A new method of sample selections for optimizing phenology model based remote sensing data

2015 ◽  
Vol 39 (3) ◽  
pp. 264-274
Author(s):  
MA Yong-Gang ◽  
◽  
ZHANG Chi ◽  
CHEN Xi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
New Method ◽  
Sensing Data ◽  
Model Based ◽  
Phenology Model

scholarly journals Remote sensing data assimilation for a prognostic phenology model

2008 ◽  
Vol 113 (G4) ◽  
Author(s):  
R. Stöckli ◽  
T. Rutishauser ◽  
D. Dragoni ◽  
J. O'Keefe ◽  
P. E. Thornton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Phenology Model

scholarly journals Land Degradation Model Based on Vegetation and Erosion Aspects Using Remote Sensing Data

2012 ◽  
Vol 44 (1) ◽  
pp. 19-34
Author(s):  
Adhi Wibowo ◽  
Ishak H. Ismullah ◽  
Bobby S. Dipokusumo ◽  
Ketut Wikantika
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Degradation Model ◽  
Sensing Data ◽  
Model Based

scholarly journals Fusion of High- and Medium-Resolution Optical Remote Sensing Imagery and GlobeLand30 Products for the Automated Detection of Intra-Urban Surface Water

2020 ◽  
Vol 12 (24) ◽  
pp. 4037
Author(s):  
Zhi Li ◽  
Xiaomei Yang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Automated Detection ◽  
Urban Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Model Based

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance.

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