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Sahara mustard (Brassica tournefortii) is an invasive species common to the Mojave and Sonoran Deserts in the southwestern US. Our objective was to assess WorldView-2 (WV2) satellite imagery potential to detect Sahara mustard presence, cover, and biomass. We compared WV2 images (2.4 m and 30 m resolution) to Landsat ETM+ image both classified using a mixture tuned matched filtering ( MTMF). A total of 1,885 field plots (30 × 30 m) were established across a 8,715 km 2 study area in spring of 2012, an exceptionally dry year. Average target canopy cover (7.5 percent) and biomass (0.82 g/m 2 ) were extremely low. The WV2 MTMF classification had a much greater overall accuracy of 88 percent, while the resampled WV2 and the Landsat ETM+ MTMF classification overall accuracies were 67 percent and 59 percent, respectively. Producer’s and user’s accuracies in target detection were 86 percent and 94 percent, respectively, although the exceptionally low canopy cover and biomass were not well correlated with image-based estimates.

Measurement error introduced by mismatches of scale and location between response and predictor variables is one of the major sources of error in forest structure mapping. It affects the evaluation of analytical models, compromises the results of accuracy assessments, and reduces the accuracy of mapping products. Using forest structure attributes measured in a specifically-designed ground plot system, we isolated the measurement error from the total mapping errors that are related to multiple factors, and examined the distribution and magnitude of this error caused by a scale mismatch between a relatively larger forest unit and a relatively smaller forest unit, as well as location mismatch of a specific distance between two forest units of the same size. We demonstrated the effects of measurement error on the analytical models and resulting maps for three common mapping scenarios linking ground data with remote sensing imagery. Our results indicated that this scale- and location-related error can be analyzed using the Classical and Berkson error models in most practical mapping exercises involving data measured on-ground and remotely-sensed imagery, and that the distinct error pattern of each type of measurement error can be used to identify the major error source. Based on this analysis, we can adjust the plot design or adjust the resolution of imagery, and select the optimal analytical method to achieve the best mapping result.

USGS Digital Orthophoto Quadrangles (DOQs) are a form of inexpensive, high spatial resolution (1 m ground resolution) imagery available for most regions of the United States. Typically, DOQs have been used in the construction of “basemaps” or as training datasets. In this paper we present a technical approach that uses DOQs as the primary data source to map regional forest canopy cover. This approach, a form of advanced exploratory data analysis (AEDA), can separate areas of crown, shadow, and non-crown vegetation using a single value threshold and a “value range” threshold obtained by analyzing the statistical plots built on a multifractal model. By applying AEDA, we can distinguish the crown, crown boundary zone, shadow areas, and non-crown areas within a DOQ mosaic by their distinctive multifractal properties. Over a period of two years, we mapped canopy cover of 20,000 km 2 of ponderosa pine-dominated forest across northern Arizona using this technique. We used two hundred ground plot measurements from an independent source to assess the accuracy of the canopy cover map across an 8,000 km 2 region.