scholarly journals Mapping invasive alien Acacia dealbata Link using ASTER multispectral imagery: a case study in central-eastern of Portugal

2016 ◽  
Vol 25 (3) ◽  
pp. e078 ◽  
Author(s):  
Filipe Martins ◽  
Cristina Alegria ◽  
Gil Artur
Keyword(s):  
Invasive Species ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Alien Invasive Species ◽  
Support Vector ◽  
Kappa Statistics ◽  
Monitoring And Control ◽  
Acacia Dealbata ◽  
Flowering Season ◽  
Central Eastern

Aim of the study: Acacia dealbata is an alien invasive species that is widely spread in Portugal. The main goal of this study was to produce an accurate and detailed map for this invasive species using ASTER multispectral imagery.Area of study: The central-eastern zone of Portugal was used as study area. This whole area is represented in an ASTER scene covering about 321.1 x 103 ha.Material and methods: ASTER imagery of two dates (flowering season and dry season) were classified by applying three supervised classifiers (Maximum Likelihood, Support Vector Machine and Artificial Neural Networks) to five different land cover classifications (from most generic to most detailed land cover categories). The spectral separability of the land cover categories was analyzed and the accuracy of the 30 produced maps compared.Main results: The highest classification accuracy for acacia mapping was obtained using the flowering season imagery, the Maximum Likelihood classifier and the most detailed land cover classification (overall accuracy of 86%; Kappa statistics of 85%; acacia class Kappa statistics of 100%). As a result, the area occupied by acacia was estimated to be approximated 24,770 ha (i.e. 8% of the study area).Research highlights: The methodology explored proved to be a cost-effective solution for acacia mapping in central-eastern of Portugal. The obtained map enables a more accurate and detailed identification of this species’ invaded areas due to its spatial resolution (minimum mapping unit of 0.02 ha) providing a substantial improvement comparably to the existent national land cover maps to support monitoring and control activities.Keywords: remote sensing; invasive alien species; land cover mapping; vegetation mapping.

scholarly journals A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

2021 ◽  
Vol 64 (3) ◽  
pp. 265-274
Author(s):  
Fatima Mushtaq ◽  
Khalid Mahmood ◽  
Mohammad Chaudhry Hamid ◽  
Rahat Tufail
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Support Vector ◽  
Kappa Statistics ◽  
Error Matrix ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
Hyper Plane ◽  
Better Than

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.  

scholarly journals DETERMINATION OF LAND COVER/LAND USE USING SPOT 7 DATA WITH SUPERVISED CLASSIFICATION METHODS

2016 ◽  
Vol XLII-2/W1 ◽  
pp. 143-146 ◽  
Author(s):  
F. Bektas Balcik ◽  
A. Karakacan Kuzucu
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Support Vector ◽  
Kappa Statistics ◽  
Land Use Land Cover ◽  
Study Region ◽  
Error Matrix ◽  
Land Cover Maps

Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management in the rural part of the cities. There is a strong need to produce accurate, reliable, and up-to-date land use/land cover maps for sustainable monitoring and management. In this study, SPOT 7 imagery was used to test the potential of the data for land cover/land use mapping. Catalca is selected region located in the north west of the Istanbul in Turkey, which is mostly covered with agricultural fields and forest lands. The potentials of two classification algorithms maximum likelihood, and support vector machine, were tested, and accuracy assessment of the land cover maps was performed through error matrix and Kappa statistics. The results indicated that both of the selected classifiers were highly useful (over 83% accuracy) in the mapping of land use/cover in the study region. The support vector machine classification approach slightly outperformed the maximum likelihood classification in both overall accuracy and Kappa statistics.

scholarly journals TESTING THE POTENTIAL OF VEGETATION INDICES FOR LAND USE/COVER CLASSIFICATION USING HIGH RESOLUTION DATA

2017 ◽  
Vol IV-4/W4 ◽  
pp. 279-283 ◽  
Author(s):  
A. Karakacan Kuzucu ◽  
F. Bektas Balcik
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Rural Areas ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Kappa Statistics ◽  
Suitability Analysis ◽  
Agricultural Field

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

scholarly journals Land use land cover analysis with pixel-based classification approach

2019 ◽  
Vol 16 (3) ◽  
pp. 1327
Author(s):  
Haslina Hashim ◽  
Zulkiflee Abd Latif ◽  
Nor Aizam Adnan
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Urban Area ◽  
Rapid Development ◽  
Development Planning ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Classification Approach

<p>Rapid development in certain urban area will affect its natural features. Therefore, it is important to identify and determine the changes occur for further analysis and future development planning. This process will influence several factors such as area development, environmental issues and human social activities. The selection of remote sensing data and method will derive the accurate land use land cover maps. This research study accessed the classification accuracy of different classifier approach for land use land cover classification in urban area. The objective of this paper is to compare the accuracy of the classification for each technique used. The study was conducted in a highly urbanized area in Kuala Lumpur, Malaysia. The dataset used for this study is the multi temporal LANDSAT satellite imageries for the year of 2001,2006,2011 and 2016. The pre-processing and analysis of the dataset has been done using software ENVI 5.3. Five land use classes (Urban/built up area, Forest, Agriculture, Water Body and fallow land) were identify for classification process. The classification approach for this study is the supervised classification with two algorithms namely Maximum Likelihood (ML) and Support Vector Machine (SVM). The overall accuracy and kappa statistic of the classification indicate that support vector machine algorithm was more accurate than maximum likelihood algorithm for five different time intervals.Therefore, this classification approach is acceptable and highly recommended for mapping the changes of land cover.</p>

scholarly journals A COMPARATIVE STUDY OF ADVANCED LAND USE/LAND COVER CLASSIFICATION ALGORITHMS USING SENTINEL-2 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 665-670 ◽  
Author(s):  
K. Nivedita Priyadarshini ◽  
M. Kumar ◽  
S. A. Rahaman ◽  
S. Nitheshnirmal
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Unsupervised Classification ◽  
Sensor Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Land Use Land Cover ◽  
Sentinel 2A

<p><strong>Abstract.</strong> Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10<span class="thinspace"></span>m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.</p>

scholarly journals A FIT-FOR-PURPOSE ALGORITHM FOR ENVIRONMENTAL MONITORING BASED ON MAXIMUM LIKELIHOOD, SUPPORT VECTOR MACHINE AND RANDOM FOREST

2019 ◽  
Vol XLII-3/W7 ◽  
pp. 25-32 ◽  
Author(s):  
A. Jamali
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Random Forest ◽  
Land Cover ◽  
Environmental Monitoring ◽  
Support Vector ◽  
Earth Surface ◽  
Land Use Land Cover ◽  
Fit For Purpose

<p><strong>Abstract.</strong> Due to concerns of recent earth climate changes such as an increase of earth surface temperature and monitoring its effect on earth surface, environmental monitoring is a necessity. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modelling as a key factor to investigate impact of climate change phenomena such as droughts and floods on earth surface land cover. There are several free and commercial multi/hyper spectral data sources of Earth Observation (EO) satellites including Landsat, Sentinel and Spot. In this paper, for land use land cover modelling (LULCM), image classification of Landsat 8 using several mathematical and machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood (ML) and a combination of SVM, ML and RF as a fit-for-purpose algorithm are implemented in R programming language and compared in terms of overall accuracy for image classification.</p>

scholarly journals WorldView-2 Satellite Image Classification using U-Net Deep Learning Model

2021 ◽  
Vol 5 (2) ◽  
pp. 502-509
Author(s):  
Ilyas Ilyas ◽  
Lalu Muhamad Jaelani ◽  
Muhammad Aldila Syariz ◽  
Husnul Hidayat
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Maximum Likelihood ◽  
Random Forest ◽  
Land Cover ◽  
Satellite Image ◽  
Support Vector ◽  
Classification Methods ◽  
Land Cover Map ◽  
Land Cover Maps

Land cover maps are important documents for local governments to perform urban planning and management. A field survey using measuring instruments can produce an accurate land cover map. However, this method is time-consuming, expensive, and labor-intensive. A number of researchers have proposed using remote sensing, which generates land cover maps using an optical satellite image with various statistical classification procedures. Recently, artificial intelligence (AI) technology, such as deep learning, has been used in multiple fields, including satellite image classification, with satisfactory results. In this study, a WorldView-2 image of Terangun in Aceh Province, which was acquired on Aug 2, 2016, was classified using a commonly used deep-learning-based classification, namely, U-net. There were eight classes used in the experiment: building, road, open land (such as green open space, bare land, grass, or low vegetation), river, farm, field, aquaculture pond, and garden. For comparison, three classification methods: maximum-likelihood, random forest, and support vector machine, were performed compared to U-Net. A land cover map provided by the government was used as a reference to evaluate the accuracy of land cover maps generated using two classification methods. The results with 100 randomly selected pixels revealed that U-Net was able to obtain a 72% and 0.585 for overall and kappa accuracy, respectively; whereas, overall accuracy and kappa accuracy for the maximum likelihood, random forest and support vector machine methods were  49% and 0.148; 59% and 0.392; and 67% and 0. 511; respectively. Therefore, U-Net outperformed those three of classification methods in classifying the image.  

scholarly journals Geospatial analysis of wetlands based on land use/land cover dynamics using remote sensing and GIS in Sindh, Pakistan

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110261
Author(s):  
Hamza Islam ◽  
Habibuulah Abbasi ◽  
Ahmed Karam ◽  
Ali Hassan Chughtai ◽  
Mansoor Ahmed Jiskani
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Kappa Statistics ◽  
Natural Ecosystem ◽  
Land Use Land Cover ◽  
Dry Land ◽  
Lulc Changes ◽  
Manchar Lake

In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.

scholarly journals Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1377
Author(s):  
Weifang Shi ◽  
Nan Wang ◽  
Aixuan Xin ◽  
Linglan Liu ◽  
Jiaqi Hou ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Path Analysis ◽  
Temperature Variation ◽  
Direct Effect ◽  
Air Temperature ◽  
Urban Areas ◽  
Support Vector ◽  
Urban Air ◽  
Air Temperatures

Mitigating high air temperatures and heat waves is vital for decreasing air pollution and protecting public health. To improve understanding of microscale urban air temperature variation, this paper performed measurements of air temperature and relative humidity in a field of Wuhan City in the afternoon of hot summer days, and used path analysis and genetic support vector regression (SVR) to quantify the independent influences of land cover and humidity on air temperature variation. The path analysis shows that most effect of the land cover is mediated through relative humidity difference, more than four times as much as the direct effect, and that the direct effect of relative humidity difference is nearly six times that of land cover, even larger than the total effect of the land cover. The SVR simulation illustrates that land cover and relative humidity independently contribute 16.3% and 83.7%, on average, to the rise of the air temperature over the land without vegetation in the study site. An alternative strategy of increasing the humidity artificially is proposed to reduce high air temperatures in urban areas. The study would provide scientific support for the regulation of the microclimate and the mitigation of the high air temperature in urban areas.

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