estudi general)
- Fecha de lectura de la tesis: 01/12/2008
Dirección y tribunal
- Director de la tesis
- Jose Moreno Méndez
- Tribunal
- Presidente del tribunal: malcolm walter john Davidson
- soledad Gandía franco (vocal)
- francesco Mattia (vocal)
- laurent Kergoat (vocal)