RESUMEN
The protection and sustainability of forest assets is possible with planned production of forest products to lead to minimum loss. One of the products obtained from forests is the industrial wood, which is the most important raw material for many sectors. Thus, changes in industrial wood production amounts directly affect these sectors. For this reason, it is important to detect and examine the factors affecting industrial wood production amounts for optimum production and use of this raw material. This study aims to investigate and assess the effects of two biotic and two abiotic harmful factors on the amount of industrial wood production by building a deep learning estimation model. These factors are forest fires, insect outbreaks, diseases, and severe weather events. The study shows that the most harmful factor decreasing the industrial wood production level is diseases. The second effective factor, however, appears to be severe weather events. The third and the fourth factors were determined to be insect outbreaks and burned forest areas, respectively.
Asunto(s)
Aprendizaje Profundo , Madera , Agricultura Forestal , Bosques , Industrias , Conservación de los Recursos Naturales , ÁrbolesRESUMEN
Forest crimes are among the serious threats destroying forests. To prevent the forest crimes there are various solutions proposed, such as fortification of the laws, increasing the penalties, or increasing the public awareness. This article, however, suggests an alternative solution of preventing the forest crimes by investigating the relations between the individual financial supports provided to forest villagers and the levels of various forest crime types in Turkey. The study shows that, when the forest villagers are given financial supports, the levels of illegal logging, illegal transferring of forest products, illegal expansion of private lands into forests, illegal processing of trees, and illegal pasturage crimes decrease significantly. However, the financial supports do not affect the levels of illegal occupation of forestlands crime.
Asunto(s)
Conservación de los Recursos Naturales , Agricultura Forestal , Bosques , Árboles , CrimenRESUMEN
Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires, and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of 11 human-induced factors, a natural factor, and temperature, which is one of the risk factors that determine the conditions for the occurrence of forest fires. Moreover, k-means clustering algorithm, which is an artificial intelligence method, was employed in preparation of the fire risk zone map. Turkey was selected as the study area because there are social and economic variations among its regions. Thus, the regional forest directorates in Turkey were separated into four clusters as extreme-risk zone, high-risk zone, moderate-risk zone, and low-risk zone. Also, a map presenting these risk zones were provided. The map reveals that, in general, the western and southwestern coastal areas of Turkey are at high risk of forest fires. On the other hand, the fire risk is relatively low in the northern, central, and eastern areas.
Asunto(s)
Incendios , Incendios Forestales , Humanos , Inteligencia Artificial , Bosques , Factores de Riesgo , ÁrbolesRESUMEN
Cybersickness, i.e. visually induced motion sickness, remains as a negative effect that is detrimental to the user experience of VEs (virtual environments) developed for VR (virtual reality) consumers. As the VR technology evolves, it is rather triggered by application aspects rather than hardware limitations. For this reason, there is still a need for a measurement method to assess and compare VEs for cybersickness effects. SSQ (Simulation Sickness Questionnaire) is used for measuring users' level of sickness symptoms and is highly appreciated in VR research. However, it is criticized for its psychometric qualities and applicability in VR, as a measure of cybersickness. Recently, two variants of SSQ were offered for measuring cybersickness, CSQ (Cybersickness Questionnaire) and VRSQ (Virtual Reality Sickness Questionnaire). There is also another variant with a different factor structure, which we call FSSQ, that is based on French translation of SSQ. Our study compares SSQ and these variants for their psychometric qualities; construct validity, discriminant validity, internal reliability, test-retest reliability and sensitivity to distinguish application aspects of VEs that are related to cybersickness. Using a within-subjects experiment design, we evaluated 7 different VEs with 32 participants through 9 sessions, resulting with 288 responses to the 16-item SSQ. Results suggested that both VRSQ and CSQ were valid and reliable measures of cybersickness, as well as being sensitive to application aspects such as translational and rotational movements required by users for navigation in VEs. Compared to SSQ and FSSQ; the cybersickness questionnaires, CSQ and VRSQ, revealed better indicators of validity. On the other hand, we assume that the development of the two cybersickness scales had limitations in sample size to represent VR consumers and limitations in stimuli to represent the applications aspects of consumer VEs. We suggest further evaluation of cybersickness symptoms with larger samples and broader range of applications to identify the symptoms and the construct of a subjective measurement tool.