The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana, USA

Abstract

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.

Impacts of crop rotation on vegetation condition index for species-level drought monitoring

“…For crop field, crop rotation could be the main factor affects the inter-annual land cover. Yagci et al [4] and Deng et al [6][7] explored the impacts of land cover change on VCI index, and found that the index is sensitive to changes in land cover. Brown et al [8] alleged that it is difficult to isolate land cover change with weather interference on NDVI-derived satellite drought index.…”

“…NDVI-based satellite indices (e.g., VCI index) are suitable for agricultural drought monitoring [4]. However, VCI index is sensitive to the inter-annual variations of NDVI caused by land use and land cover changes [5].…”

Agricultural Drought Assessment of Post Monsoon Season of Vaijapur Taluka Using Landsat 8

“…where, NIR= near infrared band, RED= Red band; The most of the visible light is absorbed by healthy vegetation and it reflects NIR light [36]. Unhealthy vegetation reflects more visible light and less infrared light.…”

“…The 100% value of VCI indicates that healthy vegetation and value is less than 50% indicate the different degree of drought severity. The VCI is sensitive to vegetation [36]. …”

Reliability of meteorological drought indices for predicting soil moisture droughts

“…Drought is one of the most complex, harmful and least understood type of climatic events, causing an annual average of 6-8 billion USD of damage globally (Keyantash and Dracup, 2002;Yagci et al, 2013;Saghafian et al, 2003;Edwards, 1997); and it is expected that the severity and frequency of droughts will increase in the future due to climate change (Heim Jr, 2002). Droughts are classified into meteorological, agricultural, hydrological, and socioeconomic droughts (Shiau et al, 2012;Zargar et al, 2011;Passioura, 2007).…”