The use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes.

Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014–2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (gs) and stem water potential (Ψstem), reaching determination coefficients (r2) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods.

This study aims to determine if there is variation in precipitation concentrations in Chile. We analyzed daily and monthly records from 89 pluviometric stations in the period 1970–2016 and distributed between 29°12′ S and 39°30′ S. This area was divided into two climatic zones: arid–semiarid and humid–subhumid. For each station, the Gini coefficient or Gini Index (GI), the precipitation concentration index (PCI), and the maximum annual precipitation intensity in a 24-h duration were calculated. These series of annual values were analyzed with the Mann–Kendall test with 5% error. Overall, it was noted that positive trends in the GI are present in both areas, although most were not found to be significant. In the case of PCI, the presence of positive trends is only present in the arid–semiarid zone; in the humid–subhumid zone, negative trends were mostly observed, although none of them were significant. Although no significant changes in all indices are evident, the particular case of the GI in the humid–subhumid zone stands out, where mostly positive trends were found (91.1%), of which 35.6% were significant. This would indicate that precipitation is more likely to be concentrated on a daily scale.

With increasing population and freshwater shortages worldwide, it is necessary to protect vital groundwater resources using innovative methods. The main objective of this study is to use a GIS-based approach with the Groundwater Quality Index (GWQI) to analyze groundwater quality in Marvdasht located in the semi-arid region of Iran. For this purpose, we used groundwater quality data that were collected in a five-year period (2010–2015). The most influential water quality parameters were determined by performing map removal sensitivity analysis. Mean maps of the groundwater parameters showed that total dissolved solid (TDS), electrical conductivity (EC) and total hardness (TH) were the most important parameters that exceed the maximum permissible limits for drinking water. The groundwater quality of the study area is generally desirable for drinking (GWQI = 71). The GWQI map indicated that groundwater was higher quality in northern regions of the study area. The GWQI also revealed that only 2% of the study area (11 km²) was below the low quality class. According to map removal sensitivity analysis, Mg²⁺, TH and Na⁺ were identified as the most sensitive water quality parameters. Therefore, these parameters need to be monitored regularly and with increased precision.