Vegetation Health Index (VHI) analysis during drought season in Brantas Watershed

Abstract

The Brantas Watershed located in East Java has the vulnerability of drought as one of hydrometeorological disasters. The Vegetation Health Index (VHI) as one of remote sensing index was used to analyse drought. VHI can be derived based on both the Land Temperature Surface (LST) and Normalized Differenced Vegetation Index (NDVI). This research aimed to determine the influence of LST and NDVI, respectively, to VHI, especially in dry season of 2008 - 2017. The data used were MODIS Vegetation Indices (MOD13A1) and MODIS Land Surface Temperature (MOD11B1). The influence of LST and NDVI to VHI in the Brantas Watershed was analysed using correlation and regression testing. The LST - NDVI correlation of Brantas Watershed was negative (-0.73). The high temperature distribution was dominantly located in the low-density vegetation areas. The LST - VHI correlation was 0.35, and NDVI - VHI correlation was 0.63. This illustrated that the influence of land surface temperature to the vegetation drought was weak. Drought indicated by VHI was more likely to be influenced by internal conditions of vegetation and other environmental elements.

Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security

“…The three Vegetation Health indexes developed by Kogan have been used extensively in the literature for monitoring the vegetation activity in response to weather-related drivers such as drought (Zuhro et al, 2020;Baniya et al, 2019;Kamble et al, 2019;Liang et al, 2017;Marufah et al, 2017;Masitoh and Rusydi, 2019;Pei et al, 2018) and to evaluate crop production (Kogan, 1990;Orlovsky et al, 2010;Rahman et al, 2009). The findings in the literature highlight that VHI correlates well with meteorological drought and agricultural drought in monsoonal rainfall areas (Marufah et al, 2017) and, most importantly, is useful to predict the yield of several grain crops such as corn in China (Kogan et al, 2005), wheat in the USA (Salazar et al, 2007), and rice in Bangladesh (Rahman et al, 2009) several months in advance of the harvest with considerable implications for food security.…”

Spatial-Temporal Assessment of Drought in Hoa Vang district, Da Nang City, Vietnam Using Remote Sensing and Google Earth Engine

“…The VHI values were validated using correlation tests using LST and NDVI as influencing factors. The VHI values were classified into four categories: Extreme drought (<10), Severe drought (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), Moderate drought (20-30), Mild drought (30-40), and No drought (>40) [21,26,27].…”

“…The NDVI is used to determine the Vegetation Condition Index (VCI), which reflects the health and vigor of vegetation. On the other hand, the LST is used to determine the Temperature Condition Index (TCI), which indicates the thermal stress experienced by vegetation [10]. By combining these indices, the VHI provides a comprehensive assessment of drought stress on the vegetation canopy [11].…”

Application of Probabilistic Machine Learning to the prediction of Remotely Sensed Vegetation Health

“…The three Vegetation Health indexes developed by Kogan have been used extensively in the literature for monitoring the vegetation activity in response to weather-related drivers such as drought (Baniya et al, 2019;Kamble et al, 2019;Liang et al, 2017;Ma'rufah et al, 2017;Masitoh and Rusydi, 2019;Pei et al, 2018;Zuhro et al, 2020) and to evaluate crop production (Kogan, 1990;Orlovsky et al, 2010;Rahman et al, 2009). The findings in the literature highlight that VHI correlates well with meteorological drought and agricultural drought in monsoonal rainfall areas (Ma'rufah et al, 2017) and, most importantly to be useful in predicting the yield of several grain crops such as corn in China (Kogan et al, 2005), wheat in the USA (Salazar et al, 2007), and rice in Bangladesh (Rahman et al, 2009) several months in advance of the harvest with considerable implication for advanced projections of food insecurity scenarios.…”