Lin Li
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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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Item Determine characteristics requirement for the surrogate road edge objects for road departure mitigation testing(2019) Chien, Stanley; Yi, Qiang; Lin, Jun; Saha, Abir; Li, Lin; Chen, Yaobin; Chen, Chi-Chih; Sherony, Rini; Electrical and Computer Engineering, School of Engineering and TechnologyRoad departure mitigation system (RDMS), a vehicle active safety feature, uses road edge objects to determine potential road departure. In the U.S., 45%, 16%, and 15% of car-mile (traffic flow * miles) roads have grass, metal guardrail, and concrete divider as road edge, respectively. It is difficult to test RDMS with real roadside objects. Lightweight and crashable surrogate roadside objects that have representative radar, LIDAR and camera characteristics of real objects have been developed for testing. This paper describes the identification of automotive radar, LIDAR, and visual characteristics of metal guardrail, concrete divider, and grass. These characteristics will be referenced for designing and fabricating the representative surrogate objects for RDMS testing. Colors and types of the roadside objects were identified from 24,735 randomly sampled locations in the US using Google street view images. The radar and LIDAR parameters were measured using 24GHz/77GHz radar and 350-2500nm IR spectrometer.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 Removal of Chlorophyll-a Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance(MDPI, 2019-08) Ogashawara, Igor; Li, Lin; Earth Sciences, School of ScienceMonitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detection of cyanobacteria is commonly based on the absorption of phycocyanin (PC), a unique pigment of freshwater cyanobacteria, at 620 nm. However, other photosynthetic pigments can contribute to absorption at 620 nm, interfering with the remote estimation of PC. To surpass this issue, we present a remote sensing algorithm in which the contribution of chlorophyll-a (chl-a) absorption at 620 nm is removed. To do this, we determine the PC contribution to the absorption at 665 nm and chl-a contribution to the absorption at 620 nm based on empirical relationships established using chl-a and PC standards. The proposed algorithm was compared with semi-empirical and semi-analytical remote sensing algorithms for proximal and simulated satellite sensor datasets from three central Indiana reservoirs (total of 544 sampling points). The proposed algorithm outperformed semi-empirical algorithms with root mean square error (RMSE) lower than 25 µg/L for the three analyzed reservoirs and showed similar performance to a semi-analytical algorithm. However, the proposed remote sensing algorithm has a simple mathematical structure, it can be applied at ease and make it possible to improve spectral estimation of phycocyanin from space. Additionally, the proposed showed little influence from the package effect of cyanobacteria cells.Item Remote Observation in Habitat Suitability Changes for Waterbirds in the West Songnen Plain, China(MDPI, 2019-01) Tian, Yanlin; Wang, Zongming; Mao, Dehua; Li, Lin; Liu, Mingyue; Jia, Mingming; Man, Weidong; Lu, Chunyan; Earth Sciences, School of ScienceBeing one of the most important habitats for waterbirds, China’s West Songnen Plain has experienced substantial damage to its ecosystem, especially the loss and degradation of wetlands and grasslands due to anthropogenic disturbances and climate change. These occurrences have led to an obvious decrease in waterbird species and overall population size. Periodic and timely monitoring of changes in habitat suitability and understanding the potential driving factors for waterbirds are essential for maintaining regional ecological security. In this study, land cover changes from 2000 to 2015 in this eco-sensitive plain were examined using Landsat images and an object-based classification method. Four groups of environmental factors, including human disturbance, water situation, food availability, and shelter safety, characterized by remote sensing data were selected to develop a habitat suitability index (HSI) for assessing habitat suitability for waterbirds. HSI was further classified into four grades (optimum, good, general, and poor), and their spatiotemporal patterns were documented from 2000 to 2015. Our results revealed that cropland expansion and wetland shrinkage were the dominant land cover changes. Waterbird habitat areas in the optimum grade experienced a sharp decline by 7195 km2. The habitat area in good suitability experienced reduction at a change rate of −8.64%, from 38,672 km2 to 35,331 km2. In addition, waterbird habitats in the general and poor grades increased overall by 10.31%. More specifically, the total habitat areas with optimum suitable grade, in five national nature reserves over the study region, decreased by 12.21%, while habitat areas with poor suitable grade increased by 3.89%. Changes in habitat suitability could be largely attributed to the increase in human disturbance, including agricultural cultivation from wetlands and grasslands and the expansion of built-up lands. Our findings indicate that additional attention should be directed towards reducing human impact on habitat suitability for sustainable ecosystems.Item Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention(MDPI, 2019-05-19) Mao, Dehua; Liu, Mingyue; Wang, Zongming; Li, Lin; Man, Weidong; Jia, Mingming; Zhang, Yuanzhi; Earth Sciences, School of ScienceGiven the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management.Item Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm(MDPI, 2018-12-16) Ren, Chunying; Zhang, Bai; Wang, Zongming; Li, Lin; Jia, Mingming; Earth Sciences, School of ScienceForest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing'an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.Item Spatial Expansion and Soil Organic Carbon Storage Changes of Croplands in the Sanjiang Plain, China(MDPI, 2017-04) Man, Weidong; Yu, Hao; Li, Lin; Liu, Mingyue; Mao, Dehua; Ren, Chunying; Wang, Zongming; Jia, Mingming; Miao, Zhenghong; Lu, Chunyan; Li, Huiying; Earth Sciences, School of ScienceSoil is the largest pool of terrestrial organic carbon in the biosphere and interacts strongly with the atmosphere, climate and land cover. Remote sensing (RS) and geographic information systems (GIS) were used to study the spatio-temporal dynamics of croplands and soil organic carbon density (SOCD) in the Sanjiang Plain, to estimate soil organic carbon (SOC) storage. Results show that croplands increased with 10,600.68 km2 from 1992 to 2012 in the Sanjiang Plain. Area of 13,959.43 km2 of dry farmlands were converted into paddy fields. Cropland SOC storage is estimated to be 1.29 ± 0.27 Pg C (1 Pg = 103 Tg = 1015 g) in 2012. Although the mean value of SOCD for croplands decreased from 1992 to 2012, the SOC storage of croplands in the top 1 m in the Sanjiang Plain increased by 70 Tg C (1220 to 1290). This is attributed to the area increases of cropland. The SOCD of paddy fields was higher and decreased more slowly than that of dry farmlands from 1992 to 2012. Conversion between dry farmlands and paddy fields and the agricultural reclamation from natural land-use types significantly affect the spatio-temporal patterns of cropland SOCD in the Sanjiang Plain. Regions with higher and lower SOCD values move northeast and westward, respectively, which is almost consistent with the movement direction of centroids for paddy fields and dry farmlands in the study area. Therefore, these results were verified. SOC storages in dry farmlands decreased by 17.5 Tg·year−1 from 1992 to 2012, whilst paddy fields increased by 21.0 Tg·C·year−1.Item Impacts of Climate Change on Tibetan Lakes: Patterns and Processes(MDPI, 2018-02-26) Mao, Dehua; Wang, Zongming; Yang, Hong; Li, Huiying; Thompson, Julian; Li, Lin; Song, Kaishan; Chen, Bin; Gao, Hongkai; Wu, Jianguo; Earth Sciences, School of ScienceHigh-altitude inland-drainage lakes on the Tibetan Plateau (TP), the earth’s third pole, are very sensitive to climate change. Tibetan lakes are important natural resources with important religious, historical, and cultural significance. However, the spatial patterns and processes controlling the impacts of climate and associated changes on Tibetan lakes are largely unknown. This study used long time series and multi-temporal Landsat imagery to map the patterns of Tibetan lakes and glaciers in 1977, 1990, 2000, and 2014, and further to assess the spatiotemporal changes of lakes and glaciers in 17 TP watersheds between 1977 and 2014. Spatially variable changes in lake and glacier area as well as climatic factors were analyzed. We identified four modes of lake change in response to climate and associated changes. Lake expansion was predominantly attributed to increased precipitation and glacier melting, whereas lake shrinkage was a main consequence of a drier climate or permafrost degradation. These findings shed new light on the impacts of recent environmental changes on Tibetan lakes. They suggest that protecting these high-altitude lakes in the face of further environmental change will require spatially variable policies and management measures.Item Optical types of inland and coastal waters(Wiley, 2018-03-01) Spyrakos, Evangelos; O'Donnell, Ruth; Hunter, Peter D.; Miller, Claire; Scott, Marian; Simis, Stefan G. H.; Neil, Claire; Barbosa, Claudio C. F.; Binding, Caren E.; Bradt, Shane; Bresciani, Mariano; Dall'Olmo, Giorgio; Giardino, Claudia; Gitelson, Anatoly A.; Kutser, Tiit; Li, Lin; Matsushita, Bunkei; Martinez‐Vicente, Victor; Matthews, Mark W.; Ogashawara, Igor; Ruiz‐Verdú, Antonio; Schalles, John F.; Tebbs, Emma; Zhang, Yunlin; Tyler, Andrew N.; Earth Sciences, School of ScienceInland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.Item Partially-observed models for classifying minerals on Mars(IEEE, 2013) Dundar, Murat; Li, Lin; Rajwa, Bartek; Earth Sciences, School of ScienceThe identification of phyllosilicates by NASA's CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) strongly suggests the presence of water-related geological processes. A variety of water-bearing phyllosilicate minerals have already been identified by several research groups utilizing spectral enrichment techniques and matching phyllosilicate-rich regions on the Martian surface to known spectra of minerals found on earth. However, fully automated analysis of the CRISM data remains a challenge for two main reasons. First, there is significant variability in the spectral signature of the same mineral obtained from different regions on the Martian surface. Second, the list of mineral confirmed to date constituting the set of training classes is not exhaustive. Thus, when classifying new regions, using a classifier trained with selected minerals and chemicals, one must consider the potential presence of unknown materials not represented in the training library. We made an initial attempt to study these problems in the context of our recent work on partially-observed classification models and present results that show the utility of such models in identifying spectra of unknown minerals while simultaneously recognizing spectra of known minerals.