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.

The bathymetric survey of Lake Vrana included a wide range of activities that were performed in several different stages, in accordance with the standards set by the International Hydrographic Organization. The survey was conducted using an integrated measuring system which consisted of three main parts: a single-beam sonar HydroStar 4300 and GPS devices; a Ashtech ProMark 500 base, and a Thales Z-Max® rover. A total of 12 851 points were gathered. In order to find continuous surfaces necessary for analysing the morphology of the bed of Lake Vrana, it was necessary to approximate values in certain areas that were not directly measured, by using an appropriate interpolation method. The main aims of this research were as follows: (a) to compare the efficiency of 14 different interpolation methods and discover the most appropriate interpolators for the development of a raster model; (b) to calculate the surface area and volume of Lake Vrana, and (c) to compare the differences in calculations between separate raster models. The best deterministic method of interpolation was multiquadric RBF (radio basis function), and the best geostatistical method was ordinary cokriging. The root mean square error in both methods measured less than 0.3 m. The quality of the interpolation methods was analysed in two phases. The first phase used only points gathered by bathymetric measurement, while the second phase also included points gathered by photogrammetric restitution. The first bathymetric map of Lake Vrana in Croatia was produced, as well as scenarios of minimum and maximum water levels. The calculation also included the percentage of flooded areas and cadastre plots in the case of a 2 m increase in the water level. The research presented new scientific and methodological data related to the bathymetric features, surface area and volume of Lake Vrana.

Montenegro has different influences on the weather and climate; in general, according to Köppen’s classification, there are two climate zones: warm temperate (C) and cold temperate (D). The aim of this study is to determine the susceptibility to wildfires in the Montenegrin coastal municipality of Budva and the northern municipality of Rožaje, which are located in different climatic conditions, using multicriteria GIS decision analysis (GIS-MCDA). Nine natural and anthropogenic criteria were used for the analysis. Open geospatial data were used as input data for all criteria. The assignment of weighting coefficients for the criteria in relation to wildfire susceptibility importance was based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (F-AHP) procedures. The results for the AHP and F-AHP models were obtained using the Weighted Linear Combination (WLC) method. According to the AHP model, the very high and high category covers 80.93% of the total area in Budva and 80.65% in Rožaje. According to the F-AHP model, the very high and high category occupies 80.71% of the total area in Budva and 82.30% in Rožaje. The validation shows that the models of GIS-MCDA perform fair in both climatic zones. The proposed models, especially in the absence of geospatial data, can be a strategic and operational advantage in the development of plans and strategies for protection against wildfires.

Pixel-based (PB) and geographic-object-based (GEOBIA) classification approaches allow the extraction of different objects from multispectral images (MS). The primary goal of this research was the analysis of UAV imagery applicability and accuracy assessment of MLC and SVM classification algorithms within PB and GEOBIA classification approaches. The secondary goal was to use different accuracy assessment metrics to determine which of the two tested classification algorithms (SVM and MLC) most reliably distinguishes olive tree crowns and which approach is more accurate (PB or GEOBIA). The third goal was to add false polygon samples for Correctness (COR), Completeness (COM) and Overall Quality (OQ) metrics and use them to calculate the Total Accuracy (TA). The methodology can be divided into six steps, from data acquisition to selection of the best classification algorithm after accuracy assessment. High-quality DOP (digital orthophoto) and UAV_MS were generated. A new accuracy metric, called Total Accuracy (TA), combined both false and true positive polygon samples, thus providing a more comprehensive insight into the assessed classification accuracy. The SVM (GEOBIA) was the most reliable classification algorithm for extracting olive tree crowns from UAV_MS imagery. The assessment carried out indicated that application of GEOBIA-SVM achieved a TA_COR of 0.527, TA_COM of 0.811, TA_OQ of 0.745, Overall Accuracy (OA) of 0.926 or 0.980 and Area Under Curve (AUC) value of 0.904 or 0.929. The calculated accuracy metrics confirmed that the GEOBIA approach (SVM and MLC) achieved more accurate olive tree crown extraction than the PB approach (SVM and MLC) if applied to classifying VHR UAV_MS imagery. The SVM classification algorithm extracted olive tree crowns more accurately than MLC in both approaches. However, the accuracy assessment has proven that PB classification algorithms can also achieve satisfactory accuracy.

The accurate extraction of a coastline is necessary for various studies of coastal processes, as well as for the management and protection of coastal areas. Very high-resolution satellite imagery has great potential for coastline extraction; however, noises in spectral data can cause significant errors. Here, we present a newly developed Coastal Extraction Tool (CET) that overcomes such errors and allows accurate and time-efficient automated coastline extraction based on a combination of WorldView-2 (WV-2) multispectral imagery and stereo-pair-derived digital surface model (DSM). Coastline extraction is performed and tested on the Iž-Rava island group, situated within the Northern Dalmatian archipelago (Croatia). Extracted coastlines were compared to (a) coastlines extracted from state topographic map (1:25,000), and (b) coastline extracted by another available tool. The accuracy of the extracted coastline was validated with centimeter accuracy reference data acquired using a UAV system (Matrice 600 Pro + MicaSense RedEdge-MX). Within the study area, two small islets were detected that have not been mapped during the earlier coastline mapping efforts. CET proved to be a highly accurate coastline mapping technique that successfully overcomes spectral-induced errors. In future research, we are planning to integrate data obtained by UAVs infrared thermography (IRT) and in situ sensors, measuring sea and land surface temperatures (SST and LST), into the CET, given that this has shown promising results. Considering its accuracy and ease of use, we suggest that CET can be applied for automated coastline extraction in other large and indented coastal areas. Additionally, we suggest that CET could be applied in longitudinal geomorphological coastal erosion studies for the automated detection of spatio-temporal coastline displacement.