2020
DOI: 10.1016/j.ecolind.2019.105721
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Spatial probability modelling of forest productivity indicator in Italy

Abstract: The prediction of forest productivity is essential for sustainable forest management, particularly in countries, like Italy, where forest is an important part of many protected areas. A spatial predictive probability model for forest productivity rates in Italy was developed over the period 1961-1990, based on 135 annually-resolved records of site productivity and auxiliary variables measured at 219 stations. Our analysis shows that the probability of finding high (> 7.3 m 3 ha −1 yr −1) and low (< 5.8 m 3 ha … Show more

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Cited by 9 publications
(6 citation statements)
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“…Likewise, for the C2 operational condition, a weak positive correlation (𝜌 𝑠 < 0.27) was observed between the ME1 and the other machine elements. Attention and sensitivity to the correlation values were necessary, so, based on the level of statistical significance, to provide subsidies for decision making, which based on explanatory factors, in addition to being essential, as recommended by Diodato and Bellocchi (2020) for forestry operations, contributed to improving the adjustment of factors intrinsic to the log processing operation of Eucalyptus urograndis.…”
Section: Discussionmentioning
confidence: 99%
“…Likewise, for the C2 operational condition, a weak positive correlation (𝜌 𝑠 < 0.27) was observed between the ME1 and the other machine elements. Attention and sensitivity to the correlation values were necessary, so, based on the level of statistical significance, to provide subsidies for decision making, which based on explanatory factors, in addition to being essential, as recommended by Diodato and Bellocchi (2020) for forestry operations, contributed to improving the adjustment of factors intrinsic to the log processing operation of Eucalyptus urograndis.…”
Section: Discussionmentioning
confidence: 99%
“…Geoinformation has been used in forestry research to model and analyze spatial phenomena and relationships. A GIS was used to study the range of brown bears [9], bark beetle outbreaks [10], the spatial distribution of forest productivity rates [11], and the probability of wind-and snow-related damage to forest stands [12]. Mobile devices [13,14], autonomous ground vehicles [15], and unmanned aerial systems [16,17] have been tested for 3D forest mapping.…”
Section: Introductionmentioning
confidence: 99%
“…These changes caused extreme environmental degradations and destructions (Carrer et al 2018;Daoed et al 1997;Deng et al 2020). The city's development emphasizes on the aspect of economic growth (Diodato & Bellocchi 2020;Fernándezguisuraga et al 2019;Guo et al 2020), leading to several environmental problems (Hafeez & Khan 2012;Hong et al 2017;De Jager et al 2019), including water crisis, floods and pollution resulting from the traffic and the decreasing number of green spaces in Samarinda City (Daoed et al 1997).…”
Section: Introductionmentioning
confidence: 99%