@article{Gulácsi_Kovács_2018, title={Drought monitoring of forest vegetation using MODIS-based normalized difference drought index in Hungary}, volume={67}, url={https://ojs.mtak.hu/index.php/hungeobull/article/view/457}, DOI={10.15201/hungeobull.67.1.3}, abstractNote={<p>In this paper, several spectral indices based on spectral reflectance data from the Terra satellite’s Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor were evaluated in terms of their practical applicability for quantifying drought intensity and its geographical effects to support the drought monitoring applications. A total number of 358 MODIS 8-day composite images for the period between 2000 and 2014 were acquired and processed for the analysis; the frequency of drought phenomenon was increased during this time period. Vegetation indices, water indices, and combinations of both, which were called drought indices, were used. To validate the results, regression analyses were performed and Pearson’s r values were calculated. The Pálfai Drought Index (PAI) and the average annual yields of different crops were used as reference data. The Normalized Difference Vegetation Index (NDVI), the Difference Vegetation Index (DVI), the Normalized Difference Water Index (NDWI), the Difference Water Index (DWI), the Difference Drought Index (DDI), and the Normalized Difference Drought Index (NDDI) were found to be significant in quantifying drought intensity. Anomalies relative to the average of the period between 2000 and 2014 were calculated and standardized using the standard deviation, enabling the identification of above-average, drought-stricken areas. The results of this study can be used to create a cost-effective, near real-time and currently missing national drought monitoring system with high temporal resolution to detect regional climate changes, and to assess forest damage probability through changes in the chlorophyll and water content of forest vegetation.</p&gt;}, number={1}, journal={Hungarian Geographical Bulletin}, author={Gulácsi, András and Kovács, Ferenc}, year={2018}, month={Mar.}, pages={29-42} }