City Compactness: Assessing the Influence of the Growth of Residential Land Use

Abdullahi, Saleh; Pradhan, Biswajeet; Mojaddadi, Hossein

doi:10.1080/10630732.2017.1390299

Cited by 36 publications

(17 citation statements)

References 68 publications

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“…The advantage of this method is it is easy to use, however this method has certain disadvantages, such as the possibility of a mistake by the expert. The LU/LC map was obtained using Landsat 8 images [39][40][41]. The main LU/LC types identified in the study area were range, irrigation farming, and bare lands ( Figure 3k).…”

Section: Methodsmentioning

confidence: 99%

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

et al. 2018

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Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

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Section: Methodsmentioning

confidence: 99%

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

et al. 2018

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“…On the contrary, the 2014 classified map was assessed by 30 GCPs for each class that were collected from Digital Globe imagery (Google Earth) on November 2014. A reliable thematic map with the highest overall classification accuracy was used as the reference to determine the exact accuracy of the implemented classification [35,36]. We stratified randomly selected sample training and experimentation for both images to evaluate overall accuracy of classification with optimal parameter values ( Table 6).…”

Section: Sar Classification Resultsmentioning

confidence: 99%

Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

2018

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This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.

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“…The Markov chain technique is a statistical tool that characterizes the land use transformation probability from one period to another by creating a transition probability matrix between the first and second periods based on the neighborhood effects (Abdullahi, Pradhan, & Mojaddadi, ; Al‐Bakri, Duqqah, & Brewer, ; Wang et al,, ). The initial transition probability matrices of LCLU type require to be determined preceding the Markov process, as mathematically expressed by the equation below

P = ((), P_{italicij}) = (||, \begin{matrix} P_{11} & P_{12} \dots & P_{1 normaln} \\ P_{21} & P_{22} \dots & P_{2 normaln} \\ P_{normaln 1} & P_{normaln 2} \dots & P_{nn} \end{matrix}),

where P is the probability from state i to state j starting prophase to telophase.…”

Section: Methodsmentioning

confidence: 99%

Assessment of land cover and land use change impact on soil loss in a tropical catchment by using multitemporal SPOT‐5 satellite images and Revised Universal Soil Loss Equation model

et al. 2018

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Soil erosion is a common land degradation problem and has disastrous impacts on natural ecosystems and human life. Therefore, researchers have focused on detection of land cover–land use changes (LCLUC) with respect to monitoring and mitigating the potential soil erosion. This article aims to appraise the relationship between LCLUC and soil erosion in the Cameron Highlands (Malaysia) by using multitemporal satellite images and ancillary data. Land clearing and heavy rainfall events in the study area has resulted in increased soil loss. Moreover, unsustainable development and agricultural practices, mismanagement, and lack of land use policies increase the soil erosion rate. Hence, the main contribution of this study lies in the application of appropriate land management practices in relation to water erosion through identification and prediction of the impacts of LCLUC on the spatial distribution of potential soil loss in a region susceptible to natural hazards such as landslide. The LCLUC distribution within the study area was mapped for 2005, 2010, and 2015 by using SPOT‐5 temporal satellite imagery and object‐based image classification. A projected land cover–land use map was also produced for 2025 through integration of Markov chain and cellular automata models. An empirical‐based approach (Revised Universal Soil Loss Equation) coupled with geographic information system was applied to measure soil loss and susceptibility to erosion over the study area for four periods (2005, 2010, 2015, and 2025). The model comprises five parameters, namely, rainfall factor, soil erodibility, topographical factor, conservation factor, and support practice factor. Results exhibited that the average amount of soil loss increased by 31.77 t ha−1 yr−1 from 2005 to 2015 and was predicted to dramatically increase in 2025. The results generated from this research recommends that awareness of spatial and temporal patterns of high soil loss risk areas can help deploy site‐specific soil conservation measures and erosion mitigation processes and prevent unsystematic deforestation and urbanization by the authorities.

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City Compactness: Assessing the Influence of the Growth of Residential Land Use

Cited by 36 publications

References 68 publications

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

Assessment of land cover and land use change impact on soil loss in a tropical catchment by using multitemporal SPOT‐5 satellite images and Revised Universal Soil Loss Equation model

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