Search

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]

Normalized Difference Vegetation Index (NDVI) is widely recognized as a good indicator of vegetation productivity. Diagnosing the NDVI trend and understanding climatic factors influences on NDVI can predict the productivity changes under different climatic scenarios. This paper examined NDVI dynamic and its response to climate factors during a 10 year period (1998-2008) in Inner Mongolia. The main findings are as follows: (1) The NDVI multi-scale characters can be revealed well by wavelet transform, and the average NDVI and the NDVI amplitude show a gradually decreased trend from northeast to southwest in Inner Mongolia during the past 10 years, furthermore, this trend is consistent with the heat and water distribution caused by latitude difference in north-south direction and Asia monsoon effect in east-west direction. (2) The relation between NDVI and temperature is the most close, followed by precipitation, sunshine hours and relative humidity. Different vegetation cover types show different strengths in correlation between NDVI and climate variables with the correlation values decreasing from forest, meadow steppe to desert steppe in whole. (3) The precipitation and temperature have the same change cycle, both nearly 290 days in the 20 selected stations. The NDVI has the same change cycle with the precipitation and temperature or either 10 days earlier or later than precipitation and temperature, which supports the significant correlation between NDVI and its climatic factors from a new perspective. The nearly 290 days change cycle implies that the vegetation growth cycle is nearly 10 months and there are no obvious differences change cycles in different vegetations. (4) Vegetation dynamic is significantly correlated to the temperature and precipitation at the time scale of 10, 20, 40, 80, 160, and 320-day, respectively, and the S3 scale (i.e., the time scale of 80-day), nearly 3 months (one season), is most significant and suitable for evaluating the vegetation dynamic to climatic factors. [ABSTRACT FROM AUTHOR]

In a context of climate change, precipitation patterns show substantial disturbances and the occurrence of precipitation anomalies has tended to increase in the Huang-Huai-Hai River Basin. These anomalies are likely influencing vegetation dynamics and ecosystem stability. This paper aims to have a comprehensive understanding of vegetation growth response towards the precipitation pattern in the Huang-Huai-Hai River Basin. The study used NDVI (Normalized Difference Vegetation Index) data and mapped precipitation datasets from 1982 to 2011. NDVI and precipitation show a similar spatial distribution: they decrease from the southeast coast to the northwest inland. Regions with sparse vegetation are mainly distributed in arid and semi-arid areas or densely-populated areas. Vegetation coverage and the regular precipitation pattern show a positive correlation (61.6% of the whole region), while the correlation between vegetation coverage and precipitation anomalies is negative (62.7% for rainless days and 60.3% for rainstorm days). The clustering result shows that abundant vegetation is mainly situated in high precipitation or low anomaly areas. On the contrary, the degraded regions are mainly distributed in low precipitation or high anomaly areas. However, some special regions, mainly located in the Three North Shelterbelt Program region, the Tibetan Plateau, and other regions along the rivers, present improved vegetation cover when precipitation decreases or extreme events occur. [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: Spatial non-stationarity and scale-dependence are important characteristics of the relationship between NDVI and climatic factors. To improve the reliability of model prediction, it is necessary to find the scales and spatial heterogeneity in which a stationary relationship is reached. In this paper, a geographically weighted regression (GWR) model was developed to define spatial non-stationarity and scale-dependent relationships between NDVI and climatic factors. The results indicate that the spatial scale of the stationary relationship for NDVI and both temperature and precipitation is 156km over the whole Qinghai-Tibet Plateau. Both modeling performance and the spatial pattern of the GWR model are significantly better than global regression models such as OLS. Significant spatial heterogeneity of regression relationships between NDVI and climatic factors is revealed within the Qinghai-Tibet Plateau. We conclude that the dominant climatic factor influencing NDVI is not the same for all ecoregions within the study area. There are also different key scales of interaction between NDVI and the dominant climatic factor in these various ecoregions. Finally, model performance is different in the each eco-region. Therefore, this finding can provide a scientific basis for choosing a suitable scale and reliable models to solve scale-dependent problems in geography and ecology. [Copyright &y& Elsevier]

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]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In arid regions, C3 vegetation is assumed to be more sensitive to precipitation and CO2 fertilization than C4 vegetation. In this study, normalized difference vegetation index (NDVI) is used to examine vegetation growth in the arid Lower Heihe River Basin, northwestern China, for the past three decades. The results indicate that maximum NDVI (MNDVI) of the area increases over the years and is significantly correlated with precipitation (R = 0.47 and p < 0.01), not temperature (R = -0.04). The upper limit of C3 vegetation cover of the area shows a yearly rising trend of 0.6% or an overall increase of 9% over the period of 25 years, primarily due to the CO2 fertilization effect (CO2 rising 14%) over the same period. C3 dominant areas can be potentially distinguished by both MNDVI asynchronous seasonality and a significant relation between MNDVI and cumulative precipitation. This study provides a potential tool of identifying C3 vegetation from C4 vegetation and confirms the CO2 fertilization effect in this arid region. [ABSTRACT FROM AUTHOR]