Allometric equation choice impacts lidar-based forest biomass estimates

lidar point cloud from the snamp projectZhao, F., Q. Guo and M. Kelly. 2012. Allometric equation choice impacts lidar-based forest biomass estimates: A case study from the Sierra National Forest, CA. Agricultural and Forest Meteorology 165: 64–72

Plot-level estimates of biomass were derived from field data and two different allometric equations. Estimates differed between allometric equations, especially in plots with high biomass. Selection of allometric equations can influence the capacity of lidar data to estimate biomass. The best fit between field data and lidar data were found using a regional allometric equation and a combination of lidar metrics and individual tree data.

Keywords: Lidar . Allometric equation . Biomass .  Sierra Nevada .  Forest

Journal link. Pdf download.

Mapping fisher denning trees with lidar

Zhao, F., R.A. Sweitzer, Q. Guo, and M. Kelly. 2012. Characterizing habitats associated with fisher den structures in the Southern Sierra Nevada, California using discrete return lidar. Forest Ecology and Management 280: 112–119

This study explored the ability of lidar-derived metrics to capture topography and forest structure surrounding denning trees used by the Pacific fisher (Martes pennanti) as a case study to illustrate the utility of lidar remote sensing in studying mammal-habitat associations. We used Classification and Regression Trees (CART) to statistically compare the slope and lidar-derived forest height and structure metrics in the circular area (with radius of 10–50 m) surrounding denning trees and randomly selected non-denning trees. We accessed our model accuracy using resubstitution and cross-validation methods. Our results show that there is a strong association between fisher denning activity and its surrounding forested environment across scales, with high classification accuracy (overall accuracies above 80% and cross-validation accuracies above 70%) at 20, 30 and 50 m ranges. The best classification accuracies were found at 20 m (optimal resubstitution accuracy 86.2% and cross-validation accuracy 78%). Tree height and slope were important variables in classifying the area immediately surrounding denning trees; at scales larger than 20 m, forest structure and complexity became more important. Pdf download. Journal link.

Using the web as a participatory tool in adaptive management

Kelly, M., S. Ferranto, S. Lei, K. Ueda, L. Huntsinger. 2012. Expanding the table: The web as a tool for participatory adaptive management in California forests. Journal of Environmental Management 109: 1-11

Participatory adaptive management is widely promoted as the new paradigm in public lands management. It is grounded in two underlying principles - that management experiments and diverse sources of information should be used to continually refine management in complex ecological systems, and that the public must be included throughout the adaptive management process. Access to scientific results and exchange of information is at the core of both of these principles. The recent proliferation of Internet communities and web-based participation tools raises the question of how the Internet might help facilitate information exchange in participatory adaptive management. Using a case study approach, the role of web technologies in facilitating the flow of transparent and useful information was examined in a participatory adaptive management project focused on Forest Service vegetation management treatments in California’s Sierra Nevada.

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Characterizing owl nest trees with lidar

spotted owl, courtesy of the SNAMP owl team: http://snamp.cnr.berkeley.edu

García-Feced, C., D. Tempel, and M. Kelly. 2011. LiDAR as a tool to characterize wildlife habitat: California Spotted Owl nesting habitat as an example. Journal of Forestry 108(8): 436-443

We demonstrate the use of an emerging technology, airborne light detection and ranging (lidar), to assess forest wildlife habitat by showing how it can improve the characterization of California spotted owl (Strix occidentalis occidentalis) nesting habitat. We used lidar data, validated in the field, to measure the number, density and pattern of residual trees (≥ 90 cm dbh) and to estimate canopy cover within 200 m of four nest trees. Nest trees were surrounded by large numbers of residual trees and high canopy cover. We believe that lidar would greatly benefit forest managers and scientists in the assessment of wildlife-habitat relationships and conservation of important wildlife species.

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Finding trees in the lidar point cloud

individual trees extracted from the lidar point cloudLi, W., Q. Guo, M. Jakubowski and M. Kelly. 2012. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering and Remote Sensing 78(1): 75-84

In this study we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. The new algorithm adopts a top-to-bottom region growing approach that segments trees individually and sequentially from the tallest to the shortest. We experimentally applied the new algorithm to segment trees in a mixed coniferous forest in the Sierra Nevada Mountains in California, USA. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.

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Mapping downed logs with lidar + obia

downed logs in redBlanchard, S., M. Jakubowski, and M. Kelly. 2011. Object-based image analysis of downed logs in a disturbed forest landscape using lidar. Remote Sensing 3(11): 2420-2439.

Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms.

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