This summarizes a document about change detection techniques in remote sensing for analyzing land use and land cover changes. Remote sensing using aerial photographs and satellite imagery allows efficient monitoring of land cover changes compared to traditional field surveys. Change detection involves identifying transformations of land cover types over time and space using multi-temporal remote sensing data. Common techniques include comparing imagery from Landsat, QuickBird and other satellite sensors to detect changes in agriculture, deforestation, urban growth and other human and natural impacts on the earth's surface.
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
Types of Platforms
1. Airbrone Platforms
2. Spacebrone Platforms
Platforms are Vital Role in remote sensing data acquisition
Necessary to correct the position the remote sensors that collect data from the objects of interest
hyperspectral remote sensing and its geological applicationsabhijeet_banerjee
this is an introductory presentation on hyperspectral remote sensing, which essential deals with the distinguishing features, imaging spectrometers and its types, and some of the geological applications of hyperspectral remote sensing.
Types of Platforms
1. Airbrone Platforms
2. Spacebrone Platforms
Platforms are Vital Role in remote sensing data acquisition
Necessary to correct the position the remote sensors that collect data from the objects of interest
hyperspectral remote sensing and its geological applicationsabhijeet_banerjee
this is an introductory presentation on hyperspectral remote sensing, which essential deals with the distinguishing features, imaging spectrometers and its types, and some of the geological applications of hyperspectral remote sensing.
Change detection analysis in land use / land cover of Pune city using remotel...Nitin Mundhe
Lecture delivered in the National Conference entitled “Monitoring Degraded Lands” jointly organized by Agasti Arts, Commerce and Dadasaheb Rupwate Science
College, Akole and Maharashtra Bhugolshastra Parishad Pune to be held on 4 to 6 February 2014.
Application of Remote Sensing in AgricultureUTTAM KUMAR
Remote sensing has been found to be a valuable tool in evaluation, monitoring and management of land, water and crop resources. The launching of the Indian remote sensing satellite (IRS) has enhanced the capabilities for better utilization of this technology and significant progress has been made in soil and land cover mapping, land degradation studies, monitoring of waste land, assessment of crop conditions crop acreage and production estimates
Nagios Conference 2014 - Ricardo Barquero - Case Study Issue Detection Algori...Nagios
Ricardo Barquero's presentation on Case Study Issue Detection Algorithm In A Cable Company.
The presentation was given during the Nagios World Conference North America held Oct 13th - Oct 16th, 2014 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/conference
Psdot 7 change detection algorithm for visualZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS
Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Grow...Bayes Ahmed
This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.
Application of Remote Sensing Techniques for Change Detection in Land Use/ La...iosrjce
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Applied Geology and Geophysics. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Applied Geology and Geophysics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Remote sensing application in agriculture & forestry_Dr Menon A R R (The Kera...India Water Portal
This presentation by Dr A R R Menon, Emeritus scientist, CED on Remote Sensing applications in agriculture and forestry was made at at the Kerala Environment Congress, Trivandrum organised by the Centre for Environment and Development
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
Effectiveness and Capability of Remote Sensing (RS) and Geographic Informatio...nitinrane33
In this research paper, the effectiveness and capability of remote sensing (RS) and geographic information systems (GIS) are investigated as powerful tools for analyzing changes in land use and land cover (LULC), as well as for accuracy assessment. The study employs the literature of satellite imagery and GIS data to evaluate LULC changes over a period and to assess the accuracy of the analysis. Moreover, the research investigates the land use and land cover change detection analysis using RS and GIS, application of artificial intelligence (AI), and Machine Learning (ML) in LULC classification, environment and risk evaluation, stages of process LULC classification, factors affecting the LULC classification, accuracy assessment, and potential applications of RS and GIS in predicting future LULC changes and supporting decision-making processes. The findings of the study suggest that RS and GIS are highly effective and accurate for LULC analysis and assessment, with substantial potential for predicting and managing future changes in land use and land cover. The paper emphasizes the importance of utilizing RS and GIS techniques in the field of sustainable environmental management and resource planning.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Performance analysis of change detection techniques for land use land coverIJECEIAES
Remotely sensed satellite images have become essential to observe the spatial and temporal changes occurring due to either natural phenomenon or man-induced changes on the earth’s surface. Real time monitoring of this data provides useful information related to changes in extent of urbanization, environmental changes, water bodies, and forest. Through the use of remote sensing technology and geographic information system tools, it has become easier to monitor changes from past to present. In the present scenario, choosing a suitable change detection method plays a pivotal role in any remote sensing project. Previously, digital change detection was a tedious task. With the advent of machine learning techniques, it has become comparatively easier to detect changes in the digital images. The study gives a brief account of the main techniques of change detection related to land use land cover information. An effort is made to compare widely used change detection methods used to identify changes and discuss the need for development of enhanced change detection methods.
Radar and optical remote sensing data evaluation and fusion; a case study for...rsmahabir
The recent increase in the availability of spaceborne radar in different wavelengths with multiple polarisations provides new opportunities for land surface analysis. This research effort explored how different radar data, and derived texture values, indepen- dently and in combination with optical imagery influence land cover/use classification accuracies for a study site in Washington, DC, USA. Two spaceborne radar images, Radarsat-2L-band and Palsar C-band quad-polarised radar, were registered with Aster optical data for this study. Traditional methods of classification were applied to various components and combinations of this data set, and overall and class-specific thematic accuracies obtained for comparison. The results for the two despeckled radar data sets were quite different, with Radarsat-2 obtaining an overall accuracy of 59% and Palsar 77%, while that of the optical Aster was 90%. Combining the original radar and a variance texture measure increased the accuracy of Radarsat-2 to 71% but that of Palsar only to 78%. One of the sensor fusions of optical and radar obtained an accuracy of 93%. For this location, radar by itself does not obtain classification accuracies as high as optical data, but fusion with optical imagery provides better overall thematic accuracy than the optical independently, and results in some useful improvements on a class-by-class basis. For those regions with high cloud cover, quad polarisation radar can independently provide viable results but it may be wavelength-dependent.
Land Use Land Cover Change Detection of Gulbarga City Using Remote Sensing an...ijsrd.com
Land use and land cover(LULC) recently these days became a major component to handle natural resources and managing changes occurring in the environment.which is due to expansion of the urban area it has lead to critical losses of agriculture land,vegetation land and water bodies.followed by this the urban sprawl created a environmental issues. For example :decreased air quality and increase in the temperature etc. Land use and land cover change is driven by human actions and also drives changes that limit availability of products and services for human and animals, and it can undermine ecological wellbeing also. Land use and land cover is an important component in understanding various interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. Therefore, this study was aimed at understanding land use and land cover change in Gulbarga city. In this work we took Gulbarga city to study the urban expansion and LULC change that took place in 2001 and 2012 to know the changes happened in the year 2012 by comparing with data of 2001.remote sensing methodology is used in this study which provides major coverage mapping & classification of land cover features such as vegetation,soil,water,forest etc. A wide range of environmental parameters can be measured including the land use, vegetation types, surface temperatures , soil types, precipitation, phytoplankton, turbidity, surface elevation and geology.satellite images of two different years i.e 2001 and 2012 are taken in to consideration.after image processing classification is done so as to classify images in to various different land use categories.
Identification Of Ground Water Potential Zones In Tamil Nadu By Remote Sensin...IJERA Editor
A case study was conducted to find out the groundwater potential zones in Salem, Erode and Namakkal districts, Tamil Nadu, India with an aerial extent of 360.60 km2. The thematic maps such as geology, geomorphology, soil hydrological group, land use / land cover and drainage map were prepared for the study area. The Digital Elevation Model (DEM) has been generated from the 10 m interval contour lines (which is derived from SOI, Toposheet 1:25000 scale) and obtained the slope (%) of the study area. The groundwater potential zones were obtained by overlaying all the thematic maps in terms of weighted overlay methods using the spatial analysis tool in Arc GIS 9.3. During weighted overlay analysis, the ranking has been given for each individual parameter of each thematic map and weights were assigned according to the influence such as soil −25%, geomorphology − 25%, land use/land cover −25%, slope − 15%, lineament − 5% and drainage / streams − 5% and find out the potential zones in terms of good, moderate and poor zones with the area of 49.70 km2, 261.61 km2 and 46.04 km2 respectively. The potential zone wise study area was overlaid with village boundary map and the village wise groundwater potential zones with three categories such as good, moderate and poor zones were obtained. This GIS based output result was validated by conducting field survey by randomly selecting wells in different villages using GPS instruments. The coordinates of each well location were obtained by GPS and plotted in the GIS platform and it was clearly shown that the well coordinates were exactly seated with the classified zones.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
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In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
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1. Civil and Environmental Research
ISSN 2224-5790 (Paper) ISSN 2225-0514 (Online)
Vol.3, No.10, 2013
www.iiste.org
Change Detection Process and Techniques
Jwan Al-doski*, Shattri B. Mansor and Helmi Zulhaidi Mohd Shafri
Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia
43400 Serdang, Selangor, Malaysia
* E-mail of the corresponding author: Jwan-83@hotmail.com
Abstract
Land use / land cover changes studies have become very interesting over the past decades through using remote
sensing because of the availability of a suite of sensors operating at various imaging scales and scope of using
various techniques as well as considered the good ways for effective monitoring and accurate land use /land
cover changes. This paper looks into the following aspects related to the remote sensing technology, change
detection process and techniques for land cover changes, and factor affecting change detection techniques and
considerations.
Keywords: Remote Sensing, Land Use / Land Cover, Change Detection
1. Introduction
The earth's surface is changing as a result of natural phenomena or human activity, for example, wildfires,
lightning strikes, storms, pests, agro-forestry, agricultural expansion, social, economic, technological, historical
factors and urban growth and the like (Borak, Lambin & Strahler 2000). Generally, the earth’s surface changes
are divided into two categories: land use and land cover (Barnsley, Moller-Jensen & Barr 2001). Land cover
initially describes the physical state of the land surface, which includes cropland, forests, and wetlands, but it has
broadened in subsequent usage to contain human structures such as buildings, pavements and other aspects of the
natural environment, including soil type, biodiversity, surface water and groundwater (Cheng et al. 2008, Jaiswal,
Saxena & Mukherjee 1999) In contrast, land use refers to the way in which human beings exploit the land and its
resources including agriculture, urban development, grazing, logging and mining. However, land cover and land
use are often used interchangeably because the two terms are interdependent and closely related (Verburg, De
Groot & Veldkamp 2003, Verburg et al. 2009). Regardless of that, land use/land cover change (LULCC) is
defined as the transformation of the land or replacement of one land-cover type on the earth's surface (Meyer,
Turner 1992).
LULCC is the impact of several related processes operating over a wide range of scales in space and time (Foody
2002b) . Lambin, and Ehrlich 1997, suggest that there are three major causes of land use/land cover changes that
happen, with differing rates and on different scales: biophysical factors, technological and economic
considerations, and institutional and political arrangements. Besides these, the changes are resulting from
military conflicts. In order to be able to plan and implement meaningful policies and effective schemes to sustain
regional development, there is a crucial need to know the land use/land cover patterns in a particular region
(Lillesand, Keifer 1994, Lillesand, Kiefer & Chipman 2004, Lu et al. 2004) . Fortunately, there is now available
documentation of the spatiotemporal pattern of land use/cover using satellite imagery, which allows scientists the
ability to determine the causes and results of change in relation to human activity patterns (Cardille, Foley 2003),
therefore, studying changes in land use/land cover has become an important research theme in the remote
sensing spatial analysis Since the 1970s (Lo, Shipman 1990).
For extracting LULC change information, traditional methods and remote sensing technology may be used.
Traditional methods such as field surveys, map interpretation, collateral and ancillary data analysis are not
effective to acquire LULC changes because they are time consuming, date-lagged and often too expensive,
whereas the remote sensing technology method includes using aerial photographs, satellite images, spatial data
set and other data (Paradzayi et al. 2008) , and is a much more cost-effective and time-efficient means to study
LULC changes, especially over regional or national areas in comparison with the traditional methods (Nordberg,
Evertson 2003).
2. Remote Sensing Technology
The term "remote sensing," first used in the United States in the 1950s by Ms. Evelyn Pruitt of the U.S. Office of
Naval Research. Remote Sensing (RS) is now commonly used to describe the science and art of obtaining
information about an object, area, or phenomenon under investigation by a device that records the spectral
properties of surface materials on the earth from a distance (Singh 1989a, Rogan, Chen 2004). Basically, there
are two types of remote sensing instruments; passive and active. Passive instruments detect the natural energy
that is reflected or emitted from the observed scene whereas active instruments provide their own energy
(electromagnetic radiation) to illuminate the object or scene they observe.
Remote sensing from airborne and space borne platforms provide a huge amount of valuable data about our
37
2. Civil and Environmental Research
ISSN 2224-5790 (Paper) ISSN 2225-0514 (Online)
Vol.3, No.10, 2013
www.iiste.org
earth's surface including aerial photographs, satellite images, spatial data set and other data (Paradzayi et al.
2008). The increased availability of mid- to fine-resolution satellite imageries since the early 1990s, offers
repetitive data variance among themselves in terms of spatial, radiometric, spectral, temporal resolution and its
synoptic view (Stoney 2006), also the digital format makes them suitable for many computer image processing
softwares. All these have made remotely sense data the main source for various remote sensing applications (Lu
et al. 2004, Kennedy et al. 2009, Lu et al. 2010b, Lu et al. 2010a). Remote sensing satellite imagery has given
scientists a remarkable way to determine the reasons for land use/land cover changes and the resultant
consequences due to human activity (Cardille, Foley 2003).
The latest remote sensing and spatial analysis technology has become available to researchers such a powerful
equipment used to map and identify land use/land cover changes, especially in the crop rotation of agriculture
(Macleod, Congalton 1998), in deforestation assessment (Binh et al. 2005) , in yield assessment, and yield
estimation (Rao, Rao & Venkataratnam 2002), in coastal zone changes(Hongquan Xie, Yanyan Zhang & Xia Lu
2011; Kesgin, Nurlu 2009), in land degradation detection (Fadhil 2009), vegetation mapping(Müllerová 2005),
in wetland landscape changes (Wang Huiliang et al. 2009) , in urban change detection (Martínez et al. 2007) ,
and other applications.
RS presents a useful tool for understanding and managing earth resources and LULC change detection (Matinfar
et al. 2007) . Enormous efforts have been made to delineate LULC on a local scale as well as global scale by
applying different multi-temporal and multi-source remotely sensed data from both airborne and space borne
sensors. Medium resolution satellite imagery such as Landsat satellite data, are the most widely used data types
of monitoring and mapping land cover changes(Williams, Goward & Arvidson 2006) . They have been
successfully utilized for monitoring LULC changes especially in the land that has been affected by human
activity to various degrees, for example, Lu Junfeng et al. (2011) used Landsat Multi-Spectral System (MSS),
Landsat TM and ETM+ remote-sensing data for land cover changes. Fan, Weng and Wang ( 2007), used TM and
ETM+ images for detecting and predicting land use and land cover in the Core corridor of Pearl River Delta
(China). Zaki, Abotalib (2011) applied Landsat images (TM) for detecting land cover changes in Northeast Cairo,
Egypt. Not only Landsat data have shown the ability to detect LULC changes but data from other sensors are
equally useful in monitoring LULC changes, for instance, Chavula, and Bauer (2011) showed the radar and
Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectro-radiometer
(MODIS) sensor data is able to detect LULC in the Lake Malawi Drainage Basin, while, Zhang and Zhu (2011)
employed Quick Bird remote sensing data for land cover changes. A study by Perea, and Aguilera (2009) utilized
the digital aerial images to produce thematic maps for detecting changes. Huiran and Wenjie (2008), showed in
their study that bi-temporal hyper spectral of Compact High Resolution Imaging Spectrometer (CHRIS/PRBOA)
satellite images are good for identifying LULC changes.
Moreover, several researchers worked with combining different sensor data in monitoring LULC changes,
among them, Wen (2011), who used Landsat Multi-Spectral System (MSS) and Quick Bird data to derive land
cover information and changes in Guam, USA, while Geymen and Baz (2008) used Landsat TM and Landsat
Geo-Cover LC satellite images for detecting land cover changes on the Istanbul metropolitan area. Zoran and
Anderson (2006) used multi-temporal and multi-spectral satellite data Landsat MSS, TM, ETM, SAR ERS,
ASTER, and MODIS for change detection analysis of the Romanian Black Sea. However, some of these studies
show the ability of multi-temporal and multi-source data from sensors with different spatial, temporal and
spectral resolutions that can be used for global LULC changes while others cannot provide regional land cover
maps because of their spatial resolution. Still, medium resolution satellite imagery such as Landsat is ideal for
monitoring regional land cover changes (Franklin, Wulder 2002).
3. Change Detection
Detecting and analyzing LULCC over large geographic areas as well as over regional areas have been
highlighted both in a manner of discrete long-time span and in sequential time series with high temporal
resolution remote sensing satellites through a process commonly called ‘change detection’ (Coppin, Bauer 1996).
Change detection has been defined as a “process of identifying differences in the state of an object or
phenomenon by observing it in different times” (Singh 1989b). This is considered an important process in
monitoring LULCC because it provides quantitative analysis of the spatial distribution of the population of
interest and this makes LULC study a topic of interest in remote sensing applications (Song et al. 2001, Gallego
2004). Using remotely-sensed data to detect LULC changes, six main steps are important as mentioned by
Jensen in 2005 (see figure 1).
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Figur1. Major Change Detection Procedure Steps
4. Change Detection Techniques
Since launching the first of the Landsat satellite system in 1972, a wide range of data have been provided
(Williams, Goward & Arvidson 2006). The availability of a large archive of data leads to the development and
2006).
evaluation of many digital change detection techniques and methods for analyzing and detecting LULC changes
(Dewidar 2004). Various methods have been extensively reviewed and provided with excellent descriptions and
.
comprehensive summaries ( Singh 1989a, Xiubin 1996, Lu et al. 2004, Corresponding et al. 2004) Basically,
2004).
there are two categories of change detection methods: pre
pre-classification and post- classification change detection
techniques (Lu et al. 2004).
4.1 Pre-classification Techniques
classification
The pre-classification techniques, also known as binary change or non change information detecting techniques,
classification
non-change
include various techniques that directly use the multiple dates of satellite imagery to generate ‘‘change’’ vs. ‘‘no
‘‘nochange’’ maps(see table 1). Many pre classification techniques have been used and compared to assess and
pre-classification
identify LULCC changes such as, Image Differencing (ID) (Hayes, Sader 2001), Imp
, Improved Change-Vector
Analysis (Green, Kempka & Lackey 1994) Band Image Differencing (Chavez, MacKinnon 1994) RGB-NDVI
1994),
1994),
Change Detection Method(Wen, Yang 2009, Geun
(Wen,
Geun-Won Yoon, Young Bo Yun & Jong-Hyun Park 2003, Johnson,
Hyun
Kasischke 1998), Spectral Change Vector Analysis (Wen, Yang 2009), Principal Component D
,
,
Differencing (PCD),
Change Vector Analysis (CVA)(Chen et al. 2003) and others.
(Chen
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Table1. Summary of Different Pre-Classification Change Detection Methods
Code
Definition
ID
Image differencing is the subtraction of two spatially registered imageries,
pixel by pixel. The pixels of changed area are expected to be distributed in the
two tails of the histogram of the resultant image, and the unchanged area is
grouped around zero. This simple method easily interprets the resultant image;
however, it is crucial to properly define the thresholds to detect the change
from non-change areas.
MID
Different image bands have their own reflectance characteristics of each landModified Image
cover type. The image differencing results between images bands of two dates
Differencing
have varying capabilities in identifying land cover changes. Thus, the majority
rule is used in this paper. First, the image differencing was done for each
band, i.e. ID (TMi) = TMi (t1)–TMi (t2). Thresholds were determined to
identify the land cover change and to provide a binary image for each band, 1
as change and 0 as non-change. Then, the binary images were developed to
provide a new summarized image. For a TM image (six bands), if the value of
a pixel is greater than or equal to 4, then the pixel belongs to change class;
otherwise, it belongs to unchanged class based on the majority rule
PCD
PCA is often as accepted as an effective transforms to derive information and
Principal
compress dimensions. Most of the information is focused on the first two
Component
components. Particularly, the first component has the most information. The
Differencing
difference of the first PCA component of two dates has the potential to
improve the change detection results, i.e. PCD= PC1 (t1)–PC1 (t2). The
change detection is implemented based on thresholds.
MPCA
Two dates of image data are superimposed and treated as a single dataset.
Multi-temporal
PCA is implemented on the stacked dataset. The main component images
PCA
often contain the overall radiation difference that reflects different land cover
types. The minor component images contain land cover changes between the
different dates. Normally, the third and fourth components are employed to
analyze the land cover change. However, it is often not easy to determine the
change areas in the absence of a careful and complete examination of the
resultant image and field data or in combination with visual interpretation of
the multi-date composite image.
IDPCA
Similar to the multi-temporal PCA. The only difference between them is to
The combination
change the single image to resultant image from image differencing. The
of
difficulty is to determine which component images indicate the main land
ID and PCA
cover changes. It is necessary to examine thoroughly the components and
multi-date composite image to determine which component can give the best
change information
IR
Ratioing is also a simple and rapid means to identify changed areas. It implies
Image Ratioing
calculation of the ratio of two registered images from different dates, on a
band-by-band basis. In the changed areas, the ratio values will be significantly
greater than 1 or less than 1 depending on the nature of the changes between
two dates of the images. Ratioing has received criticism due to the non-normal
histogram distribution of the resultant image.
MIR
Similar method as modified image differencing. The only difference between
Modified Image
them is the replacement of the differencing images with ratioed images.
Ratioing
IRPCA
Same method as the IDPCA. The only difference between them is
The combination
replacement the differencing images with ratioed images before
of
implementation of PCA.
IR and PCA
CVA
CVA generates two outputs: a change vector image and a magnitude image.
Change Vector
The spectral change vector explains the direction and magnitude of change
Analysis
from the first to the second date. The total change extent per pixel is
calculated by determining the Euclidean distance between end points through
dimensional change space of CVA is its ability to process any number of
spectral bands required and to obtain detailed information on any detected
change,
Source: Lu et al. (2005)
Method
Image
Differencing ID
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The basic premise in these techniques is measuring the nature of changes, which means changes in the features
of interest that will result changes in radiance or reflectance values (Lu et al. 2004). Most of the preclassification techniques are identified as the most accurate change detection techniques because, they are
straight forward, effective for identifying and locating change and are easy to implement (Sunar 1998). However,
three aspects are critical for pre-classification techniques: selecting suitable thresholds or vegetation index to
identify the changed areas, being sensitive to mis-registration of pixels and they cannot provide details of the
nature of change or provide a matrix of change information (Lu et al. 2004).
4.2 Post-Classification Comparison Technique
The post-classification comparison has been proven to be the most popular approach in change detection analysis
(Foody 2002b). This approach is based on rectification of more than one classified image; where it involves the
classification of each of the images independently, then the thematic maps are generated, followed by a
comparison of the corresponding labels or themes to identify areas where change has occurred (see figure 2).
There are several advantages to this technique: it minimizes sensor, atmospheric, and environmental differences
because data from two dates are separately classified, thereby minimizing the problem of normalizing for
atmospheric and sensor differences between two dates and it provides a complete matrix of land cover change
when using multiple images (Lu et al. 2004, Jensen 2005, Naumann, Siegmund 2004, Teng et al. 2008). A series
of “from-to” matrixes can be built by comparing on a pixel by pixel basis, and these matrices include pixel
conversion matrix, percentages conversion matrix, and area conversion matrix. However, results derived from
this method are only as accurate as the individual classification images themselves (Jensen 2005, Corresponding
et al. 2004, Civco et al. 2002). Furthermore, this method could lead to wrong results especially when using
multi-date or multi-sensor images due to the differences in the radiometric characteristics of the images from
which thematic maps were obtained (Foody 2002b, Foody 2002a)
Figure2. Diagram of Post-classification Comparison Change Detection
The post-classification comparison approach used by many researchers such as (Diallo, Hu & Wen 2009,
Bayarsaikhan et al. 2009 ,Shalaby, Tateishi 2007, Muttitanon, Tripathi 2005 Torahi, Rai 2011) employed postclassification change detection techniques based on Maximum Likelihood Supervised Classification to detect
land use/land cover change detection and concluded that it has achieved high overall accuracy for a variety of
data. Dewidar, (2004) applied Maximum Likelihood Supervised Classification and Post-classification Change
Detection Techniques to Landsat images for mapping and monitoring land cover and land use changes in the
North-western coastal zone of Egypt. Sun, Ma and Wang (2009) utilized the post-classification comparison
approach method base on maximum likelihood algorithm to determine land use changes in Datong basin, China
using multi-temporal Landsat data. Similarly, Fan, Weng and Wang(2007)Maximum Likelihood (ML) procedure
and combined post-classification images of Landsat TM and ETM+ classification and socioeconomic data used
in an effective way to research land use land cover change dynamically.Recently, post-classification has been
employed in different areas around the world based on the use of the new classification algorithms for different
purposes so as to quantify land cover change, improve spectral classification, reduce the classification error
propagation and improve the land use and land cover change classification accuracy.
5. Factor Effecting Change Detection Techniques and Considerations
It is not an easy to determine the most appropriate method of detecting changes in a particular area under study.
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This is because of the varying nature of the physical characteristics of the features of interest, problems with
image registration, cloud/haze detection, sensor anisotropy or hysteresis, advantages and disadvantages of
change detection methods themselves and lack of knowledge about approach (Macleod, Congalton 1998, Nielsen,
Conradsen & Simpson 1998). Generally speaking, to select a suitable method of detecting change is very
significant because there is no single method that is can be efficiently applied to all study areas. Selection of an
appropriate change-detection technique, in practice, often depends on the nature of the change detection problem
under investigation, which considers a critical step in change detection studies, the requirement of information,
application , the data sets availability and quality, time and cost constraints of the data sets, analysis skill and
experience, and registration of multiple image data sets (Macleod, Congalton 1998, Johnson, Kasischke 1998,
Nielsen, Conradsen & Simpson 1998, Cracknell 1998, Dai, Khorram 1998)
Regardless of the technique used, the success of change detection from imagery can be affected by many factors:
the quality of image registration between multi-temporal images, the atmospheric conditions, acquisition times,
illumination, viewing angles, soil moisture, noise , shadow present in the images (Singh 1989a), vegetation
phenological variability or differences (Lu et al. 2002, Rogan, Franklin & Roberts 2002); sensor calibration
(Lillesand, Keifer 1994) in addition to the landscape and topography characteristics of the study areas, analyst’s
skill and experience, selection of the change detection technique, besides, the different steps during the
implementation of change detection procedure that can produce problems and errors and affect the success of
change detection, for example, image pre-processing (Lu et al. 2004, Jensen 2005).
6. Conclusion
One of the most important uses of remote sensing is the production of Land Use and Land Cover maps. Land
Use: refers to the purpose the land surveys, for example, recreation, wildlife habitat, or agriculture, urban
development, and most areas impacted by human activity. Knowledge of land use helps us to develop strategies
to balance conservation, conflicting uses, and developmental pressures. Some of the issues which are of concern
include the removal or disturbance of productive land, urban encroachment, and depletion of forests. Land Cover:
refers to the surface cover whether vegetation, water, bare soil, urban development or other. Identifying,
delineating and mapping land cover are important for global monitoring studies, resource management, and
planning activities
Detecting and analyzing LULC changes by using remote sensing satellites can be done through a process
commonly called ‘change detection’. Since launching the first of the Landsat satellite system in 1972, a wide
range of data has been provided thus lead to the development and evaluation of many digital change detection
techniques for analyzing and detecting LULC changes and can be divided basically into two categories: preclassification and post- classification change detection techniques. Most of the pre-classification techniques are
identified as the most accurate change detection techniques because, they are straight forward, effective for
identifying and locating change and are easy to implement. While the post-classification comparison technique
has been proven to be the most popular approach in change detection analysis because of there are several
advantages to this technique: it minimizes sensor, atmospheric, and environmental differences and it provides a
complete matrix of land cover change when using multiple images. For many researchers in the field of remote
sensing is not an easy to determine the most appropriate method of detecting LULC changes in a particular area
under study because of the varying nature of the physical characteristics of the features of interest, problems with
image registration, cloud/haze detection, sensor anisotropy or hysteresis, advantages and disadvantages of
change detection methods themselves and lack of knowledge about approach.
In summary there are many considerations that considered very important for most of researcher in the remote
sensing field which are considerations about remote sensing systems and environmental characteristics must be
satisfied. These include geometric and registration of multi-temporal images, radiometric and atmospheric
calibration or normalization between multi-temporal images, and similar phonological states between multitemporal images. There is also the need to consider the selection of the same spatial and spectral resolution
images if possible as well as understanding the major steps of image processing in order to reduce errors or
uncertainties in each step.
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