An integrated airborne laser scanning approach to forest management and cultural heritage issues: a case study at Porolissum, Romania

Abstract

Abstract. This paper explores the opportunities that arise where forest ecosystem management and cultural heritage monuments protection converge. The case study area for our analysis was the landscape surrounding the Moigrad-Porolissum Archaeological site. We emphasize that an Airborne Laser Scanning (ALS or LiDAR-Light Detection and Ranging) approach to both forest management and cultural heritage conservation is an outstanding tool, assisting policy-makers and conservationists in decision making for integrated planning and management of the environment. LiDAR-derived surface models enabled a synoptic, never-seen-before view of the ancient Roman frontiers defensive systems while also revealing the present forest road network. The thorough and accurate road inventory data are very useful for updating and modifying forest base maps and registries and also for identifying the priority sectors for archaeological discharge. The ability to identify and determine optimal routes for forest management and to locate previously unmapped ancient archaeological remains aids in reducing costs and creating operational efficiencies as well as in complying with the legislation and avoiding infringements. The potential of LiDAR to demonstrate the long-term and comprehensive human impact on wooded areas is discussed. We identified a significant historical landscape change, consisting of a deforestation period, spanning over more than 160 years, during the Roman Period in Dacia (106-271 AD). The transdisciplinary analysis of the LiDAR data provides the base for combining knowledge from archaeology, forestry and environmental history in order to achieve a thorough analysis of the landscape changes and history. In the "nature versus culture" dichotomy, the landscape, outfield areas and forests are primarily perceived as nature, while in reality they are often heavily marked by human impact. LiDAR offers an efficient method for broadening our knowledge regarding the character and extent of human interaction with landscapes -forested or otherwise.

Application of Parallel Factor Analysis (PARAFAC) to the Regional Characterisation of Vineyard Blocks Using Remote Sensing Time Series

“…Soil spectral reflectance is typically characterised by a steady increase with wavelengths from the Vis to the NIR, except at 950, 1200, and 1350 nm, where reflectance decreases [32]. As for the typical vegetation spectral reflectance, it exhibits the following characteristics: (i) a low reflection in the blue (458-523 nm) and red wavelengths (650-680 nm), (ii) relatively more reflection in green wavelengths (543-578 nm), (iii) reflectance in the NIR range (785-875 nm) is the highest, and iv) the short-wave infrared region (1360-2200 nm) is mainly characterised by less reflectance [33]. Given that grapevine cultivation is seasonal and is a row crop, the PARAFAC model clearly identifies the mixture of these two components.…”

Forest Road Detection Using LiDAR Data and Hybrid Classification

“…Three levels of automation exist: manual, automatic and semi-automatic (which combines automatic processes and manual editing) (Figure 1). The first group includes traditional methods based on surveying techniques or GPS to capture spatial data [8] as well as photointerpretation and manual digitization of roads (which use aerial/satellite images or the LiDAR-derived layers) [13]. Although these techniques are the most accurate and robust, they are also time-consuming and costly [14].…”

Early Detection of Potential Infestation by Capnodis tenebrionis (L.) (Coleoptera: Buprestidae), in Stone and Pome Fruit Orchards, Using Multispectral Data from a UAV

“…A few of its applications in agriculture include crop health monitoring, soil fertility monitoring, estimation of crop yield, irrigation equipment monitoring, and identification of invasive species of weed [10,13]. Crop health monitoring is based on the fact that stressed plants have a different spectral signature than healthy ones [14]. Normally, healthy vegetation absorbs 70-90% of the radiation of the visible spectrum (400-700 nm, or photosynthetic active radiation, PAR) [9,14].…”

“…Crop health monitoring is based on the fact that stressed plants have a different spectral signature than healthy ones [14]. Normally, healthy vegetation absorbs 70-90% of the radiation of the visible spectrum (400-700 nm, or photosynthetic active radiation, PAR) [9,14]. More specifically, radiation of the blue and red bands of the visible spectrum is used in photosynthesis by the leaves, while radiation of the green band is mostly reflected by chlorophylls of the leaves.…”

“…On the contrary, stressed vegetation absorbs a smaller amount of blue and red radiation and at the same time, the reflection of green and NIR radiation is lower, mainly due to the fact that chlorophylls and many cell structures of the leaves have been destroyed. Vegetation indices combine the reflectance of the spectral bands; thus, they provide information on the health of crops [14].…”