VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

Environmental Armed Conflict Assessment Using Satellite Imagery

Armed conflicts not only affect human populations but can also cause considerable damage to the environment. Its consequences are as diverse as its causes, including; water pollution from oil spills, land degradation due to the destruction of infrastructure, poisoning of soils and fields, destruction of crops and forests, over-exploitation of natural resources and paradoxically and occasionally reforestation. In this way, the environment in the war can be approached as beneficiary, stage, victim or/and spoil of war. Although there are few papers that assess the use of remote sensing methods in areas affected by warfare, we found a gap in these studies, being both outdated and lacking the correlation of remote sensing analysis with the causes-consequences, biome features and scale. Thus, this paper presents a methodical approach focused on the assessment of the existing datasets and the analysis of the connection between geographical conditions (biomes), drivers and the assessment using remote sensing methods in areas affected by armed conflicts. We aimed to find; weaknesses, tendencies, patterns, points of convergence and divergence. Then we consider variables such as biome, forest cover affectation, scale, and satellite imagery sensors to determine the relationship between warfare drivers with geographical location assessed by remote sensing methods. We collected data from 44 studies from international peer-reviewed journals from 1998 to 2019 that are indexed using scientific search engines. We found that 62% of the studies were focused on the analysis of torrid biomes as; Tropical Rainforest, Monsoon Forest / Dry Forest, Tree Savanna and Grass Savanna, using the 64% Moderate-resolution satellite imagery sensors as; Landsat 4-5 TM and Landsat 7 ETM+. Quantitative analysis of the trends identified within these areas contributes to an understanding of the reasons behind these conflicts

Sensing Mountains

Sensing mountains by close-range and remote techniques is a challenging task. The 4th edition of the international Innsbruck Summer School of Alpine Research 2022 – Close-range Sensing Techniques in Alpine Terrain brings together early career and experienced scientists from technical-, geo- and environmental-related research fields. The interdisciplinary setting of the summer school creates a creative space for exchanging and learning new concepts and solutions for mapping, monitoring and quantifying mountain environments under ongoing conditions of change

다중 규모 LiDAR 데이터를 활용한 도시생태계 구조 및 연결성 평가

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2021.8. 송영근.Integrated multiscale light detection and ranging (LiDAR) datasets are required for managing urban ecosystems because 1) LiDAR datasets can represent various spatial structures across the urban landscape and 2) the multitemporal LiDAR approach can derive the changes of urban landscape structures. This dissertation aimed to find the various spatiotemporal availabilities (i.e., from the tree-level spatial scale to the city-level regional scale with the multitemporal approach) of LiDAR or laser scanning (LS) datasets for monitoring urban ecosystems in the following three chapters. Chapter 2: Collecting tree inventory data in urban areas is important for managing green areas. Surveying using airborne laser scanning (ALS) is effective for collecting urban tree structures but less efficient regarding the economic costs and its operation. Terrestrial laser scanning (TLS), and mobile laser scanning (MLS) datasets could have the potential in complementing those of ALS in the respect to efficiency. However, to the best of my knowledge, there were limited studies for seeking the similarities and variations among the canopy metrics derived from various LiDAR platforms. In Chapter 2, I compared structural canopy metrics among ALS, TLS, and MLS datasets in the urban parks. The purpose of Chapter 2 was to test whether the estimates of tree metrics differed depending on single or clustered trees and to test whether the errors in LiDAR-derived metrics were related to the tree structures. Small, urban parks were selected for surveying trees using the three LiDAR platforms. The ALS datasets were acquired on 14 May, 2017. The TLS and MLS datasets were acquired from 10–11 May, 2017, and 21–25 April, 2020, respectively. The tree point clouds were classified into single and clustered trees. The structural metrics were compared in each pair (i.e., ALS and TLS, ALS and MLS, and TLS and MLS pairs). The heights related metrics (e.g., percentile heights and the distribution of the heights values), the complexity metric (e.g., the Rumple index) and area were calculated for comparisons. The root mean square error (RMSE), bias, and the Pearson’s correlation coefficient (r) were calculated to evaluate the difference in each metric among the LiDAR platforms. The results showed that ZMAX, max and mean CHM, and area showed good consistencies (RMSE% 0.900). Especially, the biases of CHM-derived metrics did not present significant differences (p > 0.05) regardless of single or clustered trees. Moreover, the biases from the comparisons in each pair showed linear relations with the tree heights and vertical canopy complexity (i.e., Pearson’s correlation coefficient showed significant; r >0.29, p < 0.05). My results could be references when combining multiple LiDAR systems to estimate the canopy structures of urban park areas. Chapter 3: Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional (3D) changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal ALS datasets enable me to quantify the vertical and lateral growth of trees across a landscape scale. The goal of Chapter 3 is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, I generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests. Chapter 4: Although many studies have considered urban structure when investigating urban ecological networks, few have considered the 3D structure of buildings as well as urban green spaces. In Chapter 4, I examined an urban ecological network using the 3D structure of both green spaces and buildings. Using breeding-season bird species observations and ALS data collected, I assessed the influence of 3D structural variables on species diversity. I used correlation analyses to determine if vertical distribution, volume, area, and height of both buildings and vegetation were related to bird species diversity. Then I conducted circuit theory-based current flow betweenness centrality (CFBC) analysis using the LiDAR-derived structural variables. I found that the volumes of buildings and 8–10 m vegetation heights were both highly correlated with species richness per unit area. There were significant differences between 2D and 3D connectivity analysis using LiDAR-derived variables among urban forest patches, boulevards, and apartment complexes. Within urban forest patches and parks, 3D CFBC represented canopy structural characteristics well, by showing high variance in spatial distributions. The 3D CFBC results indicated that adjacent high-rise buildings, dense apartment complexes, and densely urbanized areas were isolated, as characterized by low centrality values, but that vegetation planted in open spaces between buildings could improve connectivity by linking isolated areas to core areas. My research highlights the importance of considering 3D structure in planning and managing urban ecological connectivity. In this dissertation, the availability of integrated multiscale LiDAR datasets was found via three standalone studies. It was revealed that 3D information could enhance the quality of urban landscape monitoring and ecological connectivity analysis by elaborately explaining spatial structures. However, the spatiotemporal scales of each standalone study were limited to the city scale and to five years. The recently launched Global Ecosystem Dynamics Investigation (GEDI) would help to solve these limitations. Furthermore, the GEDI dataset could help researchers understand the relationship between ecosystem structures and their functions.본 학위논문은 다양한 시공간 스케일에서 도시생태계 모니터링을 위한 LiDAR 데이터의 활용과 생태적 의미 도출에 관한 내용을 다룬다. LiDAR란 Light Detection and Ranging의 약어로, LiDAR 센서에서 발사된 레이저가 대상에 도달한 뒤 반사되어 돌아오는 레이저의 세기와 시간을 계산하여 대상의 위치 정보를 3차원 점군 데이터로 변환해주는 능동형 원격탐사 도구이다. LiDAR 원격탐사 도구의 등장으로 자연과 도시의 3차원 공간정보의 취득이 기능해짐에 따라, 서식지의 3차원 공간정보와 생물 종 사이의 관계 도출, 시계열 LiDAR 데이터를 활용한 녹지 모니터링 연구 등이 이뤄지고 있다. 또한 항공 LiDAR(ALS), 지상 LiDAR(TLS), 이동형 LiDAR(MLS) 등 다양한 LiDAR 시스템의 개발로 연구 목적에 알맞은 시공간 해상도의 3차원 공간정보를 취득할 수 있게 되었다. 본 학위논문에서는 도시녹지를 대상으로 LiDAR 원격탐사 도구의 다양한 시공간 스케일 적용 측면에서, Chapter 2 항공, 지상, 이동형 LiDAR 시스템 사이 수목구조관련 변수들의 일치성 평가, Chapter 3 시계열 분석을 통한 도시녹지 동태 모니터링, Chapter 4 도시의 생태적 연결성 분석을 진행하였다. Chapter 2: 도시의 수목정보를 취득하는 것은 도시녹지 관리에 있어 필수적이다. LiDAR 기술의 발달로 도시수목의 3차원 정보를 취득할 수 있게 되었으며, 이를 통해 수목높이와 수목구조, 지상 바이오매스 등을 높은 정확도로 산출할 수 있게 되었다. 항공 LiDAR는 넓은 범위의 공간정보를 높은 정확도로 측정하는 특성을 지녀 산림 모니터링 분야에서 활발히 활용되고 있다. 하지만 항공 LiDAR 데이터의 취득은 항공기 운용비, 장비관련 막대한 비용이 발생하고 운용에 있어 전문성을 요구하며 대상의 점군밀도가 상대적으로 낮다는 단점을 지닌다. 반면 지상 LiDAR와 이동형 LiDAR는 운용하기 편리하고 높은 점군밀도를 출력한다는 점에서 항공 LiDAR의 단점을 극복할 수 있다. 이처럼 다양한 LiDAR 시스템의 등장과 이를 활용한 생태계 모니터링 연구 시도가 증가하면서 LiDAR 시스템간 효율적인 운용과 데이터의 보완 방법들이 요구되고 있다. 하지만 현재까지 ALS, TLS, MLS의 3개의 시스템을 통해 취득된 수목 정보를 서로 비교하고, 서로 대체가능한 수목정보를 도출한 연구는 많이 진행된 바 없다. 따라서 본 학위논문의 Chapter 2에서는 ALS, TLS, MLS 통해 취득된 도시의 수목정보를 서로 비교하여 일치성을 평가하고, 어떠한 수목구조관련 지표가 세 LiDAR 시스템 사이에서 대체가능한지 다루고 있다. 세부적으로 Chapter 2는 수목구조관련 지표가 단목차원과 군집차원, 수목구조에 따라 ALS, TLS, MLS 시스템에서 차이가 발생하는지에 대한 내용을 담고 있다. 천안시 도시공원 9개소에서 ALS 데이터는 2017년 5월 14일, TLS 데이터는 2017년 5월 10일과 11일, MLS 데이터는 2020년 4월 21에서 25일 취득되었다. 취득된 데이터셋은 수관의 겹침 여부에 따라 단목과 군집으로 분류되었으며, 3개의 페어(ALS-TLS, MLS-TLS, ALS-MLS)로 수관의 퍼센타일 높이, 수관복잡성, 면적 등의 수목구조관련 변수들을 1:1 비교하였다. 항공 LiDAR 데이터를 통해 도출된 수목구조관련 변수들을 참조로 하여 평균제곱근오차(RMSE), 편향(bias), 피어슨 상관계수(r) 등을 계산하고 세 LiDAR 시스템 사이의 일치성을 평가하였다. 평가 결과 ZMAX, CHM관련 수관높이 관련 변수들, 그리고 수관면적이 높은 일치성을 보였다(RMSE% 0.900). 특히 CHM을 통해 도출된 수관높이 관련 변수들은 단목과 군집에서 세개의 LiDAR 시스템간 통계적인 차이를 보이지 않았다(p > 0.05). 반면 퍼센타일 수관높이와 평균 수관높이 등은 매우 낮은 일치성을 나타냈으며, 세 페어에서 도출된 편향은 수고, 수관복잡성과 약한 선형관계를 나타냈다(r >, p < 0.05). Chapter 3: 수관동태는 숲의 건강성을 반영한다. 특히, 자연적·인위적인 교란에 의해 발생한 숲틈은 숲 내부에 빛의 투과율, 온도, 습도 등에 영향을 끼쳐 주변 환경의 변화를 야기한다. 따라서 숲틈을 탐지하고 모니터링하는 것은 숲의 동태를 이해하는데 있어 매우 중요하다. 항공 LiDAR 센서를 활용할 경우 위성영상이나 항공사진 등 2차원 데이터로 탐지하기 어려운 숲틈 또는 개방공간의 탐지와 수관의 3차원 형상의 취득이 가능하다. Chapter 3에서는 2012년도부터 2015년도 4개년의 항공 LiDAR를 활용하여 자연형 도시공원(봉서산)의 수관과 숲틈의 수평적 수직적 변화양상을 추정하였다. 수관은 높이 5m를 기준으로 상층부와 하층부 수관으로 분류되었으며, 수관높이모델(canopy height model, CHM)을 생성하여 연간변화를 탐지하였다. 연구결과 상층부 및 하층부 수관의 수직생장량과 엽면적지수는 일정한 연간 변화양상을 보인 반면, 수평적 변화와 엽면적밀도는 불규칙적인 연간 변화양상을 보였다. 전반적으로 상층부 수관은 높이 12m에서 측방향 생장을 하는 것으로 나타났으며, 하층부 수관 중 숲틈에서는 높이 5m에서 측방향 생장이 활발하게 나타났다. LiDAR 데이터의 연간 변화 탐지를 통해 자연적으로 형성된 숲틈의 경우 생장과 교란 측면에서 매우 활발한 동태가 발생하고 있으며, 인위적으로 형성된 개방공간의 경우 수관의 동태가 다소 침체됨을 도출하였다. Chapter 4는 도시 내 건물과 녹지의 3차원 구조를 입력자료로 활용하여 도시의 생태적 연결성을 평가하는 연구를 다룬다. 도시 내 생태적 연결성 도출과 관련한 연구는 도시와 녹지의 형태 등을 주요 변수로 하여 진행이 되고 있다. 그러나 3차원적인 특성인 도시 건물의 부피, 수목의 수직적인 구조 등을 고려한 연결성 분석은 많이 진행된 바 없다. 연구 대상지는 천안시 시청을 중심으로한 4 km × 4 km 지역으로, 2015년에 취득된 항공 LiDAR와 같은 해 취득된 조류 종 조사 데이터를 활용하여 1)도시 내 건물과 녹지의 3차원 구조와 조류 종 다양성 사이 관계를 살피고, 2)조류 종 다양성과 상관관계를 가지는 3차원 구조변수를 활용하여 전류흐름기반 매개중심성 연결성 분석(CFBC)을 진행하였다. 연구결과 건축물의 부피와 수목높이 8-10m의 녹지 부피비가 면적당 조류 종 풍부도와 스피어만 순위상관관계에서 높은 상관관계(ρ> 0.6)를 나타냈다. 연결성 분석의 결과는 입력변수의 공간차원(2D 및 3D)에 따라 다르게 나타났다. 특히 도시숲, 대로변, 아파트단지내 녹지 등에서 2D 기반 CFBC와 3D기반 CFBC는 통계적으로 유의미한 차이를 보였다. 또한 도시녹지의 3D 기반 CFBC의 경우 같은 녹지 면적임에도 수관의 구조적인 특성에 따라 높은 차이가 나타남을 확인하였다. 3D CFBC 분석결과를 통해 고층 건물 주변부, 고밀도 아파트단지, 고밀 시가화지역 등이 낮은 중심성을 보여 고립지역으로 나타났으며, 건물 사이 공지 내 식생은 연결성이 고립된 지역과 핵심지역을 연결하는 기능을 나타냈다. 이 학위논문은 서로 다른 LiDAR 시스템을 활용하여 단목, 경관 지역단위 등 다양한 공간 스케일에서의 도시경관구조 분석, 도시녹지구조와 토지이용 등에 따른 시간적 변화양상, 도시경관구조가 가지는 생태적 의미 등과 관련된 내용을 다루고 있다. 향후 Global Ecosystem Dynamics Investigation(GEDI) 미션의 데이터를 활용하여 본 학위논문에서 다루는 지역규모의 연구를 국가단위, 대륙단위 등으로 확장할 수 있을 것이며 이를 통해 도시생태계 구조와 그 기능 사의 관계를 이해하는데 도움을 줄 수 있을 것이다.Chapter 1. Introduction 1 1. Background 1 1.1. Urbanization and the importance of urban green spaces 1 1.2. Urban landscape and Light detection and ranging application 1 2. Purpose 6 Chapter 2. Comparing tree structures derived among multiple LiDAR systems in urban parks 10 1. Introduction 10 2. Methods and materials 12 2.1. Study site and tree classification 12 2.2. LiDAR survey and processing 14 2.3. Deriving the structural variables of the parks 17 2.4. Assessing the accuracy of the LiDAR-derived indices 18 3. Results 19 3.1. Comparing height metrics among the three LiDAR systems 19 3.2. Comparing CHM-derived canopy height metrics from each LiDAR systems 22 3.3. Comparing the area and the Rumple index determined using the LiDAR systems 23 4. Discussion 25 4.1. LiDAR configurations and data acquisition time intervals 25 4.2. Uncertainty of the structural indices derived from the three LiDAR systems 28 Chapter 3. Urban forest growth and gap dynamics detected by yearly repeated airborne LiDAR 31 1. Introduction 31 2. Methods and Materials 33 2.1. Field survey 33 2.2. Canopy opening detection 34 2.3. Airborne LiDAR dataset acquisition and registration 35 2.4. Generation of height models and change detection 36 2.5. Gap detection and classification 38 2.6. Estimating changes of vertical canopy distribution and canopy complexity 38 3. Results 39 3.1. Pixel and hexagon height model-based change detection 39 3.2. Continuous one-year vertical growth area 41 3.3. Open canopy change detection 42 3.4. Changes in vertical canopy structures in High Canopy and Open Canopy 43 4. Discussion 45 4.1. What are the differences between the canopy structural changes derived from annual change detections and three-year interval change detection? 45 4.2. What are the characteristics of the structural changes according to the different canopy classes (e.g., high canopies and low canopies) in the urban forest? 46 Chapter 4. LiDAR-derived three-dimensional ecological connectivity mapping 49 1. Introduction 49 2. Materials and Methods 51 2.1. Study area and avian species observation 52 2.2. Airborne LiDAR acquisition, preprocessing and classification and deriving structural variables 53 2.3. Correlation analysis and selection of structural variables 55 2.4. 2D and 3D ecological networks 55 3. Results 56 3.1. Avian species survey 56 3.2. Correlation analyses and variable selection 56 3.3. Connectivity analysis results 58 3.4. Correlation between connectivity results with bird species diversity 59 3.5. Differences between 2D- and 3D-based CFBCs 60 4. Discussion 61 4.1. Vegetation and building structures and bird species diversity 61 4.2. 3D-based connectivity results 62 4.3. Differences between 2D and 3D network analyses 64 4.3.1. Forest and artificial green area 65 4.3.2. Roads and residential areas 66 Appendix 67 Chapter 5. Conclusion 70 1. Combination with multiple LiDAR data for surveying structures of urban green spaces 70 2. Multi-temporal urban forest gap monitoring 71 3. Ecological connectivity analysis using LiDAR 71 4. LiDAR application to Urban ecosystem monitoring 72 5. Expanding spatiotemporal scale and further works 73 Acknowledgments 75 Reference 76 Abstract in Korean 85박

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Laser vision : lidar as a transformative tool to advance critical zone science

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hydrology and Earth System Sciences 19 (2015): 2881-2897, doi:10.5194/hess-19-2881-2015.Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demonstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.The workshop forming the impetus for this paper was funded by the National Science Foundation (EAR 1406031). Additional funding for the workshop and planning was provided to S. W. Lyon by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT grant no. 2013-5261). A. A. Harpold was supported by an NSF fellowship (EAR 1144894)

UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (bare earth, grassland, trees), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&thinsp;m3 is estimated for the test site (48&thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland