Developments in Landsat Land Cover Classification Methods: A Review ...

Developments in Landsat Land Cover 

Classification Methods: A Review

remote sensing 

Review 

Developments in Landsat Land Cover Classification Methods: A Review 

Darius Phiri * and Justin Morgenroth ID 

New Zealand School of Forestry, University of Canterbury, Christchurch 8140, New Zealand; 

justin.morgenroth@canterbury.ac.nz 

* Correspondence: 

darius.phiri@pg.canterbury.ac.nz; Tel.: +64-22-621-4280 

Received: 1 August 2017; Accepted: 13 September 2017; Published: 19 September 2017 

http://www.mdpi.com/2072-4292/9/9/967

Abstract: 

  Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification. 

Keywords: Landsat; land cover; classification methods; remote sensing; OBIA; pixel-based

1. Introduction 

  The launch of the Earth Resource Technology Satellite (ERTS) 1, later called Landsat 1 in July 1972, has contributed significantly to the development of remote sensing applications such as land cover classification [1,2]. The main aim of the Landsat satellite program was to provide a tool for continuous monitoring of Earth’s resources [1,3,4]. With the Landsat program running for over four decades now, different methods for classifying land cover were developed. The development of these methods was largely attributed to the improvements in Landsat images, advancement of computer technology, development of geographic information systems (GIS) and the Landsat free access policy [5,6]. Land cover classification using Landsat images has evolved over the last four decades. Land cover is the physical substance covering the Earth’s surface, for example forests, water and grasslands [7].

   Thus, land cover classification involves the discrimination of land cover types through different classification methods which were developed in the field of remote sensing [8,9]. The launch of new satellites with high spatial, spectral, temporal and radiometric resolution, and increasing knowledge in the field of information technology were the major advancement in the development of contemporary land cover classification methods. Land cover classification methods using Landsat images originated from early aerial photo interpretation methods which were common in the 1950s and 1960s [10,11]. During this period, land cover was classified based on visible image properties such as texture, color, shape and compactness [12,13]. The visual image analysis was done on printed images from which, boundaries of different land cover types were delineated and represented with different symbols. Improvements in computer software and hardware have contributed significantly to the development of image interpretation methods through the development of pattern recognition techniques [5]. The introduction of numeric-based pattern recognition algorithms was a major breakthrough in land cover classification and it is the basis of modern classification methods [5,6]. The last four decades have seen the development of land cover classification such as pixel-based, knowledge-based, object-based and many other classification algorithms highlighted in this review. Furthermore, the change in the Landsat data access policy from a commercial to a free access approach in 2008 and the advent of high performance computing capabilities have led to wider applications of these remote sensing classification methods to Landsat images [4,14–16].

  Since the launch of the first satellite, Landsat 1, in 1972, the Landsat program has launched seven other satellites, six of these satellites were successfully launched, with the objectives of maintaining continuity of the Earth’s monitoring mission and developing improvements to the sensors [4,17, 18]. The Landsat program provides four types of images (Table 1): Multispectral Scanner (MSS) by Landsat 1, 2 and 3; Thematic Mappers (TM) by Landsat 4 and 5, which also provided MSS images; Enhanced Thematic Mappers (ETM+) by Landsat 7; and Observation Land Images (OLI) provided by Landsat 8 [18]. Landsat MSS, TM, ETM+ and OLI have all been used in land cover classification using different methods of land cover classification [9,19]. In order to maintain continuity in the provision of Landsat data, Landsat 9 will be launched in 2023 with improved qualities [4].

   Research on land cover classification methods based on Landsat images has been an important topic over the past four decades, especially with the current effects of climate change [20–22]. While many review articles covered topics related to Landsat and land cover classification [9,14,15,19]; there is no review of the development of Landsat land cover classification methods. In this review, we address the major developments in land cover classification methods based on Landsat images by looking at: (1) the major trends in the development of classification methods; and (2) the methods suitable for specific land cover types. The first part of this paper (Section 2) presents the overview of the Landsat program. Section 3 focuses on the actual classification methods by reporting the developments, accuracy, strengths and limitations of these methods. Finally, we make recommendations for optimal ways to use Landsat images in land cover classification in Sections 4–6.

2. Developments of Landsat Data 

  The Landsat program has been providing images which have been applied in monitoring the surface of the Earth since 1972 [14]. In January 2015, the Landsat archive held over five million unique images [4]. While other satellites were launched to monitor the Earth’s surface in the last three decades, the Landsat program is unique in the application of land cover classification because: (1) it is the longest running uninterrupted Earth observation program; and (2) its archives are the first to offer global images free of charge [4,16].

The long archive period of Landsat images offers researchers a chance to gain insights into past trends which are important when monitoring land cover changes [4,14]. Haack [1] indicated that Landsat images are used to solve problems of having inadequate information on the quality and quantity of resources, especially in developing countries. Furthermore, studies which cover larger areas can be more costly if commercial satellite images are used. However, the free access to Landsat images offers opportunities to researchers who cannot afford commercial satellite images because of the higher prices [16,23,24]. This solves the problem of many resource constrained researchers as these images can be accessed free of charge.

  Landsat images are constantly improving due to new generations of satellites being launched with new and improved sensors [14,15]. The improvements are mainly defined by the richness in spectral, spatial, radiometric and temporal resolution [18]. Landsat MSS has a spatial resolution of 60 m while Landsat TM, ETM+ and OLI have a spatial resolutions of 30 m. Additionally, Landsat ETM+ and OLI have a panchromatic band with a spatial resolutions of 15 m which can be used to improve the spatial resolution of other bands by using a pan-sharpening technique [4]. Landsat MSS images have a radiometric resolution of 6 bits, Landsat TM has eight bits, Landsat ETM+ has nine bits, and Landsat OLI has 12 bits radiometric resolution [25,26]. With respect to spectral resolution, Landsat MSS has four bands, Landsat TM has seven bands and ETM+ has eight bands. However, the malfunction of the Scan Line Corrector (SLC) on the ETM+ sensor makes the application of ETM+ images limited [27,28]. The latest version of the Landsat images, the Landsat OLI, has 11 bands (Table 1). Current research indicates that Landsat OLI images give good results in many applications as they have good qualities [18,29,30]. Choosing the appropriate Landsat images is important; however, researchers will be faced with a few limitations because of the uniqueness of sensors at a particular time, and data gaps in the Landsat archives [4]. The data gaps have greatly reduced because of the on-going Landsat archive consolidation initiative which started in 2010 [4,31].

  Landsat data is stored by a network of ground systems located in different countries through a community of international co-operators (IC) and other stations owned by the United States Geological Survey (USGS) [4]. In the past, the ICs had a mandate of receiving and distributing the data to other users at a fee; however, an open access policy was adopted in 2008 [14,16]. Over the years, the ICs around the world collectively accumulated more data than the USGS archives. This means that the Landsat images held by USGS were limited compared with collective images held by ICs around the world. In 2008, the USGS recognized the need for consolidating their Landsat database through a Landsat Global Archive Consolidation (LGAC) initiative and this program, which started in 2010, was initially planned for six years; however, the program is still on-going [4]. The total number of Landsat images held by USGS archives before the consolidation process was reported to be over 5 million in 2015. The consolidation process identified additional 2.5 million images around the world to fill the data gaps. By 2016, more than 2.3 million unique images had been identified and were yet to be added to the USGS archives [4]. The consolidation program aimed at minimizing the data gaps and securing the global dataset by creating a database in a common format. The number of unique images available on the USGS Earth Explorer are a testament to the success of the LGAC program [31]. At the end of 2016, more than 57% of the images held by USGS were from this initiative; however, the USGS is still not a one-stop-shop and there are no indications if or when this may happen.

   Nevertheless, the consolidation program is on-going. The USGS still has small data gaps due to the Nevertheless, the consolidation program is on-going. The USGS still has small data gaps due to the challenges in converting the data collected from some of the ICs, because they are in unknown formats or not in good condition [4].

6- Best Practices for Landsat Land Cover Classification In order to obtain the best classification results from Landsat images, a number of factors such as the selection of an ideal classification method and classifier, the quality of pre-processing and the type of Landsat images being used need to be considered [18,39,144]. It is important to employ geometric and radiometric correction on the images using appropriate methods [39]. A lot of variation can be attained depending on the quality of the pre-processing calibration done on the images before classification, especially in areas with topographic variations [145,146]. Geometric correction includes orthorectification and registration of the images with ground points. Orthorectification involves correcting the errors resulting from tilting of the platform on which the sensor is mounted in order to produce a planimetrically correct image. This tilting usually results in distortion in the scale parameters of the images [145,147]. Although geometric corrections are important to Landsat land cover classification, most studies do not apply these corrections because National Aeronautics and Space Administration (NASA) provides images which are already geometrically corrected and orthorectified to a level called Landsat Level 1 (L1T) [147,148]. However, Tatem, Nayar and Hay [147] reported that in a few circumstances, Landsat images did not produce the desired results because they were not geometrically correct; therefore, it is important to check the geometric accuracy of the Landsat images before further processing. The major sources of geometric errors are insufficient ground control points for some scenes, errors in the geo-registration procedures and the level of calibration of a particular Landsat satellite sensor [149]. Most of the scenes have been corrected with sufficient ground control points; however, errors were identified on Landsat 4 and 5 for some scenes such as those from Brazil and Ecuador [150]. The geometric accuracy of L1T products has been increasing with the introduction of new Landsat satellites. For example, Landsat 8 has the highest geometric accuracy of less than 12 m, Landsat TM and ETM+ have accuracy of less than 50 m, while Landsat MSS has a geometric accuracy exceeding 50 m. Roy, et al. [151] highlighted that the Landsat 8 L1T products have high geometric accuracy because of the pushbroom design and the on-board global positioning system (GPS) which aids in geometric correction, unlike the other Landsat satellites which are/were dependent on ground control. For the purpose of land cover and time series studies, the acceptable geometric errors should be less than 12 m or less than half a pixel and this can be achieved by further georeferencing through image-to-image registration with geometrically accurate images or by using additional ground control points [149,150]. Another important pre-processing step on Landsat images is the radiometric correction, which involves the transformation of DN values into top of atmosphere and ground reflectance values [39]. The radiometric correction has two major components: (1) atmospheric correction, which deals with effects due to scattering and absorption of electromagnetic waves in the atmosphere; and (2) topographic correction, which comes because of variations on the Earth’s surface [145,152,153]. Tatem, Nayar and Hay [147] indicated that it is important to apply atmospheric correction when working with more than one scene in which training datasets are transferred to other scenes. Topographic effects are corrected by adjusting the surface reflectance by using digital elevation models (DEM) [154]. A number of radiative transfer codes, both simple and complex, have been developed for atmospheric correction and common application software for atmospheric corrections include Dark Object Subtraction (DOS) and FLAASH in ENVI and ATCOR which is implemented as a stand-alone software or incorporated in other software such PCI Geomatica [147]. In land cover classification, OBIA, which has become common in the last decade, has proven to be superior to other methods of classification [101,139,155]. OBIA produced high classification accuracies in most studies which were based on Landsat images for different land cover types; however, OBIA‘has limitations such as choosing the appropriate segmentation scale and dealing with different steps, which can be a source of variation if not properly handled [94]. The ability to use a diverse range of information such as shape, texture and compaction to compliment spectral values makes classification results from OBIA more accurate. Although OBIA has not been commonly applied on the first Landsat images, Landsat MSS, it has proved to perform better on Landsat TM, ETM+ and OLI [79,88]

SMA has proven to be very useful in complex environments such as the tropics, where the landscape is complex and mixed pixels are common (Table 2). It is worth noting that other classification methods can equally produce high classification accuracies when appropriate procedures are followed (Table 2). 7. Conclusions This review focused on the developments of Landsat land cover classification methods and determining the best ways of using Landsat images in land cover classification. Landsat land cover classification has continued to be an important application, especially with the continuous introduction of new sensors and the change in the data access policy from a commercial to a free access approach [4,151]. The new Landsat imagery has improved qualities such as high spectral, spatial and temporal resolution. The fact that Landsat images can be accessed for free for nearly any location on Earth is an added advantage. The land cover classification methods commonly applied to Landsat imagery can be broadly grouped into pixel-based, subpixel-based and object-based approaches. While methods for land cover classification have advanced over the last four decades, the maximum likelihood pixel-based classification method, which was developed in the 1970s, is the most commonly used method on Landsat images [9,29]. Pixel-based classification has limitations such as salt-and-pepper effects and challenges due to mixed-pixels, a common issue in medium resolution imagery like Landsat. The subpixel approach was developed to address the limitations of the pixel-based approach, especially the mixed pixel effects. However, effects due to spectral variability and challenges in selecting representative samples for endmembers still remain major challenges for the subpixel approach [66]. Most studies on Landsat land cover classification have reported the superior performance of OBIA in various landscapes such as urban areas [89,156], agricultural areas [79,85], forests [86,128] and wetlands [47,157]. The major advantage of OBIA is that it represents the classification units as real world objects on the ground and hence reduces the within class variability. Although OBIA has been commonly applied on fine spatial resolution images, most studies have indicated its superior performance on Landsat images because it combines different types of information in the classification procedure [89,128]. However, OBIA land cover classification has limitations such as challenges in selecting the optimal segmentation scale, which can generate errors due to over or under segmentation, and misclassification of small land cover types due to the low or medium spatial resolution of Landsat images [94,105]. The OBIA approach also involves many steps in its workflow such as selecting training samples, developing rule sets and choosing classifiers, all of which have the potential to affect the classification accuracy if not properly done [48]. The reviewed studies do not clearly indicate the best classification method for Landsat images, thus it is important to consider the strengths and limitations of each method as compared to other methods and hence most of the classification methods remain useful and have the potential to produce high levels of accuracy. The use of hybrid methods needs to be investigated further because the combination of different classifiers is complex, but from the limited literature, they appear to show promise for land cover classification using Landsat imagery. Acknowledgments: We would like to thank the anonymous reviewers for their valuable comments. Author Contributions: D.P. and J.M. contributed to conceptualizing the research. D.P. undertook the literature review. D.P. led the writing, with J.M. contributing some sections. Conflicts of Interest: As authors, we declare that there is no potential conflict of interest.

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