Analyzing Vegetation Trends with Sensor Data from Earth Observation Satellites
Author
Summary, in English
Abstract
This thesis aims to advance the analysis of nonlinear trends in time series of vegetation data from Earth observation satellite sensors. This is accomplished by developing fast, efficient methods suitable for large volumes of data. A set of methods, tools, and a framework are developed and verified using data from regions containing vegetation change hotspots.
First, a polynomial-fitting scheme is tested and applied to annual Global Inventory Modeling and Mapping Studies (GIMMS)–Normalized Difference Vegetation Index (NDVI) observations for North Africa for the period 1982–2006. Changes in annual observations are divided between linear and nonlinear (cubic, quadratic, and concealed) trend behaviors. A concealed trend is a nonlinear change which does not result in a net change in the amount of vegetation over the period.
Second, a systematic comparison between parametric and non-parametric
techniques for analyzing trends in annual GIMMS-NDVI data is performed at
fifteen sites (located in Africa, Spain, Italy, and Iraq) to compare how trend type and departure from normality assumptions affect each method’s accuracy in detecting long-term change.
Third, a user-friendly program (Detecting Breakpoints and Estimating Segments in Trend, DBEST) has been developed which generalizes vegetation trends to main features, and characterizes vegetation trend changes. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST is tested and evaluated using both simulated NDVI data, and actual NDVI time series for Iraq for the period 1982-2006.
Finally, a decision-making framework is presented to help analysts perform
comprehensive analyses of trend/change in time series of satellite sensor data. The framework is based on a conceptual model of the main aspects of trend analyses, including identification of the research question, the required data, the appropriate variables, and selection of efficient analysis methods. To verify the framework, it is applied to four case studies (located in Burkina Faso, Spain, Sweden, and Senegal) using Moderate-resolution Imaging Spectroradiometer (MODIS)–NDVI data for the period 2000–2013. Each of the case studies successfully achieved its
research aim(s), showing that the framework can achieve the main goal of the
study which is to advance the analysis of nonlinear changes in vegetation.
The methods developed in this thesis can help to contribute more accurate information about vegetation dynamics to the field of land cover change research.
This thesis aims to advance the analysis of nonlinear trends in time series of vegetation data from Earth observation satellite sensors. This is accomplished by developing fast, efficient methods suitable for large volumes of data. A set of methods, tools, and a framework are developed and verified using data from regions containing vegetation change hotspots.
First, a polynomial-fitting scheme is tested and applied to annual Global Inventory Modeling and Mapping Studies (GIMMS)–Normalized Difference Vegetation Index (NDVI) observations for North Africa for the period 1982–2006. Changes in annual observations are divided between linear and nonlinear (cubic, quadratic, and concealed) trend behaviors. A concealed trend is a nonlinear change which does not result in a net change in the amount of vegetation over the period.
Second, a systematic comparison between parametric and non-parametric
techniques for analyzing trends in annual GIMMS-NDVI data is performed at
fifteen sites (located in Africa, Spain, Italy, and Iraq) to compare how trend type and departure from normality assumptions affect each method’s accuracy in detecting long-term change.
Third, a user-friendly program (Detecting Breakpoints and Estimating Segments in Trend, DBEST) has been developed which generalizes vegetation trends to main features, and characterizes vegetation trend changes. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST is tested and evaluated using both simulated NDVI data, and actual NDVI time series for Iraq for the period 1982-2006.
Finally, a decision-making framework is presented to help analysts perform
comprehensive analyses of trend/change in time series of satellite sensor data. The framework is based on a conceptual model of the main aspects of trend analyses, including identification of the research question, the required data, the appropriate variables, and selection of efficient analysis methods. To verify the framework, it is applied to four case studies (located in Burkina Faso, Spain, Sweden, and Senegal) using Moderate-resolution Imaging Spectroradiometer (MODIS)–NDVI data for the period 2000–2013. Each of the case studies successfully achieved its
research aim(s), showing that the framework can achieve the main goal of the
study which is to advance the analysis of nonlinear changes in vegetation.
The methods developed in this thesis can help to contribute more accurate information about vegetation dynamics to the field of land cover change research.
Department/s
Publishing year
2014
Language
English
Full text
Document type
Dissertation
Publisher
Department of Physical Geography and Ecosystem Science, Lund University
Topic
- Physical Geography
Keywords
- Change detection
- Satellite sensor data
- Time series analysis
- Vegetation dynamics
- Vegetation index
Status
Published
Supervisor
- Jonathan Seaquist
- Lars Eklundh
- Jonas Ardö
- Per Jönsson
ISBN/ISSN/Other
- ISBN: 978-91-85793-42-6
Defence date
24 October 2014
Defence time
10:00
Defence place
Världen auditorium, Geocentrum I
Opponent
- Ronald Eastman (Professor)