An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.
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.
Geoinformatics is the science and the technology which develops and uses information science infrastructure to address the problems of geography, cartography, geosciences and related branches of science and engineering.
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
In this presentation I show my exploration into the use of a Drone, or Small Unmanned Aerial System (sUAS), to perform remote sensing. At a high-level I introduce the concepts to leveraging this platform to generate spatial data for use in a variety of disciplines. I generally discuss the process of collecting and processing data, displace the data generated, discuss a few advantages, and give my perspective on where this platform can be utilized.
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.
Geoinformatics is the science and the technology which develops and uses information science infrastructure to address the problems of geography, cartography, geosciences and related branches of science and engineering.
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
In this presentation I show my exploration into the use of a Drone, or Small Unmanned Aerial System (sUAS), to perform remote sensing. At a high-level I introduce the concepts to leveraging this platform to generate spatial data for use in a variety of disciplines. I generally discuss the process of collecting and processing data, displace the data generated, discuss a few advantages, and give my perspective on where this platform can be utilized.
APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURELagnajeetRoy
India is a country that depends on agriculture. Today in this era of technological supremacy, agriculture is also using different new technologies like some robotic machinery to remote sensing and Geographical Information System (GIS) for the betterment of agriculture. It is easy to get the information about that area where human cannot check the condition everyday and help in gathering the data with the help of remote sensing. Whereas GIS helps in preparation of map that shows an accurate representation of data we get through remote sensing. From disease estimation to stress factor due to water, from ground water quality index to acreage estimation in various way agriculture is being profited by the application of remote sensing and GIS in agriculture. The applications of those software or techniques are very new to the agriculture domain still much more exploration is needed in this part. New software’s are developing in different parts of the world and remote sensing. Today farmers understand the beneficiaries of these kinds of techniques to the farm field which help in increasing productivity that will help future generation as technology is hype in traditional system of farming.
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data. The acronym GIS is sometimes used for geographical information science or geospatial information studies to refer to the academic discipline or career of working with geographic information systems and is a large domain within the broader academic discipline of Geoinformatics. In the simplest terms, GIS is the merging of cartography, statistical analysis, and computer science technology.
A Spatial Decision Support System (SDSS) for Understanding and Reducing Long-...Global Risk Forum GRFDavos
6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland
Geographic Information Systems (GIS) play a pivotal role in military operations. The concept of Command, Control, Communication and Coordination in military operations is largely dependent on the availability of accurate, spatial information to arrive at quick decisions for operational orders.
In the present digital era, GIS is an excellent tool for military commanders in the operations. The use of GIS applications in military forces has revolutionised the way in which these forces operate and function.
LAND USE /LAND COVER CLASSIFICATION AND CHANGE DETECTION USING GEOGRAPHICAL I...IAEME Publication
Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. Geographical information system and image processing techniques used for the analysis of land use/land cover and change detection of Sukhana Basin of Aurangabad District, Maharashtra state. The tools used ArcGIS10.1 and ERDAS IMAGINE9.1, landsat images of 1996, 2003and 2014. From land use / land cover change detection it is found that during 1996-2014, water bodies cover have loss of 4 Sq. Km. Barren land have 146 Sq.Km. loss and forest area with 96 Sq.Km. loss. It is found that urbanization area has gain of 51 Sq.Km. and agricultural land cover also have gain of 195 Sq.Km.
Optical and Microwave Remote Sensing for Crop Monitoring in MexicoCIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
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.
APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURELagnajeetRoy
India is a country that depends on agriculture. Today in this era of technological supremacy, agriculture is also using different new technologies like some robotic machinery to remote sensing and Geographical Information System (GIS) for the betterment of agriculture. It is easy to get the information about that area where human cannot check the condition everyday and help in gathering the data with the help of remote sensing. Whereas GIS helps in preparation of map that shows an accurate representation of data we get through remote sensing. From disease estimation to stress factor due to water, from ground water quality index to acreage estimation in various way agriculture is being profited by the application of remote sensing and GIS in agriculture. The applications of those software or techniques are very new to the agriculture domain still much more exploration is needed in this part. New software’s are developing in different parts of the world and remote sensing. Today farmers understand the beneficiaries of these kinds of techniques to the farm field which help in increasing productivity that will help future generation as technology is hype in traditional system of farming.
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data. The acronym GIS is sometimes used for geographical information science or geospatial information studies to refer to the academic discipline or career of working with geographic information systems and is a large domain within the broader academic discipline of Geoinformatics. In the simplest terms, GIS is the merging of cartography, statistical analysis, and computer science technology.
A Spatial Decision Support System (SDSS) for Understanding and Reducing Long-...Global Risk Forum GRFDavos
6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland
Geographic Information Systems (GIS) play a pivotal role in military operations. The concept of Command, Control, Communication and Coordination in military operations is largely dependent on the availability of accurate, spatial information to arrive at quick decisions for operational orders.
In the present digital era, GIS is an excellent tool for military commanders in the operations. The use of GIS applications in military forces has revolutionised the way in which these forces operate and function.
LAND USE /LAND COVER CLASSIFICATION AND CHANGE DETECTION USING GEOGRAPHICAL I...IAEME Publication
Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. Geographical information system and image processing techniques used for the analysis of land use/land cover and change detection of Sukhana Basin of Aurangabad District, Maharashtra state. The tools used ArcGIS10.1 and ERDAS IMAGINE9.1, landsat images of 1996, 2003and 2014. From land use / land cover change detection it is found that during 1996-2014, water bodies cover have loss of 4 Sq. Km. Barren land have 146 Sq.Km. loss and forest area with 96 Sq.Km. loss. It is found that urbanization area has gain of 51 Sq.Km. and agricultural land cover also have gain of 195 Sq.Km.
Optical and Microwave Remote Sensing for Crop Monitoring in MexicoCIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
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.
Remote sensing and geographic information systems technics for spatial-based...IJECEIAES
Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless.
Land Use Growth Simulation and Optimization for Achieving a Sustainable Urban...TELKOMNIKA JOURNAL
Urban areas have been perceived as the source of environmental problems. To avoid improper land use allocation, negative sprawl effects, and other sources of environmental degradation, city planners need tools for simulating and optimizing their proposed plans. This study proposed a “what-if” analysis model that could help the planners in assessing and simulating their urban plans in Bekasi City, Indonesia. As part of Jakarta Metropolitan Area which exhibited a “post-suburbanization” phenomenon, this city faces many problems because of its high urban growth. Since the urban area has higher land use density than the rural area, especially on built-up class, it needs more consideration when allocating this kind of land use. Because each type of built-up class influences another type, it is difficult to allocate manually. Therefore, this study proposed a land-use optimization application to help planners finding the appropriate land use. This study showed that a model with simulation and optimization can be used to handle urban growth.
Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Sat...TELKOMNIKA JOURNAL
Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation
seasonal mapping both on forest and agricultural site. In order to provide a long -terms of vegetation
characteristic maps, a wide time-series images analysis is needed which require high-performance
computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in
Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of
paddy field vegetation information. This research is aimed to develop a method to produce the optimized
solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The
time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused
by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both
CPU and GPU to find the efficient and the robust result. The developed method has been tested and
implemented using the sample case of paddy fields in Java Island. The best system which can
accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are
ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%.
Topographic Information System as a Tool for Environmental Management, a Case...iosrjce
IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) multidisciplinary peer-reviewed Journal with reputable academics and experts as board member. IOSR-JESTFT is designed for the prompt publication of peer-reviewed articles in all areas of subject. The journal articles will be accessed freely online.
Paddy field classification with MODIS-terra multi-temporal image transformati...IJECEIAES
This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope >2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were >0.85 and >0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia.
Comparing of Land Change Modeler and Geomod Modeling for the Assessment of De...IJAEMSJORNAL
The forest destruction, climate change and global warming can reduce an indirect forest benefit because forest is the largest carbon sink and it plays a very important role in global carbon cycle. To support reducing emissions from deforestation and forest degradation (REDD+) program, there is a need to understand the characteristics of existing Land Use/Cover Change (LUCC) modules. The aims of this study are 1) to calculate the rate of deforestation at Poso Regency; and 2) to compare the performance of LCM and GM for simulating baseline deforestation of multiple transitions based on model structure and predictive accuracy. The data used in this study are : 1) Indonesia tophographic map scale 1; 50.000, produced by Geospatial Information Agency (BIG), 2) Landcover maps (1990, 2000, and 2011) which were collected from the Director General of Forestry Planning, Ministry of Environment and Forestry. Meanwhile independent variables (environmental variables) such as : distance from the edge of the forest, the distance from roads, the distance from streams, the distance from settlement, elevation and slope. Landcover changes analysis was assessed by using Idrisi Terrset software and Geomod software. Landcover maps from 1990 and 2000 were used to simulate land-cover of 2011. The resulting maps were compared with an observed land-cover map of 2011. The predictive accuracy of multiple transition modeling was calculated by using Relative Operating Characteristics (ROC). The results show that the deforestation on the period of 1990-2011 reached 19,944 ha (3.55 %) or the rate of deforestation 949 ha year1. Based on the model structure and predictive accuracy comparisons, the LCM was more suitable than the GM for the asssement of deforestation.
The present study focuses on the nature and pattern of urban expansion of Madurai city over its surrounding region during the period from 2003 to 2008. Based on its proximity to the Madurai city, Preparation of various thematic data such Land use and Land cover using Land sat data. Create a land use land cover map from satellite imagery using supervised classification. Find out the areas from the classified data. The study is based on secondary data, the satellite imagery has downloaded from GLCF (Global Land Cover Facility) web site, for the study area (path101 row 67), the downloaded imagery subset using Imagery software to clip the study area. The clipped satellite imagery has used to prepare the land use and land cover map using supervised classification.
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
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Land use/land cover classification using machine learning models
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 2, April 2022, pp. 2040~2046
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i2.pp2040-2046 2040
Journal homepage: http://ijece.iaescore.com
Land use/land cover classification using machine learning
models
Subhra Swetanisha1
, Amiya Ranjan Panda1
, Dayal Kumar Behera2
1
School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India
2
Faculty of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India
Article Info ABSTRACT
Article history:
Received Apr 28, 2021
Revised Sep 20, 2021
Accepted Oct 10, 2021
An ensemble model has been proposed in this work by combining the
extreme gradient boosting classification (XGBoost) model with support
vector machine (SVM) for land use and land cover classification (LULCC).
We have used the multispectral Landsat-8 operational land imager sensor
(OLI) data with six spectral bands in the electromagnetic spectrum (EM).
The area of study is the administrative boundary of the twin cities of Odisha.
Data collected in 2020 is classified into seven land use classes/labels: river,
canal, pond, forest, urban, agricultural land, and sand. Comparative
assessments of the results of ten machine learning models are accomplished
by computing the overall accuracy, kappa coefficient, producer accuracy and
user accuracy. An ensemble classifier model makes the classification more
precise than the other state-of-the-art machine learning classifiers.
Keywords:
Land use and land cover
Machine learning
Random forest
Remote sensing
Support vector machine
XGBoost
This is an open access article under the CC BY-SA license.
Corresponding Author:
Dayal Kumar Behera
Department of CSE, Silicon Institute of Technology
Bhubaneswar, India
Email: dayalbehera@gmail.com
1. INTRODUCTION
Land use and land cover classification (LULCC) is a critical technique for assessing global change
at various spatiotemporal scales [1]. It is a pervasive, accelerating, and substantial process fueled by human
activity and frequently results in changes that directly affect humans. The effects of LULCC on ecosystem
sustainability are becoming a growing focus of global change study [2]. Till today, there has been a
requirement to deliver provincial land use and land cover (LULC) maps and information for a variety of
purposes, including change detection [3], planning or monitoring of the urban environment [4], disaster
monitoring, landscape planning, resource management, site suitability analysis and ecological studies [5] or
biological investigation [6]. Traditionally, non-parametric machine-learning classifiers (ML) such as random
forests (RF) and support vector machines (SVMs) [7] have been used for geographical and easy-to-use
classification.
The focus of this work is to identify the physical aspect of the earth's surface (land cover) as well as
how we exploit the land (land use) for the twin cities of Odisha. This can be accomplished by field surveys or
through the analysis of satellite pictures (remote sensing) [6]. Conducting field surveys is more thorough and
authoritative. It is a costly endeavor that frequently takes a long time to complete. But with recent
advancements in the space sector and an increase in the availability of satellite photos (both free and
commercial), machine learning models [8] have demonstrated promising outcomes in this field. Recent
advancements in sensor technology have resulted in the development of a constellation of satellites [9] and
airborne platforms from which a significant amount of spatial resolution remotely sensed imagery is
available. Landsat-8 [10] is now circling the earth. The operational land imager sensor (OLI) offers images in
2. Int J Elec & Comp Eng ISSN: 2088-8708
Land use/land cover classification using machine learning models (Subhra Swetanisha)
2041
six distinct spectral bands on the Landsat payload. In this paper, the data of Landsat-8 is used for
classification. The contributions of the work are i) land use and land cover classification using machine
learning models; ii) generating the feature set from the raster using the shapefile of the training and test data;
iii) designing an ensemble model by combining the output of the XGBoost model with SVM;
and iv) performing efficacy analysis of ensemble models in view of user, producer, and overall accuracy.
2. LITERATURE SURVEY
Many works have been done to examine the use of LULC analysis on remotely sensed records.
From 1986 to 2001 in Pallisa District, Uganda, Otukei and Blaschke [3] carried out land cover mapping and
land cover assessing using DTs, SVMs and MLCs. They explored the use of knowledge mining to find the
required classification bands and thresholds for decision. The analysis assessed the efficiency of the
classification models, claiming that land cover elements occur at an unpredictable pace.
According to desired classes, a few image classification models are available for segmenting a
multi-dimensional component space into homogenous regions and labelling segments. Parametric classifiers
accept a normally distributed dataset and statistical parameters acquired properly from training data. The
most broadly utilized parametric classifier is the maximum-likelihood classifier (MLC), which makes
decision surfaces dependent on the mean and covariance of each class. MLC [11] was first applied to IRS
LISS-III images between 2001 and 2011 and classified into eight classes. Additionally, the study used a
unique methodological framework for post-classification adjustments. It considerably increased total
classification accuracy from 67.84% to 82.75% in 2001 and from 71.93% to 87.43% in 2011.
Islam et al. [1] used Landsat TM and Landsat 8 OLI/TIRS images to examine land use changes in
Chunati Wildlife Sanctuary (CWS) from 2005 to 2015. ArcGIS and ERDAS imagine were used for land use
change assessment. To derive supervised land use categorization, the maximum likelihood classification
technique was applied. It was discovered that around 256 ha of the degraded forest area has increased over
ten years (2005–2015), with an annual rate of change of 25.56%. Non-parametric classifiers do not accept a
particular information appropriation to isolate a multi-dimensional feature space into classes. Most normally
utilized non-parametric classifiers incorporate decision trees [4], support vector machines (SVM) [12] and
expert systems.
ML algorithms have been utilized according to pixel classifiers in remote sensing image
analysis [6]. Grippa et al. [13] describes a method for mapping urban land use at the street block level,
emphasizing residential usage by utilizing very-high-resolution satellite images and derived land-cover maps
as input. For the classification of street blocks, a random forest (RF) classifier is utilized, which achieves
accuracies of 84% and 79% for five and six land-use classifications, respectively. RF classifier applied over
urban communities Dakar and Ouagadougou, cover more than 1,000 km2
altogether, with a spatial resolution
of 0.5 m. In the year 2019, Jamali [7] compared and contrasted eight machine learning methods for image
categorization in the northern region of Iran developed in the Waikato environment for knowledge analysis
(WEKA) and R programming languages. Machine learning models [14]–[16] such as RF, SVM [17], [18],
decision tree, K-nearest-neighbors (KNN) [19], principal component analysis (PCA) [20] are successfully
applied in many application areas. We have built up an ensemble model [21], including SVM and XGBoost
[22], that gives better precision when contrasted with other individual machine learning models.
3. LULC CLASSIFICATION
3.1. Study area
Our study site is the twin cities of Odisha i.e., Bhubaneswar and Cuttack, which are situated towards
the Eastern part and lies between 20° 15' N-20° 28' N latitude and 85° 52' E-85° 54' E longitude. According
to the European petroleum survey group (EPSG), the twin cities of Odisha lies in EPSG:32645-WGS
84/UTM zone 45 N. It is surrounded by Ganjam district towards the north, Puri, Jagatsinghpur and
Kendrapara districts towards the east, Jajpur and Dhenkanal districts towards the south and Anugul, Buodh
and Nayagarh districts to the west. Bhubaneswar is the capital of Odisha, coming under the Khurda district.
The urban administrative area of the twin cities is considered for analysis.
3.2. Data acquisition
To establish land usage, land cover (LU/LC) of the study area, Landsat satellite-8 ETM+data for
2020 have been used. The spectrum consists of six electromagnetic (EM), shortwave Infra-Red1-SWER 1
and shortwave Infra-Red2-SWIR 2 including blue, green, red, near infra-red, which is used to classify into
seven land use classes such as a river, canal, pond, forest, urban, agricultural land, sand.
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 2, April 2022: 2040-2046
2042
3.3. Image pre-processing
3.3.1. Layer stacking
After data acquisition, this stage is to apply fundamental pre-processing activities framed on the raw
Satellite images before its utilization in any further upgrade, understanding, interpretation or analysis. Layer
stacking [23] is applied to consolidate various images into a single image. The LULC map after layer
stacking is portrayed in Figure 1(a).
3.3.2. Atmospheric correction
The atmospheric correction [1] is essential when working on images with more than one timestamp.
We not only implement image classification but also want to compare several images between one another.
The main aim is the conversion of raster bands of Landsat 8 images from digital numbers to reflectance.
Atmospheric correction is applied on the resultant image of layer stacking shown in Figure 1(a). In our work,
the atmospheric correction is implemented using the dark object subtraction method. After implementing
atmospheric correction, the generated map is shown in Figure 1(b).
(a) (b)
Figure 1. Image pre-processing (a) after layer stacking and (b) after atmospheric correction
3.3.3. Image composite
Each band of a multispectral image can be shown each band as a grayscale image or as a mix of
three bands simultaneously as a color composite image. The three essential shades of light are red, green, and
blue (RGB). PC screens can show a picture of three unique groups by utilizing an alternate essential color for
each band. When we consolidate these three images, the outcome is a color image with every pixel's shading
controlled by a mix of RGB of various splendors. Our study area is cropped from the LULC map shown in
Figure 1. Two different color composite formats: True color composite and false color composite [24] of the
study area is depicted in Figures 2(a) and 2(b), respectively.
(a) (b)
Figure 2. Image composite of twin cities of Odisha in (a) false color and (b) true color
4. METHOD
The proposed classification method consists of three major stages. Firstly, the study area is
identified, and data acquisition is performed. In the next stage, image pre-processing is carried out with layer
stacking and atmospheric correction. Finally, ensemble classification is carried out for thematic LULC
change analysis.
4. Int J Elec & Comp Eng ISSN: 2088-8708
Land use/land cover classification using machine learning models (Subhra Swetanisha)
2043
Image classification is an automated approach for classifying raster data belongs to satellite
images [25], [26], airborne images, and drone images. This typically includes evaluating several images and
applying statistical rules in determining the identity of the land cover for each pixel in an image. In this
paper, supervised classification algorithms are applied for LULC classification. All classes of interest are
selected to prepare the train and test dataset. The proposed model is depicted in Figure 3. The training data is
used to train the classifier, whereas the test data is used to validate the model. Raster library of R is used to
generate the features from the raster and shapefile of the train and test data. The generated features are taken
as input to implement the classifier using Python's sklearn library with default parameter setting. For the
ensemble model, voting classifier is used with 0.5 weight for both SVM and XGBoost models.
After defining the classes, the next step is training stage. Here numerous training areas for the
required land cover classes are identified. A sufficient sample size is required to ensure accurate statistical
descriptors of our training data. The LULC maps generated in different machine learning models are shown
in Figure 4. Output maps of minimum distance, RF and Hybrid model (SVM+XGBoost) are shown in
Figures 4(a), 4(b), and 4(c), respectively.
Figure 3. Proposed model
(a) (b) (c)
Figure 4. Classification using (a) minimum distance, (b) random forest (RF), and (c) SVM+XGBoost
5. ISSN: 2088-8708
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5. RESULTS AND DISCUSSION
Then the accuracy assessment is done to determine that how good the map is. If the accuracy
assessment shows that the land use land cover map is valid then the resulting map can be utilized in different
ways like dramatic maps, all kind of output tables or statistics for the various land cover classes and digital
data files amendable to inclusion in geographical information system (GIS). In supervised learning
classification, an error occurs when a pixel that belongs to one class is allotted to another class. Here the
question is how to test for it. And basically, there are two methods either visual control or quantitative control
to test this.
Visual control is basically visual assessment of the results of supervised or unsupervised learning.
Once the visual control has passed and if the results look plausible then the quantitative approach for
accuracy assessment can be done. Accuracy assessment procedure with the help of error metrics plays vital
role in any classification job and plays a vital role in LULC classification. This is done by calculating
different accuracy measures like overall accuracy (OA), user accuracy (UA), producer accuracy (PA), Kappa
coefficient from the confusion matrix. Table 1 shows a comparison between different Machine learning
models based on train accuracy, overall accuracy and Kappa index [27], [28]. Higher value indicates better
classification. Table 2 depicts PA and UA of ML classifiers for classifying seven class labels.
Table 1. Model accuracy and Kappa coefficient of ML classifiers
Model Train accuracy Test accuracy (OA) Kappa coefficient
Minimum distance 0.8592 51.77 0.413
KNN 0.9952 93.0782 0.8738
LR 0.9404 93.0049 0.8682
DT 1.0000 92.4922 0.8631
SVM 0.9514 93.3208 0.8742
XGBoost 0.9957 93.5360 0.8818
Extra tree 0.9980 93.2064 0.8760
RF 0.8776 88.6330 0.7872
SVM+XGBoost 0.9920 93.5635 0.8824
RF+XGBoost 0.9907 93.4902 0.8806
Table 2. Producer and UA of ML classifiers
Model River Canal Pond Forest Urban Agri. Land Sand
PA UA PA UA PA UA PA UA PA UA PA UA PA UA
KNN 99.60 99.98 87.28 90.49 69.23 49.32 99.93 73.54 77.33 98.00 13.07 38.89 100 99.02
LR 96.68 100 NaN 0.00 NaN 0.00 97.90 85.03 64.34 99.13 12.57 5.83 100 99.51
DT 99.41 99.95 82.02 79.75 53.89 44.29 99.51 72.87 81.20 96.73 11.91 40.00 99.92 96.17
SVM 96.68 100 0.00 0.00 0.00 0.00 98.10 85.46 69.86 98.60 23.65 21.94 100 99.59
XGBoost 99.38 99.97 76.20 82.52 62.87 47.95 100 76.60 84.14 98.00 13.13 37.78 99.92 99.51
Extra tree 99.50 99.98 84.41 88.04 79.69 46.58 99.90 74.38 80.33 98.00 12.83 39.72 100 99.35
RF 98.08 99.73 NaN 0.00 0.00 0.00 97.21 92.53 42.28 100 NaN 0.00 NaN 0.00
SVM+
XGBoost
99.60 99.96 82.16 86.20 62.89 45.66 100 77.16 80.31 98.73 10.54 28.33 99.92 99.51
RF+
XGBoost
99.13 99.97 89.05 74.85 66.67 43.84 99.82 78.54 80.06 99.07 6.95 17.50 99.92 99.51
6. CONCLUSION
Land use and land cover classification is beneficial to explore the change dynamics of the city.
Although the maximum likelihood classifier is widely used, it could not perform satisfactorily to ensure the
desired classification accuracy. This work presented the pixel-based classification of LULC using various
ML models. This will benefit the researcher to recognize the best classifiers and various evaluation metrics.
Landsat 8 geospatial data with atmospheric correction significantly improve the accuracy of LULC
classification. An ensemble model is proposed by combining the output of SVM and Extreme gradient
boosting model. The efficacy of the proposed model is shown in Table 2. It is seen that the user, producer,
and overall accuracy has been significantly improved in ensemble models.
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BIOGRAPHIES OF AUTHORS
Subhra Swetanisha has been working as an Assistant Professor in the department
of Computer Science and Engineering at Trident Academy of Technology, Bhubaneswar,
Odisha. She has completed M.Tech. degree in Computer Science and Engineering from KIIT
Deemed to be University, Bhubaneswar and also continuing her Ph.D. Her research interests
include Machine Learning, Data Science, Image Processing and Remote Sensing. She has
sixteen years of teaching experience and published more than ten Scopus/SCI indexed research
articles. She is a life member of the ISTE and IAENG. She can be contacted at email:
sswetanisha@gmail.com.
Amiya Ranjan Panda has seven years of research experience in DRDO and more
than three years of teaching experience. He received B.Tech. degree in Information
Technology from Biju Patnaik University of Technology, Rourkela, India, in 2009, and the
M.Tech. degree in Computer Science and Engineering from the Kalinga Institute of Industrial
Technology (KIIT), Bhubaneshwar, India, in 2012. He received Ph.D. degree from Siksha ‘O’
Anusandhan University, Bhubaneshwar, in 2017, working in a real-time project, ‘Design,
development and implementation of Software Defined Radio based Flight Termination
System’ of DRDO, ITR, Chandipur. He has worked as JRF, SRF and RA in DRDO, ITR,
Chandipur, for more than seven years. Then, he has worked as Assistant Professor at Siksha
‘O’ Anusandhan University for four months, and currently, he is working as Assistant
Professor in KIIT Deemed to be University, Bhubaneswar. His research interest is machine
learning, IoT, data acquisition system and software-defined radio. He has published more than
25 articles in international journals. Currently, he is working with two nos of real-time projects
of DRDO. He can be contacted at email: amiya.pandafcs@kiit.ac.in.
Dayal Kumar Behera has fifteen years of teaching experience and received Ph.D.
degree from KIIT Deemed to be University. He has obtained B.E. degree with honours in
Information Technology from the National Institute of Science and Technology, Berhampur,
Odisha, in 2006 and completed M.Tech. from the College of Engineering and Technology
Bhubaneswar in 2012. He has been working as an Assistant Professor in CSE department at
Silicon Institute of Technology, Bhubaneswar. His research interest includes Recommendation
Systems, Machine Learning, IoT, and Image Processing. He has fifteen publications in
Scopus/SCI indexed Journals and conferences. He has guided many M.Tech. Projects and two
IEDC funded projects in his area of interest. He is a lifetime member of ISTE and IAENG
societies. He can be contacted at email: dayalbehera@gmail.com.