Automatic identification of charcoal origin based on deep learning

The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil. The main contribution of this study is to identify the charcoal origin using macroscopic images and Deep Learning Algorithm. We applied a Convolutional Neural Network (CNN) using VGG-16 architecture, with preprocessing based on contrast enhancement and data augmentation with rotation over the training set images. on the performance of the CNN with fine-tuning using 360 macroscopic charcoal images from the plantation and native forests. The results pointed out that our method provides new perspectives to identify the charcoal origin, achieving results upper 95 % of mean accuracy to classify charcoal from native forests for all compared preprocessing strategies

Computer vision-based wood identification: a review

Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.info:eu-repo/semantics/publishedVersio

Legacy of wood charcoal production on subalpine forest structure and species composition

Land-use legacy on forest dynamics at both stand and landscape scale can last for centuries, affecting forest structure and species composition. We aimed to disentangle the history of the charcoal production legacies that historically shaped Mont Avic Natural Park (Aosta Valley, Italy) forests by integrating LiDAR, GIS, anthracological, and field data at the landscape scale. We adopted different geostatistical tools to relate geographic layers from various data sources. The overexploitation due to intensive charcoal production to fuel mining activities shaped the current forests by homogenising their structure and species composition into dense and young stands with a reduction in late seral species such as Norway spruce (Picea abies) and an increase in pioneer species such as Mountain pine (Pinus uncinata). The multidisciplinary and multi-scale framework adopted in this study stresses the role of historical landscape ecology in evaluating ecosystem resilience to past anthropogenic disturbances. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13280-022-01750-y

Object-Based Image Analysis of Ground-Penetrating Radar Data for Archaic Hearths

Object-based image analysis (OBIA) has been increasingly used to identify terrain features of archaeological sites, but only recently to extract subsurface archaeological features from geophysical data. In this study, we use a semi-automated OBIA to identify Archaic (8000-1000 BC) hearths from Ground-Penetrating Radar (GPR) data collected at David Crockett Birthplace State Park in eastern Tennessee in the southeastern United States. The data were preprocessed using GPR-SLICE, Surfer, and Archaeofusion software, and amplitude depth slices were selected that contained anomalies ranging from 0.80 to 1.20 m below surface (BS). Next, the data were segmented within ESRI ArcMap GIS software using a global threshold and, after vectorization, classified using four attributes: area, perimeter, length-to-width ratio, and Circularity Index. The user-defined parameters were based on an excavated Archaic circular hearth found at a depth greater than one meter, which consisted of fire-cracked rock and had a diameter greater than one meter. These observations were in agreement with previous excavations of hearths at the site. Features that had a high probability of being Archaic hearths were further delineated by human interpretation from radargrams and then ground-truthed by auger testing. The semi-automated OBIA successfully predicted 15 probable Archaic hearths at depths ranging from 0.85 to 1.20 m BS. Observable spatial clustering of hearths may indicate episodes of seasonal occupation by small mobile groups during the Archaic Period

Archaeological, Geophysical, and Geospatial Analysis at David Crockett Birthplace State Park, in Upper East Tennessee

A geophysical survey was conducted at David Crockett Birthplace State Park (40GN205, 40GN12) using ground-penetrating radar (GPR) and magnetometry. The data indicated multiple levels of occupation that were investigated by Phase II and Phase III archaeological excavations. New cultural components were discovered, including the remnants of a Protohistoric Native American structure containing European glass trade beads and Middle Woodland artifacts that suggest trade with Hopewell groups from Ohio. A circular Archaic hearth was uncovered at one meter below surface and similar deep anomalies were seen in the GPR data at this level. A semi-automated object-based image analysis (OBIA) was implemented to extract Archaic circular hearths from GPR depth slices using user-defined spatial parameters (depth, area, perimeter, length to width ratio, and circularity index) followed by manual interpretation. This approach successfully identified sixteen probable hearths distributed across the site in a semi-clustered pattern

LOCATION OF PLANTED FOREST INFLUENCING THE WOOD COMMERCIALIZATION IN THE STATE OF GOIAS, BRAZIL

Planted forests have grown substantially in Brazil, especially in states such as Goias. While they may compete with native forest areas, planted forests also present themselves as economically viable solutions for the recovery of degraded areas. This study presents the market of wood in the Brazilian state of Goias analyzing the interaction between supply and demand curves of the product in a partial equilibrium analysis. In this sense, it is essential to understand the spatial issue to offer production by planning the transaction costs related to transportation. Therefore, the distribution of companies is linked to the production chain, mapped by means of labor market bases (Ministry of Labor), wood production (IBGE) and other industry data. The spatial analysis of the planted forest area (silviculture) in Goias between 2000 and 2016 was based on data from the time series, from mapping provided by the MapBiomas Project. In Goias, considering all sectors of the forest production chain, in 2015 alone, revenues exceeded US$1.24 billion and public collections US$ 24 million, employing more than 36 thousand people in 7 thousand firms. Thus, it is fundamental to understand this process, identifying the main determinants of planted forests, through statistical and spatial analysis. From a spatial point of view, planted forests and companies involved in wood production are relatively spread throughout the state, except for the state capital of Goiania, which has a large number of timber trade and manufacturing firms