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

Evaluación de la variabilidad espacial de propiedades del suelo bajo diferentes usos/coberturas en la granja El Romeral de la Universidad de Cuenca

Las diferentes prácticas de uso y manejo del suelo tienen un gran impacto sobre las propiedades del suelo, por ende, es importante saber cómo varían estas propiedades del suelo en diferentes contextos de uso/coberturas del suelo con el fin de realizar estrategias de gestión del suelo, y que permita ejecutar la agricultura de precisión. Para ello, se realizó un estudio en diferentes parcelas experimentales de la Granja “El Romeral” de la Universidad de Cuenca, donde se evaluó la variabilidad de algunas propiedades del suelo en función de diferentes categorías de uso o cobertura de la tierra (agroforestal, pastizal y agrícola), y a continuación se mapeo su distribución espacial. Se ejecutó un muestreo aleatorio con un total de 74 puntos de muestreo en un diseño de estratos geográficos compactos. En consecuencia, se midió y recolectó muestras alteradas e inalteradas del suelo de 0 a 15 cm de profundidad y fueron analizadas para las variables: densidad aparente (Da), resistencia a la penetración (RP), materia orgánica (MO) y pH del suelo. Los resultados analizados con estadística clásica, técnicas geoestadísticas y SIG mostraron que no solo existieron cambios en los niveles de las variables evaluadas derivados del efecto del uso de la tierra y su cobertura vegetal, sino que también existió influencia sobre la variabilidad espacial entre las parcelas estudiadas. Por ello, se pudo inferir que aparte de los factores intrínsecos y extrínsecos, el uso de del suelo y la cobertura vegetal presente desempeñaron un papel vital sobre la distribución espacial. Finalmente, algunos mapas de variabilidad espacial generados proporcionan información útil para para los administradores e investigadores que intervengan sobre las parcelas experimentales para sus respectivos intereses.Different land use and management practices have a great impact on soil properties, therefore, it is important to know how these soil properties vary in different land use/cover contexts in order to carry out soil management strategies, that allows precision agriculture to be carried out. For this reason, a study was conducted in different experimental plots of the “El Romeral” Farm of the University of Cuenca, where the variability of some soil properties was evaluated according to different categories of land use or coverage (agroforestry, pasture and agricultural), and later its spatial distribution was mapped. Random sampling was performed with a total of 74 sampling points in a compact geographic strata design. Consequently, altered and undisturbed soil samples from 0 to 15 cm deep were measured and collected and analyzed for the variables: bulk density (Da), penetration resistance (RP), organic matter (OM) and soil pH. The results analyzed with classical statistics, geostatistical techniques and GIS confirmed that there were not only changes in the levels of the evaluated variables derived from the effect of land use and its vegetation cover, but also influenced the spatial variability between the studied plots. Therefore, it could be inferred that apart from intrinsic and extrinsic factors, the actual land use and the vegetation cover played a vital role on the spatial distribution. Finally, some spatial variability maps provide useful information for administrators and researchers who intervene on the experimental plots for their interests.Ingeniero AgrónomoCuenc

Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review.

BACKGROUND: There is an increasing volume of literature investigating the links between urban environments and human health, much of which involves spatial conceptualisations and research designs involving various aspects of geographical information science. Despite intensifying research interest, there has been little systematic investigation of pragmatic methodological concerns, such as how studies are realised in terms of the types of data that are gathered and the analytical techniques that are applied, both of which have the potential to impact results. The aim of this systematic review is, therefore, to understand how spatial scale, datasets, methods, and analytics are currently applied in studies investigating the relationship between green and blue spaces and human health in urban areas. METHOD: We systematically reviewed 93 articles following PRISMA protocol, extracted information regarding different spatial dimensions, and synthesised them in relation to various health indicators. RESULTS AND DISCUSSION: We found a preponderance of the use of neighbourhood-scale in these studies, and a majority of the studies utilised land-use and vegetation indices gleaned from moderate resolution satellite imagery. We also observed the frequent adoption of fixed spatial units for measuring exposure to green and blue spaces based on physical proximity, typically ranging between 30 and 5000 m. The conceptual frameworks of the studies (e.g., the focus on physical vs. mental health or the definition of exposure to green space) were found to have an influence on the strength of association between exposure and health outcomes. Additionally, the strength and significance of associations also varied by study design, something which has not been considered systematically. CONCLUSION: On the basis of our findings, we propose a set of recommendations for standardised protocols and methods for the evaluation of the impact of green-blue spaces on health. Our analysis suggests that future studies should consider conducting analyses at finer spatial scales and employing multiple exposure assessment methods to achieve a comprehensive and comparable evaluation of the association between greenspace and health along multiple pathways

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes

Multi-temporal assessment of diversity and condition in UK semi-natural grasslands using optical reflectance

With 40% of the world’s plants estimated to be under threat of extinction and ever lowering levels of ecological intactness of biological systems, the requirement to effectively monitor plant species and diversity has never been more pressing. Globally, natural, and semi-natural grassland ecosystems are at particular risk of degradation and conversion. Semi-natural grasslands in the UK currently make up about 1-2% of the permanent lowland grassland cover. Once degraded due to agricultural additions or inappropriate management, they can be difficult and costly to restore. As these systems display high levels of plant and invertebrate diversity, there is a need to safeguard their decline. However, there are currently significant challenges to providing the data needed to assess the condition of these systems. Remote sensing could contribute by providing information on herbaceous plant diversity and vegetation state across a wide range of spatial scales and time. Optical traits are a subset of plant traits that are detectable using reflectance data from leaf to canopy scales, dependent on the configuration of the sensor employed and can be linked to taxonomic diversity and condition of vegetation. Very high spatial resolution hyperspectral imaging technologies are, for the first time, enabling in-situ grassland plant phenotyping at the leaf, individual and high-resolution canopy scale. Analyses of these spectra have demonstrated promising results in application of mapping of taxonomic units and diversity metrics. However there is little evidence of the temporal stability of these observations. At the landscape scale, openly available, higher spatial resolution satellite data is also enabling examination of smaller field parcels, which are typical of UK fragmented landscapes. In this context, spectral time-series have the potential to be used to predict the condition of vegetation communities of conservation interest. In this thesis, the use of optical remote sensing data to further our understanding of semi-natural grasslands and to safeguard their decline, is examined, with a particular focus on the exploitation of multi-temporal sampling. Firstly, spectral variation in space, as a surrogate measure for species or community type diversity (also known as the spectral variation hypothesis), is assessed via a meta-analysis of existing studies. The results of the synthesis reveal some promise for the approach, but a large amount of variation between study outcomes is observed, suggesting that methodological approaches are important in the effectiveness of the proxy. Secondly, spectral data is collected alongside botanical and phenological diversity data at high spatial resolution over a growing season to test the stability of the spectral variation hypothesis over time. The results of these experiments show that the ability to detect biodiversity using this method is seasonally, and possibly, site dependent. Next, the suitability of hyperspectral leaf reflectance for distinguishing 17 herbaceous species growing within a calcareous grassland is examined. The application of machine learning classification models to multi-temporal leaf spectra show that although species are distinguishable at most sampling times within the year, the transferability of these models is very limited between sampling dates. Finally satellite time-series of vegetation indices are used to predict favourable or unfavourable vegetation condition criteria in calcareous fields across two years. A number of indices were successful in distinguishing between the different condition criteria but there was variation in results found between the two years sampled, due to differences in intra-annual vegetation phenology. Overall the results of this thesis, show promise for remote sensing of grassland biodiversity and condition. Both high spatial resolution hyperspectral data, as well as coarser resolution multi-spectral data sets, can be useful in evaluation of these systems. However, the dynamic nature of leaves and canopies over time, will require a multi-temporal approach to model building, which should be an integral part of developing these methods in the future