Use this URL to cite or link to this record in EThOS:
Title: Multi-sensor remote sensing for desertification monitoring in the dry sub-humid coastal lowland of Vietnam
Author: Hoang, Viet Anh
ISNI:       0000 0004 2744 6109
Awarding Body: University of Newcastle Upon Tyne
Current Institution: University of Newcastle upon Tyne
Date of Award: 2011
Availability of Full Text:
Access from EThOS:
Desertification, even though not a new problem, in recent years has become one of the major global threats widely recognized not only by research community but also by public interest. Traditionally, the term desert is typically used to refer to vast areas of sand dune spreading over thousands miles such as in the Sahara Desert, Africa. However, in the new concept of desertification as a degradation process, today we are facing a new trend of desertification in the sub-humid regions with an accelerating rate because of deforestation, poor agricultural land use practice and overgrazing. Mapping desertification in sub-humid areas, however, is difficult due to cloud cover, poor geo- database infrastructure and limited investment. Current desertification mapping techniques are primarily developed for arid regions and are inappropriate for sub-humid areas, both in terms of scale and ecosystem characterization. There is an obvious need for new monitoring approaches developed specifically for sub-humid areas, which utilize readily available Earth observation systems in a cost effective solution. The goal of this study is to develop an imagery-based mapping method that can be used to monitor desertification in sub-humid areas of Vietnam, which is transferable to areas with similar conditions in South East Asia. Specific objectives are: 1) to characterise the desertification process in sub- humid areas from a remote sensing perspective, 2) to develop a method for desertification mapping which combines advantages of several types of satellite imagery, thus overcoming the limitations in spatial, temporal and spectral resolution of individual satellite image systems, 3) to test and evaluate the new desertification mapping method in the coastal lowlands of Vietnam. In the first step, characterising the desertification, field surveys were conducted in the coastal lowland of Vietnam to understand the background of land processes: vegetation fluctuation in savannah landscape, land use patterns and human activities, land form and soil properties. Archived remote sensing data including Landsat, ASTER, MODIS, MERIS, and different types of SAR imagery were used to investigate the spatial and spectral responses of desertification surfaces. Through this step a set of remote sensing imagery most suitable for sub-humid desertification was selected. Next, different parameters extracted from remote sensing data and their relationships with desertification features were examined. Soil temperature and vegetation index were selected as the components required to develop the Vegetation Temperature Angle Index (VTAI) algorithm. These two parameters have strong relationships with the thermal dynamics of land processes and vegetation moisture stress, thus providing a simple yet robust representation of desertification land spectrum. The methods to extract these parameters are mature and have led to standard products being available from data providers, therefore making the implementation of the mapping method simple and comparable between geographic regions. In addition a method for rapid soil moisture estimation from SAR images was investigated. Soil moisture represents a direct and measurable indicator of dryness and therefore provides an additional dimension to verify the desertification index algorithm. The Vegetation Temperature Angle Index (VTAI), and soil moisture estimation method are evaluated in the coastal lowland of Vietnam using data from ASTER and MODIS, and ENVISAT ASAR imagery. In particular, the efficiency of the VT AI index in detecting the vegetation condition and soil water availability was investigated. The results indicate that the VT AI index is simple to construct and capable of discriminating different variation in vegetation and soil, effectively identifying vegetation stress, and is able to separate inactive vegetation from bare soil. At the same time, soil moisture estimation from SAR images can be used to verify the desertification index, and to provide a weather-independent monitoring method. The VTAI and soil moisture estimation was combined into the Vegetation Temperature Angle and Moisture (VT AMI) index. The new combined index showed improved performance compared to VT AI and produced reliable results across a wider range of land cover and surface conditions. A desertification risk class map of the study area was produced from the VT AMI index. From the results of the testing project, the elements required for effective monitoring and management of sub-humid desertification are discussed in terms of optimal remote sensing imagery and land process characteristics, in order to identify areas at risk of desertification.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available