Spatial Relationships between Trees and Snow in a Cold Regions Montane Forest

Vegetation structure is one of the primary factors that drives spatial variation of snow accumulation in forests due to interactions between falling snow, intercepted snow, and the forest canopy. These processes result in spatially heterogeneous snowpacks and snowpack energy fluxes, driving areal snow cover depletion rates during melt periods with repercussions for stand- and basin-scale ablation rates and snowmelt runoff quantities and timings. While spatial variation of forest snowpack has been documented at scales from individual tree branches to forest stands, the underlying processes are not fully understood. Understanding these relationships is critical to understanding the combined effects of climate and vegetation changes on streamflow and ecology in basins with seasonal snowpacks. To better understand these processes, this study examined the spatial relationships between branch-scale canopy structure and subcanopy snow accumulation over two accumulation events in February of 2019, at an instrumented montane forest site in Marmot Creek Research Basin on the eastern slope of the Canadian Rockies. Repeated UAV lidar surveys were paired with manual snow surveys to produce estimates of snowpack snow water equivalent (SWE) and change in snowpack (ΔSWE) over each event at high spatial resolutions. Lidar observations of the forest canopy were combined with contemporary hemispherical photography to produce a diverse set of canopy metrics, including light transmittance metrics from a novel voxel ray sampling method. Results showed that over 75% of the spatial variance in subcanopy ΔSWE for each event was found within 2.0 m of horizontal distance, indicating that the spatial scale of canopy effects on snow interception and redistribution were primarily found at the scale of tree branches in this forest. Significant vertical asymmetry was seen in the relationships between snow accumulation and surrounding vegetation which was explained by prevailing wind directions. A descriptive Gaussian snowfall model that was consistent with the tight coupling observed between near-overhead canopy characteristics and snow accumulation explained more of the spatial variation in observed ΔSWE than any canopy metric considered and performed better than two other forest snow accumulation models based on larger scale canopy characteristics found in the literature. These findings emphasize the importance of representing branch-scale forest heterogeneity in models of snow accumulation and suggest that representation of vertical asymmetry in parametrizations of snow-vegetation relationships may yield more physically realistic models

GPU 상에서의 빠르고 가벼운 Path Guiding 알고리즘

학위논문(석사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 김영민.We propose a simple, yet practical path guiding algorithm that runs on GPU. Path guiding renders photo-realistic images by simulating the iterative bounces of rays, which are sampled from the radiance distribution. The radiance distribution is often learned by serially updating the hierarchical data structure to represent complex scene geometry, which is not easily implemented with GPU. In contrast, we employ a regular data structure and allow fast updates by processing a significant number of rays with GPU. We further increase the efficiency of radiance learning by employing SARSA used in reinforcement learning. SARSA does not include aggregation of incident radiance from all directions nor storing all of the previous paths. The learned distribution is then importance-sampled with an optimized rejection sampling, which adapts the current surface normal to reflect finer geometry than the grid resolution. All of the algorithms have been implemented on GPU using megakernal architecture with NVIDIA OptiX. Through numerous experiments on complex scenes, we demonstrate that our proposed path guiding algorithm works efficiently on GPU, drastically reducing the number of wasted paths.본 연구는 GPU 상에서 작동하는 간단하지만 효과적인 path guiding 알고리즘을 제안한다. Path guiding은 path tracing의 노이즈를 줄이기 위해 제안된 기법으로 샘플링 과정에서 복사 휘도(radiance)를 배우고 이를 이용해 중요도 샘플링(importance sampling)을 수행한다. 복사 휘도의 복잡한 분포를 배우기 위해 이전의 논문들에서는 복잡한 재귀적 데이터 구조를 제안하고 이를 순차적으로 업데이트 하였지만 이는 CPU상에서의 path tracing만을 가정한 것으로 GPU상에서는 쉽게 구현하기 어려우며 효과적으로 작동하지 않는다. 본 논문에서는 GPU 친화적인 간단한 그리드 형태의 데이터를 사용해 path guiding 알고리즘을 진행하였다. 또한 path guiding의 두 가지 목표-(1) 복사 휘도 학습과 (2) 학습된 복사 휘도 분포를 이용한 중요도 샘플링-를 GPU 상에서 효과적으로 구현하기 위해 다음과 같은 방법을 제시한다. 우선 복사 휘도 학습의 경우, 강화학습과 복사 휘도 학습의 구조적 유사성을 밝힌 이전 연구를 확장하여 가볍고 빠른 SARSA를 이용한 학습 방법을 제안하였다. 학습된 복사 휘도는 공간-방향을 그리드 형태로 분할한 GPU상의 데이터 구조에 저장된다. 학습된 복사 휘도를 사용한 중요도 샘플링의 경우 법선 벡터 방향에 유효하지 않은 샘플들은 제외한 뒤, 리젝션 샘플링(rejection sampling)을 이용해 중요도 샘플링(importance sampling)을 수행하였다. 모든 알고리즘은 NVIDIA OptiX를 사용해 GPU상에서 megakernel 구조로 구현되었다. 복잡한 구조의 씬 데이터에 대해 여러 번 실험을 수행하였으며 본 연구에서 제안한 방법의 우수성을 확인하였다.Abstract i Chapter 1 Introduction 1 Chapter 2 Background and Related Works 4 2.1 Ray Tracing on GPU 4 2.2 Path Guiding 5 2.3 Reinforcement Learning and Light Transport 6 Chapter 3 Problem Setting and Overview 7 Chapter 4 Fast and Lightweight Radiance Learning 10 4.1 Analogy between the Rendering Equation and Reinforcement Learning 10 4.2 Fast and Lightweight Radiance Learning with SARSA 12 Chapter 5 Efficient Importance Sampling from Learned Radiance 16 5.1 Importance Sampling on Hemispherical Domain 16 5.2 Fast and Efficient Importance Sampling with Optimized Rejection Sampling 18 5.3 Normalizing Term Calculation with Memoization 20 Chapter 6 Experiments and Results 22 6.1 GPU-based Path Guiding with a Regular Grid 23 6.2 Comparison for Radiance Learning Methods 25 6.3 Comparison for Radiance Sampling Methods 27 Chapter 7 Conclusion 35 Appendix A Additional Experimental Results 36 A.1 Comparison for Spatial Directional Resolution 36 A.2 Equal SPP Comparison 36 Appendix B Pseudocode for the Algorithm 39 초록 46 Acknowledgements 47석

Towards interactive global illumination effects via sequential Monte Carlo adaptation

Journal ArticleThis paper presents a novel method that effectively combines both control variates and importance sampling in a sequential Monte Carlo context while handling general single-bounce global illumination effects. The radiance estimates computed during the rendering process are cached in an adaptive per-pixel structure that defines dynamic predicate functions for both variance reduction techniques and guarantees well-behaved PDFs, yielding continually increasing efficiencies thanks to a marginal computational overhead

Spin-scanning Cameras for Planetary Exploration: Imager Analysis and Simulation

In this thesis, a novel approach to spaceborne imaging is investigated, building upon the scan imaging technique in which camera motion is used to construct an image. This thesis investigates its use with wide-angle (≥90° field of view) optics mounted on spin stabilised probes for large-coverage imaging of planetary environments, and focusses on two instruments. Firstly, a descent camera concept for a planetary penetrator. The imaging geometry of the instrument is analysed. Image resolution is highest at the penetrator’s nadir and lowest at the horizon, whilst any point on the surface is imaged with highest possible resolution when the camera’s altitude is equal to that point’s radius from nadir. Image simulation is used to demonstrate the camera’s images and investigate analysis techniques. A study of stereophotogrammetric measurement of surface topography using pairs of descent images is conducted. Measurement accuracies and optimum stereo geometries are presented. Secondly, the thesis investigates the EnVisS (Entire Visible Sky) instrument, under development for the Comet Interceptor mission. The camera’s imaging geometry, coverage and exposure times are calculated, and used to model the expected signal and noise in EnVisS observations. It is found that the camera’s images will suffer from low signal, and four methods for mitigating this – binning, coaddition, time-delay integration and repeat sampling – are investigated and described. Use of these methods will be essential if images of sufficient signal are to be acquired, particularly for conducting polarimetry, the performance of which is modelled using Monte Carlo simulation. Methods of simulating planetary cameras’ images are developed to facilitate the study of both cameras. These methods enable the accurate simulation of planetary surfaces and cometary atmospheres, are based on Python libraries commonly used in planetary science, and are intended to be readily modified and expanded for facilitating the study of a variety of planetary cameras

A Landsat-based analysis of tropical forest dynamics in the Central Ecuadorian Amazon : Patterns and causes of deforestation and reforestation

Tropical deforestation constitutes a major threat to the Amazon rainforest. Monitoring forest dynamics is therefore necessary for sustainable management of forest resources in this region. However, cloudiness results in scarce good quality satellite observations, and is therefore a major challenge for monitoring deforestation and for detecting subtle processes such as reforestation. Furthermore, varying human pressure highlights the importance of understanding the underlying forces behind these processes at multiple scales but also from an interand transdisciplinary perspective. Against this background, this study analyzes and recommends different methodologies for accomplishing these goals, exemplifying their use with Landsat timeseries and socioeconomic data. The study cases were located in the Central Ecuadorian Amazon (CEA), an area characterized by different deforestation and reforestation processes and socioeconomic and landscape settings. Three objectives guided this research. First, processing and timeseries analysis algorithms for forest dynamics monitoring in areas with limited Landsat data were evaluated, using an innovative approach based in genetic algorithms. Second, a methodology based in image compositing, multisensor data fusion and postclassification change detection is proposed to address the limitations observed in forest dynamics monitoring with timeseries analysis algorithms. Third, the evaluation of the underlying driving forces of deforestation and reforestation in the CEA are conducted using a novel modelling technique called geographically weight ridge regression for improving processing and analysis of socioeconomic data. The methodology for forest dynamics monitoring demonstrates that despite abundant data gaps in the Landsat archive for the CEA, historical patterns of deforestation and reforestation can still be reported biennially with overall accuracies above 70%. Furthermore, the improved methodology for analyzing underlying driving forces of forest dynamics identified local drivers and specific socioeconomic settings that improved the explanations for the high deforestation and reforestation rates in the CEA. The results indicate that the proposed methodologies are an alternative for monitoring and analyzing forest dynamics, particularly in areas where data scarcity and landscape complexity require approaches that are more specialized.Landsat-basierte Analyse der Dynamik tropischer Wälder im Zentral-Ecuadorianischen Amazonasgebiet: Muster und Ursachen von Abholzung und Wiederaufforstung Die tropische Entwaldung stellt eine große Bedrohung für den AmazonasRegenwald dar. Daher ist die Überwachung von Walddynamiken eine notwendige Maßnahme, um eine nachhaltige Bewirtschaftung der Waldressourcen in dieser Region zu gewährleisten. Jedoch verschlechtert Bewölkung die Qualität der Satellitenaufnahmen und stellt die hauptsächliche Herausforderung für die Überwachung der Entwaldung sowie die Detektierung einhergehender Prozesse, wie der Wiederaufforstung, dar. Darüber hinaus zeigt der unterschiedliche menschliche Nutzungsdruck, wie wichtig es ist, die zugrundeliegenden Kräfte hinter diesen Prozessen auf mehreren Ebenen, aber auch interund transdisziplinär, zu verstehen. Variierender anthropogener Einfluss unterstreicht die Notwendigkeit, unterschwellige Prozesse (oder "Driver") auf multiplen Skalen aus interund transdisziplinärer Sicht zu verstehen. Darauf basierend analysiert und empfiehlt die vorliegende Studie unterschiedliche Methoden, welche unter Verwendung von LandsatZeitreihen und sozioökonomischen Daten zur Erreichung dieser Ziele beitragen. Die Untersuchungsgebiete befinden sich im ZentralEcuadorianischen Amazonasgebiet (CEA). Einem Gebiet, das einerseits durch differenzierte Entwaldungsund Aufforstungsprozesse, andererseits durch seine sozioökonomischen und landschaftlichen Gegebenheiten geprägt ist. Das Forschungsprojekt hat drei Zielvorgaben. Erstens werden auf genetischen Algorithmen basierten Verfahren zur Verarbeitung der Zeitreihenanalyse für die Überwachung der Walddynamik in Gebieten, für die nur begrenzte LandsatDaten vorhanden waren, bewertet. Zweitens soll eine Methode in Anlehnung an Satellitenbildkompositen, Datenfusion von mehreren Satellitenbildern und Veränderungsdetektion gefunden werden, die Einschränkungen der Walddynamik durch Entwaldung mithilfe von ZeitreihenAlgorithmen thematisiert. Drittens werden die Ursachen der Entwaldung/Abholzung im CEA anhand der geographischen gewichteten RidgeRegression, die zur einen verbesserten Analyse der sozioökonomischen Information beiträgt, bewertet. Die Methodik für das WalddynamikMonitoring zeigt, dass trotz umfangreicher Datenlücken im LandsatArchiv für das CEA alle zwei Jahre die historischen Entwaldungsund Wiederaufforstungsmuster mit einer Genauigkeit von über 70% gemeldet werden können. Eine verbesserte Analysemethode trägt außerdem dazu bei, die für die Walddynamik verantwortlichen treibenden Kräfte zu identifizieren, sowie lokale Treiber und spezifische sozioökonomische Rahmenbedingungen auszumachen, die eine bessere Erklärung für die hohen Entwaldungsund Wiederaufforstungsraten im CEA aufzeigen. Die erzielten Ergebnisse machen deutlich, dass die vorgeschlagenen Methoden eine Alternative zum Monitoring und zur Analyse der Walddynamik darstellen; Insbesondere in Gebieten, in denen Datenknappheit und Landschaftskomplexität spezialisierte Ansätze erforderlich machen

Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica

The surface reflection of solar radiation comprises an important boundary condition for solar radiative transfer simulations. In polar regions above snow surfaces, the surface reflection is particularly anisotropic due to low Sun elevations and the highly anisotropic scattering phase function of the snow crystals. The characterization of this surface reflection anisotropy is essential for satellite remote sensing over both the Arctic and Antarctica. To quantify the angular snow reflection properties, the hemispherical-directional reflectance factor (HDRF) of snow surfaces was derived from airborne measurements in Antarctica during austral summer in 2013/14. For this purpose, a digital 180∘ fish-eye camera (green channel, 490–585 nm wavelength band) was used. The HDRF was measured for different surface roughness conditions, optical-equivalent snow grain sizes, and solar zenith angles. The airborne observations covered an area of around 1000 km × 1000 km in the vicinity of Kohnen Station (75∘0′ S, 0∘4′ E) at the outer part of the East Antarctic Plateau. The observations include regions with higher (coastal areas) and lower (inner Antarctica) precipitation amounts and frequencies. The digital camera provided upward, angular-dependent radiance measurements from the lower hemisphere. The comparison of the measured HDRF derived for smooth and rough snow surfaces (sastrugi) showed significant differences, which are superimposed on the diurnal cycle. By inverting a semi-empirical kernel-driven bidirectional reflectance distribution function (BRDF) model, the measured HDRF of snow surfaces was parameterized as a function of solar zenith angle, surface roughness, and optical-equivalent snow grain size. This allows a direct comparison of the HDRF measurements with the BRDF derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product MCD43. For the analyzed cases, MODIS observations (545–565 nm wavelength band) generally underestimated the anisotropy of the surface reflection. The largest deviations were found for the volumetric model weight fvol (average underestimation by a factor of 10). These deviations are likely linked to short-term changes in snow properties