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Han P.,China Agricultural University | Wang P.,China Agricultural University | Zhang S.,Remote Sensing Information Center for Agriculture of Shaanxi Province | Zhu D.,China Agricultural University
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2010

The drought forecasting models is developed using the time series of the quantitative drought monitoring results of vegetation temperature condition index (VTCI) in the Guanzhong Plain of Northwest China. The autoregressive integrated moving average (ARIMA) is used to simulate the VTCI series of each pixel and forecast their changes in the future. A new way of modeling the spatio-temporal series is presented by extending of the forecasting models of some pixels to the whole area. The AR(1) models are suitable for all VTCI series of the 36 pixels. Therefore, the AR(1) models are applied to each pixel of the whole area, and the forecast is done with 1-2 lead-times. Comparing the monitoring and forecasting results, the forecasting accuracies of the AR(1) models are better, and the accuracies of the 1 lead-time are less than those of the 2 lead-times. The VTCI series of pixels in the whole study area are fitted by the selected best models. Comparing the fitting data with the historical data, the results show that VTCI series are better fitted by AR(1) models. Most of the simulating errors are small. All these results demonstrate that AR(1) models are suitable for drought forecasting using the VTCI series. Source


Han P.,China Agricultural University | Wang P.X.,China Agricultural University | Zhang S.Y.,Remote Sensing Information Center for Agriculture of Shaanxi Province | Zhu D.H.,China Agricultural University
Mathematical and Computer Modelling | Year: 2010

Regarded as a near real time drought monitoring method, the VTCI index based on remote sensing data is applied to the drought forecasting in the Guanzhong Plain. ARIMA models are used in the VTCI series, and forecast its changes in the future. A new way of modeling for the spatio-temporal series is used in the VTCI series. The time series of 36 pixels are studied firstly for their fitting models. Then the ARIMA model fitting for the whole area is determined. The AR(1) model are chosen to be the best model used in each pixel of the whole area, and the forecast is done with 1-2 steps. The results show that forecasting accuracy is better, 1 step is better than 2 steps. The historical VTCI data are simulated by AR(1) models. Comparing the simulating data with the historical data, the results show that the simulating accuracy is better. Most of the simulating errors are small. All results demonstrate that AR(1) model developed for VTCI series can be used for the drought forecasting in the Guanzhong Plain. © 2009 Elsevier Ltd. All rights reserved. Source


Gao B.,Shaanxi Normal University | Gao B.,Remote Sensing Information Center for Agriculture of Shaanxi Province | Wei H.-Y.,Shaanxi Normal University | Guo Y.-L.,Shaanxi Normal University | Gu W.,Shaanxi Normal University
Chinese Journal of Ecology | Year: 2015

Abies chensiensis is listed as a national third-class endangered wildlife species. It is one of the evergreen coniferous trees that are distributed primarily in Qinling Mountain across a range of elevations extending from 1350 m to 2500 m. Using the current geographic distribution records of A. chensiensis in Qinling Mountains and 20 factors, including 14 climate factors, 3 soil factors and 3 topographic factors, the potential geographic distribution of A. chensiensis in Qinling Mountains was assessed by MaxEnt model and ArcGIS spatial analysis. Results showed that the major factors impacting the suitable distribution area of A. chensiensis included 6 climate factors (annual mean temperature, annual extreme highest temperature, average temperature in January, ≥0 ºC accumulated temperature, aridity index and annual sunshine duration), 1 soil factor (soil pH) and 1 terrain factor (elevation). The most suitable and suitable areas of A. chensiensis in Qinling Mountains were 19498.87 and 32219.61 km2, respectively, and were mainly concentrated in southeastern Gansu, central and southern Shaanxi, northeastern Sichuan, northwestern Hubei and northwestern Henan. With complex terrain, there were lots of secondary vegetations in these areas. The marginally suitable and unsuitable areas of A. chensiensis were 51874.76 and 106307.97 km2, respectively. This study showed that the maximum entropy model and ArcGIS spatial analysis could be used to regionalize the potential geographic distribution of A. chensiensis, providing information for the resource conservation and management of A. chensiensis. © 2015, Chinese Journal of Ecology. All rights reserved. Source


Gao B.,Shaanxi Normal University | Gao B.,Remote Sensing Information Center for Agriculture of Shaanxi Province | Wei H.Y.,Shaanxi Normal University | Guo Y.L.,Shaanxi Normal University | Gu W.,Shaanxi Normal University
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2015

Amorphophallus rivieri (the corpse flower) is a traditional edible and medicinal plant in China. This species is distributed in the south part of the Qinling Mountains, China. We assimilated data about A. rivieri cultivation, environmental information from 28 sampling sites in the Qinling Mountains, climate data from 45 weather stations in the Qinling Mountains from 1961 to 2010, soil data with 1 km × 1 km spatial resolution and DEM data with 30 m ×30 m spatial resolution in the Qinling Mountains, A. rivieri data collected throughout China, and a specific report on A. rivieri in Shaanxi Province. We obtained 20 assessment factors that were significantly correlated when evaluating A. rivieri yield against environmental factors. The key environmental factors affecting the distribution of A. rivieri cultivation included 13 dominant climate factors, 4 dominant soil factors, and 3 dominant topographical factors. These dominant factors were 1) Frost-free duration (D), 2) Annual average temperature (Tn), 3) Annual total active temperature(≥10°C)(T≥10djw), 4) Monthly mean maximum temperature from July to August (T78zg), 5) Annual precipitation (Pn), 6) Monthly mean daily temperature range from July to September (T79gc), 7) Monthly mean temperature from May to October (T510p), 8) Monthly mean temperature from July to August (T78p), 9) Monthly mean relative air humidity from July to August (Q78), 10) June precipitation (P6), 11) July precipitation (P7), 12) August precipitation (P8),13) September precipitation (P9), 14) Topsoil depth (H), 15) Topsoil pH(H2O)(pH), 16) topsoil texture classification (C), 17) Topsoil organic matter (O), 18) Aspect (A), 19) Slope (S), 20) Altitude elevation (h). Using Geographic Information System (GIS) and a multivariate regression model, the climate factors were rasterized. Then, we used fuzzy mathematics analysis, analytic hierarchy process (AHP), and the weighted means method to set up the subjection function and determine the weight of each factor. We set up a model of ecological suitability for A. rivieri in the Qinling Mountains of Shaanxi Province, and determined the spatial distribution of suitable planting areas for this species. The root-mean-square error (RMSE) was used to evaluate the accuracy of the model predictions. The RMSE value reached 7.8%, which indicated that the predictive accuracy of the model was “Excellent.”The results showed that the ecological planting suitability model identified a relationship between potential A. rivieri cultivation distribution and the environmental factors. Highly suitable, moderately suitable, marginally suitable, and unsuitable planting areas of 1 214.42 km2, 2 015.60 km2, and 3 115.03 km2, and 5 580.02km2 were identified for A. rivieri, respectively. The potential suitable planting areas for A. rivieri were mainly distributed in the south central part of Hanzhong district, the south central part of Ankang district, and the southeast part of Shanluo district. This information on the potential suitable planting area of A. rivieri is valuable for providing baseline data, scientific information, and a research platform for understanding the ecology, geography, and environmental science of this important medicinal species. © 2015, Ecological Society of China. All rights reserved. Source


Xu W.-N.,China Agricultural University | Wang P.-X.,China Agricultural University | Han P.,China Agricultural University | Yan T.-L.,China Agricultural University | Zhang S.-Y.,Remote Sensing Information Center for Agriculture of Shaanxi Province
Journal of Natural Disasters | Year: 2011

Kappa coefficient is widely used to assess the accuracy of classification on remote sensing images. The assessment is generally done through statistics of error matrix among the classification types on the images. In this paper, Kappa coefficient was used to assess accuracy of drought forecast for Guanzhong Plain, using an approach to integrate the coefficient with the weighted Markov model and the ARIMA model. Commission error, omission error, overall accuracy and Kappa coefficient based on SPI and VTCI were obtained by establishing error matrix between original data and forecast data. Above all, commission error and omission error could show the local applicability of the two models, overall accuracy could not figure the truly accuracy of the models at different spatial and temporal scales but Kappa coefficient could. However, when the number of the forecasting samples increased to a certain extent, overall accuracy could be the same as Kappa coefficient, it could be used to assess drought forecasting accuracy. Source

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