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Restoration of natural vegetation in arid and semi‐arid grasslands is facing severe challenges. The vegetation is easy to lose their vitality, resulting in the loss of the cover in natural grasslands under the high grazing pressure. To address this situation, this paper proposes a novel method for accurately predicting the grazing pressure using the nonlinear autoregressive with exogenous input (NARX) network based on the remote sensing data of normalized difference vegetation index (NDVI) and precipitation. The proposed method uses the NARX networks to predict the temporal variations of the NDVI with respect to the precipitation. The grazing pressure can be thus calculated using the predicted values of the NDVI. For practical application, this study investigated an arid and semi‐arid grassland with heavy grazing pressure in Hulunbuir, China. The results demonstrate that the proposed method can provide an accurate prediction of the grazing pressure (mean absolute error 0.103, root‐mean‐square error 0.122, mean absolute percentage error 8.36% and coefficient of determination 0.899 at the confidence interval of 95%). In addition, the predicted values of the grazing pressure in the study area during the years from 2016 to 2020 can be obtained using the proposed method. The proposed method can obtain a good prediction of the grazing pressure, which can be further used as a guidance for the rangeland managers to reduce the occurrence of the overgrazing. [ABSTRACT FROM AUTHOR]

The latest research indicates that there are time-lag effects between the normalized difference vegetation index (NDVI) and the precipitation variation. It is well known that the time-lags are different from region to region, and there are time-lags for the NDVI itself correlated to the precipitation. In the arid and semi-arid grasslands, the annual NDVI has proved not only to be highly dependent on the precipitation of the concurrent year and previous years, but also the NDVI of previous years. This paper proposes a method using recurrent neural network (RNN) to capture both time-lags of the NDVI with respect to the NDVI itself, and of the NDVI with respect to precipitation. To quantitatively capture these time-lags, 16 years of the NDVI and precipitation data are used to construct the prediction model of the NDVI with respect to precipitation. This study focuses on the arid and semi-arid Hulunbuir grasslands dominated by perennials in northeast China. Using RNN, the time-lag effects are captured at a 1 year time-lag of precipitation and a 2 year time-lag of the NDVI. The successful capture of the time-lag effects provides significant value for the accurate prediction of vegetation variation for arid and semi-arid grasslands. [ABSTRACT FROM AUTHOR]

Abstract: The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present a statistical downscaling algorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying a downscaling methodology, TRMM 3B43 0.25°×0.25° precipitation fields were downscaled to 1×1km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscaling algorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r 2 ranging from 0.72 to 0.96 at six different raingauge stations. [Copyright &y& Elsevier]

Abstract: As an index denoting vegetation cover, NDVI is influenced by many climatic factors such as precipitation and average temperature. In this paper, NDVI is calculated on the basis of waveband characteristic of NOAA/AVHRR data for the YarlungZangbo River Basin. Then, the spatial-temporal change of NDVI is analyzed. The basin''s NDVI is in consistency with season from 2001 to 2003.Large value zone of NDVI is mainly distributed in the lower reaches and part of the middle reaches. Meanwhile, point-data of principal climatic factors are interpolated to spatial grid data with Kriging method consistent with NDVI based on DEM of the YarlungZangbo River Basin. Then, the relation of NDVI and precipitation or average air temperature is analyzed with F checking and two-sample variance test in the basin. The result is P=0, which shows that correlation analysis has larger confidence. The linear and logarithmic correlation of NDVI and precipitation or average air temperature is analyzed at confidence level of 0.05.The conclusion is that NDVI and precipitation and average air temperature in the basin vary with seasons and are closely correlated. The linear correlation coefficient of NDVI and precipitation is 0.8, and that of NDVI and average air temperature is about 0.77.And the logarithmic correlation coefficient of NDVI and precipitation is 0.71, and that of NDVI and average air temperature is 0.7. [Copyright &y& Elsevier]