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ISPRS Int. J. Geo-Inf. 2017, 6(11), 331; https://doi.org/10.3390/ijgi6110331

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

1
College of Computer and Information Engineering, Xiamen University of Technology, 600 Ligong Road, Xiamen 361024, China
2
School of Electronics and Information Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China
3
The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 9 Dengzhuang South Road, Beijing 100101, China
4
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
*
Author to whom correspondence should be addressed.
Received: 13 August 2017 / Revised: 1 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
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Abstract

Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user’s accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities. View Full-Text
Keywords: decision tree classification; feature-based approach; urban land use and land cover; remote sensing; modeling; coastal city; Xiamen decision tree classification; feature-based approach; urban land use and land cover; remote sensing; modeling; coastal city; Xiamen
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Hua, L.; Zhang, X.; Chen, X.; Yin, K.; Tang, L. A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China. ISPRS Int. J. Geo-Inf. 2017, 6, 331.

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