Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine

• Published in: Journal of environmental planning and management Vol. 66; no. 3; pp. 665 - 697
• Main Authors: Feizizadeh, Bakhtiar, Omarzadeh, Davoud, Kazemi Garajeh, Mohammad, Lakes, Tobia, Blaschke, Thomas
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 23.02.2023; Taylor & Francis Ltd
• Subjects: Algorithms; Analysis; Automation; Change detection; Classification; Cloud computing; Cognitive style; comparative approach Google Earth Engine; Data; data collection; Daylight saving; Decision analysis; Decision makers; Decision making; Decision trees; Earth; Environmental protection; Internet; Iran; Land cover; Land use; land use/cover mapping; Landsat; Landsat satellites; Learning algorithms; Machine learning; Mapping; Regression analysis; Reliability; Reliability analysis; Remote monitoring; Remote sensing; Satellite imagery; Satellites; Spatial analysis; spatial uncertainty analysis; Support vector machines; Time series; time series analysis; Trend analysis; Uncertainty; Uncertainty analysis; Urmia lake basin; Iran
• DOI: 10.1080/09640568.2021.2001317
• ISSN: 0964-0568; 1360-0559

With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Online platforms such as Google Earth Engine (GEE) bring data-driven techniques to the desktops of researchers while changing workflows and making excessive data downloads redundant. We present a study that utilizes machine learning algorithms on the GEE cloud computing platform for land use/land cover (LULC) mapping and change detection analysis using a Landsat satellite image time series. We applied different machine learning techniques to data from an environmentally sensitive area in Northern Iran. We tested their efficiency for LULC mapping and change detection analysis using the support vector machine (SVM), random forest (RF) and classification and regression tree (CART). We obtained LULC maps for the years 2000, 2005, 2010, 2015 and 2020. Training data was collected from field operations and historical datasets, and the respective LULC maps were validated using ground control points. In addition, we validated the reliability of the results through a spatial uncertainty analysis using Dempster-Shafer Theory (DST). The resulting accuracies of the classification outcomes varied significantly. SVM performed best with accuracies of 90.25%, 91.84%, 89.02%, 93.35% and 95.65% for 2000, 2005, 2010, 2015 and 2020, respectively. The spatial uncertainty analysis also validated the efficiency of SVM compared to RF and CART. The results confirm the potential of machine learning techniques for time series LULC mapping on the GEE platform while lowering the barriers to analyzing large amounts of satellite data. The results are also critical for decision-makers and authorities for analyzing the LULC changes and developing the respective environmental protection and polices in Northern Iran.