scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

TL;DR: Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment as mentioned in this paper, and Earth observation (EO) technology provides an in...
Abstract: Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology provides an in...

Summary (1 min read)

Introduction

  • 1 The aim of the present review work is to provide all-inclusive critical reflection on the state of 30 the art in the use of EO technology in LULC mapping and change detection.
  • The emphasis is placed 31 on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of 32 satellite data and classification approaches employed in land cover classification.
  • The review 33 concludes providing recommendations and remarks on what should be done in order to overcome 34 hurdle faced using above-mentioned problems in LULC mapping.
  • This also provides information on 35 using classifier algorithms depending upon the data types and dependent on the regional ecosystems.

1.1 Introductory concepts 46

  • Land Use/Land Cover (LULC) and its changes has been considered as one of the factor of global 47 environmental change (Erdogan et al. 2015).
  • Land cover demonstrates the terrain features 50 on the Earth surface whereas land use reflects the utilization of available land by the human beings i.e. 51 built environment/human use of terrains (Fisher et al. 2005; Hansen and Loveland 2012).
  • LULC change has been perceived as a key driver of 58 worldwide environmental change by affecting the land surface (Petropoulos et al. 2013).
  • The use of remotely sensed dataset depends upon the user's need, 109 requirement, and type of assessment of the landscape.
  • 127 In overall, remotely sensed data are sharing the stages for LULC change and pattern analysis 128 according to need and availability.

7 LISS- III IRS- 1C

  • The conventional methods like ground truthing, surveying, etc., that employ field surveys and on site 150 human-made observations, are generally reliable methods of mapping, however, they are considered 151 as time consuming and expensive methods (Koutsias et al.
  • Each remote sensing data deliver complementary and 364 additional information in term of spatial, spectral or textural, hence LULC classification and mapping 365 can exploit the combination of the two or more information types to deliver the enhanced precision 366 mapping results, using fusion techniques.
  • Thus, employment of spatial-spectral information has resulted to more accurate 497 and reliable classification results as compared to their individual use.
  • 777 ISPRS Journal of Photogrammetry and Remote Sensing.
  • Land-use and land-cover analysis with remote sensing images.

Did you find this useful? Give us your feedback

Content maybe subject to copyright    Report

THIS IS A POST
PRINT
Published in journal
Geocarto International

1
Land Use/Land Cover in view of Earth Observation: Data Sources, Input 1
Dimensions and Classifiers -a Review of the State of the Art 2
3
Prem Chandra Pandey
1,*
, Nikos Koutsias
2
, George P. Petropoulos
3,4
, Prashant K. 4
Srivastava
5
, Eyal Ben Dor
6
5
6
1
Center for Environmental Sciences and Engineering, School of Natural Sciences, Shiv Nadar 7
University, Greater Noida Dadri, Gautam Buddha Nagar Uttar Pradesh, India-201314; 8
prem26bit@gmail.com; prem.pandey@snu.edu.in 9
2
Department of Environmental and Natural Resources Management, University of Patras, G. Seferi 2, 10
GR-30100, Agrinio, Greece; nkoutsia@upatras.gr 11
3
School of Mineral & Resources Engineering, Technical University of Crete, Crete, Greece 12
4
Department of Soil & Water Resources, Institute of Industrial & Forage Crops, Hellenic Agricultural 13
Organization “Demeter”, Larisa, Greece 14
Email: petropoulos.george@gmail.com 15
5
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India-16
221005;, email: prashant.iesd@bhu.ac.in 17
6
Department of Geography, School of Geo-Sciences, Faculty of Exact Sciences, Tel Aviv University, 18
Israel- 6997801; bendor@post.tau.ac.il bendor@tauex.tau.ac.il 19
20
* Corresponding author: Email: prem26bit@gmail.com 21
22
ABSTRACT: Land use/Land cover (LULC) is a fundamental concept of the Earth's system 23
intimately connected to many phases of the human and physical environment. Earth Observation (EO) 24
technology provides an informative source of data covering the entire globe in a spatial and spectral 25
resolution appropriate to better and easier classify land cover than traditional or conventional 26
methods. The use of high spatial and spectral resolution imagery from EO sensors has increased 27
remarkably over the past decades, as more and more platforms are placed in orbit and new 28
applications emerge in different disciplines. 29
The aim of the present review work is to provide all-inclusive critical reflection on the state of 30
the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed 31
on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of 32
satellite data and classification approaches employed in land cover classification. The review 33
concludes providing recommendations and remarks on what should be done in order to overcome 34
hurdle faced using above-mentioned problems in LULC mapping. This also provides information on 35
using classifier algorithms depending upon the data types and dependent on the regional ecosystems. 36
One of the main messages of our review is that in future, there will be a need to assemble 37
techniques specifically used in LULC with their merit and demerits that will enable more 38
comprehensive understanding at regional or global scale and improve understanding between different 39
land cover relationship and variability among them and these remains to be seen. 40
41
Keywords: LULC mapping; Landsat; Hyperspectral; spatial-spectral dimensions; 42
Multi-temporal; multi-source 43
44

2
1 Introduction 45
1.1 Introductory concepts 46
Land Use/Land Cover (LULC) and its changes has been considered as one of the factor of global 47
environmental change (Erdogan et al. 2015). Accurate identification and monitoring of LULC is 48
important for land resource management, since LULC mapping constitutes an important part of the 49
land management system (Chatziantoniou et al. 2017). Land cover demonstrates the terrain features 50
on the Earth surface whereas land use reflects the utilization of available land by the human beings i.e. 51
built environment/human use of terrains (Fisher et al. 2005; Hansen and Loveland 2012). Accurate 52
knowledge of LULC provides critical information for planning and management activities (Elatawneh 53
2015). This is attributed to the fact that land is one of the most important natural resource of the earth 54
system contributing to life and various other development activities (Whyte et al. 2018). 55
LULC information and its spatial distribution patterns are essential for a wide spectrum of research 56
themes especially urban studies characterized by heterogeneous classes and for maintenance and 57
developmental plans (Stefanov et al. 2001). LULC change has been perceived as a key driver of 58
worldwide environmental change by affecting the land surface (Petropoulos et al. 2013). Being in 59
steady change, urban perimeter, river basins, wetlands, agricultural areas are constantly subjected to 60
LULC changes, particularly by decreasing forest cover to give a path for agricultural extension, 61
urbanization, industrialization and so on (Stamou et al. 2016). Land cover in urban environments is 62
changing rapidly and conversion from agricultural/fallow to concrete forest resulting in urban sprawl 63
(Pandey et al. 2012), hence play a key role in environment changes (Vargo et al. 2013). 64
The assessment of LULC and of its change is important for understanding several environmental 65
issues related to urban as well as to surrounding landscapes. The primary impact on many other 66
processes need to be assessed, such as utilisation of land cover, surface temperature variation due to 67
concrete forest, (Rani et al. 2018), habitat fragmentation, biodiversity loss (Trisurat et al. 2010; 68
Theobald et al. 2011), soil and land degradation (Zucca et al. 2010; Bajocco et al. 2012; Pandey et al. 69
2013), decreased air quality, waste disposal problem (Pandey et al. 2012), decreased water seepage, 70
increased runoff along with subsequent flooding/flash flood, water quality deterioration (Tu 2011; 71
Uriarte et al. 2011), and decreased agricultural productivity. An improved understanding of historical 72
LULC change patterns provides a better means to understand the present and project future trends of 73
LULC change using different remote sensing (RS) datasets at multiple spatial, spectral and temporal 74
resolutions (Pocewicz et al. 2008). One of the key concerns about LULC and its impact has emerged 75
on a global stage due to the realisation that changes occurring on the land surface also influence 76
climate (Mahmood et al. 2014), ecosystem and its services and in return reduces biotic diversity 77
(Dezso et al. 2005). As a result, the requirements for mapping and monitoring LULC at multiple 78
scales are well-suited with demands associated to the EU habitats Directive (Petropoulos et al. 2013; 79
Singh Priyadarshini et al. 2017). 80
Nowadays remote sensing is the primary sources used extensively for LULC analysis in the recent 81
decades. Remote sensing often combined with Geographic Information System (GIS) has been used 82
extensively in mapping LULC in the analysis of their dynamics (Zucca et al. 2010). Several research 83
works were carried out by considering the importance of LULC changes at multiple scales, for spatio-84
temporal change patterns and identification of composition and its rate among different study sites 85
(Gessner et al. 2009; Chen G et al. 2012; Modica et al. 2012; Sharma et al. 2012; Grecchi et al. 2014). 86
The purpose of this review is to present LULC classification using multi-sensors, multi-source, multi-87
temporal datasets, input dimension and use of classifiers, and present the standard on improving the 88

3
change analysis, depending upon the user needs and requirements according to the landscape or data 89
availability. Figure 1 represents the user inputs, input dimension and classifier algorithms for LULC 90
mapping. The use of more than one data attributes helps in enhancing the results, such as high spatial 91
resolution, high spectral resolution, providing high temporal resolution to study change patterns at a 92
regular interval and may contribute a large coverage of the landscape. 93
94
95
Figure 1 An illustration showing types of datasets, spatio-spectral-temporal dimension and classifiers 96
algorithms for LULC and dynamic changes (author generated figure). 97
98
Thus, the objective of this review is two-fold: first to highlight various aspects of LULC classification 99
using multi-sensor, multi-source, multi-temporal datasets, input requirements and use of classifiers 100
and second to present the standard on improving the change analysis, depending upon the user needs 101
and requirements according to the landscape or data availability. In this background, the importance 102
of input dimension, remotely sensed datasets, as well as algorithms is discussed which is certainly 103
dependent upon how they are being utilised during LULC assessment. 104
105
1.2 EO datasets: Multispectral, Hyperspectral, LiDAR, SAR 106
Remote sensing has emerged as very powerful technology providing accurate spatial information and 107
LULC distribution in the temporal period (Bora and Goswami 2016; Gidey et al. 2017; Rani et al. 108
2018; Kabisch et al. 2019). The use of remotely sensed dataset depends upon the user's need, 109

4
requirement, and type of assessment of the landscape. While other factors such as regional coverage 110
(either large or small-MODIS, MISR), spatial and spectral (AVIRIS, ASTER, AVHRR), high spatial-111
spectral resolution (AISA -airborne hyperspectral images), temporal coverage (LANDSAT TM, MSS, 112
ETM+) and Synthetic Aperture Radar (SAR) data (to counter cloud effects) play an important role in 113
choosing the particular data for a specific type of study (See Figure 2). 114
LULC change patterns and dynamic changes have been presented with conventional methods, 115
individual remote sensing data, multi-sensor, multi-source, multi-sensor-temporal data are widely 116
used for assessment and evaluation of LULC change and patterns of the landscape (Figure 2). More 117
recently the synergy between different Earth Observation (EO) datasets in obtaining LULC mapping 118
has been examined. The motivation behind the synergy of different datasets is to harness the different 119
properties such as spatial, spectral, topographic, texture for improving the accuracy of land cover 120
mapping and temporal for improving the change dynamics. Therefore, user needs and requirements 121
play an important role in the selection of types of remotely sensed datasets, input dimension, and 122
implementing classifiers. With the advancement of EO technology, the broad spectral resolution 123
was replaced with high spectral resolution and filled the gap in limitation of multispectral imaging 124
(Heiden et al. 2007). EO datasets classification along with a range of classification approach varies 125
with the complexity of study site, the content and details of the classification scheme, spatial/spectral 126
resolution of datasets, and thus remains a challenge in the remote sensing community. 127
In overall, remotely sensed data are sharing the stages for LULC change and pattern analysis 128
according to need and availability. The implementation of different remotely sensed data is according 129
to the users' needs and the requirement for large area coverage, high spatial resolution, spectral 130
resolution, temporal resolution or combination of one or more together. 131
132
133
Figure 2: Conceptual model to demonstrate the user needs and requirements revolve around the basic 134
characteristics/properties of remotely sensed data and their combinations (author generated figure). 135

Citations
More filters
01 Jan 2016
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading remote sensing and image interpretation. As you may know, people have look hundreds times for their favorite novels like this remote sensing and image interpretation, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. remote sensing and image interpretation is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the remote sensing and image interpretation is universally compatible with any devices to read.

1,802 citations

01 Jan 2012

208 citations

Journal ArticleDOI
TL;DR: This systematic review presents trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection, and highlights the possibility of using medium-resolution time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles.
Abstract: Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

118 citations

Journal ArticleDOI
01 Jun 2021
TL;DR: The techniques for land classification and development stages that began from 1950 and continuously getting upgraded till now are reported, to assist researchers to select the efficient classifier for their study as well as to motivate them to develop new classifiers with appropriate accuracy.
Abstract: This article demonstrations the techniques for land classification and development stages that began in 1950 and till now It highlights the findings of the research efforts from 220 studies that worked in this domain The land classification was manual till classification processes evolved into numerical and digital with the emergence of technology and the revolution in Artificial Intelligence algorithms It included an inventory of all the methods traditional and recent used in land classification Most land use and land cover classification classifiers have been comparing to determine the best classifiers and the characteristics of each to determine points that will help develop classification accuracy This article will be significant for the upcoming researchers to understand the land classification and various techniques It will help determine the efficient classifier and motivates to development of new classifiers

45 citations

Book ChapterDOI
01 Jan 2020
TL;DR: This chapter describes the latest developments in remote sensing for precision agriculture with particular emphasis placed on the use of hyperspectral sensors and includes information about HRS sensors and also includes a discussion on the advancement and challenges of spaceborne satellites faced during agriculture monitoring.
Abstract: The rapid development of remote sensing has made it possible to study environmental processes and changes in agriculture and also to provide important assistance in relevant practices, even operationally. This chapter describes the latest developments in remote sensing for precision agriculture with particular emphasis placed on the use of hyperspectral sensors. This chapter provides practical information regarding the identification of research challenges, limitations, and advantages of different platforms and sensors for precision agriculture. Hyperspectral remote sensing (HRS) is more effective as compared to multispectral remote sensing because it records radiation in narrow contiguous spectral channels reflected from any feature or target. More accurate spectral information retrieved using HRS can be combined with other techniques to retrieve useful information for precision agriculture. The chapter includes information about HRS sensors and also includes a discussion on the advancement and challenges of spaceborne satellites faced during agriculture monitoring. It concludes with summarizing the hurdles faced during agriculture research using hyperspectral data discussing possible pathways in which relevant research should be directed.

39 citations

References
More filters
Journal ArticleDOI
01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

79,257 citations


"Land use/land cover in view of eart..." refers methods in this paper

  • ...In order to exploit the properties of individual data such as texture, backscattering amplitudes, Breiman (2001) employed an unsupervised classification algorithm for mapping purposes....

    [...]

Journal ArticleDOI
15 Nov 2013-Science
TL;DR: Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally, and boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms.
Abstract: Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

7,890 citations


"Land use/land cover in view of eart..." refers background or methods in this paper

  • ...Therefore, mapping methods have generally exploited the properties of optical-multispectral (for example spatial), hyperspectral (spectral) as well as radar (for example texture) remote sensing for land use analysis and classification using different algorithms (Miettinen and Liew 2011; Hansen et al. 2013; Jin et al. 2014; G omez et al. 2016)....

    [...]

  • ...…the properties of optical-multispectral (for example spatial), hyperspectral (spectral) as well as radar (for example texture) remote sensing for land use analysis and classification using different algorithms (Miettinen and Liew 2011; Hansen et al. 2013; Jin et al. 2014; G omez et al. 2016)....

    [...]

OtherDOI
01 Jan 1976
TL;DR: The framework of a national land use and land cover classification system is presented for use with remote sensor data and uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources.
Abstract: The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The pro-posed system uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in US Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.

4,154 citations

Journal ArticleDOI
TL;DR: This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way.
Abstract: Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

3,809 citations


"Land use/land cover in view of eart..." refers methods in this paper

  • ...Thus, object-based techniques become popular and perform better that pixel-based when classifying LULC especially when high spatial resolution data are used (Blaschke 2010)....

    [...]

Journal ArticleDOI
TL;DR: This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature and summarizes and reviews these techniques.
Abstract: Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

2,785 citations


"Land use/land cover in view of eart..." refers background in this paper

  • ...(See Zhu (2017) for a comprehensive review on change detection and algorithms using Landsat time series data and Lu et al. (2004) and Jianya et al. (2008) for comparative study on change detection methods.)...

    [...]

Frequently Asked Questions (18)
Q1. What are the contributions in this paper?

The aim of the present review work is to provide all-inclusive critical reflection on the state of 30 the art in the use of EO technology in LULC mapping and change detection. This also provides information on 35 using classifier algorithms depending upon the data types and dependent on the regional ecosystems. 

The techniques used in 389 above study include image object segmentation and rule based techniques which harness the spectral 390 and spatial attributes of the LiDAR datasets. 

The motivation behind the synergy of different datasets is to harness the different 119 properties such as spatial, spectral, topographic, texture for improving the accuracy of land cover 120 mapping and temporal for improving the change dynamics. 

To overcome the low spectral and spatial resolution, hyperspectral imaging systems have been 260 developed that can detect subtle changes in the spectral ranges, and thus discriminate between 261 vegetation types, crops and other features during LULC classification (Pandey et al. 2018). 

Other techniques such as Principal Component Analysis (PCA), Spectral 560 Mixture Analysis (SMA), Minimum Noise Fraction transformation (MNF), Linear Spectral Unmixing 561 (LSU) Matched filtering techniques (Braswell et al., 2003), have been also applied to reduce the data 562 dimensionality especially of big datasets (either space-borne or air-borne hyperspectral images) for 563 LULC mapping. 

With 194 the recent advancement in the space-borne missions, advanced remote sensing imageries with higher 195 spatial resolution are used that achieve higher accuracies nowadays. 

Data fusion 425 enhances the information and the composite images are visually more interpretable and better for 426 being used for LULC mapping and achieve higher accuracy than individual data. 

Unsupervised change detection in VHR remote 987 sensing imagery–an object-based clustering approach in a dynamic urban environment. 

Bartels and Wei (2006) performed LiDAR based maximum 386 likelihood classifications fused with co-registered spectral bands achieving accurate results. 

In order to 374 exploit the properties of individual data such as texture, backscattering amplitudes, Breiman (2001) 375 employed an unsupervised classification algorithm for mapping purposes. 

multi-sensors and multi-source 400 remotely sensed images require downscaling process to match the spatial resolution between the all 401 employed images. 

the use of advanced fuzzy approach helps to generate 550 meaningful crisp image objects using segmentation techniques (Kindu et al. 2013). 

the use 284 of hyperspectral images has overcome the inability of multispectral images to differentiate the 285 different types within same features (crop types, plant types), and therefore, hyperspectral images 286 have been in use for mapping and change analysis though it is expensive in case of airborne images. 

Adar et al. 255 (2014) utilised multispectral and hyperspectral images (HyMap) acquired at two or more different 256 times to detect spatial, spectral and temporal changes. 

Reducing landscape heterogeneity for improved land 939 use and land cover (LULC) classification across the large and complex Ethiopian highlands. 

Improved classification accuracy based on the output-level fusion of high-resolution 951 satellite images and airborne LiDAR data in urban area. 

The implementation of different remotely sensed data is according 129 to the users' needs and the requirement for large area coverage, high spatial resolution, spectral 130 resolution, temporal resolution or combination of one or more together. 

114LULC change patterns and dynamic changes have been presented with conventional methods, 115 individual remote sensing data, multi-sensor, multi-source, multi-sensor-temporal data are widely 116 used for assessment and evaluation of LULC change and patterns of the landscape (Figure 2).