Prescribed burning impact on forest soil properties--a Fuzzy Boolean Nets approach.
The Portuguese northern forests are often and severely affected by wildfires during the Summer season. These occurrences significantly affect and negatively impact all ecosystems, namely soil, fauna and flora. In order to reduce the occurrences of natural wildfires, some measures to control the availability of fuel mass are regularly implemented. Those preventive actions concern mainly prescribed burnings and vegetation pruning. This work reports on the impact of a prescribed burning on several forest soil properties, namely pH, soil moisture, organic matter content and iron content, by monitoring the soil self-recovery capabilities during a one year span. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, which was kept intact from prescribed burnings during a period of four years. Soil samples were collected from five plots at three different layers (0-3, 3-6 and 6-18) 1 day before prescribed fire and at regular intervals after the prescribed fire. This paper presents an approach where Fuzzy Boolean Nets (FBN) and Fuzzy reasoning are used to extract qualitative knowledge regarding the effect of prescribed fire burning on soil properties. FBN were chosen due to the scarcity on available quantitative data. The results showed that soil properties were affected by prescribed burning practice and were unable to recover their initial values after one year.
Castro
Ana C Meira
AC
CIGAR, LEMA, Instituto Superior de Engenharia do Porto, R. S. Tomé, 4200-072 Porto, Portugal. ana.meira.castro@eu.ipp.pt
Paulo Carvalho
Joao
J
Ribeiro
S
S
D003247:Conservation of Natural Resources / Q000379:methods*; D017753:Ecosystem; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods; D004787:Environmental Pollution / Q000706:statistics & numerical data; D005390:Fires / Q000517:prevention & control / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D006863:Hydrogen-Ion Concentration; D007501:Iron / Q000032:analysis; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011174:Portugal; D012987:Soil / Q000032:analysis / Q000737:chemistry*; D014197:Trees
D012987:Soil; D007501:Iron