Junming Liu

Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China

The aim of this study was to develop and validate a method of determining the warm and cold edges of the Vegetation Temperature Condition Index (VTCI) drought monitoring approach, and to analyse the time series profiles of the VTCI in croplands under both rainfed and irrigated conditions. The linear correlation coefficients between the VTCI and the cumulative precipitation at one or two periods of 10‐day intervals are the highest. There are significant linear correlations between the VTCI and soil moisture at the 0–10‐cm layer for each 10‐day interval during the winter wheat growing seasons. These results indicate that the VTCI is an effective approach for monitoring drought occurrence after the crops turn green. The time series analysis of the VTCI in the 10‐day intervals shows that the VTCI profiles are different for irrigated and rainfed conditions. The time series VTCI values under rainfed conditions have a good response to recent precipitation, while those under irrigated conditions have less agreement with recent precipitation due to irrigation practices. The results show that the VTCI is a better indicator of droughts than indices developed from precipitation data.

Application of the ARIMA Models in Drought Forecasting Using the Standardized Precipitation Index

The standardized precipitation index (SPI) was used to quantify the classification of drought in the Guanzhong Plain, China. The autoregressive integrated moving average (ARIMA) models were developed to fit and forecast the SPI series. Most of the selected ARIMA models are seasonal models (SARIMA). The forecast results show that the forecasting power of the ARIMA models increases with the increase of the time scales, and the ARIMA models are more powerful in short-term forecasting. Further study was made on the correlation coefficient between the actual SPIs and the predicted ones for the forecasting. It is shown that the ARIMA models can be used to forecast 1-month leading values of all SPI series, and 6-month leading values for SPI with time scales of 9, 12 and 24 months. Our study shows that the ARIMA models developed in the Guanzhong Plain can be effectively used in drought forecasting.

Reconstructing Vegetation Temperature Condition Index Based on the Savitzky–Golay Filter

Vegetation temperature condition index (VTCI) is a near-real-time drought monitoring approach which is derived from normalized difference vegetation index (NDVI) changes in a given region to land surface temperature (LST) changes of pixels with a given NDVI value. It can be physically explained as the ratio of temperature differences among the pixels which have the same NDVI values. Due to the noise in the NDVI and LST, results of VTCI have much deviation. The Savitzky.Golay filter, a weighted moving average filter as a polynomial of a certain degree, is applied to smooth out noise in NDVI and LST time-series. VTCI were taken into Guanzhong Plain of Shaanxi Province of each 10-days from March to May in 2007 and 2008 as the study data. In order to reconstruct VTCI space-time (temporal and spatial) series data, the Savitzky.Golay filter was used to reconstruct the VTCI time-series of each pixel in remote sensing images. Then, the results were extended to the surface from the point. The results show that the Savitzky.Golay filter could improve the quality of VTCI and could get a better drought monitoring result.

The Estimation of Tree Height Based on LiDAR Data and QuickBird Imagery

The estimation of tree height is advanced following the development of LiDAR technique. The estimation model of tree height considering suppressed trees is developed in order to extract tree height accurately using LiDAR data. Filtered LiDAR data and Quickbird imagery are segmented using watershed segmentation method based on mathematical morphology to get the boundary of trees. And the highest point in each canopy object is used to estimate tree height. Weibull distribution is used to estimate height distribution of the suppressed trees. The experiment results indicate that the watershed segmentation method based on mathematical morphology is an effective method to extract the boundary of trees. And the R2 between the tree height estimated using estimation model of tree height considering suppressed trees and the tree height measured by field work is 0.93.