scholarly journals Mapping Wetland Burned Area from Sentinel-2 across the Southeastern United States and Its Contributions Relative to Landsat-8 (2016–2019)

Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 52
Author(s):  
Melanie K. Vanderhoof ◽  
Todd J. Hawbaker ◽  
Casey Teske ◽  
Andrea Ku ◽  
Joe Noble ◽  
...  
Keyword(s):  
United States ◽  
Learning Algorithm ◽  
Southeastern United States ◽  
Open Water ◽  
Burned Area ◽  
Landsat 8 ◽  
Wetland Ecosystems ◽  
Clear Sky ◽  
Southeastern Us ◽  
Sentinel 2

Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and (3) high annual precipitation limiting clear-sky satellite observations. We developed a machine learning algorithm specifically for burned area in wetlands, and applied the algorithm to the Sentinel-2 archive (2016–2019) across the Southeastern US (>290,000 km2). Combining Landsat-8 imagery with Sentinel-2 increased the annual clear-sky observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validated with WorldView imagery, the Sentinel-2 burned area had a 29% and 30% omission and commission rates of error for burned area, respectively, compared to the US Geological Survey Landsat-8 Burned Area Product (L8 BA), which had a 47% and 8% omission and commission rate of error, respectively. The Sentinel-2 algorithm and the L8 BA mapped burned area within 78% and 60% of wetland fire perimeters (n = 555) compiled from state and federal agencies, respectively. This analysis demonstrated the potential of Sentinel-2 to support efforts to track the burned area, especially across challenging ecosystem types, such as wetlands.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Public entrepreneurs and the adoption of broad-based merit aid beyond the Southeastern United States

2013 ◽  
Vol 21 ◽  
pp. 58
Author(s):  
William Kyle Ingle ◽  
Ruth Ann Petroff
Keyword(s):  
United States ◽  
Diffusion Theory ◽  
Full Range ◽  
Southeastern United States ◽  
Merit Aid ◽  
Theoretical Frameworks ◽  
Southeastern Us ◽  
Public Colleges And Universities ◽  
Aid Programs ◽  
K 12

The concentration of broad-based merit aid adoption in the southeastern United States has been well noted in the literature. However, there are states that have adopted broad-based merit aid programs outside of the Southeast.  Guided by multiple theoretical frameworks, including innovation diffusion theory (e.g., Gray, 1973, 1994; Rogers, 2003), Roberts and King’s (1991) typology of public entrepreneurs, and Anderson’s (2003) stages of the policymaking process, this qualitative study sought to answer the following questions. First, in the absence of regional diffusion pressures, what internal determinants are reported as accounting for the diffusion of broad-based merit aid programs outside of the Southeastern US?  What types of public entrepreneurs were identified as playing key roles in establishing merit aid in states outside the southeastern US?  During which stages of the policymaking process were they active? We found that merit aid was a means of addressing an array of public problems, including low college going rates at in-state public colleges and universities, and weak K-12 accountability. Consistent factors reported as facilitating merit aid creation included a strong, vocal public advocate (governors and a university system president) and a desire to strengthen state economies and diversify workforces.  A full range of public entrepreneurs played key roles in developing merit aid in the sampled states. Political and executive entrepreneurs were in the forefront of merit aid efforts, but our data suggest that a cast of supporting public entrepreneurs were integral to the eventual adoption of broad-based merit aid in the sampled states.

scholarly journals Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0232962 ◽  
Author(s):  
Fiona Ngadze ◽  
Kudzai Shaun Mpakairi ◽  
Blessing Kavhu ◽  
Henry Ndaimani ◽  
Monalisa Shingirayi Maremba
Keyword(s):  
Burned Area ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Burned Area Mapping ◽  
Sentinel 2

scholarly journals Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

2019 ◽  
Vol 231 ◽  
pp. 111254 ◽  
Author(s):  
David P. Roy ◽  
Haiyan Huang ◽  
Luigi Boschetti ◽  
Louis Giglio ◽  
Lin Yan ◽  
...  
Keyword(s):  
Change Detection ◽  
Temporal Change ◽  
Burned Area ◽  
Landsat 8 ◽  
Detection Approach ◽  
Burned Area Mapping ◽  
Multi Temporal ◽  
Combined Sensor ◽  
Sentinel 2

scholarly journals Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

2017 ◽  
Vol 17 (22) ◽  
pp. 13559-13572 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Loretta J. Mickley ◽  
Eric M. Leibensperger ◽  
Michael J. Iacono
Keyword(s):  
United States ◽  
Radiative Transfer ◽  
Southeastern United States ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

Abstract. In situ surface observations show that downward surface solar radiation (SWdn) over the central and southeastern United States (US) has increased by 0.58–1.0 Wm−2 a−1 over the 2000–2014 time frame, simultaneously with reductions in US aerosol optical depth (AOD) of 3.3–5.0  ×  10−3 a−1. Establishing a link between these two trends, however, is challenging due to complex interactions between aerosols, clouds, and radiation. Here we investigate the clear-sky aerosol–radiation effects of decreasing US aerosols on SWdn and other surface variables by applying a one-dimensional radiative transfer to 2000–2014 measurements of AOD at two Surface Radiation Budget Network (SURFRAD) sites in the central and southeastern United States. Observations characterized as clear-sky may in fact include the effects of thin cirrus clouds, and we consider these effects by imposing satellite data from the Clouds and Earth's Radiant Energy System (CERES) into the radiative transfer model. The model predicts that 2000–2014 trends in aerosols may have driven clear-sky SWdn trends of +1.35 Wm−2 a−1 at Goodwin Creek, MS, and +0.93 Wm−2 a−1 at Bondville, IL. While these results are consistent in sign with observed trends, a cross-validated multivariate regression analysis shows that AOD reproduces 20–26 % of the seasonal (June–September, JJAS) variability in clear-sky direct and diffuse SWdn at Bondville, IL, but none of the JJAS variability at Goodwin Creek, MS. Using in situ soil and surface flux measurements from the Ameriflux network and Illinois Climate Network (ICN) together with assimilated meteorology from the North American Land Data Assimilation System (NLDAS), we find that sunnier summers tend to coincide with increased surface air temperature and soil moisture deficits in the central US. The 1990–2015 trends in the NLDAS SWdn over the central US are also of a similar magnitude to our modeled 2000–2014 clear-sky trends. Taken together, these results suggest that climate and regional hydrology in the central US are sensitive to the recent reductions in aerosol concentrations. Our work has implications for severely polluted regions outside the US, where improvements in air quality due to reductions in the aerosol burden could inadvertently pose an enhanced climate risk.

scholarly journals A Fused Radar-Optical Approach for Mapping Wetlands and Deepwaters of the Mid-Atlantic and Gulf Coast Regions of the United States

2021 ◽  
Vol 13 (13) ◽  
pp. 2495
Author(s):  
Brian T. Lamb ◽  
Maria A. Tzortziou ◽  
Kyle C. McDonald
Keyword(s):  
United States ◽  
Gulf Coast ◽  
Open Water ◽  
The United States ◽  
Common Reed ◽  
Wildlife Habitat ◽  
Tidal Marshes ◽  
Tidal Wetlands ◽  
Landsat 8 ◽  
Marsh Vegetation

Tidal wetlands are critically important ecosystems that provide ecosystem services including carbon sequestration, storm surge mitigation, water filtration, and wildlife habitat provision while supporting high levels of biodiversity. Despite their importance, monitoring these systems over large scales remains challenging due to difficulties in obtaining extensive up-to-date ground surveys and the need for high spatial and temporal resolution satellite imagery for effective space-borne monitoring. In this study, we developed methodologies to advance the monitoring of tidal marshes and adjacent deepwaters in the Mid-Atlantic and Gulf Coast United States. We combined Sentinel-1 SAR and Landsat 8 optical imagery to classify marshes and open water in both regions, with user’s and producer’s accuracies exceeding 89%. This methodology enables the assessment of marsh loss through conversion to open water at an annual resolution. We used time-series Sentinel-1 imagery to classify persistent and non-persistent marsh vegetation with greater than 93% accuracy. Non-persistent marsh vegetation serves as an indicator of salinity regimes in tidal wetlands. Additionally, we mapped two invasive species: wetlands invasive Phragmites australis (common reed) with greater than 80% accuracy and deepwater invasive Trapa natans (water chestnut) with greater than 96% accuracy. These results have important implications for improved monitoring and management of coastal wetlands ecosystems.

scholarly journals Modelling the High Mercury Wet deposition in the Southeastern US by WRF-GC

2022 ◽  
Author(s):  
Xiaotian Xu ◽  
Xu Feng ◽  
Haipeng Lin ◽  
Peng Zhang ◽  
Shaojian Huang ◽  
...  
Keyword(s):  
United States ◽  
Wet Deposition ◽  
Deep Convection ◽  
Southeastern United States ◽  
Convective Precipitation ◽  
Mercury Deposition ◽  
Southeastern Us ◽  
Different Types ◽  
High Mercury ◽  
Simulation Results

Abstract. High mercury wet deposition in southeastern United States has been noticed for many years. Previous studies came up with a theory that it was associated with high-altitude divalent mercury scavenged by convective precipitation. Given the coarse resolution of previous models (e.g. GEOS-Chem), this theory is still not fully tested. Here we employed a newly developed WRF-GEOS-Chem (WRF-GC) model implemented with mercury simulation. We conduct extensive model benchmarking by comparing WRF-GC with different resolutions (from 50 km to 25 km) to GEOS-Chem output (4° × 5°) and data from Mercury Deposition Network (MDN) in July–September 2013. The comparison of mercury wet deposition from two models both present high mercury wet deposition in southeastern United States. We divided simulation results by heights, different types of precipitation and combination of these two variations together and find most of mercury wet deposition concentrates on higher space and caused by convective precipitation. Therefore, we conclude that it is the deep convection caused enhanced mercury wet deposition in the southeastern United States.

scholarly journals Locating Forest Management Units Using Remote Sensing and Geostatistical Tools in North-Central Washington, USA

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2454
Author(s):  
Palaiologos Palaiologou ◽  
Maureen Essen ◽  
John Hogland ◽  
Kostas Kalabokidis
Keyword(s):  
Machine Learning ◽  
Forest Management ◽  
Random Forests ◽  
Burned Area ◽  
Landsat 8 ◽  
Management Units ◽  
Entire Study ◽  
North Central ◽  
Machine Learning Classification ◽  
Sentinel 2

In this study, we share an approach to locate and map forest management units with high accuracy and with relatively rapid turnaround. Our study area consists of private, state, and federal land holdings that cover four counties in North-Central Washington, USA (Kittitas, Okanogan, Chelan and Douglas). This area has a rich history of landscape change caused by frequent wildfires, insect attacks, disease outbreaks, and forest management practices, which is only partially documented across ownerships in an inconsistent fashion. To consistently quantify forest management activities for the entire study area, we leveraged Sentinel-2 satellite imagery, LANDFIRE existing vegetation types and disturbances, monitoring trends in burn severity fire perimeters, and Landsat 8 Burned Area products. Within our methodology, Sentinel-2 images were collected and transformed to orthogonal land cover change difference and ratio metrics using principal component analyses. In addition, the Normalized Difference Vegetation Index and the Relativized Burn Ratio index were estimated. These variables were used as predictors in Random Forests machine learning classification models. Known locations of forest treatment units were used to create samples to train the Random Forests models to estimate where changes in forest structure occurred between the years of 2016 and 2019. We visually inspected each derived polygon to manually assign one treatment class, either clearcut or thinning. Landsat 8 Burned Area products were used to derive prescribed fire units for the same period. The bulk of analyses were performed using the RMRS Raster Utility toolbar that facilitated spatial, statistical, and machine learning tools, while significantly reducing the required processing time and storage space associated with analyzing these large datasets. The results were combined with existing LANDFIRE vegetation disturbance and forest treatment data to create a 21-year dataset (1999–2019) for the study area.

scholarly journals Aerosol optical properties in the southeastern United States in summer – Part 2: Sensitivity of aerosol optical depth to relative humidity and aerosol parameters

2016 ◽  
Vol 16 (8) ◽  
pp. 5009-5019 ◽  
Author(s):  
Charles A. Brock ◽  
Nicholas L. Wagner ◽  
Bruce E. Anderson ◽  
Andreas Beyersdorf ◽  
Pedro Campuzano-Jost ◽  
...  
Keyword(s):  
United States ◽  
Refractive Index ◽  
Relative Humidity ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mixed Boundary ◽  
Southeastern United States ◽  
Geometric Standard Deviation ◽  
Aerosol Mass ◽  
Southeastern Us

Abstract. Aircraft observations of meteorological, trace gas, and aerosol properties were made between May and September 2013 in the southeastern United States (US). Regionally representative aggregate vertical profiles of median and interdecile ranges of the measured parameters were constructed from 37 individual aircraft profiles made in the afternoon when a well-mixed boundary layer with typical fair-weather cumulus was present (Wagner et al., 2015). We use these 0–4 km aggregate profiles and a simple model to calculate the sensitivity of aerosol optical depth (AOD) to changes in dry aerosol mass, relative humidity, mixed-layer height, the central diameter and width of the particle size distribution, hygroscopicity, and dry and wet refractive index, while holding the other parameters constant. The calculated sensitivity is a result of both the intrinsic sensitivity and the observed range of variation in these parameters. These observationally based sensitivity studies indicate that the relationship between AOD and dry aerosol mass in these conditions in the southeastern US can be highly variable and is especially sensitive to relative humidity (RH). For example, calculated AOD ranged from 0.137 to 0.305 as the RH was varied between the 10th and 90th percentile profiles with dry aerosol mass held constant. Calculated AOD was somewhat less sensitive to aerosol hygroscopicity, mean size, and geometric standard deviation, σg. However, some chemistry–climate models prescribe values of σg substantially larger than we or others observe, leading to potential high biases in model-calculated AOD of  ∼  25 %. Finally, AOD was least sensitive to observed variations in dry and wet aerosol refractive index and to changes in the height of the well-mixed surface layer. We expect these findings to be applicable to other moderately polluted and background continental air masses in which an accumulation mode between 0.1–0.5 µm diameter dominates aerosol extinction.

scholarly journals Aerosol optical properties in the southeastern United States in summer – Part 1: Hygroscopic growth

2015 ◽  
Vol 15 (18) ◽  
pp. 25695-25738 ◽  
Author(s):  
C. A. Brock ◽  
N. L. Wagner ◽  
B. E. Anderson ◽  
A. R. Attwood ◽  
A. Beyersdorf ◽  
...  
Keyword(s):  
United States ◽  
Layer Structure ◽  
Southeastern United States ◽  
Field Studies ◽  
Hygroscopic Growth ◽  
Particle Volume ◽  
Aerosol Composition ◽  
Optical Extinction ◽  
Airborne Measurements ◽  
Southeastern Us

Abstract. Aircraft observations of meteorological, trace gas, and aerosol properties were made during May–September 2013 in the southeastern United States (US) under fair-weather, afternoon conditions with well-defined planetary boundary layer structure. Optical extinction at 532 nm was directly measured at three relative humidities and compared with extinction calculated from measurements of aerosol composition and size distribution using the κ-Köhler approximation for hygroscopic growth. Using this approach, the hygroscopicity parameter κ for the organic fraction of the aerosol must have been < 0.10 to be consistent with 75 % of the observations within uncertainties. This subsaturated κ value for the organic aerosol in the southeastern US is consistent with several field studies in rural environments. We present a new parameterization of the change in aerosol extinction as a function of relative humidity that better describes the observations than does the widely used power-law (gamma, γ) parameterization. This new single-parameter κext formulation is based upon κ-Köhler and Mie theories and relies upon the well-known approximately linear relationship between particle volume (or mass) and optical extinction (Charlson et al., 1967). The fitted parameter, κext, is nonlinearly related to the chemically derived κ parameter used in κ-Köhler theory. The values of κext we determined from airborne measurements are consistent with independent observations at a nearby ground site.

Sign in / Sign up

Export Citation Format

Share Document