Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of “precision agriculture” combined with “vegetation index”, this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies.

In recent decades, precision agriculture and geospatial technologies have made it possible to ensure sustainability in an olive-growing sector. The main goal of this study is the extraction of olive tree canopies by comparing two approaches, the first of which is related to geographic object-based analysis (GEOBIA), while the second one is based on the use of vegetation indices (VIs). The research area is a micro-location within the Lun olives garden, on the island of Pag. The unmanned aerial vehicle (UAV) with a multispectral (MS) sensor was used for generating a very high-resolution (VHR) UAVMS model, while another mission was performed to create a VHR digital orthophoto (DOP). When implementing the GEOBIA approach in the extraction of the olive canopy, user-defined parameters and classification algorithms support vector machine (SVM), maximum likelihood classifier (MLC), and random trees classifier (RTC) were evaluated. The RTC algorithm achieved the highest overall accuracy (OA) of 0.7565 and kappa coefficient (KC) of 0.4615. The second approach included five different VIs models (NDVI, NDRE, GNDVI, MCARI2, and RDVI2) which are optimized using the proposed VITO (VI Threshold Optimizer) tool. The NDRE index model was selected as the most accurate one, according to the ROC accuracy measure with a result of 0.888 for the area under curve (AUC).

<p>Flood hazard prediction is a critical component of flood risk assessment, flood risk management plans, and implementation of flood mitigation measures. In the EU, there is currently a growing interest in floods caused by extreme heavy rainfall, commonly known as pluvial floods. Due to the rapid development of computational and remote sensing technology, as well as the public availability of high-resolution spatial data, pluvial floods are now simulated using integrated hydrological-hydraulic approaches consisting of time-dependent 2D numerical models and so-called rain-on-grid approaches with spatially variable infiltration. In this paper, we will present the recent progress and methodological framework for pluvial flood hazard assessment in the city of Poreč in the northern coastal part of Croatia, focusing on the interpretation and modification of spatial input data, precipitation data processing, and numerical modelling of pluvial flooding. We show what spatial data were collected and improved, what spatial data were generated, how the precipitation data were processed for this purpose, and discuss some modelling aspects specific to pluvial flooding in urban areas. Finally, we present the results of the pluvial flood hazard assessment for the city of Poreč and its catchment area and provide some recommendations for further research.</p>