This study aims to evaluate the effects of natural and human conditions on vegetation covers using VCI and TCI indices (the satellite-based Vegetation Health Indices (VHIs)) in Dak Nong province. Factors affecting the VHIs included in the analysis involve land use, soil, population, topography, distance to roads and surface water. The data analysis period is the dry season from 2000 to 2020. The trend of the VHIs’ change in this study is analyzed using Sen’s method with monitoring data from Modis. The effects of factors affecting the VHIs are based on logistic regression and discriminant analysis. The analysis results show that the VHIs are clear and show both increasing and decreasing trends. Based on logistic regression analysis, the influence of land use types on the trend of the VHIs in the direction of increasing from negative to positive will be from PdF, UnL, AnC, SpF, PtF, PeA and then to PdR. Corresponding to the soil will be from ACa, FRp, FRx, FRr, ACh, LVx, FLg and then to LVg. Based on the discriminant analysis method, it was found that there are only four primary factors affecting the trend of the VHIs in order of decreasing level of closure: population density, land use, soil and population growth rate and to the road. These results show that in places where labour resources are available, plants are interested in investment due to high profits, nutrient-rich soil and convenience for plant care; the VHIs tend to increase and vice versa. Compared with VCI, the trend of TCI tends to be sloping negative and more pessimistic. Through the coefficients of the logistic regression equation and the difference function, the TCI is more sensitive than the VCI when the independent variables change, especially for changes in population density and land use. Thus, TCI can be considered the preferred option for assessing vegetation health trends in the context of climate change.

The study aimed to forecast early the vegetation health index (VHI) in the Srepok river basin and evaluate the contribution of drought indices to the forecast accuracy. Stepwise Multiple Linear Regression (SMLR) is used to build forecast models. The model input variables include the Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and VHI in the previous period. The analysis shows that VHI during the dry and early wet seasons is strongly dependent on rainfall and streamflow at the end of the previous wet season. Vegetation shows high sensitivity to drought indices at the end of the dry season and the beginning of the wet season, when streamflow is at its lowest. The evaluation results show that VHI can be forecasted in the dry and early rainy season months with a 3-month lead time. The contribution of the variables to the forecast quality depends on the forecast time. With a 1-month forecast, VHI is dominant and from 2 to 3 months it is replaced by SPI and SSI. The t-Test shows that the forecast accuracy is higher for agricultural crops than for forests. The study provides a useful tool for drought monitoring and early warning in agricultural management in this basin.

This study developed machine learning models to predict the Vegetation Health Index (VHI) using predictors derived from the Standardized Streamflow Index (SSI), Standardized Precipitation Index (SPI), and lagged VHI values. Three machine learning approaches were evaluated: Stepwise Quadratic...

This study aims to evaluate drought dynamics using the Vegetation Health Index (VHI) in the Srepok River Basin during the period 2000–2022 and to analyze the relationship between meteorological drought and vegetation condition. The Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data derived from MODIS satellite imagery were utilized to calculate the VHI, while rainfall data from meteorological stations were employed to compute the Standardized Precipitation Index (SPI). The analysis shows that VHI exhibits the strongest correlation with SPI at a 6-month scale (SPI6), particularly in April, marking the end of the dry season. During the 23-year study period, drought events demonstrated a recurrence pattern of approximately 3–5 years, with severe episodes occurring in 2005 and 2016, coinciding with strong El Niño events. Spatial analysis indicates that the northwestern middle and lower sub-basins were most severely affected, with drought durations exceeding six months and frequencies greater than 14%. Crop-specific assessment shows that annual crops and rice were the most drought-sensitive groups, while perennial crops exhibited higher drought tolerance but still sustained significant damage due to their extensive cultivation area. These findings underscore the effectiveness of integrating VHI and SPI6 as tools for agricultural drought monitoring and early warning in the region.