6533b854fe1ef96bd12ae745

RESEARCH PRODUCT

Vegetation monitoring through retrieval of NDVI and LST time series from historical databases.

subject

description

The PhD dissertation presented here falls into the Earth Observation field, specifically vegetation monitoring. This work consists in the extensive exploitation of historical databases of satellite images for vegetation monitoring through two parameters, which are the land surface temperature (LST) and a vegetation index (NDVI). Up to now, vegetation monitoring has been limited to the use of vegetation indices, so the addition of the land surface temperature parameter represents the main innovative character of this PhD study.This dissertation is divided into 5 chapters. The first chapter begins by introducing the theoretical aspects of NDVI and LST parameters, addressing the means for retrieving them from remotely sensed observations, as well as their main limitations. Then, an introduction to vegetal physiology is developed, which allows for understanding how NDVI and LST parameters are linked to plants. A bibliographical study is then presented, which stresses out the gaps in the exploitation of historical databases.The second describes the data used in this PhD. The instrument providing most of these data is embarked on the NOAA (National Oceanic and Atmospheric Administration) satellite series. This instrument is the AVHRR (Advanced Very High Resolution Radiometer). The AVHRR databases used in this work are the PAL (Pathfinder AVHRR Land) and GIMMS (Global Inventory Modeling and Mapping Studies) databases. Additional data used punctually are also described briefly.The third chapter describes the operations applied to the data to prepare their temporal analysis. These operations start with the calculations of vegetation index and land surface temperature parameters. The AVHRR data used in this work are contaminated by the orbital drift of NOAA satellites, so an important part of this doctorate consisted in developing a technique for correcting this effect. We chose to develop our own technique, which we validated by direct comparison with data retrieved by geostationary satellites.In the fourth chapter, the different methods used for data temporal analysis are presented. Those methods consist of trend detection, harmonic analysis, and fitting the temporal series to annual NDVI evolution curves. Then, a phenological analysis is presented, which allows for retrieval of trends in spring and autumn dates for most of the globe. These trends are validated by comparison with previous studies. The trend analysis for spring dates is then extended to the 1948-2006 period using air temperature data. The long-term observation of different NDVI indicators also allows for the detection of land vegetation changes, even in our case of coarse spatial resolution. Finally, two methods for NDVI temporal analysis are compared.In the fifth chapter, a quick presentation of simultaneous study of NDVI and LST is developed through a revision of previous results, followed by the observations carried out from the orbital drift corrected data. These observations allowed for the determination of indicators of NDVI and LST, thus enabling for the characterization of the vegetation at global scale. A harmonic analysis of NDVI and LST at European scale is also presented. The application of the developed indicators for simultaneous monitoring of NDVI and LST shows promising results.As a conclusion, the main results described above are summarized, and plans for a close future are presented. This PhD has also demonstrated that such work could be carried out in a small structure with limited resources.