Spatio-temporal change analysis of Perak river basin using remote sensing and GIS

Hanif, Muhammad; Mustafa, Muhammad Raza Ul; Hashim, Ahmad Mustafa; Yusof, Khamaruzaman Wan

doi:10.1109/iconspace.2015.7283793

Cited by 7 publications

(9 citation statements)

References 29 publications

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“…The recent study by [28] analyzed LULC changes in Kenya by using GIS and remote sensing techniques. Similarly, numerous studies have been reported to observe LULC changes in different catchments in Malaysia [29][30][31].…”

Section: Introductionmentioning

confidence: 93%

“…Perak is situated in the western part of peninsular Malaysia, bordered to the side by Kedah and the south coast of Thailand, to the northwest by Palau Penang east by Kelantan and Pahang, and to the south by Selangor, and occupies an area of km 2 . It ranges geographically from 3°30′ N to 6°0′ N longitude and100°0′ E to 1 latitude, as shown in Figure 1 [31]. The shapefile of the study area was obtained website providing data-interpolating variational analysis (DIVA-GIS) (Retrieved Feb 2021, https://gadm.org/maps/MYS.html)from which study area map was clippe second-largest river basin in peninsular Malaysia is the Perak River basin.…”

Section: Study Areamentioning

confidence: 99%

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Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Zeshan

Mustafa

Baig

2021

Water

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Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km2) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.

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Section: Introductionmentioning

confidence: 93%

Section: Study Areamentioning

confidence: 99%

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Zeshan

Mustafa

Baig

2021

Water

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“…Typical temperatures range between 21 and 34°C, with an annual mean of 28°C. The state has an average humidity of 81 % and receives an annual rainfall of about 2,400 mm (Hanif et al 2015). Rainfall in Perak state drains largely to three main rivers; Sungai Perak, Sungai Manjung and Sungai Bernam (Pour and Hashim 2015).…”

Section: Study Areamentioning

confidence: 99%

Prediction of pore-water pressure response to rainfall using support vector regression

et al. 2016

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Nonlinear complex behavior of pore-water pressure responses to rainfall was modelled using support vector regression (SVR). Pore-water pressure can rise to disturbing levels that may result in slope failure during or after rainfall. Traditionally, monitoring slope pore-water pressure responses to rainfall is tedious and expensive, in that the slope must be instrumented with necessary monitors. Data on rainfall and corresponding responses of pore-water pressure were collected from such a monitoring program at a slope site in Malaysia and used to develop SVR models to predict pore-water pressure fluctuations. Three models, based on their different input configurations, were developed. SVR optimum metaparameters were obtained using k-fold cross validation and a grid search. Model type 3 was adjudged the best among the models and was used to predict three other points on the slope. For each point, lag intervals of 30 min, 1 h and 2 h were used to make the predictions. The SVR model predictions were compared with predictions made by an artificial neural network model; overall, the SVR model showed slightly better results. Uncertainty quantification analysis was also performed for further model assessment. The uncertainty components were found to be low and tolerable, with d-factor of 0.14 and 74 % of observed data falling within the 95 % confidence bound. The study demonstrated that the SVR model is effective in providing an accurate and quick means of obtaining pore-water pressure response, which may be vital in systems where response information is urgently needed.

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“…However, due to the fact that it neglects the temporal correlation between corresponding pixels in multi-temporal images, the post-classification approach usually suffers from an accumulation of misclassification errors [32]. In order to solve this problem, some researchers have combined historical land use vector maps (HVMs) with remote sensing images to create a classification system, set decision rules, and used existing GIS knowledge to aid the CD process [26,[28][29][30]. These studies use GIS and remote sensing tools to monitor land use and land cover changes at different spatiotemporal scales.…”

Section: Introductionmentioning

confidence: 99%

“…2018, 10, x FOR PEER REVIEW 3 of 23 knowledge to accomplish high-resolution remote sensing image CD [26][27][28][29][30]. Post-classification with multi-temporal remote sensing images is one of the most popular change detection methods, providing the detailed "from-to" change information in real applications [31,32].…”

Section: Introductionmentioning

confidence: 99%

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Feng

Sui

et al. 2018

Remote Sensing

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Abstract:In the process of object-based change detection (OBCD), scale is a significant factor related to extraction and analyses of subsequent change data. To address this problem, this paper describes an object-based approach to urban area change detection (CD) using rotation forest (RoF) and coarse-to-fine uncertainty analyses of multi-temporal high-resolution remote sensing images. First, highly homogeneous objects with consistent spatial positions are identified through vector-raster integration and multi-scale fine segmentation. The multi-temporal images are stacked and segmented under the constraints of a historical land use vector map using a series of optimal segmentation scales, ranging from coarse to fine. Second, neighborhood correlation image analyses are performed to highlight pixels with high probabilities of being changed or unchanged, which can be used as a prerequisite for object-based analyses. Third, based on the coarse-to-fine segmentation and pixel-based pre-classification results, change possibilities are calculated for various objects. Furthermore, changed and unchanged objects identified at different scales are automatically selected to serve as training samples. The spectral and texture features of each object are extracted. Finally, uncertain objects are classified using the RoF classifier. Multi-scale classification results are combined using a majority voting rule to generate the final CD results. In experiments using two pairs of real high-resolution remote sensing datasets, our proposed approach outperformed existing methods in terms of CD accuracy, verifying its feasibility and effectiveness.

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Spatio-temporal change analysis of Perak river basin using remote sensing and GIS

Cited by 7 publications

References 29 publications

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Prediction of pore-water pressure response to rainfall using support vector regression

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

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