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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Impacts of forest fuel treatments and wildfire on forests

Tempel, DJ, RJ Gutiérrez, JJ Battles, DL Fry, Y Su, Q Guo, MJ Reetz, SA Whitmore, GM Jones, BM Collins, SL Stephens, M Kelly, WJ Berigan, and MZ Peery. 2015. Modeling short- and long-term impacts of fuel treatments and wildfire on an old-forest species. Ecosphere 6(12) DOI:10.1890/ES15-00234.1

Fuels-reduction treatments are commonly implemented in the western U.S. to reduce the risk of high-severity fire, but they may have negative short-term impacts on species associated with older forests.   Therefore, we modeled the effects of a completed fuels-reduction project on fire behavior and California spotted owl (Strix occidentalis occidentalis) habitat and demography in the Sierra Nevada to assess the potential short- and long-term trade-offs.  We combined field-collected vegetation data and LiDAR data to develop detailed maps of forest structure needed to parameterize our fire and forest-growth models.  We simulated wildfires under extreme weather conditions (both with and without fuels treatments), then simulated forest growth 30 years into the future under four combinations of treatment and fire: treated with fire, untreated with fire, treated without fire, and untreated without fire.

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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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Delineating individual trees from lidar data

One SNAMP forest plot viewed with lidar dataJakubowski, MK, L Wenkai, Q Guo, and M Kelly. 2013. Delineating individual trees from lidar data: a comparison of vector- and raster-based segmentation approaches. Remote Sensing 5, 4163-4186; doi:10.3390/rs5094163

This work concentrates on delineating individual trees from discrete lidar data in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m²), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types.

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Capturing forest fuel characteristics with lidar

Jakubowski, M. K., Q. Guo, B. Collins, S. Stephens, and M. Kelly. 2013. Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense, mountainous forest. Photogrammetric Engineering and Remote Sensing 79(1):37-49

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans across a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m2), discrete return, smallfootprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. While the general fuel types were predicted accurately, specific surface fuel model predictions were poor using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions.

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Low density lidar provides valuable plot-level forest data

Jakubowski, M., Q. Guo, and M. Kelly. 2013. Tradeoffs between lidar pulse density and forest measurement accuracy. Remote Sensing of Environment. 130: 245–253.

lidar density examples: 9 - 0.01 pl/m2

Discrete lidar is increasingly used to analyze forest structure. Technological improvements in lidar sensors have led to the acquisition of increasingly high pulse densities, possibly reflecting the assumption that higher densities will yield better results. In this study, we systematically investigated the relationship between pulse density and the ability to predict several commonly used forest measures and metrics at the plot scale. The accuracy of predicted metrics was largely invariant to changes in pulse density at moderate to high densities. In particular, correlations between metrics such as tree height, diameter at breast height, shrub height and total basal area were relatively unaffected until pulse densities dropped below 1 pulse/m2. Metrics pertaining to coverage, such as canopy cover, tree density and shrub cover were more sensitive to changes in pulse density, although in some cases high prediction accuracy was still possible at lower densities.

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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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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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