Lin Li

Permanent URI for this collection

https://hdl.handle.net/1805/25251

Remote sensing of inland water quality

Outbreaks of cyanobacteria in inland water bodies, especially water sources, can pose a serious threat to public health. Studies have shown that public exposure or ingestion of cyanobacterial cells and toxins can cause a series of harmful health consequences, such as skin irritation, allergic reactions, mucous membrane blistering, muscle and joint pain, gastroenteritis, lung consolidation, liver and kidney damage, and various neurological effects. Furthermore, the outbreak of cyanobacteria can cause factory shutdowns and tremendous economic losses. Therefore, the scientific value and socio-economic significance of inland water quality remote sensing monitoring and early warning research are beyond doubt. Chlorophyll-a and phycocyanin are important indicators for monitoring and early warning of outbreaks of cyanobacteria in inland water bodies with remote sensing because both pigments exhibit diagnostic absorption spectral features in the visible spectral region.

Over the last 15 years, Dr. Lin Li's research group has been making efforts to improve remote sensing approaches to monitoring inland water quality. Particularly, the effort was put in deriving the absorption and scattering coefficients of optically active substances (OACs) in water from remote sensing reflectance spectra and decomposing the total absorption of OACs to achieve the separation of the absorption coefficients of algal pigments from other constituents such as soluble organic matter and suspended sediment. An accurate retrieval of pigment absorption coefficients makes it possible to reliably map chlorophyll-a and phycocyanin and assess inland water quality. This would provide water management authorities and managers with an effective remote sensing tool for dealing with the outbreak of cyanobacteria in inland water bodies.

Dr. Li's work to improve remote sensing and monitoring of inland water quality is another excellent example of how IUPUI's faculty members are TRANSLATING their RESEARCH INTO PRACTICE.

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Now showing 1 - 10 of 29

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 Science
Retrieval 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.
Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment
(IEEE, 2022-10) Zolfaghari, Kiana; Pahlevan, Nima; Binding, Caren; Gurlin, Daniela; Simis, Stefan G. H.; Ruiz Verdú, Antonio; Li, Lin; Crawford, Christopher J.; VanderWoude, Andrea; Errera, Reagan; Zastepa, Arthur; Duguay, Claude R.; Earth Sciences, School of Science
Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ( N=905 ) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ∼73 % and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.
A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze
(Elsevier, 2021-02) Lei, Shaohua; Xu, Jie; Li, Yunmei; Li, Lin; Lyu, Heng; Liu, Ge; Chen, Yu; Lu, Chunyan; Tian, Chao; Jiao, Wenzhe; Earth Sciences, School of Science
The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment.
Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters
(Elsevier, 2021-02) Pahlevan, Nima; Smith, Brandon; Binding, Caren; Gurlin, Daniela; Li, Lin; Bresciani, Mariano; Giardino, Claudia; Earth Sciences, School of Science
Following more than two decades of research and developments made possible through various proof-of-concept hyperspectral remote sensing missions, it has been anticipated that hyperspectral imaging would enhance the accuracy of remotely sensed in-water products. This study investigates such expected improvements and demonstrates the utility of hyperspectral radiometric measurements for the retrieval of near-surface phytoplankton properties1, i.e., phytoplankton absorption spectra (aph) and biomass evaluated through examining the concentration of chlorophyll-a (Chla). Using hyperspectral data (409–800 nm at ~5 nm resolution) and a class of neural networks known as Mixture Density Networks (MDN) (Pahlevan et al., 2020), we show that the median error in aph retrievals is reduced two-to-three times (N = 722) compared to that from heritage ocean color algorithms. The median error associated with our aph retrieval across all the visible bands varies between 20 and 30%. Similarly, Chla retrievals exhibit significant improvements (i.e., more than two times; N = 1902), with respect to existing algorithms that rely on select spectral bands. Using an independent matchup dataset acquired near-concurrently with the acquisition of the Hyperspectral Imager for the Coastal Ocean (HICO) images, the models are found to perform well, but at reduced levels due to uncertainties in the atmospheric correction. The mapped spatial distribution of Chla maps and aph spectra for selected HICO swaths further solidify MDNs as promising machine-learning models that have the potential to generate highly accurate aquatic remote sensing products in inland and coastal waters. For aph retrieval to improve further, two immediate research avenues are recommended: a) the network architecture requires additional optimization to enable a simultaneous retrieval of multiple in-water parameters (e.g., aph, Chla, absorption by colored dissolved organic matter), and b) the training dataset should be extended to enhance model generalizability. This feasibility analysis using MDNs provides strong evidence that high-quality, global hyperspectral data will open new pathways toward a better understanding of biodiversity in aquatic ecosystems.
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 Science
Laboratory 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.
Variations in the light absorption coefficients of phytoplankton, non-algal particles and dissolved organic matter in reservoirs across China
(Elsevier, 2021-10) Shang, Yingxin; Jacinthe, Pierre-Andre; Li, Lin; Wen, Zhidan; Liu, Ge; Lyu, Lili; Fang, Chong; Zhang, Bai; Hou, Junbin; Song, Kaishan; Earth Sciences, School of Science
Reservoirs were critical sources of drinking water for many large cities around the world, but progress in the development of large-scale monitoring protocols to obtain timely information about water quality had been hampered by the complex nature of inland waters and the various optical conditions exhibited by these aquatic ecosystems. In this study, we systematically investigated the absorption coefficient of different optically-active constituents (OACs) in 120 reservoirs of different trophic states across five eco-regions in China. The relationships were found between phytoplankton absorption coefficient at 675 nm (aph (675)) and Chlorophyll a (Chla) concentration in different regions (R2:0.60-0.82). The non-algal particle (NAP) absorption coefficient (aNAP) showed an increasing trend for reservoirs with trophic states. Significant correlation (p < 0.05) was observed between chromophoric dissolved organic matter (CDOM) absorption and water chemical parameters. The influencing factors for contributing the relative proportion of OACs absorption including the hydrological factors and water quality factors were analyzed. The non-water absorption budget from our data showed the variations of the dominant absorption types which underscored the need to develop and parameterize region-specific bio-optical models for large-scale assessment in water reservoirs.
National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images
(Elsevier, 2020-06) Mao, Dehua; Wang, Zongming; Du, Baojia; Li, Lin; Tian, Yanlin; Jia, Mingming; Zeng, Yuan; Song, Kaishan; Jiang, Ming; Wang, Yeqiao; Earth Sciences, School of Science
Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China’s wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China’s wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China’s wetlands and biodiversity conservation.
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 Science
Urban 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.
Cyanobacteria in Inland Waters: Remote Sensing
(CRC Press, 2020) Li, Lin; Earth Sciences, School of Science
Remote sensing plays important roles in managing harmful cyanobacterial blooms. Remote sensing algorithms for monitoring cyanobacterial blooms are grouped into empirical, semi-empirical, and semi-analytical methods. In this chapter, 12 of these methods were selected to be reviewed for their performances when applied to in situ measured field reflectance spectra and airborne or satellite sensor collected image spectra. Five empirical PC algorithms based on either band ratio or baseline calculation showed data-dependent performances, empirical band ratios and the baseline can be used to build semi-empirical models such as double three band baseline (DTBB) and four band baseline model (FBBM) showing stronger performance than the three band model (TBM), and the DTBB even performing stronger than the nested band ratio (NBR). As far as three semi-analytical models concern, the NBR and EIIMIW consistently performed well compared to the QAA pc , but care or recalibration should be practiced for applying both EIIMIW and NBR given caring inherent optical property of non-phycocyanin (PC) constituent in the water column. Although neither DTBB nor FBM cannot be evaluated with satellite MEdium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Color Instrument (OLCI) images, they should be tested in future with hyperspectral satellite images acquired by PRISMA, EnMAP, and HyspIRI.
Radiation dimming and decreasing water clarity fuel underwater darkening in lakes
(Elsevier, 2020-10) Zhang, Yunlin; Qin, Boqiang; Shi, Kun; Zhang, Yibo; Deng, Jianming; Wild, Martin; Li, Lin; Zhou, Yongqiang; Yao, Xiaolong; Liu, Miao; Zhu, Guangwei; Zhang, Lu; Gu, Binhe; Brookes, Justin D.; Earth Sciences, School of Science
Long-term decreases in the incident total radiation and water clarity might substantially affect the underwater light environment in aquatic ecosystems. However, the underlying mechanism and relative contributions of radiation dimming and decreasing water clarity to the underwater light environment on a national or global scale remains largely unknown. Here, we present a comprehensive dataset of unprecedented scale in China’s lakes to address the combined effects of radiation dimming and decreasing water clarity on underwater darkening. Long-term total radiation and sunshine duration showed 5.8% and 7.9% decreases, respectively, after 2000 compared to 1961–1970, resulting in net radiation dimming. An in situ Secchi disk depth (SDD) dataset in 170 lakes showed that the mean SDD significantly decreased from 1.80 ± 2.19 m before 1995 to 1.28 ± 1.82 m after 2005. SDD remote sensing estimations for 641 lakes with areas ≥ 10 km2 showed that SDD markedly decreased from 1.26 ± 0.62 m during 1985–1990 to 1.14 ± 0.66 m during 2005–2010. Radiation dimming and decreasing water clarity jointly caused an approximately 10% decrease in the average available photosynthetically active radiation (PAR) in the euphotic layer. Our results revealed a more important role of decreasing water clarity in underwater darkening than radiation dimming. A meta-analysis of long-term SDD observation data from 61 various waters further elucidated a global extensive underwater darkening. Underwater darkening implies a decrease in water quality for potable water supplies, recession in macrophytes and benthic algae, and decreases in benthic primary production, fishery production, and biodiversity.

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