Application of vegetation health index (VHI) to identify distribution of agricultural drought in Indramayu Regency, West Java Province

Abstract

The drought that occurred in Indramayu Regency was caused by a shift of the beginning season and a long dry season which affected the availability of water storage for plants. Indramayu Regency is one of the rice centers in West Java with 56% of its area is rice fields. But in recent years rice productivity has been reduced due to drought. The Indramayu District Agriculture Office noted that in 2012, 2015 and 2018 paddy fields. The purpose of this study was to determine the distribution of 2012, 2015 and 2018 wetland agricultural drought areas and their relationship with rainfall in Indramayu Regency. The VHI drought index (Vegetation Health Index) is used to determine the pattern of distribution of the drought area of agricultural land. VHI is a combination of VCI (Vegetation Condition Index) and TCI (Temperature Condition Index) derived from NDVI data processing (Normalized Difference Vegetation Index), LST (Land Surface Temperature) of Landsat 7 and 8 images. The processing results of the VHI index show the distribution of drought levels no drought to extreme drought, where in 2012, 2015 and 2018 the distribution of drought in agricultural land has the same pattern, which is dominated by the coastal areas of Indramayu Regency due to the influence of less rainfall. While the level of mild drought is in the western and center regions of Indramayu Regency.

Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany

“…Looking at the calculated drought index maps from TCI, VCI, and VHI (Figure 6), it can be seen that MODIS data can primarily capture the temporal and spatial dynamics of NDVI and LST. Comparing this with drought index calculations from other satellites (e.g., [72,73]), this is an advantage in drought monitoring, especially in the temporal dimension. However, some compromises have to be made within the spatial resolution.…”

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.…”

“…The scientific community has made extensive use of satellite imagery for mapping and monitoring changes in land cover and estimating geophysical and biophysical characteristics of the soil (Shanmugapriya et al, 2019;Weiss et al, 2020), as well as to develop and validate vegetation health indicators (Kogan, 2002;Kogan et al, 2004;Kogan, 2019;Kogan et al, 2005;Xue and Su, 2017). The Vegetation Health Index (VHI) (Kogan, 1997;Kogan, 1987) is one such indicators and it has largely been used to monitor crop vegetation over large areas and predict crop yield (Kogan, 1990;Orlovsky et al, 2010;Rahman et al, 2009;Zuhro et al, 2020). In parallel, learning-based statistical algorithms have opened up new frontiers and enabled the development of tools for analysing satellite imagery, providing better and more nuanced insights thanks to their ability to find patterns underlying the complex nonlinear relations that characterise environmental variables.…”

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.…”

“…The field's technical and methodological advances have brought a multitude of indexes to monitor several vegetation aspects (Xue and Su, 2017). The Vegetation Health Index (Kogan, 1997(Kogan, , 1987) is one such index that characterises vegetation health and has been often used to estimate crop condition and predict yield (Kogan, 1990;Orlovsky et al, 2010;Rahman et al, 2009;Zuhro et al, 2020).…”