Colin J. Ferster

Review: A review of earth observation using mobile personal communication devices

Earth observation using mobile personal communication devices (MPCDs) is a recent advance with considerable promise for acquiring important and timely measurements. Globally, over 5 billion people have access to mobile phones, with an increasing proportion having access to smartphones with capabilities such as a camera, microphone, global positioning system (GPS), data storage, and networked data transfer. Scientists can view these devices as embedded sensors with the potential to take measurements of the Earths surface and processes. To advance the state of Earth observation using MPCDs, scientists need to consider terms and concepts, from a broad range of disciplines including citizen science, image analysis, and computer vision. In this paper, as a result of our literature review, we identify a number of considerations for Earth observation using MPCDs such as methods of field collection, collecting measurements over broad areas, errors and biases, data processing, and accessibility of data. Developing effective frameworks for mobile data collection with public participation and strategies for minimizing bias, in combination with advancements in image processing techniques, will offer opportunities to collect Earth sensing data across a range of scales and perspectives, complimenting airborne and spaceborne remote sensing measurements.

Aboveground large tree mass estimation in a coastal forest in British Columbia using plot-level metrics and individual tree detection from lidar

Plot-level large tree and snag aboveground mass (TSAM) in a second-growth coastal Douglas-fir forest stand in British Columbia was estimated using light detection and ranging (lidar) combining metrics from individually identified trees and snags and plot-level lidar canopy return density. Individual trees were identified using the tree variable window (TreeVaW) algorithm, which identifies tree crowns using a circular moving filter and the relationship between tree height and crown diameter. A multiple linear regression model was then developed to predict plot-level TSAM as determined from ground plots. The predicted heights of individually identified trees were very accurate (r2 = 0.92, SEE = 0.69 m). Plot TSAM was predicted with an r2 = 0.75 and SEE = 29.68 Mg/ha using lidar density and height metrics alone, and a slightly lower r2 = 0.71 and SEE = 31.95 Mg/ha using lidar density metrics with individually identified tree heights. Using individual tree metrics did not improve plot-level TSAM estimation, since a large component of TSAM is contained in complex canopy levels where individual trees are difficult to identify.

Assessing the quality of forest fuel loading data collected using public participation methods and smartphones

Effective wildfire management in the wildland–urban interface (WUI) depends on timely data on forest fuel loading to inform management decisions. Mobile personal communication devices, such as smartphones, present new opportunities to collect data in the WUI, using sensors within the device – such as the camera, global positioning system (GPS), accelerometer, compass, data storage and networked data transfer. In addition to providing a tool for forest professionals, smartphones can also facilitate engaging other members of the community in forest management as they are now available to a growing proportion of the general population. Approaches where the public participates in the data-collection process (inspired by citizen science) may be beneficial for fire hazard issues. This research note demonstrates a smartphone application for measuring forest fuel loading in the WUI by forestry professionals and non-professionals, and evaluates the quality of the collected data. Smartphones and their associated applications may provide new tools for collecting forest structural data in the WUI, but forest managers need to ensure that measurement protocols provide the required precision for analysis and enforce the logical consistency of observations made by a diverse set of data collectors, and that sufficient training is provided. If these recommendations are followed, we conclude that data acquired by volunteers in collaborative projects through smartphone applications can be of acceptable quality to help inform forest management decisions.

Integrating volunteered smartphone data with multispectral remote sensing to estimate forest fuels

Volunteered data sources are readily available due to advances in electronic communications technology. For example, smartphones provide tools to collect ground-based observations over broad areas from a diverse set of data collectors, including people with, and without, extensive training. In this study, volunteers used a smartphone application to collect ground-based observations. Forest structural components were then estimated over a broader area using high spatial resolution RapidEye remote sensing imagery (5 spectral bands 440–850 nm, 5 m spatial resolution) and a digital elevation model following a three nearest neighbor approach (K-NN). Participants with professional forestry experience on average chose high-priority fuel load locations near buildings, while nonprofessional participants chose a broader range of conditions over a larger extent. When used together, the professional and nonprofessional observations provided a more complete assessment of forest conditions. A generalized framework is presented that utilizes K-NN imputation tools for estimating the distribution of forest fuels using remote sensing and topography variables, ensuring spatial representation, checking attribute accuracy, and evaluating predictor variables. Frameworks to integrate volunteered data from smartphone platforms with remote sensing may contribute toward more complete Earth observation for Digital Earth.

Comparison of carbon-stock changes, eddy-covariance carbon fluxes and model estimates in coastal Douglas-fir stands in British Columbia

BackgroundThe global network of eddy-covariance (EC) flux-towers has improved the understanding of the terrestrial carbon (C) cycle, however, the network has a relatively limited spatial extent compared to forest inventory data and plots. Developing methods to use inventory-based and EC flux measurements together with modeling approaches is necessary evaluate forest C dynamics across broad spatial extents.MethodsChanges in C stock change (ΔC) were computed based on repeated measurements of forest inventory plots and compared with separate measurements of cumulative net ecosystem productivity (ΣNEP) over four years (2003 – 2006) for Douglas-fir (Pseudotsuga menziesii var menziesii) dominated regeneration (HDF00), juvenile (HDF88 and HDF90) and near-rotation (DF49) aged stands (6, 18, 20, 57 years old in 2006, respectively) in coastal British Columbia. ΔC was determined from forest inventory plot data alone, and in a hybrid approach using inventory data along with litter fall data and published decay equations to determine the change in detrital pools. These ΔC-based estimates were then compared with ΣNEP measured at an eddy-covariance flux-tower (EC-flux) and modelled by the Carbon Budget Model - Canadian Forest Sector (CBM-CFS3) using historic forest inventory and forest disturbance data. Footprint analysis was used with remote sensing, soils and topography data to evaluate how well the inventory plots represented the range of stand conditions within the area of the flux-tower footprint and to spatially scale the plot data to the area of the EC-flux and model based estimates.ResultsThe closest convergence among methods was for the juvenile stands while the largest divergences were for the regenerating clearcut, followed by the near-rotation stand. At the regenerating clearcut, footprint weighting of CBM-CFS3 ΣNEP increased convergence with EC flux ΣNEP, but not for ΔC. While spatial scaling and footprint weighting did not increase convergence for ΔC, they did provide confidence that the sample plots represented site conditions as measured by the EC tower.ConclusionsMethods to use inventory and EC flux measurements together with modeling approaches are necessary to understand forest C dynamics across broad spatial extents. Each approach has advantages and limitations that need to be considered for investigations at varying spatial and temporal scales.

An early warning system to forecast the close of the spring burning window from satellite-observed greenness

Spring represents the peak of human-caused wildfire events in populated boreal forests, resulting in catastrophic loss of property and human life. Human-caused wildfire risk is anticipated to increase in northern forests as fuels become drier, on average, under warming climate scenarios and as population density increases within formerly remote regions. We investigated springtime human-caused wildfire risk derived from satellite-observed vegetation greenness in the early part of the growing season, a period of increased ignition and wildfire spread potential from snow melt to vegetation green-up with the aim of developing an early warning wildfire risk system. The initial system was developed for 392,856 km2 of forested lands with satellite observations available prior to the start of the official wildfire season and predicted peak human-caused wildfire activity with 10-day accuracy for 76% of wildfire-protected lands by March 22. The early warning system could have significant utility as a cost-effective solution for wildfire managers to prioritize the deployment of wildfire protection resources in wildfire-prone landscapes across boreal-dominated ecosystems of North America, Europe, and Russia using open access Earth observations.