대형 폐기물량 산정을 위한 UAS와 TLS 기반 공간정보 구축기법 연구

학위논문(박사)--서울대학교 대학원 :환경대학원 협동과정 조경학,2019. 8. 이동근.대형재난 발생에 대한 사전예방부터 대응단계까지 전과정의 체계적이고 효율적인 대처를 통해 인명, 재산, 환경 등의 피해를 최소화하여야 한다. 본 연구는 대형재난 발생 시 대응 과정 중 폐기물량 산정에 집중하여 연구를 수행하였다. 대형폐기물량 산정에 대한 연구는 과거부터 수행되고 있지만 실질적인 측정이 어렵기 때문에 발생 이전의 정보를 이용하여 모델링, 원격탐사 등의 기술을 이용하여 폐기물량을 예측하는 연구가 다수 수행되고 있다. 본 연구에서는 최근 활발하게 이용되고 있는 UAS (Unmanned Aerial System)를 기반으로 폐기물량을 산정하고 정확도를 평가하며 기존 기술과의 비교와 분석을 수행하고자 하였다. UAS는 UAV (Unmanned Aerial Vehicle)를 이용하여 영상을 취득하고 분석하는 전반적인 과정이라고 볼 수 있다. UAS를 이용하여 3차원 공간정보를 구축하고 정확도를 평가하는 연구가 과거부터 주로 수행되고 있으며 다양한 분야에 적용되고 있다. 이와 유사하게 TLS (Terrestrial Laser Scanning)를 이용하여 3차원 공간정보를 구축할 수 있는데 측량 분야에서 주로 이용되고 있으며 그 정확성 또한 우수하여 식생, 건축, 토목, 문화재, 지형측량 등 다양한 분야에서 널리 이용되고 있다. 대형폐기물량 또한 TLS를 이용하여 3차원 공간정보 구축 후 산정할 수 있지만 비용, 시간 등의 제약사항으로 인해 활용이 불가능하다고 볼 수 있다. 본 연구는 크게 3가지 부분으로 구분할 수 있다. 첫 번째는 UAS를 이용한 3차원 공간정보 구축과 폐기물량 산정 가능성 모색이다. UAS를 이용하여 3차원 공간정보 구축까지의 과정을 정밀 분석하여 최적의 비행변수와 기타 변수를 도출하여 폐기물량 산정의 가능성을 보고자 하였다. 두 번째는 TLS 기술과 UAS 기술 기반의 3차원 공간정보의 비교와 분석이다. 각각의 3차원 공간정보를 M3C2알고리즘을 이용하여 비교하고 분석하여 최적의 폐기물량 산정 기법을 도출하고자 하였다. 마지막으로 세 번째는 3차원 공간정보의 융합과 효율성 분석이다. 두 가지 기술을 융합하여 3차원 공간정보를 구축하고 효율성을 분석하여 UAS, TLS, 융합기법 세가지 방법론간의 차이와 최적의 폐기물량 산정 기법을 도출하고자 하였다. 주요 비행변수는 비행고도와 영상의 중복도이며 이외 변수는 지상기준점 개수이다. 이 외에도 카메라 내부표정, 짐벌의 흔들림 정도를 분석하였다. 본 연구를 통해 56개의 케이스 중 최적의 변수를 도출하였으며 과거 연구와는 다르게 고도차이가 많이 나는 폐기물 지역에서는 DW (Distance covered on the ground by on image in Width direction)에 의해 결과가 도출되었다. 일반적으로 고도가 낮을수록 높은 정확도를 가지는 3차원 공간정보를 구축하지만 본 연구에서는 고도가 낮을수록 정확도가 낮아지는 것을 확인하였다. 56개의 케이스 모두 정확도 분석을 실시하였으며 정확도와 폐기물량간의 상관성이 있음을 도출하였다. 3차원 공간정보의 정확도가 높을수록 산정한 폐기물량이 유사했으며 이와 반대로 정확도가 낮은 3차원 공간정보들에서는 폐기물량이 제각각으로 나타나는 것을 확인할 수 있었다. 이러한 일련의 과정을 통해 폐기물량 산정을 위한 UAS 최적 변수를 도출하였으며 3차원 공간정보 기반의 폐기물량 산정 가능성을 확인할 수 있었다. M3C2알고리즘을 이용하여 UAS와 TLS 기반의 3차원 공간정보를 비교하였으며 이를 통해, 각각의 공간정보가 가지고 있는 장단점을 확인할 수 있었다. 정확도의 경우, UAS기반 3차원 공간정보의 RMSE는 0.032m, TLS의 RMSE는 0.202m로 UAS의 정확도가 더 높은 것으로 나타났다. 두 가지 기술을 융합한 3차원 공간정보의 RMSE는 0.030m로써 세 가지 방법론 중에서 가장 높은 정확도를 보였다. 하지만 효율성 관점에서 분석한 결과, UAS 기반의 3차원 공간정보가 단시간에 높은 정확도를 보이는 결과로 도출됨으로써 대형폐기물량 산정에 최적화된 기술과 방법론을 가지고 있는 것으로 확인할 수 있었다. 이 외에도 비용을 분석한 결과, UAS 기반의 3차원 모형 구축까지 소비된 비용이 TLS에 비해 적은 비용이 소비된 것을 확인할 수 있었다. 대형재난 시 비교적 단시간에 대응하여 피해를 최소화 하고 다양한 의사결정을 진행해야 하는데, 본 연구를 통해 도출한 UAS 기반의 3차원 공간정보 구축 기법은 대형 폐기물량산정과 공간적 의사결정에 활용할 수 있을 기대한다.Damage to people, property, and the environment must be minimized through systematic and efficient handling of large-scale disasters throughout the entire process from prevention to the response stage. This study focused on the waste quantity calculations that are part of the response process during large-scale disasters. Studies on large-scale waste quantity calculations have been performed in the past, but actual measurements are difficult. Therefore, many studies are being performed on using information from previous instances to perform modeling and using technologies such as remote sensing to estimate waste quantities. This study calculated waste quantities based on UAS (unmanned aerial system), which is a technology that is often used these days. It evaluated the accuracy of this technology, and it analyzed and compared the technology with existing technologies. UAS can be seen as an overall process of using UAVs (Unmanned Aerial Vehicle) to capture images and analyzing them. Studies have been conducted in the past on using UAS to build 3D spatial information and evaluate accuracy, and they are being used integrally in a variety of fields. Similarly, 3D spatial information can be built using TLS (Terrestrial Laser Scanning), and these are chiefly used in the surveying field. This methods accuracy is excellent, and it is widely used in a variety of fields such as vegetation, construction, civil engineering, cultural assets, and topographical surveys. Large-scale waste can also be calculated by using TLS to build a 3D spatial information, but it is seen as unfeasible to use due to cost and time limitations. This study is broadly divided into 3 parts. The first part is examining the feasibility of using UAS to build a 3D spatial information and calculate waste quantity. The process up to the point of using UAS to build a 3D spatial information was analyzed in detail, and optimal flight variables and other variables were found in order to examine the feasibility of calculating waste quantity. The second part is comparing and analyzing 3D spatial information based on TLS and UAS technology. The 3D spatial information were compared and analyzed using the M3C2 algorithm, and the optimal waste quantity calculation methods were found. Finally, the third part is analyzing a combination of the 3D spatial information and the 3D spatial information efficiency. The two technologies were combined to build a 3D spatial information, and their efficiency was analyzed to find the differences between the three methodologies (UAS, TLS, and the combined method), as well as find the optimal waste quantity calculation method. The major flight variables are the flight altitude and image overlap. Another variable is the number of ground control points. In addition to this, the camera interior orientation and degree of gimbal shaking were analyzed. Through this study, the optimal variables among 56 cases were found. Unlike past studies, it was discovered that the results were contrary to previous studies due to the DW (Distance covered on the ground by on image in Width direction) in waste regions with a lot of altitude differences. Normally, as the altitude becomes lower, the accuracy of the 3D spatial information becomes higher, but in this study it was found that the accuracy became lower as the altitude became lower. The accuracy of all 56 cases was analyzed, and it was found that there is a correlation between accuracy and the amount of waste. As the accuracy of the 3D spatial information increased, the calculated waste amounts became similar. Conversely, in 3D spatial information with low accuracy, it was found that the waste amounts were different. Through this sequential process, the optimal UAS variables for calculating waste amounts were found, and it was possible to confirm the feasibility of calculating waste amounts based on 3D spatial iformation. The M3C2 algorithm was used to compare the UAS and TLS-based 3D spatial information, and by doing so, it was possible to confirm the advantages and disadvantages of each model. As for accuracy, the RMSE of the UAS-based 3D spatial information was 0.032 m, and the RMSE of the TLS model was 0.202, making the UAS models accuracy higher. The RMSE of the 3D spatial information which combined the two technologies was 0.030 m, and it showed the highest accuracy of the three methodologies. However, in terms of efficiency, the analyzed results were able to confirm that the UAS-based 3D spatial information had the optimal technology and methodology for large-scale waste amount calculations by creating a model which shows high accuracy in a short time. In addition, cost analysis results were able to confirm that the cost of building the UAS-based 3D spatial information was lower than that of TLS. During large-scale disasters, it is necessary to respond in a relatively short time to minimize damage and perform a variety of decision-making. The UAS-based 3D spatial information building method found in this study can be used for large-scale waste amount calculations and spatial decision-making.I. Introduction 1 II. Literature Review 7 1. Studies on Applying the UAS to Disaster Management 7 2. Accuracy of UAS-based 3D Model Construction 14 3. Disaster Waste Quantity 26 III. Materials and Methods 34 1. Optimal Flight Parameters for UAV Generating 3D Spatial Information 36 1.1. Design of UAV Flight 36 1.2. Photogrammetric Processing for the Acquisition of 3D Spatial Information 41 1.3. Assessment of the 3D Spatial Information Accuracy 43 1.4. Computation of the Amount of Waste 45 2. Comparison and Analysis of TLS and UAS Methodology for Optimal Volume Computation 47 2.1. TLS and UAS-based 3D Spatial Information Generation and Volume Computation 49 2.2. Comparison and Analysis of 3D Spatial Information 55 3. Multispace Fusion Methodology-based 3D Spatial Information Generating and Efficiency Analysis 57 3.1. Multispace Fusion Methodology-based 3D Spatial Information 57 3.2. Efficiency Analysis of 3D Spatial Information for Responding to Large-scale Disasters 58 III. Result and Discussion 59 1. Optimal Flight Parameters for UAV Generating 3D Spatial Information and Investigation of Feasibility 59 1.1. Generation of 3D Spatial Information using UAS 59 1.2. Assessment of the 3D Spatial Information Accuracy 64 1.3. Computation of the Amount of Waste and Optimal flights parameters 76 2. Comparison and Analysis of TLS and UAS-based 3D Spatial Information 84 2.1. Generation of 3D Spatial Information and Volume Computation using UAS 84 2.2. Spatial Comparison and Analysis 88 3. Multispace Fusion Methodology-based 3D Spatial Information Generating and Efficiency Analysis 93 3.1. Multispace Fusion Methodology-based 3D Spatial Information 93 3.2. 3D Spatial information Efficiency Analysis for Responding to Large-scale Disasters 96 IV. Conclusion 100 V. Bibliography 103Docto

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazard

Efficient Semantic Segmentation on Edge Devices

Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module

Rice Response to Nitrogen Fertilization and Comparison of Unmanned Aerial Systems and Active Crop Canopy Sensors Vegetative Index to Estimate Rice Yield Potential

Nitrogen (N) fertilization is a key component in producing profitable, maximized rice grain yields because yield is directly affected by N fertilizer applications. Economical optimum N rate (EONR) is used to estimate where the N fertilization rate impacts rice grain yield but is still economically efficient. Three common response models, linear-plateau, quadratic-plateau, and quadratic models were used to determine the response of rice to N fertilizer to determine the optimum N fertilization rate. The objective of the first part of this study was to evaluate the models by assessing the coefficients of determination (R2), maximum rice grain yields each model produced, and the estimated EONRs of fertilization. Coefficients of determination (R2) of the linear-plateau, quadratic-plateau, and quadratic were found to be similar (0.77, 0.79, 0.78). Other factors beyond just R2 alone need to be taken into consideration when choosing which response model best fits a data set and should be used to estimate the EONR of fertilization for an individual variety. Normalized difference vegetation index (NDVI) is a known indication of yield potential, one component needed to determine mid-season N requirements. The GreenSeeker has been the pre-dominant tool used to collect NDVI measurements. Unmanned aerial systems (UAS) have shown potential to collect NDVI measurements also. The objectives of the second part of this study were to: 1) evaluate the relationship between GreenSeeker (an active sensor) derived NDVI and UAS (a passive sensor) derived NDVI, and 2) evaluate the ability of GreenSeeker and UAS derived NDVI to estimate rice yield potential. This research was done in 2017 and 2018 at 5 locations in Louisiana. Remote sensor data was taken between panicle initiation and panicle differentiation using a GreenSeeker and UAS mounted remote sensor. All 5 locations showed a highly significant correlation between GreenSeeker and UAS derived NDVI. The linear relationship between GreenSeeker and UAS derived NDVI to rice grain yield were not similar. The different relationships could have been caused by the differences between ground and air-borne based sensors. More research will need to be conducted before UAS mounted sensors can be used to accurately predict mid-season N needs in rice

Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

There is increasing interest in using Unmanned Aircraft Systems (UAS) in flood risk management activities including in response to flood events. However, there is little evidence that they are used in a structured and strategic manner to best effect. An effective response to flooding is essential if lives are to be saved and suffering alleviated. This study evaluates how UAS can be used in the preparation for and response to flood emergencies and develops guidelines for their deployment before, during and after a flood event. A comprehensive literature review and interviews, with people with practical experience of flood risk management, compared the current organizational and operational structures for flood emergency response in both England and India, and developed a deployment analysis matrix of existing UAS applications. An online survey was carried out in England to assess how the technology could be further developed to meet flood emergency response needs. The deployment analysis matrix has the potential to be translated into an Indian context and other countries. Those organizations responsible for overseeing flood risk management activities including the response to flooding events will have to keep abreast of the rapid technological advances in UAS if they are to be used to best effect

Mapping Topobathymetry in a Shallow Tidal Environment Using Low-Cost Technology

Detailed knowledge of nearshore topography and bathymetry is required for a wide variety of purposes, including ecosystem protection, coastal management, and flood and erosion monitoring and research, among others. Both topography and bathymetry are usually studied separately; however, many scientific questions and challenges require an integrated approach. LiDAR technology is often the preferred data source for the generation of topobathymetric models, but because of its high cost, it is necessary to exploit other data sources. In this regard, the main goal of this study was to present a methodological proposal to generate a topobathymetric model, using low-cost unmanned platforms (unmanned aerial vehicle and unmanned surface vessel) in a very shallow/shallow and turbid tidal environment (Bahia Blanca estuary, Argentina). Moreover, a cross-analysis of the topobathymetric and the tide level data was conducted, to provide a classification of hydrogeomorphic zones. As a main result, a continuous terrain model was built, with a spatial resolution of approximately 0.08 m (topography) and 0.50 m (bathymetry). Concerning the structure from motion-derived topography, the accuracy gave a root mean square error of 0.09 m for the vertical plane. The best interpolated bathymetry (inverse distance weighting method), which was aligned to the topography (as reference), showed a root mean square error of 0.18 m (in average) and a mean absolute error of 0.05 m. The final topobathymetric model showed an adequate representation of the terrain, making it well suited for examining many landforms. This study helps to confirm the potential for remote sensing of shallow tidal environments by demonstrating how the data source heterogeneity can be exploited.Fil: Genchi, Sibila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Vitale, Alejandro José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; ArgentinaFil: Seitz, Carina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable