scholarly journals Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data

2021 ◽  
Vol 13 (5) ◽  
pp. 897 ◽  
Author(s):  
Christopher William Smith ◽  
Santosh K. Panda ◽  
Uma Suren Bhatt ◽  
Franz J. Meyer
Keyword(s):  
Boreal Forest ◽  
Spectral Resolution ◽  
Vegetation Type ◽  
Hyperspectral Data ◽  
Landsat 8 ◽  
Percent Cover ◽  
Fuel Type ◽  
Field Plot ◽  
Type Mapping ◽  
In Fire

In Alaska the current wildfire fuel map products were generated from low spatial (30 m) and spectral resolution (11 bands) Landsat 8 satellite imagery which resulted in map products that not only lack the granularity but also have insufficient accuracy to be effective in fire and fuel management at a local scale. In this study we used higher spatial and spectral resolution AVIRIS-NG hyperspectral data (acquired as part of the NASA ABoVE project campaign) to generate boreal forest vegetation and fire fuel maps. Based on our field plot data, random forest classified images derived from 304 AVIRIS-NG bands at Viereck IV level (Alaska Vegetation Classification) had an 80% accuracy compared to the 33% accuracy of the LANDFIRE’s Existing Vegetation Type (EVT) product derived from Landsat 8. Not only did our product more accurately classify fire fuels but was also able to identify 20 dominant vegetation classes (percent cover >1%) while the EVT product only identified 8 dominant classes within the study area. This study demonstrated that highly detailed and accurate fire fuel maps can be created at local sites where AVIRIS-NG is available and can provide valuable decision-support information to fire managers to combat wildfires.

scholarly journals GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

2020 ◽  
Vol 12 (4) ◽  
pp. 656 ◽  
Author(s):  
Luoma Wan ◽  
Yinyi Lin ◽  
Hongsheng Zhang ◽  
Feng Wang ◽  
Mingfeng Liu ◽  
...  
Keyword(s):  
Hong Kong ◽  
Spectral Resolution ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Species Discrimination ◽  
Infrared Range ◽  
Landsat 8 ◽  
Mangrove Species ◽  
Spectral Bands ◽  
Species Mapping

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

2017 ◽  
Vol 33 (10) ◽  
pp. 1064-1083 ◽  
Author(s):  
A. Stefanidou ◽  
E. Dragozi ◽  
D. Stavrakoudis ◽  
I. Z. Gitas
Keyword(s):  
Image Analysis ◽  
Landsat 8 ◽  
Fuel Type ◽  
Landsat 8 Oli ◽  
Object Based Image Analysis ◽  
Type Mapping ◽  
Object Based

scholarly journals National fuel type mapping methodology using geographic object based image analysis and landsat 8 oli imagery

10.3990/2.398 ◽  
2016 ◽  
Author(s):  
M. Tompoulidou ◽  
A. Stefanidou ◽  
D. Grigoriadis ◽  
E. Dragozi ◽  
D. Stavrakoudis ◽  
...  
Keyword(s):  
Image Analysis ◽  
Landsat 8 ◽  
Fuel Type ◽  
Landsat 8 Oli ◽  
Object Based Image Analysis ◽  
Type Mapping ◽  
Object Based ◽  
Geographic Object

scholarly journals Rotational hyperspectral scanner and related image reconstruction algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Longqiang Luo ◽  
Shuo Li ◽  
Xinli Yao ◽  
Sailing He
Keyword(s):  
Image Reconstruction ◽  
Spectral Resolution ◽  
Reconstruction Algorithm ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Spatial Accuracy ◽  
Scanning Method ◽  
Image Reconstruction Algorithm

AbstractWe design and implement a compact and lightweight hyperspectral scanner. Based on this, a novel rotational hyperspectral scanner was demonstrated. Different from translational scanning, rotational scanning is a moveless and stable scanning method. We also designed a relevant image algorithm to reconstruct the image from an angular recorded hyperspectral data cube. The algorithm works well even with uncertain radial and tangential offset, which is caused by mechanical misalignment. The system shown a spectral resolution of 5 nm after calibration. Finally, spatial accuracy and spectral precision were discussed, based on some additional experiments.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

scholarly journals Production of peroxy nitrates in boreal biomass burning plumes over Canada during the BORTAS campaign

2016 ◽  
Vol 16 (5) ◽  
pp. 3485-3497 ◽  
Author(s):  
Marcella Busilacchio ◽  
Piero Di Carlo ◽  
Eleonora Aruffo ◽  
Fabio Biancofiore ◽  
Cesare Dari Salisburgo ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Forest Fire ◽  
Biomass Burning ◽  
Forest Fires ◽  
Chemical Mechanism ◽  
Fire Emissions ◽  
Fire Plumes ◽  
Peroxy Nitrates ◽  
In Fire ◽  
The Impact

Abstract. The observations collected during the BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) campaign in summer 2011 over Canada are analysed to study the impact of forest fire emissions on the formation of ozone (O3) and total peroxy nitrates ∑PNs, ∑ROONO2). The suite of measurements on board the BAe-146 aircraft, deployed in this campaign, allows us to calculate the production of O3 and of  ∑PNs, a long-lived NOx reservoir whose concentration is supposed to be impacted by biomass burning emissions. In fire plumes, profiles of carbon monoxide (CO), which is a well-established tracer of pyrogenic emission, show concentration enhancements that are in strong correspondence with a significant increase of concentrations of ∑PNs, whereas minimal increase of the concentrations of O3 and NO2 is observed. The ∑PN and O3 productions have been calculated using the rate constants of the first- and second-order reactions of volatile organic compound (VOC) oxidation. The ∑PN and O3 productions have also been quantified by 0-D model simulation based on the Master Chemical Mechanism. Both methods show that in fire plumes the average production of ∑PNs and O3 are greater than in the background plumes, but the increase of ∑PN production is more pronounced than the O3 production. The average ∑PN production in fire plumes is from 7 to 12 times greater than in the background, whereas the average O3 production in fire plumes is from 2 to 5 times greater than in the background. These results suggest that, at least for boreal forest fires and for the measurements recorded during the BORTAS campaign, fire emissions impact both the oxidized NOy and O3,  but (1 ∑PN production is amplified significantly more than O3 production and (2) in the forest fire plumes the ratio between the O3 production and the ∑PN production is lower than the ratio evaluated in the background air masses, thus confirming that the role played by the ∑PNs produced during biomass burning is significant in the O3 budget. The implication of these observations is that fire emissions in some cases, for example boreal forest fires and in the conditions reported here, may influence more long-lived precursors of O3 than short-lived pollutants, which in turn can be transported and eventually diluted in a wide area.

scholarly journals Abundance and Impacts of Competing Species on Conifer Regeneration Following Careful Logging in the Eastern Canadian Boreal Forest

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 177 ◽  
Author(s):  
Louis-Philippe Ménard ◽  
Jean-Claude Ruel ◽  
Nelson Thiffault
Keyword(s):  
Boreal Forest ◽  
Basal Area ◽  
Low Fertility ◽  
Percent Cover ◽  
Large Area ◽  
Competing Species ◽  
Competing Vegetation ◽  
Conifer Regeneration ◽  
Ericaceous Shrubs ◽  
Canadian Boreal Forest

Managing competing vegetation is crucial in stand establishment strategies; forecasting the abundance, composition, and impact of competing vegetation after harvesting is needed to optimize silviculture scenarios and maintain long-term site productivity. Our main objective was to identify factors influencing the short-term abundance and composition of competing vegetation over a large area of the Canadian boreal forest. Our second objective was to better understand the mid-term evolution of the regeneration/competing vegetation complex in cases of marginal regeneration conditions. We used operational regeneration surveys of 4471 transects sampled ≈5 years after harvesting that contained data on regeneration, competing vegetation, elevation, ecological classification, soil attributes, and pre-harvest forest stands. We performed a redundancy analysis to identify the relationships between competing vegetation, harvesting and biophysical variables. We then estimated the probability of observing a given competing species cover based on these variables. In 2015, we re-sampled a portion of the sites, where conifer regeneration was marginal early after harvesting, to assess the temporal impact of different competing levels and species groups on the free-to-grow stocking, vigour and basal area of softwood regeneration. Results from the first inventory showed that, after careful logging around advance growth, ericaceous shrubs and hardwoods were not associated with the same sets of site attributes. Ericaceous shrubs were mainly found on low fertility sites associated with black spruce (Picea mariana (Mill.) BSP) or jack pine (Pinus banksiana Lamb.). The distinction between suitable environments for commercial shade-intolerant hardwoods and non-commercial hardwoods was less clear, as they responded similarly to many variables. Analysis of data from the second inventory showed a significant improvement in conifer free-to-grow stocking when commercial shade-intolerant hardwood competing levels were low (stocking 0%–40%) and when ericaceous shrubs competing levels were moderate (percent cover 26%–75%). In these conditions of marginal regeneration, the different types and intensities of competition did not affect the vigour or basal area of softwood regeneration, 9–14 years after harvesting.

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