The digital elevation model (DEM) and its derivative attributes are important parameters for evaluating any process using digital terrain analysis. Five freely available global DEM products including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model version 2 (ASTER GDEM2), Shuttle Radar Topographic Mission version 4.1 (SRTM V4.1), Global Multiresolution Terrain Elevation Data 2010 (GMTED2010), EarthEnv-DEM90, and Global 30 Arc-Second Elevation (GTOPO30) were assessed in this study. The objective of this study was to compare the differences of elevations, slopes, and topographic wetness indices (TWIs) derived from these five DEM products. SRTM V4.1 showed a better accuracy [root mean square error (RMSE)=4.87 m] than ASTER GDEM2 (RMSE=7.08 m) based on ICESat/GLAS (the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) laser altimetry points. ICESat/GLAS data were then selected as the benchmark to rectify the SRTM V4.1 data using the simple kriging (SK) interpolation method. The corrected high-accuracy SRTM V4.1 data (RMSE=1.14 m) were then regarded as the reference data. EarthEnv-DEM90 displayed the best accuracy in the DEM and slope, whereas the TWI accuracy of GMTED2010 was best. The accuracy of topographic attributes was sensitive to the roughness of the terrain. DEM and slope displayed a larger error variance as the elevation increased. DEM was sensitive to the data source and slope was sensitive to the data source and spatial resolution. TWI was influenced by data source and spatial resolution. As the spatial resolution decreased, the differences of topographic attributes tended to decrease.
An approach of the multi-scale texture classification for urban land cover /use using high-spatial resolution satellite
imagery was proposed in this paper, in which the decision tree classifier was employed. The comparison with the band to
be extraction was performed for three images. The grey-level co-occurrence matrix was adopted to calculate texture
values of twenty windows. The J-M distance was used to optimize the texture scales for the eight classes of land cover
/use. It was founded that maximum J-M distance appears in the window 15×15 for broadleaf-evergreen, conifer, 27×27
for grass land, 47×47 for bare soil, 67×67 for building and water, respectively. The experimental results showed that
overall accuracy with multi-scale texture was 81.7% for eight urban types. The comparison with both the single scale
texture and original spectrum showed that the overall accuracy of multi-scale texture was higher than ~6% of the single
scale texture and ~11% of the original spectrum respectively. The results also indicate that multi-scale texture method is
more accurate and reasonable with real world, and can reduce the "salt-and-pepper" effect. This is achieved by the
proposed method, in which the classification with optimization the texture scales is of the most critical value for
mapping urban land cover/use using high spatial resolution satellite image.
Accurate estimation of impervious surface and vegetation is a key issue in monitoring urban area and assessing urban
environments. It has been proved that the nonlinear models for spectral mixture analysis outperform the linear models in
the literature. However, the mapping functions of nonlinear models require to be predefined which are difficult to be
determined. Support vector regression (SVR) has shown success in dealing with nonlinear problem, such as estimation
and prediction. In this paper, genetic algorithm (GA) was employed to determine the optimal parameters of SVR
automatically, which were applied to SVR model. Further, a GA-SVR model with multi sets of parameters (Multi-GA-SVR)
was applied to estimate the distributions of impervious surface and vegetation. The results showed that Multi-GA-SVR
achieved a higher accuracy than GA-SVR with single set of parameters (Single-GA-SVR) and the traditional linear
mixture model (LMM), with an overall root mean square error measure (RMSE) of 0.15 for three distributions. It is
demonstrated that the proposed approach is a promising approach for estimation of impervious surface and vegetation.
Based on the new idea of harmony for human with water, a macro strategy for the flood-control and for the lake storage
during low water standing period in Dongting Lake watershed and the middle reach of Yangtze River is suggested in the
paper under normal operations of the Three Gorges Reservoir. The strategy includes three components that the first
region must maintain suitable water level through low construction projects to keep original scene for the natural lake
and wetland in low water standing period, the followed is the construction flood storage when water level over the
limitation level of the bank, the last one is the planning extended flood storage areas to regulate extreme floods like 1954,
1935 and 1931, as well as 1870. The strategy not only meets the sustainable development of human society, but can also
protect the nature. It provides a new approach to basically moderating or effective solve various problems among human,
water and land in the Dongting Lake area.
An approach for extraction and detection urban impervious surface was proposed in this paper, in which a decision tree classifier based on data learning algorithm was employed using Landsat TM/ETM data in 1988, 1994 and 2002 at same season. The feature subset was constructed with spectral, spatial and change information related to the characters of urban impervious surface. The samples from the higher spatial resolution image were dealt with CART algorithm. The extraction and change detection were performance with the decision tree classifier, and change information of 1994-2002 and 1988-1992 was verified by overlay analysis from GIS for the reasonability. The result of extraction impervious surface for six urban types was shown that the overall accuracy was 88.1% compared with 69.3% of MLC (maximum-likelihood Classifier) in 2002, and the detection accuracy for the five change types was 89.1% and 91.4% between 1994 and 2002, 1988 and 1994 respectively. The research has been demonstrated that the proposed approach is of capability for the change detection and can be achieved better accuracy using medium spatial resolution remotely sensed data.
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