253 research outputs found

    Advancing the Monitoring Capabilities of Mountain Snowpack Fluctuations at Various Spatial and Temporal Scales

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    Snow is a critical water resource for the western US and many regions across the globe. However, our ability to accurately monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge in mountain regions. No single sensor currently has the ability to directly measure snow water equivalent (SWE) from space at a spatial scale suitable for water supply forecasting in mountain environments. This knowledge gap calls for the innovative use of remote sensing techniques, computational tools, and data science methods to advance our ability to estimate mountain snowpacks across a range of spatial and temporal scales. The goal of this dissertation is to advance our capabilities for understanding snowpack across watershed-relevant spatial and temporal scales. Two research approaches were used to accomplish this goal: quantifying the physiographic controls and sensitivities of hydrologically important snow metrics and progressing our ability to use L-band interferometric synthetic aperture radar (InSAR) to measure SWE changes. First, we quantify the physiographic controls and various snowpack metrics in the Sierra Nevada using a novel gridded SWE reanalysis dataset. Such work demonstrates the complexity of snowpack processes and the need for fine-resolution snowpack information. Next, using L-band Interferometric Synthetic Aperture Radar (InSAR) from the NASA SnowEx campaign, both snow ablation and accumulation are estimated in the Jemez Mountains, NM. The radar-derived retrievals are evaluated utilizing a combination of optical snow-cover data, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). Lastly, we compare multisensor optical-radar approaches for SWE retrievals and find that moderate-resolution legacy satellite products provide sufficient results. The results of this work show that L-band InSAR is a suitable technique for global SWE monitoring when used synergistically with optical SCA data and snowpack modeling. While two distinctive methods are present in this research, they both work towards advancing our ability to understand the dynamics of mountain snowpack

    Land Surface Monitoring Based on Satellite Imagery

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    This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest ïŹres and drought

    Estimating tropical forest above-ground biomass at the local scale using multi-source space-borne remote sensing data

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    Although forest biomass estimation has attracted a great number of studies using remote sensing data, its usage still contains high uncertainties. After transitioning from deforestation to reforestation under the development of Payments for Environmental Services (PES) programmes, young forests that are dominated by numerous small regenerating understory trees are found in many areas of many developing countries. However, the lack of analysis on the effect of this understory vegetation on total AGB is one the limitations of biomass studies. Moreover, it is always challenging to estimate the biomass of tropical forest due to its complex structure, high diversity of species, and dense canopy of understory trees. Taking into account these factors, this study, therefore, aims to investigate the effect of including understory trees in accuracy of AGB estimation in complex tropical heterogeneous forest at the local scale. The research conducted three consecutive experiments, using different remote sensing data sources, being: optical data, synthetic aperture radar (SAR) data and the integration of optical and SAR data, across various forest types in different test site locations. The results provide comprehensive insights into the impact of small regenerating trees on improving AGB estimation. This major finding alone demonstrates that the role of small regenerating trees should not be automatically discounted, especially for tropical forest where a number of different tree layers is common. This is especially important in areas with a large number of small regenerating trees and where open canopy layers are young. The thesis reveals that the level of influence of small regenerating trees on each forest type is different. Therefore, the study recommends an approach to including small regenerating trees for each forest type. This thesis argues there is a need to develop local-specific allometric equations for both overstory and understory layers to improve the accuracy of biomass models. Methods required for collecting field data and calculating biomass for small regenerating trees should be considered carefully in terms of evaluating cost-effective biomass estimation for each ecological region and each species. This requirement is most critical for young forest sites where there are mixtures of mature trees and young regenerating trees

    River landform dynamics detection and responses to morphology change in the rivers of North Luzon, the Philippines

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    River morphology detection has been improved considerably with the application of remote sensing and developments in computer science. However, applications that extract landforms within the active river channel remain limited, and there is a lack of studies from tropical regions. This thesis developed and then applied a workflow employing Sentinel-2 imagery for seasonal and annual river landform classification. Image downscaling approaches were investigated, and the performance of object-based image segmentation was assessed. The area to point regression kriging (ATPRK) approach was chosen to downscale coarser 20 m resolution Sentinel-2 bands to finer 10 m resolution bands. All features were set or processed at 10 m resolution before applying support vector machine (SVM) classification. To improve machine learning classification accuracy, Sentinel-2 acquisitions across one year, which incorporates multiple seasons, should be used. For rivers with different hydrological or geology settings, the thesis considered collecting river specific ground truth data to build a training model to avoid underfitting of models from other hydrological/geological settings. Applying the workflow, three landforms (water, unvegetated bars and vegetated bars) were classified within the active channel of the Bislak, Laoag, Abra and Cagayan Rivers, north Luzon, the Philippines, between 2016 to 2021, respectively. The spatial-temporal river landform datasets enabled the quantitative analysis of the river morphology changes. Water and unvegetated bars showed clear seasonal dynamics in all four rivers, whilst vegetated bars only showed seasonality in the rivers located in the northwest Luzon (the Bislak, Laoag and Abra Rivers). This thesis employed correlated coefficients to investigate the longitudinal correlation between river landforms and active width. It was found that vegetated bar areas always have strong significant correlations (≄0.67) with the active widths in all four rivers, whilst correlation coefficients between vegetated bar areas and active widths in the wet season are higher than that in the dry season. Ensemble empirical mode decomposition (EEMD) was applied to detect landform periodicity; this method indicated that water and vegetated bars commonly showed synchronised fluctuations with precipitation, while unvegetated bars had an anti-phase oscillation with precipitation. In the case of EEMD, deviations from periodic consistency in river pattern may reflect the influence of extreme events and/or human disturbance. Coefficient of variation (COV) was then used to evaluate the stability of the landforms; results suggested that the interplay of faults, elevation, confinement and tributary locations impacted landform stability. Finally, tributary inflow impacts on the mainstem river were investigated for eight tributaries of the lowland Cagayan River, also on Luzon Island. Longitudinal variations in channel morphology and stability, and temporal changes in landform frequency, using Simpson’s diversity index and COV, showed downstream widening associated with tributaries that was controlled by water discharge, with a secondary sediment flux effect. Overall, this thesis provided a novel example of combining remote sensing and GIS science, computing science, statistical science, and river morphology science to study the earth surface processes synthetically and quantitatively within river active channels in the tropical north Luzon, the Philippines. This work demonstrated how the fusion of techniques from these disciplines can be used to detect and analyse river landform changes, with potential applications for river management and restoration

    Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

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    De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large rĂ©solution spatiale (25-50 km) n’en font pas un outil adĂ©quat pour des applications hydrologiques Ă  une Ă©chelle locale telles que la modĂ©lisation et la prĂ©vision hydrologiques. Dans de nombreuses Ă©tudes, une dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol des produits satellites micro-ondes est faite puis validĂ©e avec des mesures in-situ. Toutefois, l’utilisation de ces donnĂ©es issues d’une dĂ©sagrĂ©gation d’échelle n’a pas encore Ă©tĂ© pleinement Ă©tudiĂ©e pour des applications en hydrologie. Ainsi, l’objectif de cette thĂšse est de proposer une mĂ©thode de dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol issue de donnĂ©es satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) Ă  diffĂ©rentes rĂ©solutions spatiales afin d’évaluer leur apport sur l’amĂ©lioration potentielle des modĂ©lisations et prĂ©visions hydrologiques. À partir d’un modĂšle de forĂȘt alĂ©atoire, une dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol de SMAP l’amĂšne de 36-km de rĂ©solution initialement Ă  des produits finaux Ă  9-, 3- et 1-km de rĂ©solution. Les prĂ©dicteurs utilisĂ©s sont Ă  haute rĂ©solution spatiale et de sources diffĂ©rentes telles que Sentinel-1A, MODIS et SRTM. L'humiditĂ© du sol issue de cette dĂ©sagrĂ©gation d’échelle est ensuite assimilĂ©e dans un modĂšle hydrologique distribuĂ© Ă  base physique pour tenter d’amĂ©liorer les sorties de dĂ©bit. Ces expĂ©riences sont menĂ©es sur les bassins versants des riviĂšres Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situĂ©s aux États-Unis. De plus, le modĂšle assimile aussi des donnĂ©es d’humiditĂ© du sol en profondeur issue d’une extrapolation verticale des donnĂ©es SMAP. Par ailleurs, les donnĂ©es d’humiditĂ© du sol SMAP et les mesures in-situ sont combinĂ©es par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilĂ© dans le modĂšle hydrologique pour tenter d’amĂ©liorer la prĂ©vision hydrologique sur le bassin versant Au Saumon situĂ© au QuĂ©bec. Les rĂ©sultats montrent que l'utilisation de l’humiditĂ© du sol Ă  fine rĂ©solution spatiale issue de la dĂ©sagrĂ©gation d’échelle amĂ©liore la reprĂ©sentation de la variabilitĂ© spatiale de l’humiditĂ© du sol. En effet, le produit Ă  1- km de rĂ©solution fournit plus de dĂ©tails que les produits Ă  3- et 9-km ou que le produit SMAP de base Ă  36-km de rĂ©solution. De mĂȘme, l’utilisation du produit de fusion SMAP/ in-situ amĂ©liore la qualitĂ© et la reprĂ©sentation spatiale de l’humiditĂ© du sol. Sur le bassin versant Susquehanna, la modĂ©lisation hydrologique s’amĂ©liore avec l’assimilation du produit de dĂ©sagrĂ©gation d’échelle Ă  9-km, sans avoir recours Ă  des rĂ©solutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la rĂ©solution spatiale la plus fine Ă  1- km qui offre les meilleurs rĂ©sultats de modĂ©lisation hydrologique. L’assimilation de l’humiditĂ© du sol en profondeur issue de l’extrapolation verticale des donnĂ©es SMAP n’amĂ©liore que peu la qualitĂ© du modĂšle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon amĂ©liore la qualitĂ© de la prĂ©vision du dĂ©bit, mĂȘme si celle-ci n’est pas trĂšs significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

    GFM Product User Manual

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    This Product User Manual (PUM) is the reference document for all end-users and stakeholders of the new Global Food Monitoring (GFM) product of the Copernicus Emergency Management Service (CEMS). The PUM provides all of the basic information to enable the proper and effective use of the GFM product and associated data output layers. This manual includes a description of the functions and capabilities of the GFM product, its applications and alternative modes of operation, and step-by-step guidance on the procedures for accessing and using the GFM product

    Evaluation of low-cost Earth observations to scale-up national forest monitoring in Miombo Woodlands of Malawi

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    This study explored the extent that low-cost Earth Observations (EO) data could effectively be combined with in-situ tree-level measurements to support national estimates of Above Ground Biomass (AGB) and Carbon (C) in Malawi’s Miombo Woodlands. The specific objectives were to; (i) investigate the effectiveness of low-cost optical UAV orthomosaics in geo-locating individual trees and estimating AGB and C, (ii) scale-up the AGB estimates using the canopy height model derived from the UAV imagery, and crown diameter measurements; and (iii) compare results from (ii), ALOS-PALSAR-2, Sentinel1, ESA CCI Biomass Map datasets, and Sentinel 2 vis/NIR/SWIR band combination datasets in mapping biomass. Data were acquired in 2019 from 13 plots over Ntchisi Forest in 3-fold, vis-a-vis; (i) individual tree measurements from 0.1ha ground-based (gb) plots, (ii) 3-7cm pixel resolution optical airborne imagery from 50ha plots, and (iii) SAR backscatter and Vis/NIR/SWIR bands imagery. Results demonstrate a strong correlational relationship (R2 = 0.7, RMSE = 11tCha-1) between gb AGB and gb fractional cover percent (FC %), more importantly (R2 = 0.7) between gb AGB and UAV-based FC. Similarly, another set of high correlation (R2 = 0.9, RMSE = 7tCha-1; R2 = 0.8, RMSE = 8tCha-1; and R2 = 0.7) was observed between the gb AGB and EO-based AGB from; (i) ALOS-PALSAR-2, (ii) ESA-CCI-Biomass Map, and (iii) S1-C-band, respectively. Under the measurement conditions, these findings reveal that; (i) FC is more indicative of AGB and C pattern than CHM, (ii) the UAV can collect optical data of very high resolution (3-7cm resolution with ±13m horizontal geolocation error), and (iii) provides the cost-effective means of bridging the ground datasets to the wall-to-wall satellite EO data (ÂŁ7 ha-1 compared to ÂŁ30 ha-1, per person, provided by the gb system). The overall better performance of the SAR backscatter (R2 = 0.7 to 0.9) establishes the suitability of the SAR backscatter to infer the Miombo AGB and fractional cover with high accuracy. However, the following factors compromised the accuracy for both the SAR and optical measurements; leaf-off and seasonality (fire, aridness), topography (steep slopes of 18-74%), and sensing angle. Inversely, the weak to moderate correlation observed between the gb height and UAV FC % measurements (R2 = 0.4 to 0.7) are attributable to the underestimation systematic error that UAV height datasets are associated with. The visual lacunarity analysis on S2-Vis/NIR/SWIR composite band and SAR backscatter measurements demonstrated robust, consistent and homogenous spatial crown patterns exhibited particularly by the leaf-on tree canopies along riverine tree belts and cohorts. These results reveal the potential of vis/NIR/SWIR band combination in determining the effect of fire, rock outcrops and bare land/soil common in these woodlands. Coarsening the EO imagery to ≄50m pixel resolution compromised the accuracy of the estimations, hence <50m resolution is the ideal scale for these Miombo. Careful consideration of the aforementioned factors and incorporation of FC parameter in during estimation of AGB and C will go a long way in not only enhancing the accuracy of the measurements, but also in bolstering Malawi’s NFMS standards to yield carbon off-set payments under the global REDD+ mechanism

    ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

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    Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
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