Search

In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing data can provide abundant Earth observation information for lithology classification and identification, especially in some areas with complex geological conditions where the field geological survey is difficult to carry out. Compared with mountainous areas with large outcrops of rock, the lithology information obtained based on traditional field measurement and single optical remote sensing data in semi-arid areas is greatly limited due to the uneven vegetation coverage. While the combination of microwave and optical remote sensing technologies can effectively improve the integrality and reliability of the obtained lithology information in semi-arid areas. This paper selected Duolun County of Inner Mongolia Autonomous Region as the study area, added vegetation suppression for microwave and optical images into the conventional lithology classification process, and integrated Sentinel-1A and Landsat-8 images of the study area to carry out the experiment. The improved water-cloud model with the parameter of vegetation coverage and the method of feature-oriented principal component analysis based on multiple vegetation indices were used to realize the vegetation suppression in Synthetic Aperture Radar (SAR) backscattering images in two polarization modes and multispectral images. The processed SAR backscattering, SAR texture and spectral feature images were used to form seven feature combinations for lithology classification by the maximum likelihood method. The results showed that the proposed lithology classification scheme cannot achieve high-precision classification of all lithologic types, but it was effective in identifying the major lithologies dominated in the Quaternary deposits of the study area. In all feature combinations, the combination of all the three types of features reached the highest classification accuracy, with the overall classification accuracy of 88.60% and the kappa coefficient of 0.75 for five major lithologies. The experimental results fully demonstrated the advantages of integrating microwave and optical remote sensing data in lithology classification in a semiarid area. [ABSTRACT FROM AUTHOR]

This paper concerns the comparison of two sample non parametric regression. An asymptotically correct simultaneous confidence band (SCB) is proposed for the difference of two-sample non parametric regression functions to achieve the goal of comparison. Simulation experiments provide strong evidence that corroborates the asymptotic theory. The proposed SCB is used to analyze different samples of strata pressure data from the Bullianta Coal Mine in Erdos City, Inner Mongolia, China. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD) is a global infectious disease severely threatening children's health. It has been recognized that climate factors play an important role in the transmission of HFMD. In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones. The resulting mixed GTWR model is then used to investigate spatiotemporal effect of climate factors on the HFMD incidence in Inner Mongolia, China, a provincial autonomous region with extensive area and different climatic conditions. The results demonstrate that the effect of relative humidity is global over space and time, while that of air temperature, air pressure and wind speed varies spatiotemporally. The extended bootstrap test provides a solid statistical basis for model selection. The findings from the study may provide not only a deep understanding of spatiotemporal variation characteristics of the climatic effect on the HFMD incidence, but also some useful evidences for taking measures of the disease prevention and control at the county level in different seasons. [ABSTRACT FROM AUTHOR]

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]

Most cities or leagues in Inner Mongolia are the result of resource-based industrialization processes and animal husbandry development, leading to a heterogeneous composition and configuration of urban areas seldom guided by proper urban ecological landscape regulation policies. In this paper, we chose Xilingol as a case study, and put forward novel ecological landscape regulation approaches based on ecosystem services (ES) for achieving sustainable development in it. An urban ES simulation model and an urban classification model were used to assess the change of ES and to guide the future urban ecological landscape regulation. The results showed a rapid land use change in different subsystems of Xilingol from 2008 to 2013, providing relative values of each type of land per unit in three subsystems (core urban area, suburb and rural), and detected the change of ES in three subsystems and the change of ES per capita in the whole administrative urban area. We suggest that (a) long-term, stable, and ecosystem-services-based land use policies and urban ecological landscape regulation approaches are needed; and (b) highlight an urban ecological landscape regulation approach covering economic, social and natural dimensions for achieving sustainable development. [ABSTRACT FROM PUBLISHER]

Techniques for mapping and monitoring vegetation status under human disturbance (e.g., grazing livestock) can contribute to sustainable development of grasslands. The main objective of this paper is to investigate whether the effects of differences in grazing intensity (measured by stocking rate (SR)) could be discriminated using in situ spectral data. The experiment was conducted with controlled SRs on the Sino-German experimental farm in the Xilingol grassland, Inner Mongolia, P.R. China. Canopy spectral measurements were made on experimental paddocks that varied by SRs, terrain and management type. Eight vegetation indices (VIs) were calculated from ASD field spectrometer data for each paddock. We modelled the variability within and between paddocks using a linear mixed model to isolate the effect of grazing intensity on VI response. All VIs showed significant negative relationships to grazing intensity that were ameliorated on paddocks with haying compared to traditional direct grazing. The best VIs for detecting differences in paddocks due to the levels of SR were photochemical reflectance index (PRI), broadband normalized difference vegetation index (bNDVI) and narrowband NDVI (nNDVI1). The responses of VIs to vegetation conditions as affected by different SRs offer promise for the use of airborne and satellite remote sensing to map vegetation status and to evaluate the effects of grazing intensities at regional scales. [ABSTRACT FROM AUTHOR]