Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the ...Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.
In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness ...and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.
Park, S.-I.; Hwang, Y.-S.; Lee, J.-J., and Um, J.-S., 2021. Evaluating operational potential of UAV transect mapping for wetland vegetation survey. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and ...Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 474–478. Coconut Creek (Florida), ISSN 0749-0208. There has been little research on data collection using Unmanned Aerial Vehicle (UAV) remote sensing in exploring wetland vegetation communities. The aim of this research was to integrate the concept of typical UAV remote sensing into in situ transect techniques. Transect sampling is commonly utilized to identify biological diversity and richness in vegetation communities. The Object-based Image Analysis was used to identify area-wide wetland vegetation that distinctively appears according to the UAV multispectral sensor's spectral characteristic. This study verified UAV transect mapping on wetland vegetation based on comparative evaluation between field survey and UAV transect method. This research offered an objective reference to UAV transect mapping's operational potential as the alternatives to traditional in-situ transect sampling in the coastal wetland.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. Two of these three models are data mining models: Boosted Regression Tree (BRT) ...model and Random Forest (RF) model. The other model is the Logistic Regression (LR) model. The three models are based on the relationship between groundwater-productivity data (specific capacity (SPC) and transmissivity (T)) and the related hydro-geological factors from thematic maps, such as topography, lineament, geology, land cover, and etc. The thematic maps which are generated from the remote sensing images. Groundwater productivity data were collected from 86 wells locations. The resulting GPP maps were validated through area-under-the-curve (AUC) analysis using wells data that had not been used for training the model. When T was used in the BRT, RF, and LR models, the obtained GPP maps had 81.66%, 80.21%, and 85.04% accuracy, respectively, and when SPC was used, the maps had 81.53%, 78.57%, and 82.22% accuracy, respectively. The LR model, which is a statistical model, showed the highest verification accuracy, also the other two models showed high accuracies. These observations indicate that all three models can be useful for groundwater resource development.
The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, ...telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe data redundancy—namely, that non-solar panel area occupies more than 99% of ground target, causing a serious lack of the thermal markers on solar panels. This study aims to explore the correlations between the thermal signatures of urban rooftop solar panels obtained from a UAV video stream and autopilot-based photomosaic. The thermal signatures of video imaging are strongly correlated (0.89–0.99) to those of autopilot-based photomosaics. Furthermore, the differences in the thermal signatures of solar panels between the video and photomosaic are aligned in the range of noise equivalent differential temperature with a 95% confidence level. The results of this study could serve as a valuable reference for employing video stream-based thermal imaging to urban rooftop solar panels.
Background: Treatment of critical limb ischemia (CLI) by bypass operation or percutaneous vascular intervention is occasionally difficult. The safety and efficacy of multiple intramuscular adipose ...tissue-derived mesenchymal stem cells (ATMSC) injections in CLI patients was determined in the study. Methods and Results: The study included 15 male CLI patients with ischemic resting pain in 1 limb with/without non-healing ulcers and necrotic foot. ATMSC were isolated from adipose tissue of thromboangiitis obliterans (TAO) patients (B-ATMSC), diabetes patients (D-ATMSC), and healthy donors (control ATMSC). In a colony-forming unit assay, the stromal vascular fraction of TAO and diabetic patients yielded lesser colonies than that of healthy donors. D-ATMSC showed lower proliferation abilitythan B-ATMSC and control ATMSC, but they showed similar angiogenic factor expression with control ATMSC and B-ATMSC. Multiple intramuscular ATMSC injections cause no complications during the follow-up period (mean follow-up time: 6 months). Clinical improvement occurred in 66.7% of patients. Five patients required minor amputation during follow-up, and all amputation sites healed completely. At 6 months, significant improvement was noted on pain rating scales and in claudication walking distance. Digital subtraction angiography before and 6 months after ATMSC implantation showed formation of numerous vascular collateral networks across affected arteries. Conclusions: Multiple intramuscular ATMSC injections might be a safe alternative to achieve therapeutic angiogenesis in patients with CLI who are refractory to other treatment modalities. (Circ J 2012; 76: 1750–1760)
Recently a multiple-aperture interferometry (MAI)-based azimuth shift method has been proposed to correct the ionospheric phase on synthetic aperture radar (SAR) interferograms. This method needs to ...determine integral constants required for azimuth integration. The exact estimation of the integral constants plays a key role in this MAI-based method. In this paper, we propose an efficient method for improving the performance of integral constant estimation, which functions by simultaneously removing the ionospheric and orbital phase artifacts from the interferometric SAR interferogram. We validate the performance improvement of the proposed method using two Advanced Land Observation Satellite Phase Array L-band SAR (ALOS PALSAR) interferometric pairs. The proposed method is compared with a MAI-based method, which does not work well due to azimuth integration errors. The proposed method successfully corrects the ionospheric and orbital phase artifacts. In addition, we compare the performances of the previous and proposed methods using the in situ Global Positioning System velocities. The root-mean-square error (RMSE) in line-of sight velocity from the previous method is about 7.0 mm/yr, whereas the RMSE from the proposed method is about 4.3 mm/yr. An RMSE reduction of about 38.6% is achieved when using the proposed method. These results indicate: 1) that the proposed method successfully estimates and corrects the ionosphere and orbital phase distortions; and 2) that the proposed method is superior to the previous method.
As the importance of forests has increased, continuously monitoring and managing information on forest ecology has become essential. The composition and distribution of tree species in forests are ...essential indicators of forest ecosystems. Several studies have been conducted to classify tree species using remote sensing data and machine learning algorithms because of the constraints of the traditional approach for classifying tree species in forests. In the machine learning approach, classification accuracy varies based on the characteristics and quantity of the study area data used. Thus, applying various classification models to achieve the most accurate classification results is necessary. In the literature, patch-based deep learning (DL) algorithms that use feature maps have shown superior classification results than point-based techniques. DL techniques substantially affect the performance of input data but gathering highly explanatory data is difficult in the study area. In this study, we analyzed (1) the accuracy of tree classification by convolutional neural networks (CNNs)-based DL models with various structures of CNN feature extraction areas using a high-resolution LiDAR-derived digital surface model (DSM) acquired from a drone platform and (2) the impact of tree classification by creating input data via various geometric augmentation methods. For performance comparison, the drone optic and LiDAR data were separated into two groups according to the application of data augmentation, and the classification performance was compared using three CNN-based models for each group. The results demonstrated that Groups 1 and CNN-1, CNN-2, and CNN-3 were 0.74, 0.79, and 0.82 and 0.79, 0.80, and 0.84, respectively, and the best mode was CNN-3 in Group 2. The results imply that (1) when classifying tree species in the forest using high-resolution bi-seasonal drone optical images and LiDAR data, a model in which the number of filters of various sizes and filters gradually decreased demonstrated a superior classification performance of 0.95 for a single tree and 0.75 for two or more mixed species; (2) classification performance is enhanced during model learning by augmenting training data, especially for two or more mixed tree species.
PDF
