| 1. |
- Bhardwaj, Anshuman, et al.
(author)
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Demarcation of potential avalanche sites using remote sensing and ground observations : A case study of Gangotri glacier
- 2014
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In: Geocarto International. - : Informa UK Limited. - 1010-6049 .- 1752-0762. ; 29:5, s. 520-535
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Journal article (peer-reviewed)abstract
- This study demonstrates the effectiveness of remote sensing and analytical hierarchy process for avalanche hazard mapping. The layers incorporated in this study were of slope, aspect, profile curvature, ground cover and elevation. The accuracy of output was determined using the registered avalanche sites based on ground observations and field-based modelling techniques. 93.35% of avalanche-affected areas came under maximum and moderate hazard zones, thus proving the effectiveness of this technique for Gangotri glacier basin. A parallel study was done to observe the change in the results, if any, by using high-resolution DEM and Cartosat-1 imagery. Similar methodology was adopted and the outcome was having significant improvement over the previous result as 98.8% of the preregistered avalanche area falling within maximum and moderate hazard zones
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| 2. |
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| 3. |
- Bouhennache, Rafik, et al.
(author)
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A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery
- 2019
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In: Geocarto International. - : Taylor & Francis Group. - 1010-6049 .- 1752-0762. ; 34:14, s. 1531-1551
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Journal article (peer-reviewed)abstract
- Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as Built-up Land Features Extraction Index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the Spectral Discrimination Index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.
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| 4. |
- Darabi, H., et al.
(author)
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Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood
- 2021
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In: Geocarto International. - : Taylor and Francis Ltd.. - 1010-6049 .- 1752-0762.
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Journal article (peer-reviewed)abstract
- In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.
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| 5. |
- Ehsani, Amir Houshang, et al.
(author)
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Self Organizing Maps for Multi-Scale Morphometric Feature Identification Using Shuttle Radar Topography Mission (SRTM) Data
- 2009
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In: Geocarto International. - : Informa UK Limited. - 1010-6049 .- 1752-0762. ; 24:5, s. 335-355
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Journal article (peer-reviewed)abstract
- This article presents a new procedure using artificial neural networks in the form of a self-organizing map (SOM), as a semi-automatic method for analysis and identification of morphometric features in the Man and Biosphere Reserve 'Eastern Carpathians' with nine spatial scales. The NASA Shuttle Radar Topography Mission (SRTM) has provided digital elevation models (DEM) for over 80% of the land surface on earth. The latest version 3.0 SRTM data provided by the Consultative Group for International Agricultural Research-Consortium for Spatial Information GeoPortal is the result of substantial editing effort on the SRTM digital elevation data produced by NASA. Easy availability of SRTM 3 arc second data has resulted in great advances in morphometric studies and numerical description of terrain surface features as shown by many literature references. The first derivative, slope steepness and second derivatives, minimum curvature, maximum curvature and cross-sectional curvature of elevation were derived by fitting bivariate quadratic surfaces with nine window sizes ranging from 5 to 55 cells to the processed SRTM DEM 90 m Version 3.0. These analyses represent landform entities with ground distances from 450 to 4950 m, which are local to regional features. The four morphometric parameters were used as input for the SOM algorithm. Forty-two SOMs with different learning parameter sets, e.g. initial radius, final radius and number of iterations were investigated. An optimal SOM with 10 classes based on 1000 iteration and a final neighbourhood radius of 0.01 provide a low average quantization error of 0.1780 and was used for further analysis. The effect of the random initial weights for optimal SOM was also studied. The results in this particular study are not sensitive to the randomization of initial weight vectors if many iterations are used. Feature space analysis, morphometric signatures, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of morphometric features. Results are provided as thematic maps of landform entities based on form and slopes. The result showed that a SOM is an efficient scalable tool for analysing geomorphometric features as meaningful landforms over different spatial extents, and uses the full potential of morphometric characteristics. This procedure is reproducible for the same application with consistent results.
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| 6. |
- Islam, Md Monirul, et al.
(author)
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Monitoring Mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015
- 2019
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In: Geocarto International. - : Informa UK Limited. - 1010-6049 .- 1752-0762. ; 34:13, s. 1458-1476
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Journal article (peer-reviewed)abstract
- This study used multi-date Landsat images to quantify mangrove cover changes in the whole of Bangladesh from 1976 to 2015. Images were pre-processed with an atmospheric correction using Dark Object Subtraction (DOS) and Relative Radiometric Normalization (RRN) using Pseudo-Invariant Features (PIFs). Land Use/Land Cover (LU/LC) classification map was generated using Maximum Likelihood (MaxLike) algorithm, indicating the areal extent of mangroves increased by 3.1% between 1976 and 2015, where 1.79% of this increase occurred between 2000 and 2015. Though mangrove areas remained almost constant in the Sundarbans, Chakaria Sundarbans has almost disappeared between 1976 and 1989. The overall accuracy of Landsat MSS, TM, ETM+, and L8 OLI classified images were 80%, 80%, 87%, and 97% respectively. The study also found deforestation, shrimp & salt farm, coastal erosion and sedimentation, and mangrove plantation could be responsible for mangrove changes in Bangladesh.
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| 7. |
- Khan, M., et al.
(author)
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Monitoring and assessment of heavy metal contamination in surface water of selected rivers
- 2023
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In: Geocarto International. - : Taylor & Francis. - 1010-6049 .- 1752-0762. ; 38:1
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Journal article (peer-reviewed)abstract
- The current research aimed to monitor and assess the heavy metal contamination in the surface water of 53 sampling sites along the selected rivers using principal component analysis and cluster analysis. For this purpose, both physiochemical parameters such as the temperature (T), the potential of hydrogen (pH), total dissolved solids (TDS) and electroconductivity (EC), and heavy metals such as iron (Fe), chromium (Cr), nickel (Ni), cadmium (Cd), lead (Pb) and arsenic (As) are analyzed as potential water contaminants. The average values of pH, TDS, EC and T are found at 7.75, 70.89 mg/L, 139.11 µs/cm and 20.29 °C, respectively, and heavy metals including Cr, Ni, Cd, Pb, As and Fe are observed at 0.04, 0.04, 0.04, 0.03, 0.001 and 0.04 mg/L, respectively. Moreover, it is found that in both rivers hazardous metals, including Cr (100%), Cd (92.30%), Pb (100%), Ni (100%) and Fe (91%), exceed the permissible limits of the WHO.
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| 8. |
- Niaz, R., et al.
(author)
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Proposing a new framework for analyzing the severity of meteorological drought
- 2023
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In: Geocarto International. - : Taylor & Francis. - 1010-6049 .- 1752-0762. ; 38:1
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Journal article (peer-reviewed)abstract
- The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.
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| 9. |
- Raza, Muhammad Ahmad, et al.
(author)
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Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence
- 2023
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In: Geocarto International. - : Taylor & Francis. - 1010-6049 .- 1752-0762. ; 38:1
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Journal article (peer-reviewed)abstract
- Drought is one of the disastrous natural hazards with complex seasonal and spatial patterns. Understanding the spatial patterns of drought and predicting the likelihood of inter-seasonal drought persistence can provide substantial operational guidelines for water resource management and agricultural production. This study examines drought persistence by identifying the spatial patterns of seasonal drought frequency and inter-seasonal drought persistence in the northeastern region of Pakistan. The Standardized Precipitation Index (SPI) with a three-month time scale is used to examine meteorological drought. Furthermore, Bayesian logistic regression is used to calculate the probability and odds ratios of drought occurrence in the current season, given the previous season's SPI values. For instance, at Balakot station, for the summer-to-autumn season, the value of the odds ratio is significant (6.78). It shows that one unit increase in SPI of the summer season will cause a 5.78 times to increase in odds of autumn drought occurrence. The average drought frequency varies from 37.3 to 89.1%, whereas the average inter-seasonal drought persistence varies from 21.9 to 91.7% in the study region. Results indicate that some areas in the study region, like Kakul and Garhi Dupatta, are more prone to drought and vulnerable to inter-seasonal drought persistence. Furthermore, the Bayesian logistic regression results reveal a negative relationship between spring drought occurrence and winter SPI, demonstrating that the overall study region is more prone to winter-to-spring drought persistence and less vulnerable to summer-to-autumn drought persistence. Overall study has concluded that the region's seasonal drought forecast is challenging due to uncertain drought persistence patterns. However, the Bayesian logistic regression model provides more accurate and precise regional seasonal drought forecasts. The outcome of the present study provides scientific evidence to develop early warning systems and manage seasonal crops in Pakistan.
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| 10. |
- Runnström, Micael, et al.
(author)
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Estimation of PAR over northern China from daily NOAA AVHRR cloud cover classifications
- 2006
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In: Geocarto International. - : Informa UK Limited. - 1010-6049 .- 1752-0762. ; 21:1, s. 51-60
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Journal article (peer-reviewed)abstract
- Incoming Photosynthetic Active Radiation (PAR) is an essential variable for modelling aboveground primary production of ecosystems through the light-use efficiency approach. A method is presented where daily classifications of cloud cover (CLAVR) from the NOAA AVHRR satellite sensor is used to estimate surface incident short wave (SW) flux from which PAR can be assessed. The study area is the Inner Mongolia Autonomous Region (IMAR) of northern China. Daily time steps of calculated theoretical incoming global radiation outside the atmosphere, is adjusted according to the clear, mixed or cloudy classification in the NOAA Pathfinder data set at 8x8 km grid-cells. For the different CLAVR classifications, empirical relationships to atmospheric transparency were established against ground measurements of SW flux. Clear pixels corresponded to an average 61% penetration of the theoretical radiation at the top of the atmosphere and mixed and cloudy pixels to 47% and 40% respectively. The CLAVR time series is evaluated regarding consistency and diurnal precision against measured SW flux and hours of bright sunshine. Modelled monthly fluxes over the growing season were acceptable compared to measured (NRMSE = 6. 6%) and about as good as deriving fluxes from measurements of bright sunshine hours. The global NOAA Pathfinder archive provides an opportunity to assess PAR over the past 20 years at a considerably higher spatial resolution than with methods based on geo-stationary meteorological satellite data sets and without interpolations from scarce measurements of bright sunshine hours.
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