High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015
2021
Authors
Marco Calderón-Loor, Michalis Hadjikakou, Brett A Bryan
Abstract
Computational and data handling limitations have constrained time-series analyses of land-cover change at high-spatial resolution over large (e.g., continental) extents. However, a new set of cloud-computing services offer an opportunity for improving knowledge of land change at finer grain. We constructed a historical set of seven high-resolution wall-to-wall land-cover maps at continental scale for Australia and analyzed temporal and spatial changes of land-cover from 1985 to 2015 at 5-year time-steps using Google Earth Engine (GEE). We used 281,962 Landsat scenes for producing median cloud-free composites at each time-step. We established a pseudo ground-truth dataset and used a PCA-based outlier detection method to reduce its uncertainty. A random forest model was trained at each time-step for classifying raw data into six land-cover classes: Cropland, Forest, Grassland, Built-up, Water, and Other areas, using 49 predictor datasets and nearly 20,000 training points. We further constructed uncertainty maps at each time-step as a proxy of per-pixel confidence. The average overall accuracy of the seven 30 m-resolution land-cover maps was ~93%. Built-up and Water areas displayed the highest user and producer accuracies (>93%), with Grasslands and Other areas slightly lower (~82–88%). Classification uncertainty was lower in more homogeneous landscapes (i.e., large expanses of a single land-cover class). Around 510,975 km2 (±69,877 km2) of land changed over the 30 years at an average of ~17,033 km2 yr−1 (±2329 km2 yr−1). Cropland and Forests declined by ~64,836 km2 (±16,437 km2) and ~ 152,492 km2 (±24,749 km2) over 30 years, mainly converting to Grassland. Built-up areas experienced the highest relative increases, increasing from 12,320 km2 in 1985 to 15,013 km2 in 2015 (~19.2%, ±3.1%). The sensitivity, i.e., proportion of pixels correctly classified as having changed, was over 96%, whereas the specificity, i.e., the proportion of pixels correctly classified as no-change, was over 68%. Numerous potential applications of these first-of-their-kind, detailed spatiotemporal maps of land use and land-change assessment exist spanning many areas of environmental impact assessment, policy, and management. Similarly, this methodological framework can provide a useful template for assessing continental-scale, high-resolution land dynamics more broadly.
Fig. 3. Australia's land-cover classification maps from 1985 to 2015.
