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

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.

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

“…There are different classification methods from unsupervised algorithms including K-means clustering, parametric algorithms such as maximum likelihood [10], machine learning algorithms including Artificial Neural Networks (ANNs) and SVMs [11,12], decision trees [13,14], and ensemble of classifiers [15]. Algorithms of machine learning commonly have high accuracy and efficiency in comparison to usual parametric algorithms for dealing with large and assembled databases [16].…”

How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

“…The pixel-based method has long been the major approach for the classification of remote sensing imagery [4]. Various pixel-based classification methods were proposed based on statistical distance measures [5], including classification and regression tree (CART) [6][7][8], support vector machine (SVM) [9][10][11], and random forest (RF) [12,13]. However, the pixel-based classification method has two major limitations: the first one is the problem of mixed pixels in which the features from multiple classes are presented in a single pixel [14].…”

A Fit-for-Purpose Algorithm for Environmental Monitoring Based on Maximum Likelihood, Support Vector Machine and Random Forest

“…A wide range of classification methods have been used to map land cover using remotely sensed data. Classification methods vary from unsupervised algorithms such as K-means klustring to parametric supervised algorithms such as maximum likelihood (Otukei and Blaschke, 2010); to machine learning algorithms such as artificial neural networks (Duro et al, 2012), SVMs (Mountrakis et al, 2011), decision trees (Breiman, 1984;Hua et al, 2017), and ensembles of classifiers (Breiman, 1996).…”