Lidar Applications in Precision Forestry

Precision forestry leverages advance technology sensing and analytical tools to support sitespecific economic, environmental, and sustainable decision making for the forestry sector. The discipline is highly reliant on accurate, timely and detailed forest inventory characterization and structural information, spanning extensive land holdings. Discrete, high density, lidar point cloud has become an invaluable dataset utilized in precision forestry applications. This paper gives a synopsis of a research with aerial and terrestrial lidar to provide detailed forest inventory characteristics such as canopy heights and volumes as well as diameter at breast height. The estimation of Leaf Area Index (LAI) and forest fuel metrics are also addressed. The results presented here summarize the achievable accuracies and future goals of applied lidar remote sensing research in the field of precision forestry.

[1]  Sorin C. Popescu,et al.  Mapping surface fuel models using lidar and multispectral data fusion for fire behavior , 2008 .

[2]  K. Itten,et al.  Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction , 2006 .

[3]  L. Monika Moskal,et al.  Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR , 2009 .

[4]  Tomas Brandtberg Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar , 2007 .

[5]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[6]  S. Reutebuch,et al.  A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods , 2006 .

[7]  Glen Murphy,et al.  Determining Stand Value and Log Product Yields Using Terrestrial Lidar and Optimal Bucking: A Case Study , 2008 .

[8]  Guang Zheng,et al.  Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors , 2009, Sensors.

[9]  S. Reutebuch,et al.  Light detection and ranging (LIDAR): an emerging tool for multiple resource inventory. , 2005 .

[10]  Emilio Chuvieco,et al.  Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests , 2004 .

[11]  H. L. Allen,et al.  Leaf Area, Stemwood Growth, and Nutrition Relationships in Loblolly Pine , 1988, Forest Science.

[12]  Erik Næsset,et al.  Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning , 2006 .

[13]  Randolph H. Wynne,et al.  Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA , 2004, Forest Science.

[14]  Randolph H. Wynne,et al.  Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size , 2002 .

[15]  K. Lim,et al.  Lidar remote sensing of biophysical properties of tolerant northern hardwood forests , 2003 .

[16]  Mikko Inkinen,et al.  A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners , 2001, IEEE Trans. Geosci. Remote. Sens..

[17]  Robert E. Keane,et al.  First Order Fire Effects Model: FOFEM 4.0, user's guide , 1997 .

[18]  Andrew Youngblood,et al.  Forest Structure and Fire Susceptibility in Volcanic Landscapes of the Eastern High Cascades, Oregon , 2004 .