ORCID Profile
0000-0002-1728-0008
Current Organisations
University of the Sunshine Coast
,
James Cook University
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Publisher: Elsevier BV
Date: 10-2015
Publisher: Elsevier BV
Date: 11-2013
Publisher: MDPI AG
Date: 09-11-2019
DOI: 10.3390/S19224893
Abstract: Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.
Publisher: SPIE
Date: 18-10-2016
DOI: 10.1117/12.2242563
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-8568
Abstract: & & Mountainous regions of the world are the source of water for large amount of population living downstream. This is also the case for Pamir Mountains in Tajikistan which produces majority of the water for the several countries in the region. Despite increasing impacts of climate change, last several decades, there have been critical decrease of number of monitoring networks in mountainous areas of Central Asia bringing high uncertainty to water resources management and planning. In this study we investigate the possibility to combine the remote sensing data, ground observations and a modelling approach to estimate discharge of Gunt River in the Eastern Pamir, Tajikistan. The Gunt River watershed is of great importance for the region, as about 60 settlements are concentrated along the entire length of the river, including the administrative city of Khorog. Two hydropower stations were built in the lower reaches of the river to provide electricity for the local communities. These headwater glacier-fed basins of Central Asia are particularly vulnerable as climate change threatens water supply from glacier systems and increases evaporative losses, while demand to irrigation water and electricity is rising. This uncertainty in water supply can result, to a deterioration in the development of the economy and the quality of life in the region. Therefore, for sustainable electricity production and economic development in the region, a better understanding of water availability in the river, is required.& & & & & & & & & The aim of the study is to assess the characteristics of the flow regime of the Gunt River. We used & quot Hydrograph& quot hydrological model to simulate daily discharge of the Gunt River. The model algorithms combine physically based and conceptual approaches to describe snow and glacier melting and runoff generation processes. & quot Hydrograph& quot model has also successfully used to simulate river flow in Varzob River with similar climatic conditions in Tajikistan. Parametrization of the model including the assessment of precipitation distribution in the high mountainous areas is based on the data from the research watershed of the Varzob River with long term historical data availability. The verification and evaluation of the model was conducted based on the historical data (1970-1980) using data from the Dzhavshangoz and Khorog meteorological stations. The model performance and simulations for the recent period (2000-2020) were also evaluated by using the remote sensing data. The results have shown satisfactory quality with difference between the observed and simulated runoff does not exceed 2%. In general, the results of the paper confirm the possibility of using the deterministic model & quot Hydrograph& quot to simulate the daily water runoff in the river which is critical for hydropower and irrigation purposes. However, the lack of accurate information on distribution of precipitation in the catchment, significantly reduces the model results accuracy. The study was carried out with the support of St. Petersburg State University (project 75295879).& &
Publisher: MDPI AG
Date: 13-06-2017
DOI: 10.20944/PREPRINTS201706.0056.V1
Abstract: Rainfall is the main driver of hydrological processes in dryland environments and characterising the rainfall variability and processes of runoff generation are critical for understanding ecosystem function of catchments. Using remote sensing and in situ data sets, we assess the spatial and temporal variability of the rainfall, rainfall-runoff response, and effects of antecedent soil moisture and ground cover at different spatial scales on runoff coefficients in the Upper Burdekin catchment, northeast Australia, which is a major contributor of sediment and nutrients to the Great Barrier Reef. The high temporal and spatial variability of rainfall exerts significant controls on runoff generation processes. Rainfall amount and intensity are the primary runoff controls, and runoff coefficients for wet antecedent conditions were higher than for dry conditions. The majority of runoff occurred via surface runoff generation mechanisms, with subsurface runoff likely contributing little runoff due to the intense nature of rainfall events. At annual to seasonal temporal scales and for relatively large catchments, we could not detect a significant effect of ground cover on runoff. We conclude that in the range of moderate to large catchments (193 & ndash 36,260 km2) runoff generation processes are sensitive to both antecedent soil moisture and ground cover. A higher runoff-ground cover correlation in drier months with sparse ground cover highlighted the critical role of cover at the onset of the wet season and how runoff generation is more sensitive to cover in drier months than in wetter months. The monthly water balance analysis indicates that runoff generation in wetter months (January and February) is partially influenced by saturation overland flow, most likely confined to saturated soils in riparian corridors, swales, and areas of shallow soil. By March and continuing through October, the soil & lsquo bucket& rsquo progressively empties by evapotranspiration, and Hortonian overland flow becomes the dominant, if not exclusive, flow generation process. The results of this study can be used to better understand the rainfall-runoff relationships in dryland environments and subsequent exposure of coral reef ecosystems in Australia and elsewhere to terrestrial runoff.
Publisher: SPIE
Date: 18-10-2016
DOI: 10.1117/12.2242164
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-1827
Abstract: & & & & & & & Since the successful launch of the Gravity Recovery and Climate Experiment (GRACE) on March 17& sup& th& /sup& , 2002, a number of scientists have adopted satellite gravimetry for the detection of variations on terrestrial water storage (TWS). Use of high-precision GRACE gravimetry presents advantages in hydrogeologic studies, such as providing accurate estimates of currents and gravity fields. Many studies have proven that the high-precision GRACE gravimetry can observe large-scale (over 50,000 km& sup& & /sup& ) variations in groundwater storage (GWS). However, relatively few studies conducted using satellite gravimetry have focused on scales smaller than 5,000 km& sup& & /sup& .& & & & & & & & & & & The purpose of this study is to investigate the potential for using GRACE gravimetry to observe small-scale variations in GWS specifically, this paper presents a case study of the Zhoushui River alluvial fan (~2,560 km& sup& & /sup& ) in central Taiwan as an ex le of how well GRACE data compare to field-based data for ascertaining small-scale variations in GWS. Field measurements of groundwater level in 52 observation wells (2002-2017) were used to analyze variations in GWS. Results of this field-based analysis were compared to results obtained using the GWS data (2002-2017) obtained by GRACE gravimetry. This comparison allowed us to evaluate the similarities and differences in both methods as well as to prove the feasibility of using GRACE gravimetry in small-scale regions. Results of our comparative analysis indicate that water resources in small watershed can be successfully managed using gravimetric data collected by GRACE satellite.& & & & & & & & & Keywords: Groundwater storage, GRACE, Watershed& &
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-3749
Abstract: & & Snow avalanches are one of the most predominant natural hazards in mountain areas. Every year throughout the world, they are the cause of much material destruction and loss of life. It is therefore essential for local communities and public authorities to assess areas most vulnerable to avalanches. Here, we propose a new method for automatic avalanche detection from Landsat archives, using a snow index. This open-source and user-friendly model in Google Engine is the first to automatically inventory all the avalanches that have occurred each year across wide catchment areas, over a period of 32 years. The Snow Avalanche Frequency Estimation (SAFE) model was tested in the mountains of Afghanistan - Amu Panj Basin - one of the most remote regions in the world and one of the poorest in terms of avalanche monitoring. SAFE correctly detected 76% of the actual avalanches identified on Google Earth images and in the field. Since 1990, this region of Afghanistan has been impacted by 810,000 avalanches with an average frequency of 0.88 avalanches/km& #178 yr& sup& -1& /sup& . With SAFE, it is now possible to clearly identify villages, roads, and rivers that are frequently affected by avalanches and thus help decision-makers in their investments in avalanche protection infrastructure. It was also found that the frequency of avalanches has not changed over the last 32 years, but SAFE has identified a northeast shift of these hazards, notably due to a slight increase in temperatures in the south at the beginning of winter. SAFE is the first robust model that can be used worldwide and is capable of filling data voids on snow avalanche impacts in inaccessible regions.& &
Publisher: MDPI AG
Date: 26-01-2019
DOI: 10.3390/W11020212
Abstract: The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results. Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash–Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.
Publisher: Elsevier BV
Date: 05-2015
Publisher: University of Queensland Library
Publisher: MDPI AG
Date: 02-11-2019
DOI: 10.3390/RS11212575
Abstract: Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne c aigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success.
Publisher: MDPI AG
Date: 26-09-2014
DOI: 10.3390/RS6109213
No related grants have been discovered for Ben Jarihani.