Mapping weeds with UAV and obia

On-ground photographs and UAV images of the 1x1-m frames used in the ground-truth samplingPeña JM, Torres-Sánchez J, de Castro AI, Kelly M, López-Granados F. 2013. Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLoS ONE 8(10): e77151. doi:10.1371/journal.pone.0077151

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results.

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A proposed early-warning fire detection system

our graphic of the FUEGO conceptPennypacker, CR, MK Jakubowski, M Kelly, M Lampton, C Schmidt, S Stephens, and R Tripp.  2013. FUEGO—Fire Urgency Estimator in Geosynchronous Orbit—A proposed early-warning fire detection system. Remote Sensing 5(10): 5173-5192

Current and planned wildfire detection systems are impressive but lack both sensitivity and rapid response times. A small telescope with modern detectors and significant computing capacity in geosynchronous orbit can detect small (12 m²) fires on the surface of the earth, cover most of the western United States (under conditions of moderately clear skies) every few minutes or so, and attain very good signal-to-noise ratio against Poisson fluctuations in a second. Hence, these favorable statistical significances have initiated a study of how such a satellite could operate and reject the large number of expected systematic false alarms from a number of sources. Here we present both studies of the backgrounds in Geostationary Operational Environmental Satellites (GOES) 15 data and studies that probe the sensitivity of a fire detection satellite in geosynchronous orbit.

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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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Management Without Borders? Landowner Practices and Attitudes toward Cross-Boundary Cooperation

Ferranto, S., L. Huntsinger, C. Getz, W. Stewart, G. Nakamura, and M. Kelly. 2013. Management without borders? A survey of landowner practices and attitudes towards cross-­boundary cooperation. Society and Natural Resources 26(9): 1082-1100. DOI: 10.1080/08941920.2013.779343.

Ecosystem management requires cross-jurisdictional problem-solving and, when private lands are involved, cross-boundary cooperation from many individual landowners. Fragmented ownership patterns and variation in ownership values, as well as distrust and transaction costs, can limit cooperation. Results from a landowner survey in California were analyzed using an audience segmentation approach. Landowners were grouped into four clusters according to ownership motivations: rural lifestyle, working landscape, natural amenity, and financial investment. All clusters showed willingness to cooperate for all three topics addressed in the survey (pest and disease control, fire hazard reduction, and wildlife conservation), but their degree of willingness differed by cluster, who they were expected to cooperate with, and the natural resource problem addressed. All were more willing to cooperate with neighbors and local groups than with state and federal agencies. Landowners were most willing to cooperate to reduce fire hazard, which is the most direct threat to property and well-being. Journal link.

Income and ethnicity differences among people with diabetes

Jones Smith, J. C., M. Wharton, M. Kelly, E. Kersten, A. Karter, N. Adler, D. Schillinger, H. Moffett, B. A. Laraia. 2013. Obesity and the food environment: income and ethnicity differences among people with diabetes, the Diabetes Study of Northern California (DISTANCE). Diabetes Care 36:2697-2705

The objectives of this study were to test whether there was an association between food environments and obesity among adults with diabetes and whether this relationship differed according to individual income or race/ethnicity. We found that more healthful food environments were associated with lower obesity in the highest income groups among whites, Latinos, and Asians. The association was negative, but smaller and not statistically significant, among high-income blacks. In contrast, a more healthful food environment was associated with higher obesity among participants in the lowest-income group which was statistically significant for black participants in this income category. These findings suggest that the availability of healthful food environments may have different health implications when financial resources are severely constrained. Journal link.

Plant litter influences remote sensing signatures in wetlands

Correlation between fAPAR-hig and two-band vegetation indices usingsimulated Hyperion bands using spectroradiometer data collected at Twitchell IslandSchile, L. K. Byrd, L. Windham-Myers, and M. Kelly. 2013. Accounting for plant litter in remote sensing based estimates of carbon flux in wetlands.  Remote Sensing Letters 4(6):542-551

Monitoring productivity in coastal wetlands is important due to their high carbon sequestration rates and potential role in climate change mitigation. We tested agricultural- and forest-based methods for estimating the fraction of absorbed photosynthetically active radiation (ƒ_APAR), a key parameter for modeling gross primary productivity (GPP), in a restored, managed wetland with a dense litter layer of non-photosynthetic vegetation, and we compared the difference in canopy light transmission between a tidally influenced wetland and the managed wetland.

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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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Sampling effects in PPGIS and VGI for public lands management

Brown, G., M. Kelly and D. Whital. 2013. Which “public”? Sampling effects in public participation GIS (PPGIS) and Volunteered Geographic Information (VGI) systems for public lands management. Journal of Environmental Planning and Environment 57(2): 190-214. doi: http://dx.doi.org/10.1080/09640568.2012.741045

Web 2.0 technologies including Public Participation Geographic Information Systems (PPGIS) and Volunteered Geographic Information (VGI) provide methods for engaging multiple publics in public lands management. We examined the effects of sampling in a PPGIS/VGI application for national forest planning in the U.S. A random sample (RS) of households and a volunteer public (VP) were invited to participate in an internet-based PPGIS to identify national forest values and use preferences. Spatial and non-spatial group responses were analysed. The VP group expressed stronger utilitarian values and consumptive use preferences while the RS group preferred forest amenities. These results would lead to different planning decisions. PPGIS/VGI methods should include scientific sampling to ground-truth voluntary participation. Journal Link.

Key words: public participation . PPGIS . volunteered geographic information . VGI . forest planning . public lands

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.