Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Abstract

, but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE 016.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover. org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multitemporal depiction of Earth's tree cover available to the Earth science community.

Effect of woody-plant encroachment on livestock production in North and South America

“…Because of their large size, single counties include a wide range of pixel cover values. As a result, the magnitude of the error of this variable at the county scale, although admittedly unknown, is actually much smaller than that described at the pixel scale in the literature (42). Thus, we assume that our models are robust in relation to the errors in our variables.…”

“…Errors in the independent variables will produce bias in the regression parameters when the variance of the error in X is large in comparison with the variance of X (41). In our case, our MODIS-based estimator of TC is known to be subject to errors, ranging from 10 and 31 units of rms error at the pixel scale (250 × 250 m) with a tendency of overestimation in areas of low cover and underestimation in areas of high cover (42). Here, it is important to note that our TC variable represents mean values of TC at a county scale, with each county containing tens of thousands of pixels.…”

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

“…Each Landsat scene was processed independently, and overlapping pixels from multiple images were composited by selecting the result at each location with highest posterior (water/ nonwater) probability. This 'best-pixel' compositing maximized classification certainty and filled gaps due to clouds and their shadows (Sexton, Song, Feng, et al 2013;Kim et al 2014). Finally, inland water was distinguished from marine water by referring to the Global Database of Administrative areas (GADM) version 2 (http://www.gadm.org/).…”

An improved species distribution model for Scots pine and downy oak under future climate change in the NW Italian Alps

“…Then, we computed NDVI using band TM4 (near infrared) and band TM3 (visible red) and used it as a proxy of standing forest biomass (Tucker 1979;Pettorelli et al 2005). As an additional index of competition by forest vegetation, we used percent tree cover from the recently released Landsat vegetation continuous field (VCF) dataset (Sexton et al 2013), at a resolution of 30 m, based on a Landsat 5 TM image acquired on July 27, 2001. (6) Land use intensity was assessed by using proxy variables, i.e., total road length and total building surface per 500-m pixel, as extracted from a vector regional map.…”