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DOI10.1016/j.rse.2020.112148
High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015
Calderón-Loor M.; Hadjikakou M.; Bryan B.A.
发表日期2021
ISSN00344257
卷号252
英文摘要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. © 2020 The Author(s)
英文关键词Google earth engine; Ground-truth data; Land-cover change; Landsat; Random Forest
语种英语
scopus关键词Classification (of information); Data handling; Decision trees; Environmental impact; Environmental impact assessments; Land use; Pixels; Time series analysis; Cloud computing services; Ground-truth dataset; High spatial resolution; Land cover mapping; Methodological frameworks; Overall accuracies; Random forest modeling; Temporal and spatial changes; Walls (structural partitions); detection method; environmental impact assessment; forest ecosystem; homogeneity; land cover; land use change; Landsat; pixel; satellite altimetry; software; Australia
来源期刊Remote Sensing of Environment
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/179068
作者单位Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Australia; Grupo de Investigación de Biodiversidad, Medio Ambiente y Salud–BIOMAS, Universidad de las Américas (UDLA), Quito, Ecuador
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Calderón-Loor M.,Hadjikakou M.,Bryan B.A.. High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015[J],2021,252.
APA Calderón-Loor M.,Hadjikakou M.,&Bryan B.A..(2021).High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015.Remote Sensing of Environment,252.
MLA Calderón-Loor M.,et al."High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015".Remote Sensing of Environment 252(2021).
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