Sampling with probability proportional to prediction (3P sampling) using covariates derived from spherical images

Using a two-phase sampling approach with systematic selection of large samples of covariates followed by a sampling with probability proportional to prediction (3P sampling) process to subsample field measures of the parameters of interest can be an efficient design to sample larger forest areas. To assist in obtaining predictions for each sample plot consistently and rapidly, we propose using a 360° spherical camera. In this study, three covariates derived from spherical images were evaluated: (i) basal area (P[BA]); (ii) sum of squared heights per hectare (P[SHT]); and (iii) stem fraction (P[SF]). These covariates were used to estimate volume. Sample simulations showed no biases in volume estimates for any of the three covariates. Overall, P[SF] had the lowest standard error percentages across different simulated sample sizes (10% for five subsamples to 2.5% for 50 subsamples), even though it had the lowest correlations with field volume (correlation = 0.30–0.31). This may be a result of the relatively consistent stand conditions within the study site. Based on our results, standard errors of 5% were obtainable with measurement fractions of about 25% of the number of image-based predictions when using P[SF] or P[BA] and 75% when using P[SHT].

A mobile mapping system utilizing a spherical camera

The transportation departments belonging to respective provinces currently collect highway management data with the use of several methods and systems which include visual field inspections, survey methods, aerial photogrammetry, as well as mobile data acquisition systems. Spherical cameras offer an attractive alternative to standard mobile data acquisition devices for highway management systems as they provide full coverage with a single camera. Inclusion of such a camera requires methods of determining relative, interior and exterior orientation information, as well as bore-sight and lever arm determination. Specialized methods of mosaicking[sic] the imagery are also required. This paper focuses on exploring these methods for spherical cameras. Several computer programs were developed to solve for relative, interior, and exterior orientation parameters. It was concluded that a spherical camera can be efficiently utilized for highway data collection and provides full data coverage with a single camera system.

A mobile mapping system utilizing a spherical camera

The transportation departments belonging to respective provinces currently collect highway management data with the use of several methods and systems which include visual field inspections, survey methods, aerial photogrammetry, as well as mobile data acquisition systems. Spherical cameras offer an attractive alternative to standard mobile data acquisition devices for highway management systems as they provide full coverage with a single camera. Inclusion of such a camera requires methods of determining relative, interior and exterior orientation information, as well as bore-sight and lever arm determination. Specialized methods of mosaicking[sic] the imagery are also required. This paper focuses on exploring these methods for spherical cameras. Several computer programs were developed to solve for relative, interior, and exterior orientation parameters. It was concluded that a spherical camera can be efficiently utilized for highway data collection and provides full data coverage with a single camera system.