Browsing by Author "Li, Lin"
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Item Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery(Elsevier, 2021-12) O'Shea, Ryan E.; Pahlevan, Nima; Smith, Brandon; Bresciani, Mariano; Egerton, Todd; Giardino, Claudia; Li, Lin; Moore, Tim; Ruiz-Verdu, Antonio; Ruberg, Steve; Simis, Stefan G. H.; Stumpf, Richard; Vaičiūtė, Diana; Earth Sciences, School of ScienceRetrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), PCs, and remote sensing reflectance (Rrs) measurements to estimate PC from all relevant spectral bands. The performance of the developed model is demonstrated via PC maps produced from select images of the Hyperspectral Imager for the Coastal Ocean (HICO) and Italian Space Agency's PRecursore IperSpettrale della Missione Applicativa (PRISMA) using a matchup dataset. As input to the MDN, we incorporate a combination of widely used band ratios (BRs) and line heights (LHs) taken from existing multispectral algorithms, that have been proven for both Chla and PC estimation, as well as novel BRs and LHs to increase the overall cyanobacteria biomass estimation accuracy and reduce the sensitivity to ∆Rrs. When trained on a random half of the dataset, the MDN achieves uncertainties of 44.3%, which is less than half of the uncertainties of all viable optimized multispectral PC algorithms. The MDN is notably better than multispectral algorithms at preventing overestimation on low (<10 mg m−3) PC. Visibly, HICO and PRISMA PC maps show the wider dynamic range that can be represented by the MDN. The available in situ and satellite-derived Rrs matchups and measured in situ PC demonstrate the robustness of the MDN for estimating low (<10 mg m−3) PC and the reduced impact of ∆Rrs on medium-to-high in situ PC (>10 mg m−3). According to our extensive assessments, the developed model is anticipated to enable practical PC products from PRISMA and HICO, therefore the model is promising for planned hyperspectral missions, such as the Plankton Aerosol and Cloud Ecosystem (PACE). This advancement will enhance the complementary roles of hyperspectral radiometry from satellite and low-altitude platforms for quantifying and monitoring cyanobacteria harmful algal blooms at both large and local spatial scales.Item BAND SELECTION METHOD APPLIED TO M3 (MOON MINERALOGY MAPPER)(Office of the Vice Chancellor for Research, 2012-04-13) Cavanagh, Patrick D.; Li, LinRemote sensing optical sensors, such as those on board satellites and planetary probes, are able to detect and measure solar radiation at both im-proved spectral and spatial resolution. In particular, a hyperspectral dataset often consists of tens to hundreds of specified wavelength bands and con-tains a vast amount of spectral information for potential processing. One drawback of such a large spectral dataset is information redundancy result-ing from high correlation between narrow spectral bands. Reducing the data dimensionality is critical in practical hyperspectral remote sensing applica-tions. Price’s method is a band selection approach that uses a small subset of bands to accurately reconstruct the full hyperspectral dataset. The method seeks to represent the dataset by a weighted sum of basis functions. An it-erative process is used to successively approximate the full dataset. The process ends when the last basis function no longer provides a significant contribution to the reconstruction of the dataset, i.e. the basis function is dominated by noise. The research presented examines the feasibility of Price’s method for ex-tracting an optimal band subset from recently acquired lunar hyperspectral images recorded by the Moon Mineralogy Mapper (M3) instrument on board the Chandrayaan-1 spacecraft. The Apollo 17 landing site was used for test-ing of the band selection method. Preliminary results indicate that the band selection method is able to successfully reconstruct the original hyperspectral dataset with minimal error. In a recent test case, 15 bands were used to reconstruct the original 74 bands of reflectance data. This represents an accurate reconstruction using only 20% of the original dataset. The results from this study can help to configure spectral channels of fu-ture optical instruments for lunar exploration. The channels can be chosen based on the knowledge of which wavelength bands represent the greatest relevant information for characterizing geology of a particular location.Item Climate change and ecohydrological processes in drylands : the effects of C02 enrichment, precipitation regime change and temperature extremes(2018-04-03) Lu, Xuefei; Wang, Lixin; Gilhooly, William P.; Jacinthe, Pierre Andre; Li, Lin; Wilson, JefferyDrylands are the largest terrestrial biome on the planet, and the critically important systems that produce approximately 40% of global net primary productivity to support nearly 2.5 billion of global population. Climate change, increasing populations and resulting anthropogenic effects are all expected to impact dryland regions over the coming decades. Considering that approximately 90% of the more than 2 billion people living in drylands are geographically located within developing countries, improved understanding of these systems is an international imperative. Although considerable progress has been made in recent years in understanding climate change impacts on hydrological cycles, there are still a large number of knowledge gaps in the field of dryland ecohydrology. These knowledge gaps largely hinder our capability to better understand and predict how climate change will affect the hydrological cycles and consequently the soil-vegetation interactions in drylands. The present study used recent technical advances in remote sensing and stable isotopes, and filled some important knowledge gaps in the understanding of the dryland systems. My study presents a novel application of the combined use of customized chambers and a laser-based isotope analyzer to directly quantify isotopic signatures of transpiration (T), evaporation (E) and evapotranspiration (ET) in situ and examine ET partitioning over a field of forage sorghum under extreme environmental conditions. We have developed a useful framework of using satellite data and trend analysis to facilitate the understanding of temporal and spatial rainfall variations in the areas of Africa where the in situ observations are scarce. By using a meta-analysis approach, we have also illustrated that higher concentrations of atmospheric CO2 induce plant water saving and the consequent available soil water increases are a likely driver of the observed greening phenomena. We have further demonstrated that Leuning’s modified Ball-Berry model and RuBP limited optimization model can generally provide a good estimate of stomatal conductance response to CO2 enrichment under different environmental conditions. All these findings provide important insights into dryland water-soil-vegetation interactions.Item A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content(MDPI, 2019-04) Chen, Lin; Ren, Chunying; Li, Lin; Wang, Yeqiao; Zhang, Bai; Wang, Zongming; Li, Linfeng; Earth Sciences, School of ScienceAccurate digital soil mapping (DSM) of soil organic carbon (SOC) is still a challenging subject because of its spatial variability and dependency. This study is aimed at comparing six typical methods in three types of DSM techniques for SOC mapping in an area surrounding Changchun in Northeast China. The methods include ordinary kriging (OK) and geographically weighted regression (GWR) from geostatistics, support vector machines for regression (SVR) and artificial neural networks (ANN) from machine learning, and geographically weighted regression kriging (GWRK) and artificial neural networks kriging (ANNK) from hybrid approaches. The hybrid approaches, in particular, integrated the GWR from geostatistics and ANN from machine learning with the estimation of residuals by ordinary kriging, respectively. Environmental variables, including soil properties, climatic, topographic, and remote sensing data, were used for modeling. The mapping results of SOC content from different models were validated by independent testing data based on values of the mean error, root mean squared error and coefficient of determination. The prediction maps depicted spatial variation and patterns of SOC content of the study area. The results showed the accuracy ranking of the compared methods in decreasing order was ANNK, SVR, ANN, GWRK, OK, and GWR. Two-step hybrid approaches performed better than the corresponding individual models, and non-linear models performed better than the linear models. When considering the uncertainty and efficiency, ML and two-step approach are more suitable than geostatistics in regional landscapes with the high heterogeneity. The study concludes that ANNK is a promising approach for mapping SOC content at a local scale.Item Comparison of Urban Tree Canopy Classification With High Resolution Satellite Imagery and Three Dimensional Data Derived From LIDAR and Stereoscopic Sensors(2008-08-22T13:59:51Z) Baller, Matthew Lee; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Li, LinDespite growing recognition as a significant natural resource, methods for accurately estimating urban tree canopy cover extent and change over time are not well-established. This study evaluates new methods and data sources for mapping urban tree canopy cover, assessing the potential for increased accuracy by integrating high-resolution satellite imagery and 3D imagery derived from LIDAR and stereoscopic sensors. The results of urban tree canopy classifications derived from imagery, 3D data, and vegetation index data are compared across multiple urban land use types in the City of Indianapolis, Indiana. Results indicate that incorporation of 3D data and vegetation index data with high resolution satellite imagery does not significantly improve overall classification accuracy. Overall classification accuracies range from 88.34% to 89.66%, with resulting overall Kappa statistics ranging from 75.08% to 78.03%, respectively. Statistically significant differences in accuracy occurred only when high resolution satellite imagery was not included in the classification treatment and only the vegetation index data or 3D data were evaluated. Overall classification accuracy for these treatment methods were 78.33% for both treatments, with resulting overall Kappa statistics of 51.36% and 52.59%.Item CONFOUNDING CONSTITUENTS IN REMOTE SENSING OF PHYCOCYANIN(2008-08-22T13:53:38Z) Vallely, Lara Anne; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Li, LinThis project examines the impact of confounding variables that have limited the accuracy of remotely predicting phycocyanin in three Indiana drinking and recreational water reservoirs. In-situ field reflectance spectra were collected from June to November 2006 over a wide range of algal bloom conditions using an ASD Fieldspec (UV/VNIR) spectroradiometer. Groundtruth samples were analyzed for chlorophyll a, phycocyanin, total suspended matter, and other water quality constituents. Previously published spectral algorithms for the detection of phycocyanin were evaluated against lab measured pigment concentrations using linear least squares regression. Algorithm performance varied across study sites (best performing models by reservoir resulted in r2 values of 0.32 to 0.84). Residuals of predicted versus measured pigment concentrations were analyzed against concentration of potential confounding water constituents. Residual analysis revealed optically active constituents contributed between 25% and 95% of original phycocyanin model errors. Inclusion of spectral variables into models to account for significant confounders resulted in improved spectral estimates of phycocyanin (r2 = 0.56 to 0.93).Item Construction of Late Cretaceous, Mid-Crustal Sheeted Plutons from the Eastern Transverse Ranges, Southern California(2009-01-16T16:57:10Z) Brown, Kenneth Lee; Licht, Kathy J.; Swope, R. Jeffrey; Li, LinDifferential exhumation within the eastern Transverse Ranges of southern California has revealed a tilted crustal section that provides a unique view into the architecture of the Mesozoic arc. At the base of this crustal section is a group of well-exposed sheeted plutons. Well-developed, gentle to moderately dipping magmatic and solid-state fabrics within these plutons are regionally consistent, margin-parallel, discordant to internal sheeting and layering, and are generally parallel to equivalent host rock structures and fabrics. In some plutons, magmatic foliations define regional fold structures, thus recording regional contraction during chamber construction. Collectively, field mapping and fabric analyses within these sheeted plutons show that the observed fabric patterns are better explained by regional deformation rather than internal magma chamber processes. This interpretation is in direct contrast to previous mapping in the region. The host rocks also record complex processes during sheeted pluton emplacement. Deflection of host rock foliations and structures into parallelism with pluton contacts suggest that downward ductile flow played a role in making space for these plutons. However, evidence of regional faulting and shearing is not observed, suggesting that they did not play a significant role. Although there is considerable microstructural variability within each pluton, the observed microstructures are generally consistent with a transition from magmatic to submagmatic/ high-temperature solid-state deformation. Magmatic microstructures are defined by euhedral to subhedral plagioclase, hornblende, and biotite that do not show significant internal crystal-plastic deformation. Evidence for high-temperature solid-state deformation includes high-temperature grain boundary migration in quartz, plagaioclase, potassium feldspar, and hornblende; chessboard extinction in quartz; and ductile bending in plagioclase and hornblende. Microstructural observations also indicate that mafic and intermediate compositions record stronger magmatic fabrics than felsic compositions. Based on the structural and microstructural observations presented in this study, I interpret that these sheeted plutons were emplaced into an active continental arc setting that was undergoing regional contraction. The strong magmatic fabrics and high-temperature solid-state overprinting is likely a consequence of regional deformation during crystallization. The weak fabrics within upper crustal plutons relative to the strong fabrics within the mid-crustal plutons suggest that deformation was largely localized to the more compositionally heterogeneous mid-crustal portions of the arc structure.Item The Contributions of Soil Moisture and Groundwater to Non-Rainfall Water Formation in the Namib Desert(2019-08) Adhikari, Bishwodeep; Wang, Lixin; Li, Lin; Jacinthe, Pierre-AndréNon-rainfall waters such as fog and dew are considered as important source of water in drylands, and the knowledge of possible sources of its formation is very important to make future predictions. Prior studies have suggested the presence of radiation fog in drylands; however, its formation mechanism still remains unclear. There have been earlier studies on the effects of fog on soil moisture dynamics and groundwater recharge. On the contrary, no research has yet been conducted to understand the contribution of soil moisture and groundwater to fog formation. This study, therefore, for the first time intends to examine such possibility in a fog-dominated dryland ecosystem, the Namib Desert. The study was conducted at three sites representing two different land forms (sand dunes and gravel plains) in the Namib Desert. This thesis is divided into two parts: the first part examines evidences of fog formation through water vapor movement using field observations, and the second part simulates water vapor transport using HYDRUS-1D model. In the first part of the study, soil moisture, soil temperature and air temperature data were analyzed, and the relationships between these variables were taken as one of the key indicators for the linkage between soil water and fog formation. The analysis showed that increase in soil moisture generally corresponds to similar increase in air or soil temperature near the soil surface, which implied that variation in soil moisture might be the result of water vapor movement (evaporated soil moisture or groundwater) from lower depths to the soil surface. In the second part of the study, surface fluxes of water vapor were simulated using the HYDRUS-1D model to explore whether the available surface flux was sufficient to support fog formation. The actual surface flux and cumulative evaporation obtained from the model showed positive surface fluxes of water vapor. Based on the field observations and the HYDRUS-1D model results, it can be concluded that water vapor from soil layers and groundwater is transported through the vadose zone to the surface and this water vapor likely contributes to the formation of non-rainfall waters in fog-dominated drylands, like the Namib Desert.Item Convolutional neural network model for soil moisture prediction and its transferability analysis based on laboratory Vis-NIR spectral data(Elsevier, 2021-12) Chen, Yu; Li, Lin; Whiting, Michael; Chen, Fang; Sun, Zhongchang; Song, Kaishan; Wang, Qinjun; Earth Sciences, School of ScienceLaboratory visible near infrared reflectance (Vis-NIR, 400–2500 nm) spectroscopy has the advantages of simplicity, fast and non-destructive which was used for SM prediction. However, many previously proposed models are difficult to transfer to unknown target areas without recalibration. In this study, we first developed a suitable Convolutional Neutral Network (CNN) model and transferred the model to other target areas for two situations using different soil sample backgrounds under 1) the same measurement conditions (DSSM), and 2) under different measurement conditions (DSDM). We also developed the CNN models for the target areas based on their own datasets and traditional PLS models was developed to compare their performances. The results show that one dimensional model (1D-CNN) performed strongly for SM prediction with average R2 up to 0.989 and RPIQ up to 19.59 in the laboratory environment (DSSM). Applying the knowledge-based transfer learning method to an unknown target area improved the R2 from 0.845 to 0.983 under the DSSM and from 0.298 to 0.620 under the DSDM, which performed better than data-based spiking calibration method for traditional PLS models. The results show that knowledge-based transfer learning was suitable for SM prediction under different soil background and measurement conditions and can be a promising approach for remotely estimating SM with the increasing amount of soil dataset in the future.Item Coupling Coordination Relationship between Urban Sprawl and Urbanization Quality in the West Taiwan Strait Urban Agglomeration, China: Observation and Analysis from DMSP/OLS Nighttime Light Imagery and Panel Data(MDPI, 2020-10) Lu, Chunyan; Li, Lin; Lei, Yifan; Ren, Chunying; Su, Ying; Huang, Yufei; Chen, Yu; Lei, Shaohua; Fu, Weiwei; Earth Sciences, School of ScienceUrban sprawl is the most prominent characteristic of urbanization, and increasingly affects local and regional sustainable development. The observation and analysis of urban sprawl dynamics and their relationship with urbanization quality are essential for framing integrative urban planning. In this study, the urban areas of the West Taiwan Strait Urban Agglomeration (WTSUA) were extracted using nighttime light imagery from 1992 to 2013. The spatio-temporal characteristics and pattern of urban sprawl were quantitatively analyzed by combining an urban expansion rate index and a standard deviation ellipse model. The urbanization quality was assessed using an entropy weight model, and its relationship with urban sprawl was calculated by a coupling coordination degree model. The results showed that the urban area in the WTSUA experienced a significant increase, i.e., 18,806.73 km2, during the period 1992–2013. The central cities grew by 11.08% and noncentral cities by 27.43%, with a general uneven city rank-size distribution. The urban sprawl showed a circular expansion pattern, accompanied by a gradual centroid migration of urban areas from the southeast coast to the central-western regions. The coupling coordination level between urban expansion and urbanization quality increased from serious incoordination in 1992 to basic coordination in 2013. Dual driving forces involving state-led policies and market-oriented land reform had a positive influence on the harmonious development of urban sprawl and urbanization quality of the WTSUA. This research offers an effective approach to monitor changes in urban sprawl and explore the coupling coordination relationship between urban sprawl and urbanization quality. The study provides important scientific references for the formulation of future policies and planning for sustainable development in urban agglomerations.