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Multimodal Remote Sensing of Complex Forests for Height and Biomass Estimation.
Multimodal Remote Sensing of Complex Forests for Height and Biomass Estimation.
상세정보
- 자료유형
- 학위논문(국외)
- 기본표목-개인명
- 표제와 책임표시사항
- Multimodal Remote Sensing of Complex Forests for Height and Biomass Estimation.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 235 p.
- 일반주기
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 일반주기
- Advisor: Pierce, Leland E.;Sarabandi, Kamal.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 요약 등 주기
- 요약A fundamental technical challenge of any new space-borne vegetation remote sensing mission is the determination of what sensor(s) to place on-board and what, if any, overlapping modes of operation they will employ as each on-board sensor adds significant cost to the overall mission. In this work, a multimodal sensor fusion algorithm is presented that couples remotely sensed products from radar, electro-optical, and LiDAR with one another, as well as with physics-based models for each remote sensing technology. This unique fusion enables the joint estimation of the physical structure of a forest stand along with its above ground biomass; two critical factors to aid in forest management, commerce, and agriculture.It is shown that this proposed method achieves high-accuracy estimates while using minimal ancillary data in the estimation process. This thesis presents a method for combining measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with physics-based simulators and a minimal amount of ground information and to produce an estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m2 and 1.68 m respectively.The domain of the algorithm is then expanded to regions with sparse LiDAR coverage by adding a hierarchical step to approximate the LiDAR sensor's measurements based on the available SAR and EO/IR measurements coupled with the available LiDAR data. The expanded algorithm achieved biomass and canopy height with rms errors of 2.51 kg/m2 and 5.07 m, respectively.Transitioning from sensor fusion to the effect of phenomenology on SAR and Interferometric Synthetic Aperture Radar (InSAR), this thesis presents a novel approach to accurately model the impact of the wind, a temporal decorrelator, on these two technologies. In the case of InSAR, the correlation between the two source SAR datasets is critically important. Depending on the time between passes, repeat-pass InSAR is susceptible to temporal decorrelation effects as a target area can change due to the weather and / or animal activity including that of humans. Using repeat-pass InSAR to measure the height of a forest canopy above the ground is a common method of monitoring tree growth. However, if either source SAR image is collected during a wind-event, the resulting canopy height estimate will be impacted. This thesis seeks to quantify exactly how much this parameter can change due to the wind.In order to investigate and quantify the decorrelation induced by the wind, we have developed a model for the repeat-pass interferometric SAR response of a forest including the application of a wind field. The simulation consists of multiple interconnected parts including the generation of fractal tree geometries, a wind simulator to apply variable wind forces to the generated trees, an electromagnetic model to allow us to calculate a Single Look Complex (SLC) value for the SAR return of the combined target, an image-forming technique based on antenna array theory and an image processing algorithm. The results present polarimetric coherence as well as scattering phase center heights as a function of the look angle, wind speed, and the alignment of the tree with the incident wind field. An important feature of this research is the usage of a physically based realistic wind model that is based on measurements of wind effects on trees, as well as realistic models of fluid flow and simple harmonic branch segment resonators. Allowing branches to bend and move out of the plane of the incident wind field enables our model to capture numerous features of a physical tree blowing in the wind. This realistic model is necessary for a realistic simulation of the effects that wind has on a given InSAR imaging system as expressed in this study by the interferometric coherence.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 원문정보보기
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a537
■1001 ▼aBenson, Michael L.
■24510▼aMultimodal Remote Sensing of Complex Forests for Height and Biomass Estimation.
■260 ▼a[S.l.]▼bUniversity of Michigan. ▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a235 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Pierce, Leland E.;Sarabandi, Kamal.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aA fundamental technical challenge of any new space-borne vegetation remote sensing mission is the determination of what sensor(s) to place on-board and what, if any, overlapping modes of operation they will employ as each on-board sensor adds significant cost to the overall mission. In this work, a multimodal sensor fusion algorithm is presented that couples remotely sensed products from radar, electro-optical, and LiDAR with one another, as well as with physics-based models for each remote sensing technology. This unique fusion enables the joint estimation of the physical structure of a forest stand along with its above ground biomass; two critical factors to aid in forest management, commerce, and agriculture.It is shown that this proposed method achieves high-accuracy estimates while using minimal ancillary data in the estimation process. This thesis presents a method for combining measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with physics-based simulators and a minimal amount of ground information and to produce an estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m2 and 1.68 m respectively.The domain of the algorithm is then expanded to regions with sparse LiDAR coverage by adding a hierarchical step to approximate the LiDAR sensor's measurements based on the available SAR and EO/IR measurements coupled with the available LiDAR data. The expanded algorithm achieved biomass and canopy height with rms errors of 2.51 kg/m2 and 5.07 m, respectively.Transitioning from sensor fusion to the effect of phenomenology on SAR and Interferometric Synthetic Aperture Radar (InSAR), this thesis presents a novel approach to accurately model the impact of the wind, a temporal decorrelator, on these two technologies. In the case of InSAR, the correlation between the two source SAR datasets is critically important. Depending on the time between passes, repeat-pass InSAR is susceptible to temporal decorrelation effects as a target area can change due to the weather and / or animal activity including that of humans. Using repeat-pass InSAR to measure the height of a forest canopy above the ground is a common method of monitoring tree growth. However, if either source SAR image is collected during a wind-event, the resulting canopy height estimate will be impacted. This thesis seeks to quantify exactly how much this parameter can change due to the wind.In order to investigate and quantify the decorrelation induced by the wind, we have developed a model for the repeat-pass interferometric SAR response of a forest including the application of a wind field. The simulation consists of multiple interconnected parts including the generation of fractal tree geometries, a wind simulator to apply variable wind forces to the generated trees, an electromagnetic model to allow us to calculate a Single Look Complex (SLC) value for the SAR return of the combined target, an image-forming technique based on antenna array theory and an image processing algorithm. The results present polarimetric coherence as well as scattering phase center heights as a function of the look angle, wind speed, and the alignment of the tree with the incident wind field. An important feature of this research is the usage of a physically based realistic wind model that is based on measurements of wind effects on trees, as well as realistic models of fluid flow and simple harmonic branch segment resonators. Allowing branches to bend and move out of the plane of the incident wind field enables our model to capture numerous features of a physical tree blowing in the wind. This realistic model is necessary for a realistic simulation of the effects that wind has on a given InSAR imaging system as expressed in this study by the interferometric coherence.
■590 ▼aSchool code: 0127.
■650 4▼aElectromagnetics.
■650 4▼aRemote sensing.
■650 4▼aGeophysics.
■653 ▼aInterferometric Synthetic Aperture Radar
■653 ▼aMulti-modal fusion
■653 ▼aWind
■653 ▼aSimulation
■653 ▼aSmoothed particle hydrodynamics
■690 ▼a0799
■690 ▼a0607
■690 ▼a0800
■690 ▼a0373
■71020▼aUniversity of Michigan▼bElectrical Engineering.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359862▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
