1,847 research outputs found
Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring
With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data
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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°
Applications of Remote Sensing to Alien Invasive Plant Studies
Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions
A MODIS/ASTER Airborne Simulator (MASTER) Imagery for Urban Heat Island Research
abstract: Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect
A Cross Comparison of Spatiotemporally Enhanced Springtime Phenological Measurements From Satellites and Ground in a Northern U.S. Mixed Forest
Cross comparison of satellite-derived land surface phenology (LSP) and ground measurements is useful to ensure the relevance of detected seasonal vegetation change to the underlying biophysical processes. While standard 16-day and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI)-based springtime LSP has been evaluated in previous studies, it remains unclear whether LSP with enhanced temporal and spatial resolutions can capture additional details of ground phenology. In this paper, we compared LSP derived from 500-m daily MODIS and 30-m MODIS-Landsat fused VI data with landscape phenology (LP) in a northern U.S. mixed forest. LP was previously developed from intensively observed deciduous and coniferous tree phenology using an upscaling approach. Results showed that daily MODIS-based LSP consistently estimated greenup onset dates at the study area (625 m Γ 625 m) level with 4.48 days of mean absolute error (MAE), slightly better than that of using 16-day standard VI (4.63 days MAE). For the observed study areas, the time series with increased number of observations confirmed that post-bud burst deciduous tree phenology contributes the most to vegetation reflectance change. Moreover, fused VI time series demonstrated closer correspondences with LP at the community level (0.1-20 ha) than using MODIS alone at the study area level (390 ha). The fused LSP captured greenup onset dates for respective forest communities of varied sizes and compositions with four days of the overall MAE. This study supports further use of spatiotemporally enhanced LSP for more precise phenological monitoring
Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness
The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps
A MODIS/ASTER airborne simulator (MASTER) imagery for urban heat island research
Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect
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