Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification

In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The measurement system consists of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technology, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using an experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task using only traditional TLS data.     

Early Farming in the Northern Boreal Zone: Reassessing the History of Land Use in Southeastern Finland through High‐Resolution Pollen Analysis

The early stages of agriculture in the Boreal forests of Northern Europe remain poorly understood. Although pottery appeared during the 6th millennium B.C., this has not been seen as an indication of a true Neolithic in the north. In later prehistory, vast parts of the region are thought to have remained a wilderness. In order to test these assumptions, a high‐resolution pollen analysis and an archaeological survey were carried out at Lake Huhdasjärvi, SE Finland. The results indicate signs of cultivation already by the early Neolithic, 5260–4260 B.C., and slash‐and‐burn cultivation concentrated on deciduous forests is recorded from ca. A.D. 600 onwards. By A.D. 930, an intensive form of swidden cultivation began in the coniferous forests, indicating a well‐established agricultural settlement. The discovery of Neolithic (late 6th millennium B.C.) buckwheat pollen suggests that the roots of agriculture in northernmost Europe may have to be searched for in China rather than the Near East.     

SWAN observations of 9P/Tempel 1 around the Deep Impact event

The SWAN instrument observed the coma of Comet 9P/Tempel 1 at Lyman alpha around the Deep Impact event. From these observations, a water production rate profile for 3 weeks after the impact was derived. The comet could not be identified in images taken before the impact because of the relatively low production rates. The most important feature of the profile is that the production rate increases by about a factor of two more than a week after the impact. This is too late to be directly caused by the impact plume itself. Although it is not impossible that the impact triggered a resurfacing event, the comet is known to show sudden outburts, and the elevated production rate is similar to what has been reported on previous apparitions.     

Temporal deconvolution of the hydrogen coma I. A hybrid model

This paper describes a semi-analytical model, tentatively called the time-resolved model (TRM), for calculating the proper temporally resolved column density and Lyman-alpha intensity profiles of the extended cometary hydrogen coma, with potentially arbitrary number of photodissociation sources with different initial velocity profiles. The non-analytical part consists of a true 3D particle simulator whose results are used to obtain the necessary corrective parameters for the analytical, spherically symmetric formulas for the column density and intensity profiles. The hybrid approach provides a physically accurate and computationally effective method to recreate the extended coma profiles and supports a new analysis method that can provide hydrogen lifetime estimates and a much enhanced temporal resolution for the water production curve, for which a method is described. The ability to recreate the water production profile by different analysis methods is studied with a combination of real and simulated data. This is the first part of a two part publication with the second part dedicated to real data analysis.     

Understanding tourists' travel behavior before,during, and after the trip with data from social media platforms

Analyzing tourists' travel behavior at different stages of a trip can help to better understand their travel demands, providing valuable suggestions for destination management. In this article, we propose a research framework that can help in comprehensively analyzing tourists' travel behavior from the perspective of the three main stages: before, during, and after the trip. A case study was conducted based on user-generated content data from the Old Town of Lijiang to verify the feasibility of the proposed research framework. The results show that: (1) before the trip, tourists inquire about destinations and optimal stay; (2) during the trip, tourists are attracted to natural sceneries; (3) after the trip, tourists discuss experience and services. Travel behavior in stages provide detailed (inter-, intra-, and individual attractions) guidance (from tourists) for destinations. The proposed framework provides a novel perspective for exploring the spatiotemporal behavioral features of tourists at different stages of their trips.