Inventory of standing dead trees in the surroundings of communication routes – The contribution of remote sensing to potential risk assessments

Abstract We present a comparison of three approaches to support risk assessment of dead standing trees near communication routes. The three methods consider different possible data available, workflow complexity, results accuracy and practical implementation. The following inventory methods were implemented and evaluated: (1) passive remote sensing (RS), (2) passive remote sensing + GIS (RS + GIS) and (3) active remote sensing (RS + ALS). The study was carried out in the Polish part of Bialowieza Forest. The roadside analysis performed throughout Bialowieza Forest showed that 1671 sections of routes (72.1% of all those analysed) have standing dead trees in their vicinity. The comparison of the LIDAR based method with the passive remote sensing based methods demonstrated the significant advantage of the former in this kind of research, where precision, accuracy and level of detail of information enable the reliable assessment of risk (even at the single tree). However, this does not preclude the use of the RS methods in this type of analysis; the categorisation of at-risk routes obtained using the latter methods also proved to be satisfactory (with 81% compliance in threat categorisation when compared with the use of the LIDAR based method). Nevertheless, the importance and potentially fatal consequences of RS-based underestimation of potential risk in 16% of cases (episodes) cannot be ignored. At a practical level, our results describe time- and cost-effective methods that bring sufficient information for land managers to handle traffic management in areas with large scale tree mortality.

[1]  J. Rolstad,et al.  Time since death and fall of Norway spruce logs in old-growth and selectively cut boreal forest , 2002 .

[2]  W. Grodzki Mass outbreaks of the spruce bark beetle Ips typographus in the context of the controversies around the Białowieża Primeval Forest , 2016 .

[3]  Gert-Jan Nabuurs,et al.  Natural disturbances in the European forests in the 19th and 20th centuries , 2003 .

[5]  J. Holeksa Rozpad drzewostanu i odnowienie swierka a struktura i dynamika karpackiego boru gornoreglowego , 1998 .

[6]  José Antonio Manzanera,et al.  Comparing airborne laser scanning-imagery fusion methods based on geometric accuracy in forested areas , 2011 .

[7]  David W. Johnson Tree Hazards Recognition and Reduction in Recreation Sites , 1981 .

[8]  Petteri Packalen,et al.  Airborne laser scanning-based prediction of coarse woody debris volumes in a conservation area , 2008 .

[9]  Maggi Kelly,et al.  An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery , 2007 .

[10]  S. Solberg,et al.  Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA , 2015 .

[11]  Seppo Nevalainen,et al.  Detection of dead or defoliated spruces using digital aerial data , 2002 .

[12]  Joanne C. White,et al.  Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment , 2015 .

[13]  L. Sirois,et al.  Fire severity as a determinant factor of the decomposition rate of fire-killed black spruce in the northern boreal forest , 2011 .

[14]  Michael A. Wulder,et al.  Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities , 2006 .

[15]  Marco Heurich,et al.  IDENTIFYING STANDING DEAD TREES IN FOREST AREAS BASED ON 3D SINGLE TREE DETECTION FROM FULL WAVEFORM LIDAR DATA , 2012 .

[16]  Marco Heurich,et al.  Object-orientated image analysis for the semi-automatic detection of dead trees following a spruce bark beetle (Ips typographus) outbreak , 2010, European Journal of Forest Research.

[17]  G. R. Johnson,et al.  Urban Tree Risk Management:A Community Guide to Program Design and Implementation , 2003 .

[18]  Werner Rammer,et al.  Increasing forest disturbances in Europe and their impact on carbon storage. , 2014, Nature climate change.

[19]  C. Millar,et al.  Temperate forest health in an era of emerging megadisturbance , 2015, Science.

[20]  Michael J. Ellison,et al.  Quantified Tree Risk Assessment used in the Management of Amenity Trees , 2005, Arboriculture & Urban Forestry.

[21]  Andrew R. Brookes,et al.  Preventing death and serious injury from falling trees and branches , 2007 .

[22]  T. Zielonka Quantity and decay stages of coarse woody debris in old-growth subalpine spruce forests of the western Carpathians, Poland , 2006 .

[23]  Marco Heurich,et al.  DETECTION OF SINGLE STANDING DEAD TREES FROM AERIAL COLOR INFRARED IMAGERY BY SEGMENTATION WITH SHAPE AND INTENSITY PRIORS , 2015 .

[24]  R. Schlaepfer,et al.  Spruce snag quantification by coupling colour infrared aerial photos and a GIS , 2004 .

[25]  Michael A. Wulder,et al.  Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage , 2003 .

[26]  Marco Heurich,et al.  Automatic Mapping of Standing Dead Trees after an Insect Outbreak Using the Window Independent Context Segmentation Method , 2014 .

[27]  Jerry F. Franklin,et al.  Tree Death as an Ecological Process , 1987 .

[28]  Pierre Soille,et al.  Morphological Image Analysis , 1999 .

[29]  A. Paletto,et al.  Tourists’ perception of deadwood in mountain forests , 2016 .