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A method for predicting the dynamic spatio-temporal variations of the normalized difference vegetation index (NDVI) based on precipitation is proposed using combined nonlinear autoregressive with exogenous input (NARX) networks and artificial neural networks (ANNs). The proposed method is validated by applying to predict the spatio-temporal NDVI for the Hulunbuir grassland located in Inner Mongolia, China. The results show the good predictive ability for the spatio-temporal variations of NDVI with the mean absolute percentage error of 11.59%, mean absolute error of 7.11 × 10−2 and root mean square error of 8.06 × 10−2, respectively. The approach presented in the paper can be further used as the guidance to reduce the occurrence of overgrazing in the arid and semi-arid grasslands. [ABSTRACT FROM AUTHOR]

Validation of Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance products is important to effective utilization of such products for earth systems science. Ground-based measurements are normally utilized for such validation. However, the major scale mismatch between the ground ‘point’ measurement and MODIS resolution (500 m and 1 km) makes direct comparison infeasible over many land surface types. In this paper, an indirect comparison between ground ‘point’ measurements and MODIS land surface products via high-resolution remotely sensed imagery (Landsat Thematic Mapper/TM) was utilized in semi-arid grassland of Inner Mongolia in summer 2005, where ground measurements are relatively sparse in comparison with other locations around the world. Within the validation, the TM reflectance imagery was first calibrated by the ground ‘point’ measurements, and then aggregated to MODIS data resolution for determination of their accuracy. Besides common direct spectral band comparison of reflectance between TM and MODIS, empirical/indirect comparison between TM and MODIS was also implemented. Both types of validation showed that the absolute error of bidirectional reflectance from atmospheric correction (MOD09) is less than 9.4%, and for nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (MOD43B4) it is less than 3.1%, in which the error of visible bands of two data sets is less than 1.35% and 0.95%, respectively. This validation will help improve the accuracy of MODIS products used in this area. [ABSTRACT FROM AUTHOR]

Community ecologists and vegetation scientists in grassland research have a strong interest in quantifying biotic communities in detail. However, a satisfactory classification with fine biotic details has been challenged by the coarse resolutions of Landsat images, although they are easily accessible. In this paper, a hybrid fuzzy classifier (HFC) for vegetation classification with Landsat ETM+ imagery on the typical grassland in Xilinhe River Basin, Inner Mongolia, China has been developed. Three vegetation classification systems were created from different aspects: the botanical system (Bio-classes, also as the final mapping units for vegetation cover), the combined botanical and spectral system (Bio-S classes), and the spectral system (Spec-classes). The HFC designed a fuzzy logic to measure the similarity between Spec-classes, extracted by the unsupervised classification, and Bio-S classes, built from the field samples, when considering the spectral variations of samples within the same Bio-class. Then, Bio-S classes, which served as a bridge for assigning Spec-classes to the target Bio-classes, were merged to restore Bio-classes for the final mapping. To assess the classification accuracy, the HFC was compared with a conventional supervised classification (CSC). The overall result of the HFC was much better than that of the CSC, with an accuracy percentage of 80.2% as compared to 69.0% for the CSC. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study of endangered plants using remote sensing data. The aim of this study was to investigate and assess spatial distribution changes and temporal coverage changes of the following endangered rare plants: Tetraena mongolica, Helianthemum soongolicum, Reaumuria trigyna, Reaumuria soongorica, Potaninia mongolica, and Ammopiptanthus mongolicus, in the West Ordos Plateau National Conservation Region in Inner Mongolia, China. Using ETM+ (August 1999) remote sensing data, DEM data, and data from 25 field samples collected from Gangdeger Mountain in July 1999, digital maps highlighting endangered plant populations (1994) were processed using the ‘MPH feature extraction method’ to view the current spatial distribution pattern of the endangered plants. The ‘radiometric normalization’ and ‘vegetation change detection’ methods were then used to reveal temporal coverage changes of the endangered plants, using TM (September 1987) and ETM+ (August 1999) data. [ABSTRACT FROM AUTHOR]