Drought identification in the eastern Baltic region using NDVI

Egidijus Rimkus,Donatas Valiukas,Viktorija Maciulyte,Edvinas Stonevicius,Justinas Kilpys

doi:10.5194/esd-8-627-2017

Egidijus Rimkus, Donatas Valiukas + Show 3 more

Open Access

https://doi.org/10.5194/esd-8-627-2017

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Abstract

Abstract. Droughts are phenomena that affect large areas. Remote sensing data covering large territories can be used to assess the impact and extent of droughts. Drought effect on vegetation was determined using the normalized difference vegetation index (NDVI) and Vegetation Condition Index (VCI) in the eastern Baltic Sea region located between 53–60° N and 20–30° E. The effect of precipitation deficit on vegetation in arable land and broadleaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1- to 9-month timescales. Vegetation has strong seasonality in the analysed area. The beginning and the end of the vegetation season depends on the distance from the Baltic Sea, which affects temperature and precipitation patterns. The vegetation season in the southeastern part of the region is 5–6 weeks longer than in the northwestern part. The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on the NDVI values in the first half of the active growing season. Precipitation deficit in the first part of the vegetation season only has a significant impact on the vegetation on arable land. The vegetation in the forests is less sensitive to the moisture deficit. Correlation between VCI and the same month SPI1 is usually negative in the study area. It means that wetter conditions lead to lower VCI values, while the correlation is usually positive between the VCI and the SPI of the previous month. With a longer SPI scale the correlation gradually shifts towards the positive coefficients. The positive correlation between 3- and 6-month SPI and VCI was observed on the arable land and in both types of forests in the second half of vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.

Highlights

Vegetation indices derived from remote sensing data are very important for the accurate assessment of plant growing conditions, especially in the case of extreme weather events, such as droughts
The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on normalized difference vegetation index (NDVI) values in the first half of the active growing season
Negative correlation between SPI1 and Vegetation Condition Index (VCI) shows that the short-term precipitation deficit leads to a better vegetation condition

Summary

Introduction

Vegetation indices derived from remote sensing data are very important for the accurate assessment of plant growing conditions, especially in the case of extreme weather events, such as droughts. Ground-based meteorological and agrometeorological drought indices only allow the evaluation of the risks for agricultural lands, while the satellite information makes it possible to identify damaged vegetation in various land types and to assess the magnitude of damage. Remote sensing of the vegetation condition is based on the fact that healthy plants have more chlorophyll and absorb more visible (VIS) radiation and reflect more nearinfrared (NIR) radiation (Myneni et al, 1995). Often vegetation conditions are determined by calculating the normalized difference vegetation index (NDVI). During more than 30 years of measurements, the land use has been changed in many locations and it is difficult to determine the climatic signal in the NDVI changes.

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