This study presents a comparison between the use of wearable laser scanning (WLS) and terrestrial laser scanning (TLS) devices for automatic tree detection with an estimation of two dendrometric variables: diameter at breast height (DBH) and total tree height (TH). Operative processes for data collection and automatic forest inventory are described in detail. The approach used is based on the clustering of points belonging to each individual tree, the isolation of the trunks, the iterative fitting of circles for the DBH calculation and the computation of the TH of each tree. TLS and WLS point clouds were compared by the statistical analysis of both estimated forest dendrometric parameters and the possible presence of bias. Results show that the apparent differences in point density and relative precision between both 3D forest models do not affect tree detection and DBH estimation. Nevertheless, tree height estimation using WLS appears to be affected by the limited scanning range of the WLS used in this study. TH estimations for trees below a certain height are equivalent using WLS or TLS, whereas TH of taller trees is clearly underestimated using WLS.

Combinations of unmanned aerial platforms and multispectral sensors are considered low-cost tools for detailed spatial and temporal studies addressing spectral signatures, opening a broad range of applications in remote sensing. Thus, a key step in this process is knowledge of multi-spectral sensor calibration parameters in order to identify the physical variables collected by the sensor. This paper discusses the radiometric calibration process by means of a vicarious method applied to a high-spatial resolution unmanned flight using low-cost artificial and natural covers as control and check surfaces, respectively.

Temporal analyses and multi-temporal 3D reconstruction are fundamental for the preservation and maintenance of all forms of Cultural Heritage (CH) and are the basis for decisions related to interventions and promotion. Introducing the fourth dimension of time into three-dimensional geometric modelling of real data allows the creation of a multi-temporal representation of a site. In this way, scholars from various disciplines (surveyors, geologists, archaeologists, architects, philologists, etc.) are provided with a new set of tools and working methods to support the study of the evolution of heritage sites, both to develop hypotheses about the past and to model likely future developments. The capacity to “see” the dynamic evolution of CH assets across different spatial scales (e.g. building, site, city or territory) compressed in diachronic model, affords the possibility to better understand the present status of CH according to its history. However, there are numerous challenges in order to carry out 4D modelling and the requisite multi-data source integration. It is necessary to identify the specifications, needs and requirements of the CH community to understand the required levels of 4D model information. In this way, it is possible to determine the optimum material and technologies to be utilised at different CH scales, as well as the data management and visualization requirements. This manuscript aims to provide a comprehensive approach for CH time-varying representations, analysis and visualization across different working scales and environments: rural landscape, urban landscape and architectural scales. Within this aim, the different available metric data sources are systemized and evaluated in terms of their suitability.

Non-Melanoma skin cancer is one of the most frequent types of cancer. Early detection is encouraged so as to ensure the best treatment, Hyperspectral imaging is a promising technique for non-invasive inspection of skin lesions, however, the optimal wavelengths for these purposes are yet to be conclusively determined. A visible-near infrared hyperspectral camera with an ad-hoc built platform was used for image acquisition in the present study. Robust statistical techniques were used to conclude an optimal range between 573.45 and 779.88 nm to distinguish between healthy and non-healthy skin. Wavelengths between 429.16 and 520.17 nm were additionally found to be optimal for the differentiation between cancer types.