ABSTRACT
Satellite remote sensing is an important method for earth observation and research
of global environment changes. Status information of forest, grassland, water etc. can be
acquired through the use of ecological environment remote sensing products. These
products have been widely used in scientific research institutes and industry sector.
Meanwhile, lots of countries have launched a plenty of remote sensing satellite, such as
Landsat, Terra, Aqua, Spot etc., which producing huge amounts of earth observation
data every day. However, the production of global ecological remote sensing products
has been considered as a compute-intensive and data-intensive task because of the
massive input data and complex calculation model. Traditional serial production method
is unable to meet the requirements of the task since its low computation efficiency.
To solve this problem, a remote sensing products processing system is presented to
effectively produce the ecological environment remote sensing products by utilizing
Hadoop, which is a quite popular distributed computing framework. The main contents
of this thesis is list as below.
(1) This thesis implementate the algorithms of the Global Environment Monitoring
Index and Global Grassland Drought Index by using MapReduce programming model.
The format of input key, input value, and partition function are elaborately designed by
taking the operational principle of MapReduce model and the characteristics of the
algorithms into account. To deal with the complicated production work which contains
several MapReduce procedures, workflow technology is used in order to tackle the
dependence between these procedures.
(2) Different computation methods, including the serial and distributed
computation methods, are used to processing the global scale of the products mentioned
above. This thesis compared the efficiency differences between serial and distributed
computation, and also measured the efficiency of Hadoop cluster by using different
number of computing nodes in the process of the production.
(3) This thesis design and implement the Global Ecological Environment Remote
Sensing Production System by utilizing the technologies of Hadoop, J2EE, WebGIS, etc.
The system carries out the production tasks in Hadoop cluster according to the
requirement of users. The functions of order analysis, production control, data and