From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

Dang Hung Bui,László Mucsi

doi:10.3390/rs13091700

Abstract

It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

Highlights

Land cover is defined as “the observedphysical cover on the earth’s surface” [1], including vegetation, water surface, bare rock, bare soil, buildings, and roads
The high overall accuracy of the final land cover map (OA = 93.86%) and the final land use map (OA = 93.45%) produced in this study proved the suitability and effectiveness of a combination of pixel-based and object-based classifications using a random forest classifier and decision rules on free multi-temporal remote sensing data
Using multitemporal images in a pixel-based classification confirmed their effectiveness for improving the accuracy of the generated land cover map compared to those using single-date images (OA = 89.59–90.78%)

Summary

Introduction

Land cover is defined as “the observed (bio)physical cover on the earth’s surface” [1], including vegetation, water surface, bare rock, bare soil, buildings, and roads. Land use refers to “the arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it” [1]; in other words, land use is the way in which people use land cover types for one or more different purposes. They are defined differently and this issue has been discussed in previous studies [2,3,4], these two terms are still commonly used concurrently or interchangeably in many studies related to land cover and land use classification and mapping [5,6,7]. There have been attempts to use single or multiple remote sensing data independently [3,14,15] or in conjunction

Results

Discussion

Conclusion