드론을 활용한 위성 지표반사도 산출물 공간 패턴 분석

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2021.8. 조대솔.High-resolution satellites are assigned to monitor land surface in detail. The reliable surface reflectance (SR) is the fundamental in terrestrial ecosystem modeling so the temporal and spatial validation is essential. Usually based on multiple ground control points (GCPs), field spectroscopy guarantees the temporal continuity. Due to limited sampling, however, it hardly illustrates the spatial pattern. As a map, the pixelwise spatial variability of SR products is not well-documented. In this study, we introduced drone-based hyperspectral image (HSI) as a reference and compared the map with Sentinel 2 and Landsat 8 SR products on a heterogeneous rice paddy landscape. First, HSI was validated by field spectroscopy and swath overlapping, which assured qualitative radiometric accuracy within the viewing geometry. Second, HSI was matched to the satellite SRs. It involves spectral and spatial aggregation, co-registration and nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR) conversion. Then, we 1) quantified the spatial variability of the satellite SRs and the vegetation indices (VIs) including NDVI and NIRv by APU matrix, 2) qualified them pixelwise by theoretical error budget and 3) examined the improvement by BRDF normalization. Sentinel 2 SR exhibits overall good agreement with drone HSI while the two NIRs are biased up to 10%. Despite the bias in NIR, the NDVI shows a good match on vegetated areas and the NIRv only displays the discrepancy on built-in areas. Landsat 8 SR was biased over the VIS bands (-9 ~ -7.6%). BRDF normalization just contributed to a minor improvement. Our results demonstrate the potential of drone HSI to replace in-situ observation and evaluate SR or atmospheric correction algorithms over the flat terrain. Future researches should replicate the results over the complex terrain and canopy structure (i.e. forest).원격탐사에서 지표 반사도(SR)는 지표정보를 비파괴적이고 즉각적인 방법으로 전달해주는 매개체 역할을 한다. 신뢰할 수 있는 SR은 육상 생태계 모델링의 기본이고, 이에 따라 SR의 시공간적 검증이 요구된다. 일반적으로 SR은 여러 지상 기준점(GCP)을 기반으로 하는 현장 분광법을 통해서 시간적 연속성이 보장된다. 그러나 현장 분광법은 제한적인 샘플링으로 공간 패턴을 거의 보여주지 않아, 위성 SR의 픽셀 별 공간 변동성은 잘 분석되지 않았다. 본 연구에서는 드론 기반의 초분광 영상(HSI)을 참고자료로 도입하여, 이를 이질적인 논 경관에서 Sentinel 2 및 Landsat 8 SR과 비교하였다. 우선, 드론 HSI는 현장 분광법 및 경로 중첩을 통해서 관측각도 범위 내에서 정성적인 방사 측정을 보장한다고 검증되었다. 이후, 드론 HSI는 위성 SR의 분광반응특성, 공간해상도 및 좌표계를 기준으로 맞춰졌고, 관측 기하를 통일하기 위해서 드론 HIS와 위성 SR은 각각 양방향반사율분포함수 (BRDF) 정규화 반사도 (NBAR)로 변환되었다. 마지막으로, 1) APU 행렬으로 위성 SR과 NDVI, NIRv를 포함하는 식생지수(VI)의 공간변동성을 정량화 했고, 2) 대기보정의 이론적 오차를 기준으로 SR과 VI를 픽셀별로 평가했고, 3) BRDF 정규화를 통한 개선 사항을 검토했다. Sentinel 2 SR은 드론 HSI와 전반적으로 좋은 일치를 보이나, 두 NIR 채널은 최대 10% 편향되었다. NIR의 편향은 식생지수에서 토지 피복에 따라 다른 영향을 미쳤다. NDVI는 식생에서는 낮은 편향을 보여줬고, NIRv는 도시시설물 영역에서만 높은 편향을 보였다. Landsat 8 SR은 VIS 채널에 대해 편향되었다 (-9 ~ -7.6%). BRDF 정규화는 위성 SR의 품질을 개선했지만, 그 영향은 부수적이었다. 본 연구에서는 평탄한 지형에서 드론 HSI가 현장 관측을 대체할 수 있고, 따라서 위성 SR이나 대기보정 알고리즘을 평가하는데 활용될 수 있다는 것을 보였다. 향후 연구에서는 산림으로 대상지를 확대하여, 지형과 캐노피 구조가 드론 HSI 및 위성 SR에 미치는 영향을 분석할 필요가 있다.Chapter 1. Introduction 1 1.1 Background 1 Chapter 2. Method 3 2.1 Study Site 3 2.2 Drone campaign 4 2.3 Data processing 4 2.3.1 Sensor calibration 5 2.3.2 Bidirectional reflectance factor (BRF) calculation 7 2.3.3 BRDF correction 7 2.3.4 Orthorectification 8 2.3.5 Spatial Aggregation 9 2.3.6 Co-registration 10 2.4 Satellite dataset 10 2.4.2 Landsat 8 12 Chapter 3. Result and Discussion 12 3.1 Drone BRF map quality assessment 12 3.1.1 Radiometric accuracy 12 3.1.2 BRDF effect 15 3.2 Spatial variability in satellite surface reflectance product 16 3.2.1 Sentinel 2B (10m) 17 3.2.2 Sentinel 2B (20m) 22 3.2.3 Landsat 8 26 Chapter 4. Conclusion 28 Supplemental Materials 30 Bibliography 34 Abstract in Korean 43석

Daylight simulation with photon maps

Physically based image synthesis remains one of the most demanding tasks in the computer graphics field, whose applications have evolved along with the techniques in recent years, particularly with the decline in cost of powerful computing hardware. Physically based rendering is essentially a niche since it goes beyond the photorealistic look required by mainstream applications with the goal of computing actual lighting levels in physical quantities within a complex 3D scene. Unlike mainstream applications which merely demand visually convincing images and short rendering times, physically based rendering emphasises accuracy at the cost of increased computational overhead. Among the more specialised applications for physically based rendering is lighting simulation, particularly in conjunction with daylight. The aim of this thesis is to investigate the applicability of a novel image synthesis technique based on Monte Carlo particle transport to daylight simulation. Many materials used in daylight simulation are specifically designed to redirect light, and as such give rise to complex effects such as caustics. The photon map technique was chosen for its efficent handling of these effects. To assess its ability to produce physically correct results which can be applied to lighting simulation, a validation was carried out based on analytical case studies and on simple experimental setups. As prerequisite to validation, the photon map\u27s inherent bias/noise tradeoff is investigated. This tradeoff depends on the density estimate bandwidth used in the reconstruction of the illumination. The error analysis leads to the development of a bias compensating operator which adapts the bandwidth according to the estimated bias in the reconstructed illumination. The work presented here was developed at the Fraunhofer Institute for Solar Energy Systems (ISE) as part of the FARESYS project sponsored by the German national research foundation (DFG), and embedded into the RADIANCE rendering system.Die Erzeugung physikalisch basierter Bilder gilt heute noch als eine der rechenintensivsten Aufgaben in der Computergraphik, dessen Anwendungen sowie auch Verfahren in den letzten Jahren kontinuierlich weiterentwickelt wurden, vorangetrieben primär durch den Preisverfall leistungsstarker Hardware. Physikalisch basiertes Rendering hat sich als Nische etabliert, die über die photorealistischen Anforderungen typischer Mainstream-Applikationen hinausgeht, mit dem Ziel, Lichttechnische Größen innerhalb einer komplexen 3D Szene zu berechnen. Im Gegensatz zu Mainstream-Applikationen, die visuell überzeugend wirken sollen und kurze Rechenzeiten erforden, liegt der Schwerpunkt bei physikalisch basiertem Rendering in der Genauigkeit, auf Kosten des Rechenaufwands. Zu den eher spezialisierten Anwendungen im Gebiet des physikalisch basiertem Renderings gehört die Lichtsimulation, besonders in Bezug auf Tageslicht. Das Ziel dieser Dissertation liegt darin, die Anwendbarkeit eines neuartigen Renderingverfahrens basierend auf Monte Carlo Partikeltransport hinsichtlich Tageslichtsimulation zu untersuchen. Viele Materialien, die in der Tageslichtsimulation verwendet werden, sind speziell darauf konzipiert, Tageslicht umzulenken, und somit komplexe Phänomene wie Kaustiken hervorrufen. Das Photon-Map-Verfahren wurde aufgrund seiner effizienten Simulation solcher Effekte herangezogen. Zur Beurteilung seiner Fähigkeit, physikalisch korrekte Ergebnisse zu liefern, die in der Tageslichtsimulation anwendbar sind, wurde eine Validierung anhand analytischer Studien sowie eines einfachen experimentellen Aufbaus durchgeführt. Als Voraussetzung zur Validierung wurde der Photon Map bezüglich seiner inhärenten Wechselwirkung zwischen Rauschen und systematischem Fehler (Bias) untersucht. Diese Wechselwirkung hängt von der Bandbreite des Density Estimates ab, mit dem die Beleuchtung aus den Photonen rekonstruiert wird. Die Fehleranalyse führt zur Entwicklung eines Bias compensating Operators, der die Bandbreite dynamisch anhand des geschätzten Bias in der rekonstruierten Beleuchtung anpasst. Die hier vorgestellte Arbeit wurde am Fraunhofer Institut für Solare Energiesysteme (ISE) als teil des FARESYS Projekts entwickelt, daß von der Deutschen Forschungsgemeinschaft (DFG) finanziert wurde. Die Implementierung erfolgte im Rahmen des RADIANCE Renderingsystems

Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. Furthermore, this work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor crosscalibration. Eventually, North Africa can be used a continental scale PICS

Canopy structural modeling using object-oriented image classification and laser scanning

A terrestrial laser scanning (TLS) experiment was carried out in the EAGLE 2006 campaign to characterize and model the canopy structure of the Speulderbos forest. Semi-variogram analysis was used to describe spatial variability of the surface. The dependence of the spatial variability on the applied grid size showed, that in this forest spatial details of the digital surface model are lost in the case of larger than 0.3-0.4 m grid size. Voxel statistics was used for describing the density of the canopy structure. Five zones of the canopy were identified according to their density distribution. Basic geometric structures were tested for modeling the forest at the individual tree level. The results create a firm basis for modeling physical processes in the canopy

On Demand Vicarious Calibration for Analysis Ready Data: The FLARE Network

This paper introduces a new capability for performing the vicarious radiometric calibration of high, medium and low spatial resolution sensors. The SPecular Array Radiometric Calibration (SPARC) method employs convex mirrors to create calibration targets for deriving absolute calibration coefficients of Earth remote sensing systems in the solar reflective spectrum.17 The combination of these mirror arrays with a targeting station is the basis for a new, on-demand commercial calibration network called FLARE

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable

Extended Cross-Referenced Analysis Using Data from the Landsat 8 And 9 Underfly Event

The Landsat 8 and 9 Underfly Event occurred in November 2021, where Landsat 9 flew beneath Landsat 8 in the final stages before settling in its final orbiting path. An analysis was performed on the images taken during this event, which resulted in a cross-referenced with uncertainties estimated to be less than 0.5%. This level of precision was due in part to the near-identical sensors aboard each instrument as well as the underfly event itself, which allowed the sensors to take nearly the exact same image at nearly the exact same time. This initial calibration was applied before the end of the on-orbit initial verification (OIV) period, which meant the analysis was performed in less than a month. While it was an effective and efficient first look at the data, a longer-term analysis was deemed prudent to have the most accurate cross-referenced with the smallest uncertainties. The three forms of uncertainty established in the initial analysis, dubbed “Phase 1”, were geometric, spectral, and angular. This paper covers Phase 2 of the underfly analysis, and several modifications were made to the Phase 1 process to improve the cross-referenced results, including a spectral correction in the form of a spectral band adjustment factor (SBAF) and a more robust filtering system that used the statistics of the reflectance data to better include important data compared to the more aggressive filters used in Phase 1. A proper uncertainty analysis was performed to more accurately quantify the uncertainty associated with the underfly cross-referenced. The final results of Phase 2 showed that the Phase 1 analysis was within its 0.5% uncertainty estimation, and the cross-referenced gain values in this paper were used by USGS EROS to update the Landsat 9 calibration at the end of 2022

a Berlin case study

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt...thesi

Master of Science

thesisPersistent drought conditions and associated vegetation mortality in the central Sierra Nevada of California were analyzed from 2013-2015 using a combination of field-derived polygons and AVIRIS hyperspectral data. Linear Discriminant Analysis (LDA) was used to classify hyperspectral data into five land cover classes based on dominant flora. LDA accuracies were compared across years in order to determine whether classification accuracy was correlated with increasing drought severity. It was determined that 2013 had the greatest accuracy and 2015 had the lowest. However, this trend was influenced by Bidirectional Reflectance Distribution Function (BRDF) effects in the densely forested landscape. Fractional cover data of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil were obtained from the US Forest Service to analyze which land cover classes and which elevation intervals experienced the greatest fractional cover change, which are both indicators of vegetation senescence and mortality. GV loss deemed the most appropriate indicator of vegetation senescence and mortality as NPV and soil appeared to be confused by the Multiple Endmember Spectral Mixture Analysis (MESMA) method used to obtain the fractional cover images. Mixed oak woodland (MO) and mixed low conifer (LC) forests experienced the greatest and second-greatest decreases in GV, respectively. Lower elevation areas (695-1369 m) generally experienced greater GV loss than higher elevation areas (2167-2779), which coincided with both MO and LC forest classes. The MO forest class, which occurs most in lower elevation areas, was comprised of dominantly drought resistant flora and experienced the greatest GV loss during the study period (16%). Conversely, the HC forest, which occurs dominantly in higher elevation areas, was comprised of dominantly non-drought-tolerant flora but experienced less GV loss (5%). This suggests that the differences in elevation and location of vegetation within the landscape played a larger role than the dominant floras' degrees of drought tolerance. Variations in seasonal senescence may have influenced the measured loss of GV for the MO and LC classes, which contained deciduous vegetation. However, overall GV loss in all classes, even those without trees, indicates that the landscape likely experienced vegetation mortality, especially at low elevations in the MO and LC classes

Cross Calibration and Validation of Landsat 8 OLI and Sentinel 2A MSI

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross calibration procedure involves i) correction of the MSI data to account for spectral band differences with the OLI; and ii) correction of BRDF effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF corrections, standard least-squares linear regression is used to determine the cross calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. In general, the estimated cross calibration offsets in all bands were small, on the order of 0.0075 or less in magnitude. The cross calibration gains generally varied less than 1.0% from unity; for the Blue and Red bands, the gains varied by approximately -2.5% and - 1.4% from unity, respectively. For a forced zero offset, the estimated gain in all but the Blue band changed little; the Blue band gain varied by approximately 1.86% from unity. Consequently, cross calibration of the Blue band requires both the gain and nonzero offset. To maintain processing consistency, it is recommended to use the gain and (nonzero) offset in all bands. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty, on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include: seasonal changes in solar zenith and azimuth angles, on the order of 2.27%; target site non-uniformity, on the order of 1.8%; variability in atmospheric water vapor and/or aerosol concentration, on the order of 1.29%; and potential shifts in each sensor’s spectral filter central wavelength and/or bandwidth, on the order of 0.82% and 0.28%, respectively