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The contributions are of fundamental implications for improving RS-based desertification analysis and have created a firm foundation for developing a RS information model in Part II.
Overall, this Part II established a new comprehensive RS information model for desertification risk assessment and its trend deriving, and the whole study comprising Part I and Part II advanced a relatively standard framework for RS-based desertification monitoring.
To comprehensively reflect desertification and project its trend, this study attempted to develop a new comprehensive RS information model, with the scenario for test deployed at the south Junggar Basin, China in the last decade (2000 2009).
To fill this gap, this Part II was dedicated to developing a RS information model for comprehensively characterizing the desertification and deriving its trend, based on the indicators retrieved in Part I in the same case of the south Junggar Basin, China in the last decade (2000 2009).
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Both of these determinants also had a positive and significant correlation with coping practices (r information = 0.48, p < 0.000; r training = 0.74, p < 0.000; r info vs training = 0.54, p < 0.000).
Table 1 Percentage of risk-prone trajectories based on two decision criteria Greedy GMM Hybrid Coastal Human Risk-prone (f) 77%11%30%46%26% 26% Risk-prone (r) 78%12%24%45%7%% 7% Two decision criteria: the feature (f) and the risk (r) (information gain) metrics.
The content interestingness ϕ(r) of information r represents a measure to determine how much users want to share the information r with their neighbors.
Q-PCR was as described above using primers BEL3947-F/BEL3947-R, BELUTR-F/BELUTR, BP2609-F/BP2609-R, and BP1064-F/BP1064-R (Supplemental Information).
Primers CalU16-NdeI-F/CalU16- CalU16-NdeI-F/CalU16- CalU16-NdeI-F/CalU16- and CalU19-NdeI-F/CalU19- HindIII-R (Supporting Information Table S3) were used to amplify each of calU16, calU17, and calU19, respectively.
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