Effects of satellite image spatial aggregation and resolution on estimates of forest land area

Abstract

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We spatially aggregated 30 m datasets to coarser spatial resolutions (90, 150, 210, 270, 510 and 990 m) and produced estimates of forest proportion for each spatial resolution using both model-and design-based approaches. Average-based aggregation had no effect on per-image estimates of forest proportion; image variability decreased with increasing spatial resolution and local variability peaked between 210 and 270 m. Majority-based aggregation resulted in overestimation of forest land in a heavily forested landscape and underestimation of forest land in a sparsely forested landscape, with both trends following a natural log distribution. Of the spatial resolutions tested, 30 m was superior for obtaining estimates using model-based approaches. However, standard errors of design-based inventory estimates of forest proportion were smallest when accompanying stratification maps which were aggregated to between 90 and 150 m spatial resolutions and strata thresholds were optimized by study area. These results suggest that spatially aggregating existing 30 m land cover datasets can provide NFIs with gains in precision of their estimates of forest land area, while reducing image storage size and processing times; land cover datasets derived from coarser spatial resolution sensors may provide similar benefits.

Uncertainties of forest area estimates caused by the minimum crown cover criterion

“…For crown cover thresholds <1 differences in forest cover due to changes of the spatial resolution, simulated by spatial aggregation of pixels, were <10 %, an order of magnitude also reported by Nelson et al. (2009) for majority-based aggregation of Landsat images. The minor resolution effect can be highly relevant for remote sensing-based forest monitoring where information from sensors with different resolutions is combined.…”

Recent Progress on Spatial and Thematic Resolution in Landscape Ecology

“…A common experimental design for assessing scale effects in landscape ecology is to aggregate pixels or alter the thematic resolution of one or more land cover maps and quantify changes to response variables, such as landscape metric values [6,[58][59][60]. Another common multi-scale analysis method used frequently in landscape ecology characterises multiscale species-environment relationships by altering the size of circular buffers/kernel around point locations at which ecological data is sampled [61][62][63], while leaving the spatial or thematic resolution constant.…”

Improving estimates of forest disturbance by combining observations from Landsat time series with U.S. Forest Service Forest Inventory and Analysis data

“…As other studies have shown the benefit of modeling FIA plot variables with a 3 × 3 (90 × 90 m) window (Healey, Lapoint, Moisen, & Powell, 2011), there is some justification for incorporating information from surrounding pixels into future strata map assignment procedures. Filtering may be one way to aggregate information and improve performance (Nelson, McRoberts, Holden, & Bauer, 2009) and thus will be a focus of future investigations.…”