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Title: Mapping Historical Structures Using UAV Technology.
Creator: Jason Blankenship, Paulo Fernandes, Hongbo Su
Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FAU_SR00000004
Subject Headings: College students --Research --United States.
Format: Document (PDF)

Title: Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval.
Creator: Mi, Sujuan, Su, Hongbo, Zhang, Renhua, Tian, Jing
Abstract/Description: With the development of quantitative remote sensing, regional evapotranspiration (ET) modeling based on the feature space has made substantial progress. Among those feature space based evapotranspiration models, accurate determination of the dry/wet lines remains a challenging task. This paper reports the development of a new model, named DDTI (Determination of Dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between the thermal inertia and the...
Show moreWith the development of quantitative remote sensing, regional evapotranspiration (ET) modeling based on the feature space has made substantial progress. Among those feature space based evapotranspiration models, accurate determination of the dry/wet lines remains a challenging task. This paper reports the development of a new model, named DDTI (Determination of Dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between the thermal inertia and the soil moisture. The Simplified Thermal Inertia value estimated in the North China Plain is consistent with the value measured in the laboratory. Three evaluation methods, which are based on the comparison of the locations of the theoretical dry line determined by two models (DDTI model and the heat energy balance model), the comparison of ET results, and the comparison of the evaporative fraction between the estimates from the two models and the in situ measurements, were used to assess the performance of the new model DDTI. The location of the theoretical dry line determined by DDTI is more reasonable than that determined by the heat energy balance model. ET estimated from DDTI has an RMSE (Root Mean Square Error) of 56.77 W/m^2 and a bias of 27.17 W/m^2; while the heat energy balance model estimated ET with an RMSE of 83.36 W/m^2 and a bias of −38.42 W/m^2. When comparing the coeffcient of determination for the two models with the observations from Yucheng, DDTI demonstrated ET with an R^2 of 0.9065; while the heat energy balance model has an R^2 of 0.7729. When compared with the in situ measurements of evaporative fraction (EF) at Yucheng Experimental Station, the ET model based on DDTI reproduces the pixel scale EF with an RMSE of 0.149, much lower than that based on the heat energy balance model which has an RMSE of 0.220. Also, the EF bias between the DDTI model and the in situ measurements is 0.064, lower than the EF bias of the heat energy balance model, which is 0.084.
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Date Issued: 2015-08-24
PURL: http://purl.flvc.org/fau/fd/FAUIR000052
Format: Citation

Title: Non-Contact Measurement of the Spectral Emissivity through Active/Passive Synergy of CO2 Laser at 10.6 µm and 102F FTIR (Fourier Transform Infrared) Spectrometer.
Creator: Zhang, Renhua, Su, Hongbo, Tian, Jing, Mi, Su-Juan, Li, Zhao-Liang
Abstract/Description: In the inversion of land surface temperature (LST) from satellite data, obtaining the information on land surface emissivity is most challenging. How to solve both the emissivity and the LST from the underdetermined equations for thermal infrared radiation is a hot research topic related to quantitative thermal infrared remote sensing. The academic research and practical applications based on the temperature-emissivity retrieval algorithms show that directly measuring the emissivity of...
Show moreIn the inversion of land surface temperature (LST) from satellite data, obtaining the information on land surface emissivity is most challenging. How to solve both the emissivity and the LST from the underdetermined equations for thermal infrared radiation is a hot research topic related to quantitative thermal infrared remote sensing. The academic research and practical applications based on the temperature-emissivity retrieval algorithms show that directly measuring the emissivity of objects at a fixed thermal infrared waveband is an important way to close the underdetermined equations for thermal infrared radiation. Based on the prior research results of both the authors and others, this paper proposes a new approach of obtaining the spectral emissivity of the object at 8–14 µm with a single-band CO2 laser at 10.6 µm and a 102F FTIR spectrometer. Through experiments, the spectral emissivity of several key samples, including aluminum plate, iron plate, copper plate, marble plate, rubber sheet, and paper board, at 8–14 µm is obtained, and the measured data are basically consistent with the hemispherical emissivity measurement by a Nicolet iS10 FTIR spectrometer for the same objects. For the rough surface of materials, such as marble and rusty iron, the RMSE of emissivity is below 0.05. The differences in the field of view angle and in the measuring direction between the Nicolet FTIR method and the method proposed in the paper, and the heterogeneity in the degree of oxidation, polishing and composition of the samples, are the main reasons for the differences of the emissivities between the two methods.
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Date Issued: 2016-06-24
PURL: http://purl.flvc.org/fau/fd/FAUIR000056
Format: Citation

Title: A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale.
Creator: Zhang, Renhua, Rong, Yuan, Tian, Jing, Su, Hongbo, Li, Zhao-Liang, Liu, Suhua
Abstract/Description: This paper presents a method of estimating regional distributions of surface air temperature (Ta) and surface vapor pressure (ea), which uses remotely-sensed data and meteorological data as its inputs. The method takes into account the effects of both local driving force and horizontal advection on Ta and ea. Good correlation coefficients (R2) and root mean square error (RMSE) between the measurements of Ta/ea at weather stations and Ta/ea estimates were obtained; with R^2 of 0.77, 0.82 and 0...
Show moreThis paper presents a method of estimating regional distributions of surface air temperature (Ta) and surface vapor pressure (ea), which uses remotely-sensed data and meteorological data as its inputs. The method takes into account the effects of both local driving force and horizontal advection on Ta and ea. Good correlation coefficients (R2) and root mean square error (RMSE) between the measurements of Ta/ea at weather stations and Ta/ea estimates were obtained; with R^2 of 0.77, 0.82 and 0.80 and RMSE of 0.42K, 0.35K and 0.20K for Ta and with R^2 of 0.85, 0.88, 0.88 and RMSE of 0.24hpa, 0.35hpa and 0.16hpa for ea, respectively, for the three-day results. This result is much better than that estimated from the inverse distance weighted method (IDW). The performance of Ta/ea estimates at Dongping Lake illustrated that the method proposed in the paper also has good accuracy for a heterogeneous surface. The absolute biases of Ta and ea estimates at Dongping Lake from the proposed method are less than 0.5Kand 0.7hpa, respectively, while the absolute biases of them from the IDW method are more than 2K and 3hpa, respectively. Sensitivity analysis suggests that the Ta estimation method presented in the paper is most sensitive to surface temperature and that the ea estimation method is most sensitive to available energy.
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Date Issued: 2015-05-13
PURL: http://purl.flvc.org/fau/fd/FAUIR000053
Format: Citation

Title: Analysis of the Urban Heat Island Effect in Shijiazhuang, China Using Satellite and Airborne Data.
Creator: Liu, Kai, Su, Hongbo, Zhang, Lifu, Yang, Hang, Zhang, Renhua, Li, Xueke
Abstract/Description: The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in...
Show moreThe urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect existed in the study area during summer days, with a SUHI intensity of 2–4 °C. The analyses reveal that ISA% can provide an additional metric for the study of SUHI, yet its association with LST is not straightforward and this should a focus in future work. It was also found that two physically based indices, VIUPD and BCI, have the potential to account for the variation in urban LST. The results concerning on TASI indicate that diversity of impervious surfaces (rooftops, concrete, and mixed asphalt) contribute most to the SUHI, among all of the land cover features. Moreover, the effect of impervious surfaces on LST is complicated, and the composition and arrangement of land cover features may play an important role in determining the magnitude and intensity of SUHI. Overall, the analysis of urban thermal signatures at two spatial scales complement each other and the use of airborne imagery data with higher spatial resolution is helpful in revealing more details for understanding urban thermal environments.
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Date Issued: 2015-04-20
PURL: http://purl.flvc.org/fau/fd/FAUIR000054
Format: Citation

Title: Regional Estimation of Remotely Sensed Evapotranspiration Using the Surface Energy Balance-Advection (SEB-A) Method.
Creator: Liu, Suhua, Su, Hongbo, Zhang, Renhua, Tian, Jing, Chen, Shaohui, Wang, Weizhen
Date Issued: 2016-08-05
PURL: http://purl.flvc.org/fau/fd/FAUIR000192
Format: Citation

Title: AN EXAMINATION OF DOWNSCALING A FLOOD RISK SCREENING TOOL AT THE WATERSHED, SUBWATERSHED, AND MUNICIPAL LEVELS.
Creator: Hindle, Tucker, Bloetscher, Frederick, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: This research aims to develop a large-scale locally relevant flood risk screening tool, that is, one capable of generating accurate probabilistic inundation maps quickly while still detecting localized nuisance-destructive flood potential. The CASCADE 2001 routing model is integrated with GIS to compare the predicted flood response to heavy rains at the watershed, subwatershed, and municipal levels. Therefore, the objective is to evaluate the impact of scale for determining flood risk in a...
Show moreThis research aims to develop a large-scale locally relevant flood risk screening tool, that is, one capable of generating accurate probabilistic inundation maps quickly while still detecting localized nuisance-destructive flood potential. The CASCADE 2001 routing model is integrated with GIS to compare the predicted flood response to heavy rains at the watershed, subwatershed, and municipal levels. Therefore, the objective is to evaluate the impact of scale for determining flood risk in a community. The findings indicate that a watershed-level analysis captures most flooding. However, the flood prediction improves to match existing FEMA flood maps as drill-down occurs at the subwatershed and municipal scales. The drill-down modeling solution presented in this study provides the necessary degree of local relevance for excellent detection in developed areas because of the downscaling techniques and local infrastructure. This validated model framework supports the development and prioritization of protection plans that address flood resilience in the context of watershed master planning and the Community Rating System.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013779
Subject Headings: Floods--Risk assessment, Watersheds
Format: Document (PDF)

Title: Water and Soil Salinity Mapping for Southern Everglades using Remote Sensing Techniques and In Situ Observations.
Creator: Khadim, Fahad Khan, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: Everglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and...
Show moreEverglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and 2016 was carried out. For soil salinity mapping, supervised land cover classification was firstly carried out for 1996, 2000, 2006, 2010 and 2015; with the first four providing average accuracies of 82%-94% against existing NLCD classifications. The land cover classes and EC thresholds helped mapping four soil salinity classes namely, the non saline (EC = 0~2 dS/m), low saline (EC = 2~4 dS/m), moderate saline (EC = 4~8 dS/m) and high saline (EC >8 dS/m) areas.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004860, http://purl.flvc.org/fau/fd/FA00004860
Subject Headings: Everglades National Park (Fla.)--Environmental conditions., Florida Bay (Fla.)--Environmental conditions., Remote sensing., Multispectral imaging., Environmental monitoring--Remote sensing., Geographic information systems., Soils--Remote sensing., Soil moisture--Measurement., Soil mapping.
Format: Document (PDF)

Title: Using Deep Learning Semantic Segmentation to Estimate Visual Odometry.
Creator: Blankenship, Jason R., Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: In this research, image segmentation and visual odometry estimations in real time are addressed, and two main contributions were made to this field. First, a new image segmentation and classification algorithm named DilatedU-NET is introduced. This deep learning based algorithm is able to process seven frames per-second and achieves over 84% accuracy using the Cityscapes dataset. Secondly, a new method to estimate visual odometry is introduced. Using the KITTI benchmark dataset as a baseline,...
Show moreIn this research, image segmentation and visual odometry estimations in real time are addressed, and two main contributions were made to this field. First, a new image segmentation and classification algorithm named DilatedU-NET is introduced. This deep learning based algorithm is able to process seven frames per-second and achieves over 84% accuracy using the Cityscapes dataset. Secondly, a new method to estimate visual odometry is introduced. Using the KITTI benchmark dataset as a baseline, the visual odometry error was more significant than could be accurately measured. However, the robust framerate speed made up for this, able to process 15 frames per second.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00005990
Subject Headings: Image segmentation, Computer vision, Deep learning, Visual odometry
Format: Document (PDF)

Title: Real Time Traffic Monitoring System from a UAV Platform.
Creator: Biswas, Debojit, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: Today transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate...
Show moreToday transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate better understanding of traffic. More specifically, this research focused on traffic and UAV cameras to extract information about the traffic. Our first goal was to create an automatic system to count the cars using traffic cameras. To achieve this goal, we implemented Background Subtraction Method (BSM) and OverFeat Framework. BSM compares consecutive frames to detect the moving objects. Because BSM only works for ideal lab conditions, therefor we implemented a Convolutional Neural Network (CNN) based classification algorithm called OverFeat Framework. We created different segments on the road in various lanes to tabulate the number of passing cars. We achieved 96.55% accuracy for car counting irrespective of different visibility conditions of the day and night. Our second goal was to find out traffic density. We implemented two CNN based algorithms: Single Shot Detection (SSD) and MobileNet-SSD for vehicle detection. These algorithms are object detection algorithms. We used traffic cameras to detect vehicles on the roads. We utilized road markers and light pole distances to determine distances on the road. Using the distance and count information we calculated density. SSD is a more resource intense algorithm and it achieved 92.97% accuracy. MobileNet-SSD is a lighter algorithm and it achieved 79.30% accuracy. Finally, from a moving platform we estimated the velocity of multiple vehicles. There are a lot of roads where traffic cameras are not available, also traffic monitoring is necessary for special events. We implemented Faster R-CNN as a detection algorithm and Discriminative Correlation Filter (with Channel and Spatial Reliability Tracking) for tracking. We calculated the speed information from the tracking information in our study. Our framework achieved 96.80% speed accuracy compared to manual observation of speeds.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013188
Subject Headings: Traffic monitoring, Intelligent transportation systems, Neural networks (Computer science), Vehicle detectors, Unmanned aerial vehicles
Format: Document (PDF)

Title: SEAWALL DETECTION IN FLORIDA COASTAL AREA FROM HIGH RESOLUTION IMAGERY USING MACHINE LEARNING AND OBIA.
Creator: Paudel, Sanjaya, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: In this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image...
Show moreIn this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image analysis (OBIA) method were applied for image classification. However, Pixel based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning technique, knowledge-based technique and machine learning followed by knowledge-based technique were used to compare the most efficient method of classification. While performing the machine learning technique, three algorithms: Random Forest, support vector machine and decision tree were applied to test the best algorithm. Of all the approaches used, the combination of machine learning and a knowledge-based method was able to map the sea wall effectively.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013802
Subject Headings: Image analysis, Coasts--Florida, Machine learning
Format: Document (PDF)

Title: AUTOMATIC DETECTION OF BUILDING DAMAGE CAUSED BY HURRICANE ON FLORIDA COASTAL AREA FROM AERIAL IMAGES.
Creator: Gyegyiri, Joseph, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Rapid response and efficient damage assessment are life-or-death matters in the wake of natural disasters such as hurricanes and earthquakes. These events wreak havoc on infrastructure and properties and, most critically, endanger human lives. The timely and effective allocation of resources during such crises is imperative, necessitating meticulous planning based on the extent of damage incurred. This research presents an approach to automating the damage assessment using pre/post-disaster...
Show moreRapid response and efficient damage assessment are life-or-death matters in the wake of natural disasters such as hurricanes and earthquakes. These events wreak havoc on infrastructure and properties and, most critically, endanger human lives. The timely and effective allocation of resources during such crises is imperative, necessitating meticulous planning based on the extent of damage incurred. This research presents an approach to automating the damage assessment using pre/post-disaster aerial images and computer vision. Recent advancements in disaster response strategies have encouraged researchers to harness the power of satellite and aerial imagery to assess the aftermath. Usually, due to the different characteristics between training datasets and available datasets in times of disasters, retraining the model to improve detection accuracy has been the norm, even though it is time and resource intensive. Our method surpasses conventional solutions and requires no retraining or fine-tuning on disaster-specific data. An existing model was retrained and improved on a diverse building damage dataset and demonstrably generalizes to new disaster scenarios. Having achieved higher performances compared to state of the art models, we determines our models real world applicability by using Hurricane Ian as our potent study grounds.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014427
Subject Headings: Remote-sensing images, Natural disasters, Natural disasters--Data processing
Format: Document (PDF)

Title: A Critical Evaluation of the Nonparametric Approach to Estimate Terrestrial Evaporation.
Creator: Yang, Yongmin, Su, Hongbo, Qi, Jianwei
Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FAUIR000191
Format: Citation

Title: Observations and Modeling of the Climatic Impact of Land-Use Changes 2014.
Creator: Deng, Xiangzheng, Singh, R. B., Jiang, Qun’ou, Dong, Jinwei, Su, Hongbo
Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FAUIR000055
Format: Citation

FAU College Collections (7)	+ -
FAU Theses and Dissertations (6)	+ -
FAU Undergraduate Student Research (1)	+ -

Coasts--Florida (1)	+ -
College students --Research --United States. (1)	+ -
Computer vision (1)	+ -
Deep learning (1)	+ -
Environmental monitoring--Remote sensing. (1)	+ -

Everglades National Park (Fla.)--Environmental conditions. (1)	+ -
Floods--Risk assessment (1)	+ -
Florida Bay (Fla.)--Environmental conditions. (1)	+ -
Geographic information systems. (1)	+ -
Image analysis (1)	+ -
Image segmentation (1)	+ -
Intelligent transportation systems (1)	+ -
Machine learning (1)	+ -
Multispectral imaging. (1)	+ -
Natural disasters (1)	+ -
Natural disasters--Data processing (1)	+ -
Neural networks (Computer science) (1)	+ -
Remote sensing. (1)	+ -
Remote-sensing images (1)	+ -
Soil mapping. (1)	+ -
Soil moisture--Measurement. (1)	+ -
Soils--Remote sensing. (1)	+ -
Traffic monitoring (1)	+ -
Unmanned aerial vehicles (1)	+ -
Vehicle detectors (1)	+ -
Visual odometry (1)	+ -
Watersheds (1)	+ -

article (7)	+ -
Electronic Thesis or Dissertation (6)	+ -
poster (1)	+ -

Su, Hongbo (13)	+ -
College of Engineering and Computer Science (6)	+ -
Department of Civil, Environmental and Geomatics Engineering (6)	+ -
Florida Atlantic University (6)	+ -
Zhang, Renhua (5)	+ -

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