Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification

Namdar, Mohammad; Adamowski, Jan; Saadat, Hossein; Sharifi, Forood; Khiri, Afsaneh

doi:10.1080/01431161.2014.978035

Cited by 16 publications

(11 citation statements)

References 23 publications

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“…The agreement between classification results and ground truth was measured using the producer accuracy, user's accuracy, overall accuracy and Kappa index generated from the confusion matrices (Jia et al 2014). Producer's accuracy is a measure of how correct the classification is, while user's accuracy is a measure of the reliability of the map for each class (Namdar et al 2014). The different classification methods were primarily compared in performance with the traditional method, which uses reflective bands only based on the coverage per class (area), producer's accuracy, user's accuracy, overall accuracy and McNemar's tests.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Mushore

Mutanga

Odindi

et al. 2016

Geocarto International

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Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor's thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8's reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar's Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8's OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Mushore

Mutanga

Odindi

et al. 2016

Geocarto International

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“…9. BTEX contaminant contour (a) after 1 year (b) after 2 years (c) after 3 years; source: own study 2010; 2012a; ADAMOWSKI, PROKOPH 2013;ARAGHI et al 2015;BELAYNEH et al 2014;CAMPISI et al 2012;DANESHMAND et al 2014;HAIDARY et al 2013;NALLEY et al 2012;2013;NAMDAR et al 2014;PINGALE et al 2014;SAADAT et al 2014; TI-WARI, ADAMOWSKI 2014], new approaches such as the one proposed in this study are important to explore to facilitate the transition to more sustainable water resources management [BUTLER, ADAMOWSKI HALBE et al 2013;INAM et al 2015;KOLIN-JIVADI et al 2014a, b;MAHMOOD et al 2015;MEDE-MA et al 2014a, b;SAADAT et al 2011;STRAITH et al 2014]. In this study, to accelerate the biodegradation process, an engineered bioremediation process was studied using a set of injection and extraction wells.…”

Section: Discussionmentioning

confidence: 99%

Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing

Kumar¹,

Ch²,

Mathur³

et al. 2015

Journal of Water and Land Development

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Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM) was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE), Non Dominated Sorting Genetic Algorithm (NSGA II) and Simulated Annealing (SA). It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.

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“…Unlike other natural hazards, the impacts of droughts are often spread over large geographical areas. Droughts rank first among all natural hazards when measured in terms of the number of people affected DOI: 10.1515/jwld-2015-0014 [HEWITT 1997MISHRA, SINGH 2010;OBASI 1994;WILHITE et al 2007], which emphasizes the importance of investigating how this natural phenomenon disturbs ecological and socio-economic systems [DANESHMAND et al 2014;NAMDAR et al 2014;NIAZI et al 2014].…”

Section: Introductionmentioning

confidence: 99%

High accuracy Land Use Land Cover (LULC) maps for detecting agricultural drought effects in rainfed agro-ecosystems in central Mexico

Sierra-Soler¹,

Adamowski²,

Qi³

et al. 2015

Journal of Water and Land Development

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Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km 2 ) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.

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Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification

Cited by 16 publications

References 23 publications

Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing

High accuracy Land Use Land Cover (LULC) maps for detecting agricultural drought effects in rainfed agro-ecosystems in central Mexico

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