3,983 research outputs found
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κ³Ό ν μ§ νΌλ³΅ λ³ν λͺ¨λν°λ§μ μν΄ νμμ μ΄λ€. κ³ ν΄μλ μμ±μμμΌλ‘ μ§κ΅¬ νλ©΄μ κ΄μΈ‘νλ κ²μ μ©μ΄νκ² ν΄μ‘λ€. νΉν Planet Fusionμ μ΄μνμμ±κ΅° λ°μ΄ν°λ₯Ό μ΅λν νμ©ν΄ λ°μ΄ν° κ²°μΈ‘μ΄ μλ 3m κ³΅κ° ν΄μλμ μ§ν νλ©΄ λ°μ¬λμ΄λ€. κ·Έλ¬λ κ³Όκ±° μμ± μΌμ(Landsatμ κ²½μ° 30~60m)μ κ³΅κ° ν΄μλλ μμμ 곡κ°μ λ³νλ₯Ό μμΈ λΆμνλ κ²μ μ ννλ€. μ 3μ₯μμλ Landsat λ°μ΄ν°μ κ³΅κ° ν΄μλλ₯Ό ν₯μνκΈ° μν΄ Planet Fusion λ° Landsat 8 λ°μ΄ν°λ₯Ό μ¬μ©νμ¬ μ΄μ€ μ λμ μμ± λ€νΈμν¬(the dual RSS-GAN)λ₯Ό νμ΅μμΌ, κ³ ν΄μλ μ κ·ν μμ μ§μ(NDVI)μ μλ¬Ό κ·Όμ μΈμ λ°μ¬(NIRv)λλ₯Ό μμ±νλ νλ€. νμκΈ°λ° νμ₯ μμμ§μ(μ΅λ 8λ
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κ³ ν΄μλμμ μλ¬Ό κ΄ν©μ± μ§λλ ν μ§νΌλ³΅μ΄ 볡μ‘ν 곡κ°μμ νμ μν λͺ¨λν°λ§μ νμμ μ΄λ€. κ·Έλ¬λ Sentinel-2, Landsat λ° MODISμ κ°μ΄ νμ λμ‘° κΆ€λμ μλ μμ±μ κ³΅κ° ν΄μλκ° λκ±°λ μκ° ν΄μλ λμ μμ±μμλ§ μ 곡ν μ μλ€. μ΅κ·Ό λ°μ¬λ μ΄μνμμ±κ΅°μ μ΄λ¬ν ν΄μλ νκ³μ 극볡ν μ μλ€. νΉν Planet Fusionμ μ΄μνμμ± μλ£μ μκ³΅κ° ν΄μλλ‘ μ§νλ©΄μ κ΄μΈ‘ν μ μλ€. 4μ₯μμ, Planet Fusion μ§νλ°μ¬λλ₯Ό μ΄μ©νμ¬ μμμμ λ°μ¬λ κ·Όμ μΈμ 볡μ¬(NIRvP)λ₯Ό 3m ν΄μλ μ§λλ₯Ό μΌκ°κ²©μΌλ‘ μμ±νλ€. κ·Έλ° λ€μ λ―Έκ΅ μΊλ¦¬ν¬λμμ£Ό μν¬λΌλ©ν -μ νΈμν¨ λΈνμ νλμ€ νμ λ€νΈμν¬ λ°μ΄ν°μ λΉκ΅νμ¬ μλ¬Ό κ΄ν©μ±μ μΆμ νκΈ° μν NIRvP μ§λμ μ±λ₯μ νκ°νμλ€. μ 체μ μΌλ‘ NIRvP μ§λλ μ΅μ§μ μ¦μ μμ λ³νμλ λΆκ΅¬νκ³ κ°λ³ λμμ§μ μλ¬Ό κ΄ν©μ±μ μκ°μ λ³νλ₯Ό ν¬μ°©νμλ€. κ·Έλ¬λ λμμ§ μ 체μ λν NIRvP μ§λμ μλ¬Ό κ΄ν©μ± μ¬μ΄μ κ΄κ³λ NIRvP μ§λλ₯Ό νλμ€ νμ κ΄μΈ‘λ²μμ μΌμΉμν¬ λλ§ λμ μκ΄κ΄κ³λ₯Ό 보μλ€. κ΄μΈ‘λ²μλ₯Ό μΌμΉμν¬ κ²½μ°, NIRvP μ§λλ μλ¬Ό κ΄ν©μ±μ μΆμ νλ λ° μμ΄ νμ₯ NIRvPλ³΄λ€ μ°μν μ±λ₯μ 보μλ€. μ΄λ¬ν μ±λ₯ μ°¨μ΄λ νλμ€ νμ κ΄μΈ‘λ²μλ₯Ό μΌμΉμν¬ λ, μ°κ΅¬ λμμ§ κ°μ NIRvP-μλ¬Ό κ΄ν©μ± κ΄κ³μ κΈ°μΈκΈ°κ° μΌκ΄μ±μ 보μκΈ° λλ¬Έμ΄λ€. λ³Έ μ°κ΅¬ κ²°κ³Όλ μμ± κ΄μΈ‘μ νλμ€ νμ κ΄μΈ‘λ²μμ μΌμΉμν€λ κ²μ μ€μμ±μ 보μ¬μ£Όκ³ λμ μκ³΅κ° ν΄μλλ‘ μλ¬Ό κ΄ν©μ±μ μ격μΌλ‘ λͺ¨λν°λ§νλ μ΄μνμμ±κ΅° μλ£μ μ μ¬λ ₯μ 보μ¬μ€λ€.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed.
In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution.
In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery.
Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30β60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from β3.2% to 1.2% and β12.4% to β3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images.
Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2
1. Background 2
1.1 Daily gap-free surface reflectance using geostationary satellite products 2
1.2 Monitoring past vegetation changes with high-spatial-resolution 3
1.3 High spatiotemporal resolution vegetation photosynthesis maps 4
2. Purpose of Research 4
Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6
1. Introduction 6
2. Methods 11
2.1 Study sites 11
2.2 In situ measurements 13
2.3 Satellite products 14
2.4 Integrated system 17
2.5 Canopy photosynthesis 21
2.6 Evaluation 23
3. Results and discussion 24
3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24
3.2 Comparison of STIF NIRvP with in situ NIRvP 28
4. Conclusion 31
Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32
1. Introduction 32
2. Methods 38
2.1 Real-ESRGAN model 38
2.2 Study sites 40
2.3 In situ measurements 42
2.4 Vegetation index 44
2.5 Satellite data 45
2.6 Planet Fusion 48
2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49
2.8 Evaluation 54
3. Results 57
3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57
3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60
3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63
3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66
4. Discussion 70
4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70
4.2 CubeSat data in the dual RSS-GAN model 72
4.3 Perspectives and limitations 73
5. Conclusion 78
Appendices 79
Supplementary material 82
Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85
1. Introduction 85
2. Methods 89
2.1 Study sites 89
2.2 In situ measurements 92
2.3 Planet Fusion NIRvP 94
2.4 Flux footprint model 98
2.5 Evaluation 98
3. Results 105
3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105
3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108
3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114
4. Discussion 118
4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118
4.2 Roles of radiation component in GPP mapping 123
4.3 Limitations and perspectives 126
5. Conclusion 133
Appendix 135
Supplementary Materials 144
Chapter 5. Conclusion 153
Bibliography 155
Abstract in Korea 199
Acknowledgements 202λ°
An approach for heavy metal pollution detected from spatio-temporal stability of stress in rice using satellite images
Stable stressors on crops (e.g., salts, heavy metals), which are characterized by stable spatial patterns over time, are harmful to agricultural production and food security. Satellite data provide temporally and spatially continuous synoptic observations of stable stress on crops. This study presents a method for identifying rice under stable stress (i.e., Cd stress) and exploring its spatio-temporal characteristics indicators. The study area is a major rice growing region located in Hunan Province, China. Moderate-resolution imaging spectroradiometer (MODIS) and Landsat images from 2008β2017 as well as in situ measurements were collected. The coupling of a leaf canopy radiative transfer model with the World Food Study Model (WOFOST) via a wavelet transform isolated the effects of Cd stress from other abrupt stressors. An area wavelet transform stress signal (AWTS), based on a time-series Enhanced Vegetation Index (EVI), was used to detect rice under Cd stress, and its spatio-temporal variation metrics explored. The results indicate that spatial variation coefficients (SVC) of AWTS in the range of 0β1βhaβd a coverage area greater than 70% in each experimental region, regardless of the year. Over ten years, the temporal variation coefficients (TVC) of AWTS in the range of 0β1 occurred frequently (more than 60% of the time). In addition, the Pearson correlation coefficient of AWTS over two consecutive years was usually greater than 0.5. We conclude that a combination of multi-year satellite-derived vegetation index data with a physical model simulation is an effective and novel method for detecting crops under environmental stress. A wavelet transform proved promising in differentiating between the effects of stable stress and abrupt stress on rice and may offer a way forward for diagnosing crop stress at continental and global scales
Applications of satellite βhyper-sensingβ in Chinese agriculture:Challenges and opportunities
Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of βhyper-sensingβ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite βhyper-sensingβ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite βhyper-sensingβ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing βhyper-sensingβ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China
Remote Sensing in Mangroves
The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl
Microfluidic devices for high-throughput plant phenotyping and bioenergy harvesting from microbes and living plants
Microfluidics and micro/nanofabrication techniques provide powerful technological platforms to develop miniature bioassay devices for studying cellular and multicellular organisms. Microfluidic devices have many advantages over traditional counterparts, including good throughput due to parallel experiments, low infrastructural cost, fast reaction, reduced consumption of agent and reagent, and avoidance of contamination. This thesis is focused on the development of a microfluidic toolkit with several miniature devices to tackle important problems that the fields of plant phenotyping and bioenergy harvesting are facing. The ultimate goal of this research is to realize high-throughput screening methods for studying environment-genomics of plants through phenomics, and understanding microbial and plant metabolisms that contribute to harvesting bioenergy from microbes and living plants in different environments.
First, we develop vertical microfluidic plant chips and miniature greenhouses for high throughput phenotyping of Arabidopsis plants. The vertical design allows for gravitropic growth of multiple plants and continuous monitoring of seed germination and plant development at both the whole-plant and cellular levels. An automatic seed trapping method is developed to facilitate seed loading process. Also, electrospun nanofibrous membranes are incorporated with a seed germination chip to obtain a set of incubation temperatures on the device. Furthermore, miniature greenhouses are designed to house the plant and seed chips and to flexibly change temperature and light conditions for high-throughput plant phenotyping on a multi-scale level.
Second, to screen bacteria and mutants for elucidating mechanisms of electricity generation, we develop two types of miniature microbial fuel cells (Β΅MFCs) using conductive poly(3,4-ethylenedioxythiophene) nanofibers and porous graphene foam (GF) as three-dimensional (3D) anode materials. It is demonstrated that in the nanofiber-based Β΅MFC, the nanofibers are suitable for rapid electron transfer and Shewanella oneidensis can fully colonize the interior region of the nanofibers. The GF-based Β΅MFC is featured with a porous anolyte chamber formed by embedding a GF anode inside a microchannel. The interconnected pores of the GF provide 3D scaffolds favorable for cell attachment, inoculation and colonization, and more importantly, allow flowing nutritional and bacterial media throughout the anode with minimal waste. Therefore, the nutrients in bio-convertible substrates can be efficiently used by microbes for sustainable production of electrons.
Last, we develop a first miniature plant-MFC or Β΅PMFC device as a technological interface to study bioenergy harvesting from microbes and living plants. A pilot research is conducted to create the Β΅PMFC device by sandwiching a hydrophilic semi-permeable membrane between a Β΅MFC and a plant growth chamber. Mass transport of carbon-containing organic exudates from the plant roots to the Β΅MFC is quantified. This work represents an important step towards screening plants, microbes, and their mutants to maximize energy generation of PMFCs
Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesCrop monitoring, especially in developing countries, can improve food production,
address food security issues, and support sustainable development goals. Crop type
mapping and yield estimation are the two major aspects of crop monitoring that remain
challenging due to the problem of timely and adequate data availability. Existing
approaches rely on ground-surveys and traditional means which are time-consuming
and costly. In this context, we introduce the use of freely available Sentinel-2 (S2) imagery
with high spatial, spectral and temporal resolution to classify crop and estimate
its yield through a deep learning approach. In particular, this study uses patch-based
2D and 3D Convolutional Neural Network (CNN) algorithms to map rice crop and
predict its yield in the Terai districts of Nepal. Firstly, the study reviews the existing
state-of-art technologies in this field and selects suitable CNN architectures. Secondly,
the selected architectures are implemented and trained using S2 imagery, groundtruth
and auxiliary data in addition for yield estimation.We also introduce a variation
in the chosen 3D CNN architecture to enhance its performance in estimating rice
yield. The performance of the models is validated and then evaluated using performance
metrics namely overall accuracy and F1-score for classification and Root Mean
Squared Error (RMSE) for yield estimation. In consistency with the existing works,
the results demonstrate recommendable performance of the models with remarkable
accuracy, indicating the suitability of S2 data for crop mapping and yield estimation
in developing countries.
Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, preprocessing,
methods, computational environment, results)
First results of phytoplankton spatial dynamics in two NW-Mediterranean bays from Chlorophyll-a estimates using Sentinel 2: potential implications for aquaculture
Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2βderived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.info:eu-repo/semantics/publishedVersio
A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture
Agricultural research is essential for increasing food production to meet the
requirements of an increasing population in the coming decades. Recently,
satellite technology has been improving rapidly and deep learning has seen much
success in generic computer vision tasks and many application areas which
presents an important opportunity to improve analysis of agricultural land.
Here we present a systematic review of 150 studies to find the current uses of
deep learning on satellite imagery for agricultural research. Although we
identify 5 categories of agricultural monitoring tasks, the majority of the
research interest is in crop segmentation and yield prediction. We found that,
when used, modern deep learning methods consistently outperformed traditional
machine learning across most tasks; the only exception was that Long Short-Term
Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random
Forests (RF) for yield prediction. The reviewed studies have largely adopted
methodologies from generic computer vision, except for one major omission:
benchmark datasets are not utilised to evaluate models across studies, making
it difficult to compare results. Additionally, some studies have specifically
utilised the extra spectral resolution available in satellite imagery, but
other divergent properties of satellite images - such as the hugely different
scales of spatial patterns - are not being taken advantage of in the reviewed
studies.Comment: 25 pages, 2 figures and lots of large tables. Supplementary materials
section included here in main pd
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