RT Journal Article
T1 Automatic identification of forest disturbance drivers based on their geometric pattern in Atlantic forests
A1 Alonso Martinez, Laura
A1 Picos Martín, Juan
A1 Armesto González, Julia
K1 3106 Ciencia Forestal
K1 3106.08 Silvicultura
K1 3308 Ingeniería y Tecnología del Medio Ambiente
AB Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has different implications for the functioning of the ecosystem and associated management actions. Wildfires and harvesting are two of the major drivers of forest disturbances across different ecosystems. In this study, an automated methodology is presented to automatically distinguish between the two once the disturbance is detected, using the properties of its geometry and shape. A cluster analysis was performed to automatically individualize each disturbance and afterwards calculate its geometric properties. Using these properties, a decision tree was built that allowed for the distinction between wildfires and harvesting with an overall accuracy of 91%. This methodology and further research relating to it could pose an essential aid to national and international agencies for incorporating forest-disturbance-driver-related information into forest-focused reports.
PB Remote Sensing
SN 20724292
YR 2022
FD 2022-02-01
LK http://hdl.handle.net/11093/3069
UL http://hdl.handle.net/11093/3069
LA eng
NO Remote Sensing, 14(3): 697 (2022)
NO Xunta de Galicia
DS Investigo
RD 04-dic-2023