## 2 Tree Detection and Counting
Other difficulties regard mapping canopy edges due to conflicts between shade and illumination or the presence of ground vegetation [41; 42; 43; 44]. New studies on this topic aim to achieve higher accuracy and capability in dealing with such problems. The goal is to test the approaches by implementing the methods in different conditions of plantations, sensors, flight altitudes, viewing angles, spatial and spectral divergences, and phenological phases, and trying to standardize the results obtainable. The use of multispectral (MS) sensors provides the advantage of using the near infrared (NIR) band, which is the most sensitive band for mapping canopy properties of vegetation [45]. Some features are distinctive enough to be detected in a panchromatic or single-band image. However, the difference in the spectral signature of different surfaces usually allows subtle variations to be noticed and, thus, detailed identification [45]. In this regard, the use of multiple bands for canopy identification allows for overcoming the limitations highlighted in the detection based only on the trees' shape, size, and spatial pattern [11; 15]. This was shown by [PERSON] et al. [21], who proposed the use of the algorithm Arbor Crown Enumeration (ACE) on Quickbird imagery based on the combination of three procedures: blobs detection, Red band, and Normalized Difference Vegetation Index (NDVI) [46] thresholding. If applied individually, each procedure does not produce satisfactory results. Blob detection identifies all blob-like surfaces regardless if they represent trees' canopies, rocks, or other objects, leading to overestimations, especially in mixed land-uses imagery. Red band thresholding usually tends to clump tree canopies where the vegetation is denser,causing an underestimation. The Red band depends on the reflection values with which the method was calibrated. NDVI thresholding is sensitive to the different characteristics unique to each image. ACE has as its strength that it can exploit MS images that carry more information than a single band. The combination of the three procedures improved the results obtainable from using singles by compensating for the limits of each. Indeed, the results showed differences do not hinder the tree canopy size or tree density performance due to blob detection. [PERSON] et al. [20] described and exploited the algorithm Clustering Assessment (CLUAS, an add-on of the software suite ENVI), aimed at the automatic quantitative assessment of olive orchard's agronomic and environmental indicators. Quickbird satellite images and airborne MS imagery coupled with a supervised classification were used in the first stage. From the MS aerial photography, the bands Green and NIR were selected together with some indices (Blue, Green, Red, and NIR)/4, NDVI, and Ratio Vegetation Index (RVI) [47] for olive grove image assessment. Results showed the capability of CLUAS to provide the total number of clusters/olive trees, the whole surface of olive trees, the percentage of olive tree surface over the total, and the potential overall productivity. [PERSON] et al. [4] proposed a methodology for automatically counting olive trees in intensive orchards from MS UAV imagery exploiting digital surface model (DSM) information. The MS images were processed to generate a DSM which was converted into a greyscale image, where elevation information was approached as grey level values. The tree counting procedure was applied to the binary map obtained using the Otsu method and was focused on the analysis of the morphology of the connected segmented components in cases of overlapping of two crowns within rows. Elevation data were used for separating olive trees from the bare soil to obtain an image segmented into two classes. The results showed this method correctly detected trees with an accuracy of about 99%. The methodology was tested in an intensive olive grove of about 18 ha, showing its effectiveness in a complex scenario characterized by intra-row tree aggregations and a strong ground elevation variability.