Satellite based remote sensing is the most appropriate tool to study and monitor the state, productivity (carbon uptake by the vegetation), crop yields of agricultural plants and the phenological cycle of the terrestrial vegetation globally and regionally. Therefore the information provided by satellites about the vegetation state and functionality with high spatial and temporal resolution is essential. Plant production and the related ecosystem services are affected by the complex interactions between plant growth and the meteorological parameters, soil processes, disturbance and other factors. The exact cause of the observed variability of plant status and growth is not well quantified due to the complexity of the driving variables and the parallel changes in many meteorological elements and other environmental factors. In addition, biotic factors (such as herbivores) are also affecting the vegetation status causing disturbances, where the vitality of insects is sensitive to the climatic conditions in general. Moreover, it is known that the timing of insect outbreaks and thus the caused damage on forest ecosystems might also be related to the environmental conditions, since favorable weather conditions during their important life stages promote better survival rate and therefore population increase. Due to climate change, new herbivore alien and invasive species are appearing continuously in the region of the Carpathian-Basin, causing unexpected disturbances in the state of the forests and large economic losses. Although changes in biotic factors (pests) are not unrelated to climate change (i.e. changes in abiotic factors), it is important to understand their impact on the ecosystems, to quantify them separately, as well as their synergic effects. These biotic and abiotic factors must be considered together to study vegetation response to the changing environmental conditions.

The remote sensing of the vegetation in the optical spectra is based on the measurement of the radiation (originating from the Sun) reflected by the surface. In the following figure we can see a typical example for the yearly phenological curve of a broadleaf forest based on the NDVI (Normalized Difference Vegetation Index), which is determined from remotely sensed measurements.

Typical phenological curve of a broadleaf forest based on the annual NDVI curve

The most important results of our vegetation related investigations based on satellite remote sensing in the Carpathian-Basin are:
- The dynamics of leaf unfolding of deciduous broadleaf forests were studied using satellite remote sensing data in the wider Carpathian Basin, located in Central Europe, for the period of 2000-2019. The start of the season (SOS), end of the green-up (EOG), and its difference, the green-up duration (GUD), as the indicator of the speed of the leaf-unfolding, were analysed based on MODIS NDVI data. Our results clearly showed that there is considerable interannual variability in the green-up duration (GUD), where the last three years had, on average, the shortest (2018) and the two longest (2017 and 2019) recorded green-up durations in the region. The observed variability was partially attributed to the meteorological conditions. The relationship between the SOS and the green-up duration revealed that the SOS also played an important role as a driver (earlier SOS implies longer GUD, while delayed SOS is associated with short GUD). Considerable elevation dependency was also found both in the green-up duration and also in its correlation with SOS. Multiple linear regression models based on the SOS and the meteorological variables were created to explain and predict the green-up duration. For more information see: Kern et al., 2020.
- Investigating the effects of weather on crop yields of the four most important cereal crops in Hungary (winter wheat, maize, sunflower and rapeseed) we found that the minimum and maximum temperature has the most important role for all four plants, and in case of maize the soil water content during July and August is also significant. Using a stepwise linear regression-like method the most appropriate models were selected based on the statistical evaluation of the model fitting. The created models are apropriate both for estimating and forecasting the crop yields. For more information see: Kern et al., 2018.
- Determining the years with extreme weather events from the perspective of the vegetation activity during 2000-2016 revealed the differences between the main land cover types, and presented the interannual variability of the effects of weather on vegetation state. For more information see: Kern et al., 2017.
- Investigations were made based on the NDVI3g (Tucker et al., 2014) dataset for the region of the Carpathian-Basin, where we created and disseminated the MODIS adjusted, improved NDVI3g dataset (http://nimbus.elte.hu/NDVI_CE/). For more information see: Kern et al., 2016.
- The spread and the impacts caused by the new invasive Oak Lace Bug (Corythucha arcuata, Say 1832) in the forests of the Carpathian-Basin are continuously monitored and evaluated. For more information see: Kern et al., 2019.

The main goal of our research is to study the relationship between the biotic/abiotic factors (like insects and weather, respectively) and the state of the vegetation in the Carpathian Basin located in Central Europe.

Our research is based primarily on the following datasets:
- Vegetation related quantities derived from MODIS data (NDVI, EVI, LAI, FPAR, GPP, NPP),
- Sentinel-2A&B MSI data,
- FORESEE: public meteorological database: http://nimbus.elte.hu/FORESEE/

Related projects:
- OTKA FK128709, 2018-2022: Remote sensing based detection of the effects of biotic and abiotic factors on vegetation activity in the Carpathian-Basin (ongoing project). Principal Investigator: Anikó Kern
- OTKA PD111920, 2014-2017: Application of satellite remote sensing data to characterize vegetation dynamics within the Carpathian Basin (finished postoc project). Principal Investigator: Anikó Kern

Related sites:
The vegetation of the Carpathian Basin based on remote sensing data during 2000-2020
Website of our direct broadcast MODIS receiving station
Website of automatic processing of real-time MODIS data