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Mangroves are recognized for their valued ecosystem services to coastal areas, and the functional linkages between those services and ecosystem carbon stocks have been established. However, spatially explicit inventories are necessary to facilitate management and protection of mangroves, as well as providing a foundation for payment for ecosystem service programs such as REDD+. We conducted an inventory of carbon stocks in mangroves within Pongara National Park (PNP), Gabon using a stratified random sampling design based on forest canopy height derived from TanDEM-X remote sensing data. Ecosystem carbon pools, including aboveground and belowground biomass and necromass, and soil carbon to a depth of 2 m were assessed using measurements and samples from plots distributed among three canopy height classes within the park. There were two mangrove species within the inventory area in PNP, Rhizophora racemosa and R. harrisonii. R. harrisonii was predominant in the sparse, low-stature stands that dominated the west side of the park. In the east side of the park, both species occurred in tall-stature stands, with tree height often exceeding 30 m. Canopy height was an effective means to stratify the inventory area, as biomass was significantly different among the height classes. Despite those differences in aboveground biomass, the soil carbon density was not significantly different among height classes. Soils were the main component of the ecosystem carbon stock, accounting for over 84% of the total. The ecosystem carbon density ranged from 644 to 943 Mg C ha−1 among the three height classes. The ecosystem carbon stock within PNP is estimated to be 40,588 Gg C. The combination of pre-inventory information about stand conditions and their spatial distribution within the assessment area obtained from remote sensing data and the spatial decision support system were fundamental to implementing this relatively large-scale field inventory. That work exemplifies how mangrove carbon stocks can be quantified to augment national C reporting statistics, provide a baseline for projects involving monitoring, reporting and verification (i.e., MRV), and provide data on the forest composition and structure for sustainable management and conservation practices.
Carl C. Trettin; Zhaohua Dai; Wenwu Tang; David Lagomasino; Nathan Thomas; Seung Kuk Lee; Marc Simard; Médard Obiang Ebanega; Atticus Stoval; Temilola E. Fatoyinbo. Mangrove carbon stocks in Pongara National Park, Gabon. Estuarine, Coastal and Shelf Science 2021, 259, 107432 .
AMA StyleCarl C. Trettin, Zhaohua Dai, Wenwu Tang, David Lagomasino, Nathan Thomas, Seung Kuk Lee, Marc Simard, Médard Obiang Ebanega, Atticus Stoval, Temilola E. Fatoyinbo. Mangrove carbon stocks in Pongara National Park, Gabon. Estuarine, Coastal and Shelf Science. 2021; 259 ():107432.
Chicago/Turabian StyleCarl C. Trettin; Zhaohua Dai; Wenwu Tang; David Lagomasino; Nathan Thomas; Seung Kuk Lee; Marc Simard; Médard Obiang Ebanega; Atticus Stoval; Temilola E. Fatoyinbo. 2021. "Mangrove carbon stocks in Pongara National Park, Gabon." Estuarine, Coastal and Shelf Science 259, no. : 107432.
The Wax Lake Delta (WLD) is an actively prograding delta in the Mississippi River Delta Plain that is otherwise experiencing widespread degradation and submergence of its coastal wetlands. The WLD is actively accumulating mineral and organic sediment that increases soil surface elevation, changing emergent wetland communities as the young delta develops. There is uncertainty in how the dynamics of community composition respond to net changes in soil elevation and determine aboveground biomass and carbon storage. This study utilizes high resolution imaging spectrometer data captured on October 17, 2016, to map the delta's dominant vegetation species and wetland types. We validated this vegetation map (overall accuracy = 77.62%, Kappa = 0.72) and compared it with a published species map that used WorldView-2 data collected five years earlier on October 16, 2011. This allowed us to map changes resulting from five years of delta development to determine changes in wetland forest species (Salix nigra), two dominant herbaceous wetland species (Colocasia esculenta and Polygonum punctatum), and various grass species. Results show an increase in C. esculenta and a marginal increase in forested wetlands (S. nigra). C. esculenta's expansion occurred largely from the delta island heads toward the fringes into wetland area previously occupied by P. punctatum, which saw a corresponding decline. Additionally, this study leveraged these species distributions with a published aboveground biomass (AGB) dataset to examine the dominant plant types' growth patterns across elevational gradients. We characterized variability in AGB by marsh platform elevation and across different elevational zones categorized by hydroperiod, or hydrogeomorphic zones. We found that the herbaceous plant species peak in AGB in the low intertidal zone and decrease with elevation before increasing slightly in the higher elevations, consistent with previous field-based mesocosm experiments for C. esculenta. With vegetation distributions, succession, and growth patterns in the WLD characterized, this study may inform future restoration efforts throughout the Mississippi River Delta Plain regarding the changing vegetation composition that may emerge following changes in surface elevation with sediment deposition.
Daniel Jensen; Kyle C. Cavanaugh; Marc Simard; Alexandra Christensen; Andre Rovai; Robert Twilley. Aboveground biomass distributions and vegetation composition changes in Louisiana's Wax Lake Delta. Estuarine, Coastal and Shelf Science 2020, 250, 107139 .
AMA StyleDaniel Jensen, Kyle C. Cavanaugh, Marc Simard, Alexandra Christensen, Andre Rovai, Robert Twilley. Aboveground biomass distributions and vegetation composition changes in Louisiana's Wax Lake Delta. Estuarine, Coastal and Shelf Science. 2020; 250 ():107139.
Chicago/Turabian StyleDaniel Jensen; Kyle C. Cavanaugh; Marc Simard; Alexandra Christensen; Andre Rovai; Robert Twilley. 2020. "Aboveground biomass distributions and vegetation composition changes in Louisiana's Wax Lake Delta." Estuarine, Coastal and Shelf Science 250, no. : 107139.
We present Orinoco, an open-source Python toolkit that applies the fast-marching method to derive a river delta channel network from a water mask and ocean delineation. We are able to estimate flow direction, along-channel distance, channel width, and network-related metrics for deltaic analyses including the steady-state fluxes. To demonstrate the capabilities of the toolkit, we apply our software to the Wax Lake and Atchafalaya River Deltas using water masks derived from Open Street Map (OSM) and Google Maps. We validate our width estimates using the Global River Width from Landsat (GRWL) database over the Mackenzie Delta as well as in situ width measurements from the National Water Information System (NWIS) in the southeastern United States. We also compare the stream flow direction estimates using products from RivGraph, a related Python package with similar functionality. With the exciting opportunities afforded with forthcoming surface water and topography (SWOT) data, we envision Orinoco as a tool to support the characterization of the complex structure of river deltas worldwide and to make such analyses easily accessible within a Python remote sensing workflow. To support that end, all the data, analyses, and figures in this paper can be found within Jupyter notebooks at Orinoco’s GitHub repository.
Charlie Marshak; Marc Simard; Michael Denbina; Johan Nilsson; Tom Van Der Stocken. Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python. ISPRS International Journal of Geo-Information 2020, 9, 658 .
AMA StyleCharlie Marshak, Marc Simard, Michael Denbina, Johan Nilsson, Tom Van Der Stocken. Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python. ISPRS International Journal of Geo-Information. 2020; 9 (11):658.
Chicago/Turabian StyleCharlie Marshak; Marc Simard; Michael Denbina; Johan Nilsson; Tom Van Der Stocken. 2020. "Orinoco: Retrieving a River Delta Network with the Fast Marching Method and Python." ISPRS International Journal of Geo-Information 9, no. 11: 658.
Surface water occurrence in river deltas is governed by precipitation, evaporation, and the influx and outflux of water to and from the delta. Although studies of changes in water occurrence have been conducted at large scales, precise detection of changes in water occurrence is missing for most important river deltas. We take the case of the endorheic Selenga River Delta in Russia and train an accurate classification and quantification of water occurrence in its domain. We utilize remotely sensed observations of the Landsat satellite imagery during the last 33 years and implement supervised classification to map the surface water extent and its changes between periods of 1987-2002 and 2003-2019. We find that water occurrence has decreased in the Delta, with seasonally inundated areas presenting more pronounced decreases in water occurrence than permanent water bodies. We show that the change in the surface runoff is the main driver of changes in the spatial patterns of surface water with R2 = 0.58, while changes in water level in the recipient Lake Baikal do not influence water occurrence in the Delta. Our results show that the shrinkage and expansion of the water surface reflect the change in the freshwater supply of the Delta, and the management of the Selenga River needs to consider the impact of changes on the water occurrence.
Saeid Aminjafari; Ian Brown; Sergey Chalov; Marc Simard; Jerker Jarsjö; Mehdi Darvishi; Fernando Jaramillo. Temporal and Spatial Changes of Water Occurrence in the Selenga River Delta. 2020, 1 .
AMA StyleSaeid Aminjafari, Ian Brown, Sergey Chalov, Marc Simard, Jerker Jarsjö, Mehdi Darvishi, Fernando Jaramillo. Temporal and Spatial Changes of Water Occurrence in the Selenga River Delta. . 2020; ():1.
Chicago/Turabian StyleSaeid Aminjafari; Ian Brown; Sergey Chalov; Marc Simard; Jerker Jarsjö; Mehdi Darvishi; Fernando Jaramillo. 2020. "Temporal and Spatial Changes of Water Occurrence in the Selenga River Delta." , no. : 1.
Coastal wetlands are productive ecosystems driven by highly dynamic hydrological processes such as tides and river discharge, which operate at daily to seasonal timescales, respectively. The scientific community has been calling for landscape-scale measurements of hydrological variables that could help understand the flow of water and transport of sediment across coastal wetlands. While in situ water level gauge data have enabled significant advances, they are limited in coverage and largely unavailable in many parts of the world. In preparation for the NISAR mission, we investigate the use of spaceborne Interferometric Synthetic Aperture Radar (InSAR) observations of phase and coherence at L-band for landscape-scale monitoring of water level change and vegetation cover in coastal wetlands across seasons. We use L-band SAR images acquired by ALOS/PALSAR from 2007 to 2011 to study the impact of seasonal changes in vegetation cover on InSAR sensitivity to water level change in the wetlands of the Atchafalaya basin located in coastal Louisiana, USA. Seasonal variations are observed in the interferometric coherence (γ) time-series over wetlands, with higher coherence during the winter and lower coherence during the summer. We show with InSAR time-series that coherence is inversely correlated with Normalized Difference Vegetation Index (NDVI). Our analysis of polarimetric scattering mechanisms demonstrates that double-bounce is the dominant mechanism in swamps while its weakness in marshes hinders estimation of water level changes. In swamps, water level change maps derived from InSAR are highly correlated (r2 = 0.83) with in situ data from the Coastwide Reference Monitoring System (CRMS). From October to December, we observed that the water level may be below wetland elevation and thus not inundating wetlands significantly. Our analysis shows that water level can only be retrieved when both images used for InSAR are acquired when wetlands are inundated. The L-band derived-maps of water level change show large scale gradients originating from the Gulf Intracoastal Waterway rather than the main delta trunk channel, confirming its significant role as a source of hydrologic connectivity across these coastal wetlands. These results indicate that NISAR, with its InSAR observations every 12 days, will provide the measurements necessary to reveal large scale hydrodynamic processes that occur in swamps across seasons.
Tien-Hao Liao; Marc Simard; Michael Denbina; Michael P. Lamb. Monitoring Water Level Change and Seasonal Vegetation Change in the Coastal Wetlands of Louisiana Using L-Band Time-Series. Remote Sensing 2020, 12, 2351 .
AMA StyleTien-Hao Liao, Marc Simard, Michael Denbina, Michael P. Lamb. Monitoring Water Level Change and Seasonal Vegetation Change in the Coastal Wetlands of Louisiana Using L-Band Time-Series. Remote Sensing. 2020; 12 (15):2351.
Chicago/Turabian StyleTien-Hao Liao; Marc Simard; Michael Denbina; Michael P. Lamb. 2020. "Monitoring Water Level Change and Seasonal Vegetation Change in the Coastal Wetlands of Louisiana Using L-Band Time-Series." Remote Sensing 12, no. 15: 2351.
We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.
Charlie Marshak; Marc Simard; Laura Duncanson; Carlos Silva; Michael Denbina; Tien-Hao Liao; Lola Fatoyinbo; Ghislain Moussavou; John Armston. Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels. Remote Sensing 2020, 12, 2048 .
AMA StyleCharlie Marshak, Marc Simard, Laura Duncanson, Carlos Silva, Michael Denbina, Tien-Hao Liao, Lola Fatoyinbo, Ghislain Moussavou, John Armston. Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels. Remote Sensing. 2020; 12 (12):2048.
Chicago/Turabian StyleCharlie Marshak; Marc Simard; Laura Duncanson; Carlos Silva; Michael Denbina; Tien-Hao Liao; Lola Fatoyinbo; Ghislain Moussavou; John Armston. 2020. "Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels." Remote Sensing 12, no. 12: 2048.
This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.
Urška Drešček; Mojca Kosmatin Fras; Jernej Tekavec; Anka Lisec; Charlie Marshak; Marc Simard; Laura Duncanson; Carlos Alberto Silva; Michael Denbina; Tien-Hao Liao; Lola Fatoyinbo; Ghislain Moussavou; John Armston. Spatial ETL for 3D Building Modelling based on Unmanned Aerial Vehicle Data in Semi-Urban Areas. 2020, 12, 1 .
AMA StyleUrška Drešček, Mojca Kosmatin Fras, Jernej Tekavec, Anka Lisec, Charlie Marshak, Marc Simard, Laura Duncanson, Carlos Alberto Silva, Michael Denbina, Tien-Hao Liao, Lola Fatoyinbo, Ghislain Moussavou, John Armston. Spatial ETL for 3D Building Modelling based on Unmanned Aerial Vehicle Data in Semi-Urban Areas. . 2020; 12 (12):1.
Chicago/Turabian StyleUrška Drešček; Mojca Kosmatin Fras; Jernej Tekavec; Anka Lisec; Charlie Marshak; Marc Simard; Laura Duncanson; Carlos Alberto Silva; Michael Denbina; Tien-Hao Liao; Lola Fatoyinbo; Ghislain Moussavou; John Armston. 2020. "Spatial ETL for 3D Building Modelling based on Unmanned Aerial Vehicle Data in Semi-Urban Areas." 12, no. 12: 1.
Kristin B. Byrd; Laurel Ballanti; Nathan Thomas; Dung Nguyen; James R. Holmquist; Marc Simard; Lisamarie Windham-Myers. Corrigendum to “A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States” [ISPRS J. Photogram. Rem. Sens. 139 (2018) 255–271]. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 166, 63 -67.
AMA StyleKristin B. Byrd, Laurel Ballanti, Nathan Thomas, Dung Nguyen, James R. Holmquist, Marc Simard, Lisamarie Windham-Myers. Corrigendum to “A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States” [ISPRS J. Photogram. Rem. Sens. 139 (2018) 255–271]. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 166 ():63-67.
Chicago/Turabian StyleKristin B. Byrd; Laurel Ballanti; Nathan Thomas; Dung Nguyen; James R. Holmquist; Marc Simard; Lisamarie Windham-Myers. 2020. "Corrigendum to “A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States” [ISPRS J. Photogram. Rem. Sens. 139 (2018) 255–271]." ISPRS Journal of Photogrammetry and Remote Sensing 166, no. : 63-67.
Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth’s climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface.
Nicholas D. Ward; J. Patrick Megonigal; Ben Bond-Lamberty; Vanessa L. Bailey; David Butman; Elizabeth A. Canuel; Heida Diefenderfer; Neil K. Ganju; Miguel A. Goñi; Emily B. Graham; Charles S. Hopkinson; Tarang Khangaonkar; J. Adam Langley; Nate G. McDowell; Allison N. Myers-Pigg; Rebecca B. Neumann; Christopher L. Osburn; René M. Price; Joel Rowland; Aditi Sengupta; Marc Simard; Peter E. Thornton; Maria Tzortziou; Rodrigo Vargas; Pamela B. Weisenhorn; Lisamarie Windham-Myers. Representing the function and sensitivity of coastal interfaces in Earth system models. Nature Communications 2020, 11, 1 -14.
AMA StyleNicholas D. Ward, J. Patrick Megonigal, Ben Bond-Lamberty, Vanessa L. Bailey, David Butman, Elizabeth A. Canuel, Heida Diefenderfer, Neil K. Ganju, Miguel A. Goñi, Emily B. Graham, Charles S. Hopkinson, Tarang Khangaonkar, J. Adam Langley, Nate G. McDowell, Allison N. Myers-Pigg, Rebecca B. Neumann, Christopher L. Osburn, René M. Price, Joel Rowland, Aditi Sengupta, Marc Simard, Peter E. Thornton, Maria Tzortziou, Rodrigo Vargas, Pamela B. Weisenhorn, Lisamarie Windham-Myers. Representing the function and sensitivity of coastal interfaces in Earth system models. Nature Communications. 2020; 11 (1):1-14.
Chicago/Turabian StyleNicholas D. Ward; J. Patrick Megonigal; Ben Bond-Lamberty; Vanessa L. Bailey; David Butman; Elizabeth A. Canuel; Heida Diefenderfer; Neil K. Ganju; Miguel A. Goñi; Emily B. Graham; Charles S. Hopkinson; Tarang Khangaonkar; J. Adam Langley; Nate G. McDowell; Allison N. Myers-Pigg; Rebecca B. Neumann; Christopher L. Osburn; René M. Price; Joel Rowland; Aditi Sengupta; Marc Simard; Peter E. Thornton; Maria Tzortziou; Rodrigo Vargas; Pamela B. Weisenhorn; Lisamarie Windham-Myers. 2020. "Representing the function and sensitivity of coastal interfaces in Earth system models." Nature Communications 11, no. 1: 1-14.
Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.
Simon N. Topp; Tamlin M. Pavelsky; Daniel Jensen; Marc Simard; Matthew R. V. Ross. Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications. Water 2020, 12, 169 .
AMA StyleSimon N. Topp, Tamlin M. Pavelsky, Daniel Jensen, Marc Simard, Matthew R. V. Ross. Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications. Water. 2020; 12 (1):169.
Chicago/Turabian StyleSimon N. Topp; Tamlin M. Pavelsky; Daniel Jensen; Marc Simard; Matthew R. V. Ross. 2020. "Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications." Water 12, no. 1: 169.
Satellite estimates of inland water quality have the potential to vastly expand our ability to observe and monitor the dynamics of large water bodies. For almost 50 years, we have been able to remotely sense key water quality constituents like Total Suspended Sediment (TSS), Dissolved Organic Carbon (DOC), Chlorophyll a, and Secchi Disk Depth (SDD). Nonetheless, remote sensing of water quality is poorly integrated into inland water sciences, in part due to a lack of publicly available training data and a perception that remote estimates are unreliable. Remote sensing models of water quality can be improved by training and validation on larger datasets of coincident field and satellite observations, here called matchups. To facilitate model development and deeper integration of remote sensing into inland water science, we have built AquaSat, the largest such matchup dataset ever assembled. AquaSat contains more than 600,000 matchups, covering 1984‐2019, of ground‐based TSS, DOC, Chlorophyll a, and SDD measurements paired with spectral reflectance from Landsat 5, 7, and 8 collected within +/‐1 day of each other. To build AquaSat, we developed open source tools in R and Python and applied them to existing public datasets covering the contiguous United States, including the Water Quality Portal, LAGOS‐NE, and the Landsat archive. In addition to publishing the dataset, we are also publishing our full code architecture to facilitate expanding and improving AquaSat. We anticipate that this work will help make remote sensing of inland water accessible to more hydrologists, ecologists, and limnologists while facilitating novel data‐driven approaches to monitoring and understanding critical water resources at large spatiotemporal scales.
Matthew R. V. Ross; Simon N. Topp; Alison P. Appling; Xiao Yang; Catherine Kuhn; David Butman; Marc Simard; Tamlin M. Pavelsky. AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters. Water Resources Research 2019, 55, 10012 -10025.
AMA StyleMatthew R. V. Ross, Simon N. Topp, Alison P. Appling, Xiao Yang, Catherine Kuhn, David Butman, Marc Simard, Tamlin M. Pavelsky. AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters. Water Resources Research. 2019; 55 (11):10012-10025.
Chicago/Turabian StyleMatthew R. V. Ross; Simon N. Topp; Alison P. Appling; Xiao Yang; Catherine Kuhn; David Butman; Marc Simard; Tamlin M. Pavelsky. 2019. "AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters." Water Resources Research 55, no. 11: 10012-10025.
AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT was designed as a calibration and validation instrument for the forthcoming Surface Water and Ocean Topography (SWOT) mission, an international spaceborne synthetic aperture radar mission planned for launch in 2022 which will enable global mapping of WSE and WSS. As an airborne instrument, capable of quickly repeating overflights, AirSWOT enables measurement of high frequency and fine scale hydrological processes encountered in coastal regions. In this paper, we use data collected by AirSWOT in the Mississippi River Delta and surrounding wetlands of coastal Louisiana, USA, to investigate the capabilities of Ka-band interferometry for mapping WSE and WSS in coastal marsh environments. We introduce a data-driven method to estimate the time-varying interferometric phase drift resulting from radar hardware response to environmental conditions. A system of linear equations based on AirSWOT measurements is solved for elevation bias and time-varying phase calibration parameters using weighted least squares. We observed AirSWOT WSE uncertainty of 12 cm RMS compared to in situ water level measurements when averaged over an area of 0.5 km 2 at incidence angles below 15 ∘ . At higher incidence angles, the observed AirSWOT elevation bias is possibly due to residual phase calibration errors or radar backscatter from vegetation. Elevation profiles along the Wax Lake Outlet river channel indicate AirSWOT can measure WSS over a 24 km distance with uncertainty below 0.3 cm/km, 8% of the true water surface slope as measured by in situ data. The data analysis and results presented in this paper demonstrate the potential of AirSWOT to measure water surface elevation and slope within highly dynamic and spatially complex coastal environments.
Michael Denbina; Marc Simard; Ernesto Rodriguez; Xiaoqing Wu; Albert Chen; Tamlin Pavelsky. Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar. Remote Sensing 2019, 11, 2739 .
AMA StyleMichael Denbina, Marc Simard, Ernesto Rodriguez, Xiaoqing Wu, Albert Chen, Tamlin Pavelsky. Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar. Remote Sensing. 2019; 11 (23):2739.
Chicago/Turabian StyleMichael Denbina; Marc Simard; Ernesto Rodriguez; Xiaoqing Wu; Albert Chen; Tamlin Pavelsky. 2019. "Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar." Remote Sensing 11, no. 23: 2739.
Aboveground biomass (AGB) plays a critical functional role in coastal wetland ecosystem stability, with high biomass vegetation contributing to organic matter production, sediment accretion potential, and the surface elevation’s ability to keep pace with relative sea level rise. Many remote sensing studies have employed either imaging spectrometer or synthetic aperture radar (SAR) for AGB estimation in various environments for assessing ecosystem health and carbon storage. This study leverages airborne data from NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to assess their unique capabilities in combination to estimate AGB in coastal deltaic wetlands. Here we develop AGB models for emergent herbaceous and forested wetland vegetation in coastal Louisiana. In addition to horizontally emitted, vertically received (HV) backscatter, SAR parameters are expressed by the Freeman–Durden polarimetric decomposition components representing volume and double-bounce scattering. The imaging spectrometer parameters include normalized difference vegetation index (NDVI), reflectance from 290 visible-shortwave infrared (VSWIR) bands, the first derivatives from those bands, or partial least squares (PLS) x-scores derived from those data. Model metrics and cross-validation indicate that the integrated models using the Freeman-Durden components and PLS x-scores improve AGB estimates for both wetland vegetation types. In our study domain over Louisiana’s Wax Lake Delta (WLD), we estimated a mean herbaceous wetland AGB of 3.58 Megagrams/hectare (Mg/ha) and a total of 3551.31 Mg over 9.92 km2, and a mean forested wetland AGB of 294.78 Mg/ha and a total of 27,499.14 Mg over 0.93 km2. While the addition of SAR-derived values to imaging spectrometer data provides a nominal error decrease for herbaceous wetland AGB, this combination significantly improves forested wetland AGB prediction. This integrative approach is particularly effective in forested wetlands as canopy-level biochemical characteristics are captured by the imaging spectrometer in addition to the variable structural information measured by the SAR.
Daniel Jensen; Kyle C. Cavanaugh; Marc Simard; Gregory S. Okin; Edward Castañeda-Moya; Annabeth McCall; Robert R. Twilley. Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana. Remote Sensing 2019, 11, 2533 .
AMA StyleDaniel Jensen, Kyle C. Cavanaugh, Marc Simard, Gregory S. Okin, Edward Castañeda-Moya, Annabeth McCall, Robert R. Twilley. Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana. Remote Sensing. 2019; 11 (21):2533.
Chicago/Turabian StyleDaniel Jensen; Kyle C. Cavanaugh; Marc Simard; Gregory S. Okin; Edward Castañeda-Moya; Annabeth McCall; Robert R. Twilley. 2019. "Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana." Remote Sensing 11, no. 21: 2533.
The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R2 = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids.
Daniel Jensen; Marc Simard; Kyle Cavanaugh; Yongwei Sheng; Cédric G. Fichot; Tamlin Pavelsky; Robert Twilley. Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach. Remote Sensing 2019, 11, 1629 .
AMA StyleDaniel Jensen, Marc Simard, Kyle Cavanaugh, Yongwei Sheng, Cédric G. Fichot, Tamlin Pavelsky, Robert Twilley. Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach. Remote Sensing. 2019; 11 (13):1629.
Chicago/Turabian StyleDaniel Jensen; Marc Simard; Kyle Cavanaugh; Yongwei Sheng; Cédric G. Fichot; Tamlin Pavelsky; Robert Twilley. 2019. "Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach." Remote Sensing 11, no. 13: 1629.
Floodplain water flows have large volumetric flowrates and high complexity in space and time that are difficult to understand using water level gauges. We here analyze the spatial and temporal fluctuations of surface water flows in the floodplain of the Atrato River, Colombia, in order to evaluate their hydrological connectivity. The basin is one of the rainiest areas of the world with wetland ecosystems threatened by the expansion of agriculture and mining activities. We used 16 Differential Interferometric Synthetic Aperture Radars (DInSAR) phase observations from the ALOS-PALSAR L-band instrument acquired between 2008–2010 to characterize the flow of surface water. We were able to observe water level change in vegetated wetland areas and identify flooding patterns. In the lower basin, flow patterns are conditioned by fluctuations in the levels of the main river channel, whereas in the middle basin, topography and superficial channels strongly influence the flow and connectivity. We found that the variations in water level in a station on the main channel 87 km upstream explained more than 56% of the variations in water level in the floodplain. This result shows that, despite current expansion of agriculture and mining activities, there remain significant hydrological connectivity between wetlands and the Atrato River. This study demonstrates the use of DInSAR for a spatially comprehensive monitoring of the Atrato River basin hydrology. For the first time, we identified the spatiotemporal patterns of surface water flow of the region. We recommend these observations serve as a baseline to monitor the potential impact of ongoing human activities on surface water flows across the Atrato River basin.
Sebastián Palomino-Ángel; Jesús A. Anaya-Acevedo; Marc Simard; Tien-Hao Liao; Fernando Jaramillo. Analysis of Floodplain Dynamics in the Atrato River Colombia Using SAR Interferometry. Water 2019, 11, 875 .
AMA StyleSebastián Palomino-Ángel, Jesús A. Anaya-Acevedo, Marc Simard, Tien-Hao Liao, Fernando Jaramillo. Analysis of Floodplain Dynamics in the Atrato River Colombia Using SAR Interferometry. Water. 2019; 11 (5):875.
Chicago/Turabian StyleSebastián Palomino-Ángel; Jesús A. Anaya-Acevedo; Marc Simard; Tien-Hao Liao; Fernando Jaramillo. 2019. "Analysis of Floodplain Dynamics in the Atrato River Colombia Using SAR Interferometry." Water 11, no. 5: 875.
We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Québec, Canada. We evaluate four spatial arrangements including (1) single pixels, (2) square grid cells, (3) superpixels based on segmentation of the radar images, and (4) superpixels derived from ancillary optical Landsat imagery. Detection of forest loss using superpixels outperforms single pixel and regular square grid cell approaches, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Québec forestry data and Hansen et al. forest loss products. Our results indicate that this approach can be applied to monitor forest loss across large study areas using L-band radar instruments such as ALOS/PALSAR, particularly when combined with superpixels generated from ancillary optical data.
Charlie Marshak; Marc Simard; Michael Denbina. Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels. Remote Sensing 2019, 11, 556 .
AMA StyleCharlie Marshak, Marc Simard, Michael Denbina. Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels. Remote Sensing. 2019; 11 (5):556.
Chicago/Turabian StyleCharlie Marshak; Marc Simard; Michael Denbina. 2019. "Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels." Remote Sensing 11, no. 5: 556.
In response to a growing number of natural and anthropogenic threats, the long-term sustainability of coastal river deltas and wetlands has come into question worldwide. Tools such as remote sensing and numerical modeling have been implemented in an effort to monitor and predict the hydro-geomorphological evolution of our coasts. Hydrological connectivity is known to play an important role in deltaic evolution by delivering flow, sediment, and nutrients to the interior of deltaic islands/wetlands. However, estimating connectivity typically requires detailed field work or numerical modeling, which is difficult to implement over broad spatial and temporal scales. In the present work, we investigate the potential of using remote sensing to estimate hydrological connectivity in the Wax Lake Delta (WLD) and Atchafalaya Delta region of the Louisiana coast. During a three-hour window, five difference maps of water level in the WLD and surrounding wetlands were collected using UAVSAR L-band radar in repeat-pass interferometric mode. We then modeled the WLD subsection of the domain using a 2D shallow-water hydrodynamic model configured to run on the same discharge, tide, and wind conditions as recorded at nearby monitoring stations during the observational window, with vegetation parameterized as a source of additional drag in the deltaic islands. Modeling allowed us to determine the relative influence of tides, vegetation, and wind on WLD water levels, which could then be extrapolated to infer the behavior throughout the rest of the domain. Over the observational window, UAVSAR measured a cumulative loss of over 22 megatons of water from non-channelized wetlands as tides fell. We find that the model tends to under-predict the observed water level draw-down, as well as the degree of hydrological activity in proximal islands that we observe in the UAVSAR data. Models that neglect the influence of wind underestimate the volume of water leaving the islands by up to two-thirds, suggesting the importance of wind on deltaic hydrodynamics during the observational window. With the information gained from the numerical modeling, as well as the computation of information theory statistics, we extend the WLD results to analyze and quantify the water level behavior in the surrounding wetlands and Atchafalaya delta.
Kyle Wright; Paola Passalacqua; Cathleen Jones; Marc Simard; Michael Lamb. Estimating Hydrological Connectivity in Coastal Wetlands using UAVSAR and Numerical Modeling. 2019, 1 .
AMA StyleKyle Wright, Paola Passalacqua, Cathleen Jones, Marc Simard, Michael Lamb. Estimating Hydrological Connectivity in Coastal Wetlands using UAVSAR and Numerical Modeling. . 2019; ():1.
Chicago/Turabian StyleKyle Wright; Paola Passalacqua; Cathleen Jones; Marc Simard; Michael Lamb. 2019. "Estimating Hydrological Connectivity in Coastal Wetlands using UAVSAR and Numerical Modeling." , no. : 1.
This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy.
Guillaume Brigot; Marc Simard; Elise Colin-Koeniguer; Alexandre Boulch. Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features. Remote Sensing 2019, 11, 381 .
AMA StyleGuillaume Brigot, Marc Simard, Elise Colin-Koeniguer, Alexandre Boulch. Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features. Remote Sensing. 2019; 11 (4):381.
Chicago/Turabian StyleGuillaume Brigot; Marc Simard; Elise Colin-Koeniguer; Alexandre Boulch. 2019. "Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features." Remote Sensing 11, no. 4: 381.
Marc Simard; Lola Fatoyinbo; Charlotte Smetanka; Victor H. Rivera-Monroy; Edward Castaneda; Nathan Thomas; Tom Van Der Stocken. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience 2018, 12, 40 -45.
AMA StyleMarc Simard, Lola Fatoyinbo, Charlotte Smetanka, Victor H. Rivera-Monroy, Edward Castaneda, Nathan Thomas, Tom Van Der Stocken. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience. 2018; 12 (1):40-45.
Chicago/Turabian StyleMarc Simard; Lola Fatoyinbo; Charlotte Smetanka; Victor H. Rivera-Monroy; Edward Castaneda; Nathan Thomas; Tom Van Der Stocken. 2018. "Mangrove canopy height globally related to precipitation, temperature and cyclone frequency." Nature Geoscience 12, no. 1: 40-45.
Coastal wetlands provide a wealth of ecosystem services, including improved water quality, protection from storm surges, and wildlife habitat. Louisiana’s wetlands, however, are threatened by development, pollution, and relative sea level rise (RSLR)—the combination of sea level rise and subsidence rates. Despite widespread wetland loss, areas such as the Wax Lake and Atchafalaya river deltas are in fact growing due to their sediment loads, resulting in a complex of both degradation and aggradation along the Louisiana coast. In order to understand and model how coastal wetlands are responding to RSLR, there is a need for improved vegetation mapping, biomass estimation, and landscape-scale study of accretionary processes. AVIRIS-NG offers high spatial and spectral resolution data that can be integrated with external datasets—including from in situ measurements, monitoring stations, and other remote sensing platforms—to study these distributions and processes. Spectra derived from AVIRIS-NG imagery were used to parameterize Multiple Endmember Spectral Mixture Analysis (MESMA) for mapping vegetation functional types in addition to partial least squares regression (PLSR) models for plant aboveground biomass (AGB). The historical Landsat record complemented this analysis by deriving maps of change in wetland health and sediment availability through time. Each of these remotely sensed parameters were investigated to determine their combined relationship to Louisiana’s coastal accretion rates. In quantifying landscape-scale processes that impact wetland accretion, this research aids the assessment of coastal resiliency in the face of sea level rise. Further, the investigated imaging spectroscopy methods pertaining to vegetation mapping, biomass estimation, and accretionary modeling will inform future studies under the global Surface Biology and Geology mission.
Daniel Jensen; Kyle Cavanaugh; Marc Simard; Robert Twilley; Andre Rovai. Imaging Spectroscopy Applications for Assessing Wetland Vegetation Distributions and Coastal Resiliency in Louisiana. 2018, 1 .
AMA StyleDaniel Jensen, Kyle Cavanaugh, Marc Simard, Robert Twilley, Andre Rovai. Imaging Spectroscopy Applications for Assessing Wetland Vegetation Distributions and Coastal Resiliency in Louisiana. . 2018; ():1.
Chicago/Turabian StyleDaniel Jensen; Kyle Cavanaugh; Marc Simard; Robert Twilley; Andre Rovai. 2018. "Imaging Spectroscopy Applications for Assessing Wetland Vegetation Distributions and Coastal Resiliency in Louisiana." , no. : 1.