New paper: mapping vegetation with lidar and aerial imagery

Su, Y, Q Guo, D L Fry, B M Collins, M Kelly, J P Flanagan & J J Battles. 2016. A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne Lidar Data and Aerial Imagery. Canadian Journal of Remote Sensing 42:1–15 DOI: 10.1080/07038992.2016.1131114 

Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imagery have proved to be valuable inputs to the vegetation mapping process, but they can provide limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (lidar) can accurately measure the forest vertical and horizontal structures, and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and lidar data to map vegetation composition and structure over large spatial scales.

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Lidar + MODIS to upscale predictions of forest biomass

Li, L., Q. Guo, S. Tao, M. Kelly, and G. Xu. 2015. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass. ISPRS Journal of Photogrammetry and Remote Sensing. 102: 198–208

Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB.

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Lidar-derived volume metrics for aboveground biomass estimation in conifer stands

individual trees in the lidar cloudTao, S., Li, L., Q. Guo, L. Li, B. Xue, M. Kelly, W. Li, G. Xu, and Y. Su. 2014. Airborne Lidar-derived volume metrics for aboveground biomass estimation: A comparative assessment for conifer stands. Agriculture and Forest Management 198–199: 24–3

Estimating aboveground biomass (AGB) is essential to quantify the carbon balance of terrestrial ecosystems, and becomes increasingly important under changing global climate. Volume metrics of individual trees, for example stem volume, have been proven to be strongly correlated to AGB. In this paper, we compared a range of airborne Lidar-derived volume metrics (i.e. stem volume, crown volume under convex hull, and crown volume under Canopy Height Model (CHM)) to estimate AGB. In addition, we evaluated the effect of horizontal crown overlap (which is often neglected in Lidar literature) on the accuracy of AGB estimation by using a hybrid method that combined marker-controlled watershed segmentation and point cloud segmentation algorithms.

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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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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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