Suitability of MODIS-based NDVI index for forest monitoring and its seasonal applications in Central Europe
Published Online: Nov 20, 2019
Page range: 206 - 217
DOI: https://doi.org/10.2478/forj-2019-0020
Keywords
© 2019 Ivan Barka et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
The paper demonstrates the multipurpose application of the normalized difference vegetation index (NDVI) derived from MODIS products for forest monitoring across the Central-European macro-region Slovakia and Hungary (i.e., the Western Carpathians and Pannonian basin). Relationships between forest dynamics and NDVI were analysed and used for determining the onset of phenophases in spring and autumn and for the assessment of forest growth and health condition. To identify the phenophases, the NDVI profile during the year was established by fitting a double logistic sigmoid function to data and phenological metrics were developed based on the calculated extreme values of the sigmoid function and its derivatives. According to our analyses, leaf unfolding and leaf fall were significantly delayed or advanced in 2018 with the increase of altitude and latitude (p < 0.01). The longitudinal aspect was significant only in the autumn phenophase with earlier onset of leaf fall towards to the east. The duration of the growing season varied extensively within the region, mainly according to altitudinal and latitudinal occurrence of beech forests. Positive associations between annual tree-ring width and standardized summer NDVI were found for conifers at local scale. The highest correlation period was between July 12 and August 12 as the most critical periods for forest growth. Slight positive correlation can be observed during March – April that could be associated with the varying start of the growing seasons. In the forest health study, whereas NDVI values could well identify the location and extent of a recent forest damage due to a combination of snow break and wind break, an urgent demand for more detailed field data was obvious.