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Deepak R. Mishra
Department of Geography, University of Georgia, Athens, GA 30602, USA

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Journal article
Published: 27 June 2021 in Sensors
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Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

ACS Style

Iman Salehi Hikouei; S. Kim; Deepak Mishra. Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments. Sensors 2021, 21, 4408 .

AMA Style

Iman Salehi Hikouei, S. Kim, Deepak Mishra. Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments. Sensors. 2021; 21 (13):4408.

Chicago/Turabian Style

Iman Salehi Hikouei; S. Kim; Deepak Mishra. 2021. "Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments." Sensors 21, no. 13: 4408.

Journal article
Published: 10 May 2021 in Journal of Geophysical Research: Biogeosciences
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Light use efficiency (LUE) of salt marshes has not been well studied but is central to production efficiency models (PEMs) used for estimating gross primary production (GPP). Salt marshes are typically dominated by a species monoculture, resulting in large areas with distinct morphology and physiology. We measured eddy covariance atmospheric CO2 fluxes for two marshes dominated by a different species: Juncus roemerianus in Mississippi and Spartina alterniflora in Georgia. LUE for the Juncus marsh (mean = 0.160 ± 0.004 g C mol−1 photon), reported here for the first time, was on average similar to the Spartina marsh (mean = 0.164 ± 0.003 g C mol−1 photon). However, Juncus LUE had a greater range (0.073–0.49 g C mol−1 photon) and higher variability (15.2%) than the Spartina marsh (range: 0.035–0.36 g C mol−1 photon; variability: 12.7%). We compared the responses of LUE across six environmental gradients. Juncus LUE was predominantly driven by cloudiness, photosynthetically active radiation (PAR), soil temperature, water table, and vapor pressure deficit. Spartina LUE was driven by water table, air temperature, and cloudiness. We also tested how the definition of LUE (incident PAR vs. absorbed PAR) affected the magnitude of LUE and its response. We found LUE estimations using incident PAR underestimated LUE and masked day‐to‐day variability. Our findings suggest that salt marsh LUE parametrization should be species‐specific due to plant morphology and physiology and their geographic context. These findings can be used to improve PEMs for modeling blue carbon productivity.

ACS Style

Peter A. Hawman; Deepak R. Mishra; Jessica L. O’Connell; David L. Cotten; Caroline R. Narron; Lishen Mao. Salt Marsh Light Use Efficiency is Driven by Environmental Gradients and Species‐Specific Physiology and Morphology. Journal of Geophysical Research: Biogeosciences 2021, 126, 1 .

AMA Style

Peter A. Hawman, Deepak R. Mishra, Jessica L. O’Connell, David L. Cotten, Caroline R. Narron, Lishen Mao. Salt Marsh Light Use Efficiency is Driven by Environmental Gradients and Species‐Specific Physiology and Morphology. Journal of Geophysical Research: Biogeosciences. 2021; 126 (5):1.

Chicago/Turabian Style

Peter A. Hawman; Deepak R. Mishra; Jessica L. O’Connell; David L. Cotten; Caroline R. Narron; Lishen Mao. 2021. "Salt Marsh Light Use Efficiency is Driven by Environmental Gradients and Species‐Specific Physiology and Morphology." Journal of Geophysical Research: Biogeosciences 126, no. 5: 1.

Journal article
Published: 17 March 2021 in Remote Sensing
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Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.

ACS Style

Stephen Kinane; Cristian Montes; Timothy Albaugh; Deepak Mishra. A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images. Remote Sensing 2021, 13, 1140 .

AMA Style

Stephen Kinane, Cristian Montes, Timothy Albaugh, Deepak Mishra. A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images. Remote Sensing. 2021; 13 (6):1140.

Chicago/Turabian Style

Stephen Kinane; Cristian Montes; Timothy Albaugh; Deepak Mishra. 2021. "A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images." Remote Sensing 13, no. 6: 1140.

Journal article
Published: 11 March 2021 in Remote Sensing
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Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for consistent and long-term mapping of shifting cultivation using Landsat data from 1975 to 2018. Widely used vegetation indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio (NBR) and its relative difference NBR (RdNBR) were integrated with the suitable thresholds in the classification, which yielded overall accuracy above 85%. A significant decrease in total shifting cultivation area was observed with an overall reduction of 75% from 1975–1976 to 2017–2018. The methodology presented in this study is reproducible with minimal inputs and can be useful to map similar changes by optimizing the index threshold values to accommodate relative differences for other landscapes. Furthermore, the crop-suitability maps generated by incorporating climate and soil factors prioritizes suitable land use of shifting cultivation plots. The Google Earth Engine (GEE) platform was employed for automatic mapping of the shifting cultivation areas at desired time intervals for facilitating seamless dissemination of the map products. Besides the novel DTMT method, the shifting cultivation and crop-suitability maps generated in this study, can aid in sustainable land management.

ACS Style

Pulakesh Das; Sujoy Mudi; Mukunda Behera; Saroj Barik; Deepak Mishra; Parth Roy. Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India. Remote Sensing 2021, 13, 1066 .

AMA Style

Pulakesh Das, Sujoy Mudi, Mukunda Behera, Saroj Barik, Deepak Mishra, Parth Roy. Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India. Remote Sensing. 2021; 13 (6):1066.

Chicago/Turabian Style

Pulakesh Das; Sujoy Mudi; Mukunda Behera; Saroj Barik; Deepak Mishra; Parth Roy. 2021. "Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India." Remote Sensing 13, no. 6: 1066.

Journal article
Published: 20 January 2021 in Science of The Total Environment
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Cyclones can produce a wide variety of short-term and long-term ecological impacts on coastal lagoons depending on cyclone's physical-meteorological characteristics and the lagoon's geographic, geomorphic, and bathymetric characteristics. Here, we theorized that in monsoon regulated tropical coastal lagoons, another important factor that could determine the impact of a cyclone is the landfall season or time of the year with reference to the monsoon season. We analyzed the impact of two cyclones which made landfall near Chilika, Asia's largest brackish water lagoon in different seasons, Cyclone Fani and Titli before and after the monsoon season. We compared field measured and satellite-derived water quality parameters including nutrient, salinity, water temperature, transparency, Chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) before and after the cyclones. We found that although both the cyclones were of similar intensities, after their land interaction, their impact on the lagoon's water quality was contrasting. The post-monsoon cyclone produced a substantial increase in total nitrogen (TN) and total phosphorous (TP), a large drop in salinity, CDOM, and Chl-a. In contrast, after the pre-monsoon cyclone, TN and TP did not show any such hike, no substantial change in salinity and CDOM either, and only a slight increase in Chl-a was observed. We found that the controlling factor in determining the impact of a cyclone is the rate and duration of freshwater discharge to the lagoon, which is normally a strong pulse for pre-monsoon and a continued high flow for post-monsoon cyclones. We conclude that the antecedent conditions of the lagoon and the watershed at the time of a cyclone's landfall is a key criterion in determining the impact. The combined use of satellite data and field data was proved critical to capture the overall impact of cyclones on the hydrological characteristics of the monsoon-regulated coastal lagoon.

ACS Style

Deepak R. Mishra; Abhishek Kumar; Pradipta R. Muduli; Tamoghna Acharyya; Prasannajit Acharya; Sambit Singh; Gurdeep Rastogi. Landfall season is critical to the impact of a cyclone on a monsoon-regulated tropical coastal lagoon. Science of The Total Environment 2021, 770, 145235 .

AMA Style

Deepak R. Mishra, Abhishek Kumar, Pradipta R. Muduli, Tamoghna Acharyya, Prasannajit Acharya, Sambit Singh, Gurdeep Rastogi. Landfall season is critical to the impact of a cyclone on a monsoon-regulated tropical coastal lagoon. Science of The Total Environment. 2021; 770 ():145235.

Chicago/Turabian Style

Deepak R. Mishra; Abhishek Kumar; Pradipta R. Muduli; Tamoghna Acharyya; Prasannajit Acharya; Sambit Singh; Gurdeep Rastogi. 2021. "Landfall season is critical to the impact of a cyclone on a monsoon-regulated tropical coastal lagoon." Science of The Total Environment 770, no. : 145235.

Paper
Published: 10 December 2020 in Environmental Science: Processes & Impacts
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Reduced anthropogenic activities during COVID-19 lockdowns improved air quality and dampened LST in highly populated and polluted Indian megacities.

ACS Style

Dhruv Nanda; Deepk R. Mishra; Debadatta Swain. COVID-19 lockdowns induced land surface temperature variability in mega urban agglomerations in India. Environmental Science: Processes & Impacts 2020, 23, 144 -159.

AMA Style

Dhruv Nanda, Deepk R. Mishra, Debadatta Swain. COVID-19 lockdowns induced land surface temperature variability in mega urban agglomerations in India. Environmental Science: Processes & Impacts. 2020; 23 (1):144-159.

Chicago/Turabian Style

Dhruv Nanda; Deepk R. Mishra; Debadatta Swain. 2020. "COVID-19 lockdowns induced land surface temperature variability in mega urban agglomerations in India." Environmental Science: Processes & Impacts 23, no. 1: 144-159.

Communication
Published: 11 August 2020 in Remote Sensing
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The SARS-CoV-2 (or COVID-19) lockdown in India, which started at an early stage of its infection curve, has been one of the strictest in the world. Air quality has improved in all urban centers in India, a major emitter of greenhouse gases (GHG). This study is based on the hypothesis that an abrupt halt in all urban activities resulted in a massive decline in NO2 emissions and has also altered coastal nitrogen (N) inputs; in-turn, this affected the trophic status of coastal waters across the country. We present the first evidence of an overall decline in pre-monsoon chlorophyll-a, a proxy for phytoplankton biomass, in coastal waters off urban centers during the peak of the lockdown in April. The preliminary field data and indirect evidence suggests the reduction in coastal chlorophyll-a could be linked to a net decline in nutrient loading, particularly of bioavailable N through watershed fluxes and atmospheric deposition. The preliminary results stress the importance of a further understanding of the relationship between fluctuations in anthropogenic N, due to lockdown measures and coastal ecosystem responses, as countries open-up to a business-as-usual scenario.

ACS Style

Deepak R. Mishra; Abhishek Kumar; Pradipta R. Muduli; Sk. Md. Equeenuddin; Gurdeep Rastogi; Tamoghna Acharyya; Debadatta Swain. Decline in Phytoplankton Biomass Along Indian Coastal Waters due to COVID-19 Lockdown. Remote Sensing 2020, 12, 2584 .

AMA Style

Deepak R. Mishra, Abhishek Kumar, Pradipta R. Muduli, Sk. Md. Equeenuddin, Gurdeep Rastogi, Tamoghna Acharyya, Debadatta Swain. Decline in Phytoplankton Biomass Along Indian Coastal Waters due to COVID-19 Lockdown. Remote Sensing. 2020; 12 (16):2584.

Chicago/Turabian Style

Deepak R. Mishra; Abhishek Kumar; Pradipta R. Muduli; Sk. Md. Equeenuddin; Gurdeep Rastogi; Tamoghna Acharyya; Debadatta Swain. 2020. "Decline in Phytoplankton Biomass Along Indian Coastal Waters due to COVID-19 Lockdown." Remote Sensing 12, no. 16: 2584.

Journal article
Published: 30 May 2020 in Harmful Algae
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Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure referred to as “CyanoTRACKER” for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of the CyanoTRACKER framework is the collection and integration of combined community reports (social cloud), remote sensing data (sensor cloud) and digital image analytics (computation cloud) to detect and differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global water bodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs affected water bodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as an increased awareness about CyanoHABs among Twitter users. The CyanoHABs affected water bodies extracted via the social cloud were categorized, and smaller water bodies were selected for the deployment of CyanoSense, and satellite data analysis was performed for larger water bodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620 nm. The results and products from this infrastructure can be rapidly disseminated via the CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the dangers posed by these environmentally harmful phenomena.

ACS Style

Deepak R. Mishra; Abhishek Kumar; Lakshmish Ramaswamy; Vinay K. Boddula; Moumita C. Das; Benjamin P. Page; Samuel J. Weber. CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms. Harmful Algae 2020, 96, 101828 .

AMA Style

Deepak R. Mishra, Abhishek Kumar, Lakshmish Ramaswamy, Vinay K. Boddula, Moumita C. Das, Benjamin P. Page, Samuel J. Weber. CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms. Harmful Algae. 2020; 96 ():101828.

Chicago/Turabian Style

Deepak R. Mishra; Abhishek Kumar; Lakshmish Ramaswamy; Vinay K. Boddula; Moumita C. Das; Benjamin P. Page; Samuel J. Weber. 2020. "CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms." Harmful Algae 96, no. : 101828.

Preprint content
Published: 23 March 2020
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High-quality temperature data at a finer spatial-temporal scale is critical for analyzing the risk of heat hazards in urban environments. The variability of urban landscapes makes cities a challenging landscape for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts: the spatial-temporal granularities are too coarse and the ability to track the actual ambient air temperature instead of land surface temperature. Overcoming these limitations requires radically different research approaches, both the paradigms for collecting the temperature data and developing models for high-resolution heat mapping. We present a comprehensive approach for studying urban heat hazards by harnessing a high-quality hyperlocal temperature dataset from a network of mobile sensors and using it to refine the satellite-based temperature products. We mounted vehicle-borne mobile sensors on thirty city buses to collect high-frequency (5 sec) temperature data from June 2018 to Nov 2019. The vehicle-borne data clearly show significant temperature differences across the city, with the largest differences of up to 10℃ and morning-afternoon diurnal changes at a magnitude around 20℃. Then we developed a machine learning approach to derive a hyperlocal ambient air temperature (AAT) product by combining the mobile-sensor temperature data, satellite LST data, and other influential biophysical parameters to map the variability of heat hazard over areas not covered by the buses. The machine learning model output highlighted the high spatio-temporal granularity in AAT within an urban heat island. The seasonal AAT maps derived from the model show a well-defined hyperlocal variability of heat hazards which are not evident from other research approaches. The findings from this study will be beneficial for understanding the heat exposure vulnerabilities for individual communities. It may also create a pathway for policymakers to devise targeted hazard mitigation efforts such as increasing green space and developing better heat-safety policies for workers.

ACS Style

Yanzhe Yin; Andrew Grundstein; Deepak Mishra; Navid Hashemi; Lakshmish Lakshmish. A mobile sensor-based Approach for Analyzing and Mitigating Urban Heat Hazards. 2020, 1 .

AMA Style

Yanzhe Yin, Andrew Grundstein, Deepak Mishra, Navid Hashemi, Lakshmish Lakshmish. A mobile sensor-based Approach for Analyzing and Mitigating Urban Heat Hazards. . 2020; ():1.

Chicago/Turabian Style

Yanzhe Yin; Andrew Grundstein; Deepak Mishra; Navid Hashemi; Lakshmish Lakshmish. 2020. "A mobile sensor-based Approach for Analyzing and Mitigating Urban Heat Hazards." , no. : 1.

Preprint content
Published: 23 March 2020
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Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of this CyanoTRACKER framework is the integration of community reports, remote sensing data and digital image analytics to differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered enabled automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global waterbodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs infested waterbodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as increased awareness about CyanoHABs among Twitter users. The CyanoHABs infested geographic locations extracted via social cloud were utilized for the deployment of CyanoSense at smaller waterbodies and analysis of satellite data for larger waterbodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620nm. The results and products from this infrastructure can be rapidly disseminated via CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the imminent dangers posed by these environmentally harmful phenomena.

ACS Style

Deepak Mishra. CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms. 2020, 1 .

AMA Style

Deepak Mishra. CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms. . 2020; ():1.

Chicago/Turabian Style

Deepak Mishra. 2020. "CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms." , no. : 1.

Letter
Published: 08 March 2020 in Remote Sensing
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The Landsat 8 Operational Land Imager (OLI) has a panchromatic band (503–676 nm) that can be used to derive a novel virtual orange band (590–635 nm) by using the multispectral green band and red band components. The orange band is useful for the accurate detection and quantification of phycocyanin (PC), an accessory pigment in toxin-producing cyanobacterial blooms, because of the specific light absorption characteristics of PC around 600–625 nm. In this study, we compared the Landsat 8 OLI’s and Sentinel-3 Ocean and Land Color Instrument’s (OLCI) derived orange band reflectance and PC products corresponding to a same-date overpass during a severe cyanobacterial bloom in Lake Erie, USA. The goal was to determine if the OLI’s virtual orange band can produce results equivalent to the OLCI’s actual orange band. Band-by-band match-ups used the OLI’s top-of-atmosphere (TOA) reflectance versus TOA reflectance from the OLCI, and surface reflectance (SR) from the OLI versus SR from the OLCI. A significant correlation was observed between the OLI’s and OLCI’s derived orange band TOA reflectance (R2 = 0.86; p < 0.001; NRMSE = 9.01%) and orange band SR (R2 = 0.93; p < 0.001; NRMSE = 20.23%). The PC map produced using the best-fit empirical models from both sensors showed similar PC spatial patterns and concentration levels in the western basin of Lake Erie. The results from this research are particularly important for the study of smaller inland waterbodies with the 30 m resolution of the OLI, which cannot be studied with the 300 m resolution of OLCI data, and for analyzing historical bloom events before the launch of the OLCI. Although more analysis and validation need to be conducted, this study opens up Landsat 8’s applicability in research on cyanobacterial harmful algal blooms (cyanoHABs).

ACS Style

Abhishek Kumar; Deepak R. Mishra; Nirav Ilango. Landsat 8 Virtual Orange Band for Mapping Cyanobacterial Blooms. Remote Sensing 2020, 12, 868 .

AMA Style

Abhishek Kumar, Deepak R. Mishra, Nirav Ilango. Landsat 8 Virtual Orange Band for Mapping Cyanobacterial Blooms. Remote Sensing. 2020; 12 (5):868.

Chicago/Turabian Style

Abhishek Kumar; Deepak R. Mishra; Nirav Ilango. 2020. "Landsat 8 Virtual Orange Band for Mapping Cyanobacterial Blooms." Remote Sensing 12, no. 5: 868.

Journal article
Published: 12 February 2020 in Science of The Total Environment
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We studied the ecological resilience of salt marshes by deriving sea level rise (SLR) thresholds in two estuaries with contrasting upland hydrological inputs in the north-central Gulf of Mexico: Grand Bay National Estuarine Research Reserve (NERR) with limited upland input, and the Pascagoula River delta drained by the Pascagoula River, the largest undammed river in the continental United States. We applied a mechanistic model to account for vegetation responses and hydrodynamics to predict salt marsh distributions under future SLR scenarios. We further investigated the potential mechanisms that contribute to salt marsh resilience to SLR. The modeling results show that salt marshes in the riverine dominated estuary are more resilient to SLR than in the marine dominated estuary with SLR thresholds of 10.3 mm/yr and 7.2 mm/yr respectively. This difference of >3 mm/yr is mainly contributed by larger quantities of riverine-borne mineral sediments in the Pascagoula River. In both systems, sediment trapping by the above-ground vegetation appears to contribute more to marsh platform accretion than organic matter from below-ground biomass based on the medians of the accretion rates. However, below-ground biomass could contribute up to 90% of accretion in the marine dominated estuary compared to only 60% of accretion in the riverine dominated estuary. SLR thresholds of salt marshes are more sensitive to vegetation biomass in the marine dominated estuary while biomass and sediment similarly affect SLR thresholds of salt marshes in the riverine dominated estuary. This research will likely help facilitate more informed decisions on conservation/restoration policies for these two types of systems in the near-term needed to minimize future catastrophic loss of these coastal marsh habitats once SLR thresholds are exceeded.

ACS Style

Wei Wu; Patrick Biber; Deepak Mishra; Shuvankar Ghosh. Sea-level rise thresholds for stability of salt marshes in a riverine versus a marine dominated estuary. Science of The Total Environment 2020, 718, 137181 .

AMA Style

Wei Wu, Patrick Biber, Deepak Mishra, Shuvankar Ghosh. Sea-level rise thresholds for stability of salt marshes in a riverine versus a marine dominated estuary. Science of The Total Environment. 2020; 718 ():137181.

Chicago/Turabian Style

Wei Wu; Patrick Biber; Deepak Mishra; Shuvankar Ghosh. 2020. "Sea-level rise thresholds for stability of salt marshes in a riverine versus a marine dominated estuary." Science of The Total Environment 718, no. : 137181.

Chapter
Published: 01 February 2020 in Wetlands and Human Health
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A comprehensive analysis of sediment and phytoplankton dynamics in Chilika lagoon by synthesizing various remote sensing datasets is presented in this study. The goal of the study was to monitor and analyze the spatio-temporal variability of total suspended sediment (TSS) and chlorophyll-a (chl-a) concentration and associated environmental forcings in the coastal lagoon. NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance cloud free data was used to develop a TSS and chl-a model. Finally, a case study showing implication of satellite based TSS and Chl-a models to assess the impacts of natural hazards such as cyclones on water quality of Chilika Lagoon is presented. This case study is based on comparing the effect of two anniversary very severe cyclonic storms (VSCSs): category-5 Phailin (12 October, 2013) and category-4 Hudhud (12 October, 2014) that impacted the lagoon. Analysis for 14 years (2001–2014) using MODIS 8-day composites (MOD09Q1) data indicated that the seasonal variability of TSS is dominant in all the three sectors of the lagoon compared to inter-annual variability. The main reason for large variations in the northern sector is the shallow depth and intrusion of large sediment discharge from Mahanadi River from the northern side, which is the largest fresh water distributary for Chilika Lagoon. Anniversary cyclone impact analysis revealed that Phailin’s impact on Chilika Lagoon and its watershed resulted in unprecedented levels of precipitation and runoff before-during-after the landfall, which shattered the typical sectorial turbidity gradient. Exponential increase in turbidity because of a combination of run-off and wind driven re-suspension of fine sediments resulted in strong attenuation of light in water column post-Phailin. Limited light condition coupled with enhanced flushing rate due to flooded river and increased freshwater discharge reduced the Chl-a concentration after the passage of Phailin. In contrast, relatively farther landfall location, trajectory away from the lagoon, relatively lower wind intensity and short duration of stay of VSCS Hudhud, led to lesser precipitation and surface runoff compared to Phailin. Consequently, lagoon did not experience a drastic increase in turbidity and light attenuation. Sufficient light availability, stable wind, reduced flushing all favored the phytoplankton growth after passage of Hudhud and thus, Chl-a concentration increased almost threefold in all the sectors of the lagoon. The approach used in this study can be applied to other cyclone-prone coastal areas. Coupling of satellite based observation with modelling output from systems such as Giovanni can improve monitoring program implemented in numerous coastal estuaries and lagoons.

ACS Style

Abhishek Kumar; Sk. Md. Equeenuddin; Deepak R. Mishra. Long-Term Analysis of Water Quality in Chilika Lagoon and Application of Bio-optical Models for Cyclone Impact Assessment. Wetlands and Human Health 2020, 165 -202.

AMA Style

Abhishek Kumar, Sk. Md. Equeenuddin, Deepak R. Mishra. Long-Term Analysis of Water Quality in Chilika Lagoon and Application of Bio-optical Models for Cyclone Impact Assessment. Wetlands and Human Health. 2020; ():165-202.

Chicago/Turabian Style

Abhishek Kumar; Sk. Md. Equeenuddin; Deepak R. Mishra. 2020. "Long-Term Analysis of Water Quality in Chilika Lagoon and Application of Bio-optical Models for Cyclone Impact Assessment." Wetlands and Human Health , no. : 165-202.

Journal article
Published: 11 January 2020 in Sustainability
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Generally, improvement in the soil health of pasturelands can result in amplified ecosystem services which can help improve the overall sustainability of the system. The extent to which specific best management practices have this effect has yet to be established. A farm-scale study was conducted in eight beef-pastures in the Southern Piedmont of Georgia, from 2015 to 2018, to assess the effect of strategic-grazing (STR) and continuous-grazing hay distribution (CHD) on soil health indicators and runoff nitrate losses. In 2016, four pastures were converted to the STR system and four were grazed using the CHD system. Post-treatment, in 2018, the STR system had significantly greater POXC (by 87.1, 63.4, and 55.6 mg ha−1 at 0–5, 5–10, and 10–20 cm, respectively) as compared to CHD system. Soil respiration was also greater in the STR system (by 235 mg CO2 m-2 24 h−1) and less nitrate was lost in the runoff (by 0.21 kg ha−1) as compared to the CHD system. Cattle exclusion and overseeding vulnerable areas of pastures in STR pastures facilitated nitrogen mineralization and uptake. Our results showed that the STR grazing system could improve the sustainability of grazing systems by storing more labile carbon, efficiently mineralizing soil nitrogen, and lowering runoff nitrate losses.

ACS Style

Subash Dahal; Dorcas Franklin; Anish Subedi; Miguel Cabrera; Dennis Hancock; Kishan Mahmud; Laura Ney; Cheolwoo Park; Deepak Mishra. Strategic Grazing in Beef-Pastures for Improved Soil Health and Reduced Runoff-Nitrate-A Step towards Sustainability. Sustainability 2020, 12, 558 .

AMA Style

Subash Dahal, Dorcas Franklin, Anish Subedi, Miguel Cabrera, Dennis Hancock, Kishan Mahmud, Laura Ney, Cheolwoo Park, Deepak Mishra. Strategic Grazing in Beef-Pastures for Improved Soil Health and Reduced Runoff-Nitrate-A Step towards Sustainability. Sustainability. 2020; 12 (2):558.

Chicago/Turabian Style

Subash Dahal; Dorcas Franklin; Anish Subedi; Miguel Cabrera; Dennis Hancock; Kishan Mahmud; Laura Ney; Cheolwoo Park; Deepak Mishra. 2020. "Strategic Grazing in Beef-Pastures for Improved Soil Health and Reduced Runoff-Nitrate-A Step towards Sustainability." Sustainability 12, no. 2: 558.

Original paper
Published: 07 November 2019 in Theoretical and Applied Climatology
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The 2017 and 2018 Atlantic hurricane seasons poignantly illustrated the dangers tropical cyclones pose to US, Central American, and Caribbean coastlines. In particular, Hurricane Maria inflicted widespread damage, including catastrophic defoliation, to Puerto Rico, altering surface heat fluxes and possibly modifying precipitation patterns. This study assesses whether defoliation-driven changes to surface energy fluxes redistribute precipitation in the months following a powerful hurricane landfall. Remote sensing analyses of Maria-related vegetation reduction and recovery from Puerto Rico were adapted to the Georgia coastline. In this novel methodology, the resulting landscape evolution, characterized by an instantaneous vegetation reduction with a gradual recovery, was assimilated into the Weather Research and Forecasting model at a convection-allowing a 3-km grid spacing for the 1 June–1 August 2017 period. The experiment revealed that Maria-scale defoliation reduced precipitation by 14% during the month following landfall within a 50 × 50 km zone containing the hypothetical landfall location. A maximum deficit of 20.0% was reached 4 weeks after landfall. For June 2017, the modeled 14% deficit would have shifted the precipitation total from the 61st to the 47th percentile for years 1981–2016. Meanwhile, precipitation totals were unchanged on the domain scale. The near-landfall drying was also evident in three less-severe defoliation simulations, suggesting that systematic precipitation redistribution near the landfall location is possible following storms considerably weaker than Hurricane Maria. Analyses of the temperature and wind fields suggest that coastal kinematic flow is altered by the introduction of a thermally driven pressure gradient in the defoliated zone.

ACS Style

Paul W. Miller; Thomas L. Mote; Abhishek Kumar; Deepak Mishra. Systematic precipitation redistribution following a strong hurricane landfall. Theoretical and Applied Climatology 2019, 139, 861 -872.

AMA Style

Paul W. Miller, Thomas L. Mote, Abhishek Kumar, Deepak Mishra. Systematic precipitation redistribution following a strong hurricane landfall. Theoretical and Applied Climatology. 2019; 139 (3-4):861-872.

Chicago/Turabian Style

Paul W. Miller; Thomas L. Mote; Abhishek Kumar; Deepak Mishra. 2019. "Systematic precipitation redistribution following a strong hurricane landfall." Theoretical and Applied Climatology 139, no. 3-4: 861-872.

Journal article
Published: 04 November 2019 in Science of The Total Environment
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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing with frequent eutrophication and shifting climate paradigms. CyanoHABs produce a spectrum of toxins and can trigger neurological disorder, organ failure, and even death. To promote proactive CyanoHAB management, geospatial risk modeling can act as a predictive mechanism to supplement current mitigation efforts. In this study, iterative AIC analysis was performed on 17 watershed-level biophysical parameters to identify the strongest predictors based on Sentinel-2-derived cyanobacteria cell densities (CCD) for 771 waterbodies in Georgia Piedmont. This study used a streamlined watershed delineation technique, a 1-meter LULC classification with ~88% accuracy, and a technique to predict CyanoHAB risk in small-to-medium sized waterbodies. Landscape characteristics were computed utilizing the Google Earth Engine platform that enabled large spatio-temporal scope and variable inclusion. Watershed maximum winter temperature, percent agriculture, percent forest, percent impervious, and waterbody area were the strongest predictors of CCD with a 0.33 R-squared. Warmer winter temperatures allow cyanobacteria to be photosynthetically active year-round, and trigger CyanoHABs when warmer temperatures and nutrients are introduced in early spring, typically referred to as Spring Bloom in southeast U.S. The risk models revealed an unexpected significant linear relationship between percent forest and CCD. It is due to the fact that land reclamation via reforestation in the piedmont have left legacy sediment and nutrients which are mobilized as surface runoff to the watershed after rain events. A Jenks Natural Break scheme assigned waterbodies to CyanoHAB risk groups, and of the 771 waterbodies, 24.38% were low, 37.35% and 38.26% were medium and high risk respectively. This research supplements existing cyanobacteria risk modeling methods by introducing a novel, scalable, and reproducible method to determine yearly regional risk. Future studies should include factors such as demographic, socioeconomic, labor, and site-specific environmental conditions to create more holistic CyanoHAB risk outputs.

ACS Style

Samuel J. Weber; Deepak R. Mishra; Susan B. Wilde; Elizabeth Kramer. Risks for cyanobacterial harmful algal blooms due to land management and climate interactions. Science of The Total Environment 2019, 703, 134608 .

AMA Style

Samuel J. Weber, Deepak R. Mishra, Susan B. Wilde, Elizabeth Kramer. Risks for cyanobacterial harmful algal blooms due to land management and climate interactions. Science of The Total Environment. 2019; 703 ():134608.

Chicago/Turabian Style

Samuel J. Weber; Deepak R. Mishra; Susan B. Wilde; Elizabeth Kramer. 2019. "Risks for cyanobacterial harmful algal blooms due to land management and climate interactions." Science of The Total Environment 703, no. : 134608.

Article
Published: 11 September 2019 in Water Resources Management
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Upper-Brantas watershed in East Java, Indonesia, is a tropical watershed experiencing rapid landscape change, a phenomenon typical to developing countries. This study demonstrates the impact of Land Use Land Cover (LULC) changes on surface runoff in a tropical, urbanized, and data scarce watershed. The LULC changes were quantified between 1995 and 2015 and their impact on the hydrological processes was analyzed using the Soil and Water Assessment Tool (SWAT) model. During the study period, the watershed experienced an increase in settlement and dryland agriculture, and a decrease in the forest, rice field, and sugarcane plantation. The SWAT model results for the calibration (2003–2008) and validation (2009–2013) periods matched observed values [R2 > 0.91 and NSE (Nash-Sutcliffe Efficiency) >0.91]. In the long-term, the model predicted changes in runoff (+8%), water yield (+0.28%), groundwater (−1.8%), and evapotranspiration (−1.15%) due to changes in LULC. LULC changes showed a linear relationship with runoff generation, and the most significant factors affecting surface runoff were changes in the forest, agriculture, and settlements. Increasing urbanization, industrialization, and agricultural intensification will increase runoff which in turn will enhance the flow of nutrients and sediments into the water bodies.

ACS Style

Ike Sari Astuti; KamalaKanta Sahoo; Adam Milewski; Deepak Mishra. Impact of Land Use Land Cover (LULC) Change on Surface Runoff in an Increasingly Urbanized Tropical Watershed. Water Resources Management 2019, 33, 4087 -4103.

AMA Style

Ike Sari Astuti, KamalaKanta Sahoo, Adam Milewski, Deepak Mishra. Impact of Land Use Land Cover (LULC) Change on Surface Runoff in an Increasingly Urbanized Tropical Watershed. Water Resources Management. 2019; 33 (12):4087-4103.

Chicago/Turabian Style

Ike Sari Astuti; KamalaKanta Sahoo; Adam Milewski; Deepak Mishra. 2019. "Impact of Land Use Land Cover (LULC) Change on Surface Runoff in an Increasingly Urbanized Tropical Watershed." Water Resources Management 33, no. 12: 4087-4103.

Journal article
Published: 01 July 2019 in Remote Sensing of Environment
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This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) atmospheric correction and masking of MSI and OLI imagery, and (2) generating scene-based water clarity maps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correction theory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEE to generate uniform remote sensing reflectances (Rrs) products over optically variable freshwater lake surfaces. MSI-Rrs products derived from the atmospheric correction were used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for MSI (SDMSI) was then developed using a calibration dataset consisting of log-transformed SDin situ measurements (lnSDin situ) from 79 optically variable freshwater inland lakes collected within ±1 day of satellite overpass on 23-Aug 2017 (MAE = 0.53 m) and validated with 276 samples collected within ±1 day of a 12-Sep 2017 image (MAE = 0.66 m) across three ecoregions in Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SDsOLI) where cross-sensor performance can be evaluated during coincident overpass events. SDsOLI applied to both MSI and OLI (SDOLI) models were further validated using two coincident overpass sets of imagery on 27-Sep 2017 (n = 18) and 13-Aug 2018 (n = 43), yielding a range of error from 0.25 to 0.67 m. Potential sources of model errors and limitations are discussed. Data derived from this multi-sensor methodology is anticipated to be used by researchers, lake resource managers, and citizens to expedite the pre-processing steps so that actionable information can be retrieved for decision making.

ACS Style

Benjamin P. Page; Leif G. Olmanson; Deepak Mishra. A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems. Remote Sensing of Environment 2019, 231, 111284 .

AMA Style

Benjamin P. Page, Leif G. Olmanson, Deepak Mishra. A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems. Remote Sensing of Environment. 2019; 231 ():111284.

Chicago/Turabian Style

Benjamin P. Page; Leif G. Olmanson; Deepak Mishra. 2019. "A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems." Remote Sensing of Environment 231, no. : 111284.

Journal article
Published: 16 May 2019 in Weed Technology
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Hydrilla is an invasive aquatic plant that has rapidly spread through many inland water bodies across the globe by outcompeting native aquatic plants. The negative impacts of hydrilla invasion have become a concern for water resource management authorities, power companies, and environmental scientists. The early detection of hydrilla infestation is very important to reduce the costs associated with control and removal efforts of this invasive species. Therefore, in this study, we aimed to develop a tool for rapid, frequent, and large-scale monitoring and predicting spatial extent of hydrilla habitat. This was achieved by integrating in situ and Landsat 8 Operational Land Imager satellite data for Lake J. Strom Thurmond, the largest US Army Corps of Engineers lake east of the Mississippi River, located on the border of Georgia and South Carolina border. The predictive model for presence of hydrilla incorporated radiometric and physical measurements, including remote-sensing reflectance, Secchi disk depth (SDD), light-attenuation coefficient (Kd), maximum depth of colonization (Zc), and percentage of light available through the water column (PLW). The model-predicted ideal habitat for hydrilla featured high SDD, Zc, and PLW values, low values of Kd. Monthly analyses based on satellite images showed that hydrilla starts growing in April, reaches peak coverage around October, begins retreating in the following months, and disappears in February. Analysis of physical and meteorological factors (i.e., water temperature, surface runoff, net inflow, precipitation) revealed that these parameters are closely associated with hydrilla extent. Management agencies can use these results not only to plan removal efforts but also to evaluate and adapt their current mitigation efforts.

ACS Style

Abhishek Kumar; Christopher Cooper; Caren M. Remillard; Shuvankar Ghosh; Austin Haney; Frank Braun; Zachary Conner; Benjamin Page; Kenneth Boyd; Susan Wilde; Deepak Mishra. Spatiotemporal monitoring of hydrilla [Hydrilla verticillata (L. f.) Royle] to aid management actions. Weed Technology 2019, 33, 518 -529.

AMA Style

Abhishek Kumar, Christopher Cooper, Caren M. Remillard, Shuvankar Ghosh, Austin Haney, Frank Braun, Zachary Conner, Benjamin Page, Kenneth Boyd, Susan Wilde, Deepak Mishra. Spatiotemporal monitoring of hydrilla [Hydrilla verticillata (L. f.) Royle] to aid management actions. Weed Technology. 2019; 33 (3):518-529.

Chicago/Turabian Style

Abhishek Kumar; Christopher Cooper; Caren M. Remillard; Shuvankar Ghosh; Austin Haney; Frank Braun; Zachary Conner; Benjamin Page; Kenneth Boyd; Susan Wilde; Deepak Mishra. 2019. "Spatiotemporal monitoring of hydrilla [Hydrilla verticillata (L. f.) Royle] to aid management actions." Weed Technology 33, no. 3: 518-529.

Original paper
Published: 06 March 2019 in Population and Environment
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Louisiana lost nearly 5,000 km2 of its coastal land area due to relative sea level rise (including local, regional, and global factors driving relative sea level change) since 1932, mirroring both the hazards associated with sea level rise and the time horizons of sea level rise impacts expected this century. This represents an opportunity to examine the relationship between long-term population changes and shoreline change. Based on detailed land change data for the period 1932–2010 and a small area population estimation technique for the period 1940–2010, we examine intra-parish population changes in relation to shoreline changes for the one million plus residents living in the ten coastal parishes of Louisiana. We find that since 1940, only two of the ten coastal parishes exhibited landward population movement, which we define as movement perpendicular to the shoreline, exceeding 1 km. Three parishes exhibited seaward population movement in excess of 1 km. Overall, we find very little net intra-parish landward population movement for the region. Our findings suggest that coastal Louisiana’s historical population has not moved in concert with observed shoreline encroachment. We also find a potential tipping point related to population migration when a neighborhood loses at least 50% of its land area. Our findings suggest that this lack of landward population movement could be attributable to either localized adaptation strategies or migrations to other landward areas.

ACS Style

Mathew E. Hauer; R. Dean Hardy; Deepak R. Mishra; J. Scott Pippin. No landward movement: examining 80 years of population migration and shoreline change in Louisiana. Population and Environment 2019, 40, 369 -387.

AMA Style

Mathew E. Hauer, R. Dean Hardy, Deepak R. Mishra, J. Scott Pippin. No landward movement: examining 80 years of population migration and shoreline change in Louisiana. Population and Environment. 2019; 40 (4):369-387.

Chicago/Turabian Style

Mathew E. Hauer; R. Dean Hardy; Deepak R. Mishra; J. Scott Pippin. 2019. "No landward movement: examining 80 years of population migration and shoreline change in Louisiana." Population and Environment 40, no. 4: 369-387.