The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas.The objectives are:(1)to test the applicability of UAV visible a...The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas.The objectives are:(1)to test the applicability of UAV visible an near-infrared(VNIR)and geometrical data based on Z values of point dense cloud(PDC)raster to separate forest species and dead trees in the study area;(2)to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling;and(3)to explore the possibility of the qualitative classification of spruce health indicators.Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir,and for identification of dead tree category.Separation between common beech and fir was distinguished by the object-oriented image analysis.NDVI was able to identify the presence of key indicators of spruce health,such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation,while stem damage by peeling was identified at the significance margin.The results contributed to improving separation of coniferous(spruce and fir)tree species based on VNIR and PDC raster UAV data,and newly demonstrated the potential of NDVI for qualitative classification of spruce trees.The proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.展开更多
基金This work was supported by the Ministry of Education,Youth and Sports of the Czech Republic within the National Programme for Sustainability I[grant number LO1415]partly by EEA Grants if Iceland,Liechtenstein and Norway[grant number EHP-CZ02-OV-1-019-2014].
文摘The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas.The objectives are:(1)to test the applicability of UAV visible an near-infrared(VNIR)and geometrical data based on Z values of point dense cloud(PDC)raster to separate forest species and dead trees in the study area;(2)to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling;and(3)to explore the possibility of the qualitative classification of spruce health indicators.Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir,and for identification of dead tree category.Separation between common beech and fir was distinguished by the object-oriented image analysis.NDVI was able to identify the presence of key indicators of spruce health,such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation,while stem damage by peeling was identified at the significance margin.The results contributed to improving separation of coniferous(spruce and fir)tree species based on VNIR and PDC raster UAV data,and newly demonstrated the potential of NDVI for qualitative classification of spruce trees.The proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.
