scholarly journals Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6350 ◽  
Author(s):  
Nastassia Barber ◽  
Ernesto Alvarado ◽  
Van R. Kane ◽  
William E. Mell ◽  
L. Monika Moskal
Keyword(s):  
Vegetation Indices ◽  
Principal Component ◽  
Fire Season ◽  
Grass Species ◽  
Fuel Moisture ◽  
Major Species ◽  
Field Samples ◽  
Western Washington ◽  
Limited Moisture ◽  
Effective Models

Predicting wildfire behavior is a complex task that has historically relied on empirical models. Physics-based fire models could improve predictions and have broad applicability, but these models require more detailed inputs, including spatially explicit estimates of fuel characteristics. One of the most critical of these characteristics is fuel moisture. Obtaining moisture measurements with traditional destructive sampling techniques can be prohibitively time-consuming and extremely limited in spatial resolution. This study seeks to assess how effectively moisture in grasses can be estimated using reflectance in six wavelengths in the visible and infrared ranges. One hundred twenty 1 m-square field samples were collected in a western Washington grassland as well as overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices and components from principal component analysis of the wavelengths were generated and compared. The best model, a linear model based on principal components and biomass, showed modest predictive power (r² = 0.45). This model performed better for the plots with both dominant grass species pooled than it did for each species individually. The presence of this correlation, especially given the limited moisture range of this study, suggests that further research using samples across the entire fire season could potentially produce effective models for estimating moisture in this type of ecosystem using unmanned aerial vehicles, even when more than one major species of grass is present. This approach would be a fast and flexible approach compared to traditional moisture measurements.

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

2017 ◽  
Vol 26 (5) ◽  
pp. 384
Author(s):  
L. M. Ellsworth ◽  
A. P. Dale ◽  
C. M. Litton ◽  
T. Miura
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Native Grass ◽  
Tropical Ecosystems ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Live Fuel Moisture ◽  
Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

scholarly journals Evaluating the Effects of Projected Climate Change on Forest Fuel Moisture Content

2014 ◽  
Vol 37 ◽  
pp. 65-69
Author(s):  
Kellen Nelson ◽  
Daniel Tinker
Keyword(s):  
Climate Change ◽  
Moisture Content ◽  
Stand Structure ◽  
Canopy Cover ◽  
Fire Season ◽  
Climate Change Scenarios ◽  
Fuel Moisture ◽  
Mature Forest ◽  
Forest Fuel ◽  
Fuel Moisture Content

Understanding how live and dead forest fuel moisture content (FMC) varies with seasonal weather and stand structure will improve researchers’ and forest managers’ ability to predict the cumulative effects of weather on fuel drying during the fire season and help identify acute conditions that foster wildfire ignition and high rates of fire spread. No studies have investigated the efficacy of predicting FMC using mechanistic water budget models at daily time scales through the fire season nor have they investigated how FMC may vary across space. This study addresses these gaps by (1) validating a novel mechanistic live FMC model and (2) applying this model with an existing dead FMC model at three forest sites using five climate change scenarios to characterize how FMC changes through time and across space. Sites include post-fire 24-year old forest, mature forest with high canopy cover, and mature forest affected by the mountain pine beetle with moderate canopy cover. Climate scenarios include central tendency, warm/dry, warm/wet, hot/dry, and hot/wet.

scholarly journals Sprouting of pre-sprouted sugarcane seedlings and micrometeorological variables under photo-selective nets

2019 ◽  
Vol 23 (8) ◽  
pp. 625-629
Author(s):  
José J. F. Cordeiro Júnior ◽  
Cristiane Guiselini ◽  
Héliton Pandorfi ◽  
Alex S. Moraes ◽  
Dimas Menezes ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Substrate Temperature ◽  
Air Temperature ◽  
External Environment ◽  
Principal Component ◽  
Low Density Polyethylene ◽  
Grass Species ◽  
Low Density ◽  
Randomized Design ◽  
Completely Randomized Design

ABSTRACT Sugarcane is a grass species that stands out worldwide in the production of ethanol. Brazil is the world’s largest producer and leader in exports, responsible for more than 50% of the products that are marketed worldwide. The objective of this study was to investigate the effect of photo-selective nets on micrometeorological variables and on sprouting of pre-sprouted sugarcane seedlings. The experiment was carried out in a protected environment at Universidade Federal Rural de Pernambuco - UFRPE, in a completely randomized design. Seedlings of cultivar RB92579 were obtained by the technique of production of pre-sprouted seedlings. The protected environment was divided into four modules corresponding to the treatments: covered with anti-UV low-density polyethylene plastic: + Solpack® red ultranet net, + Solpack® white net, + Solpack® freshnet net and without shade net. Micrometeorological data of air temperature and substrate temperature were recorded in each module. The first count of emergence, sprouting speed index and sprouting percentage were calculated. Principal component analysis was used to verify the association between the cultivation modules and the micrometeorological and sprouting variables of the seedlings. Air temperature in the protected environment was 8.7% higher than that in the external environment. The white net led to sprouting of 78.93%. The substrate temperature above 30.4 °C favored seedling sprouting. The modules with white net and red ultranet net favored seedling sprouting.

Compositional changes over four years for binary mixtures of grass species grown with white clover

2005 ◽  
Vol 85 (2) ◽  
pp. 351-360 ◽  
Author(s):  
D. B. McKenzie ◽  
Y. A. Papadopoulos ◽  
K. B. McRae ◽  
E. Butt
Keyword(s):  
White Clover ◽  
Tall Fescue ◽  
Principal Component ◽  
Growing Season ◽  
Kentucky Bluegrass ◽  
Repeated Measurements ◽  
Grass Species ◽  
Growth Stages ◽  
Meadow Fescue ◽  
Reed Canarygrass

Kentucky bluegrass, meadow fescue, orchardgrass, tall fescue, timothy, and reed canarygrass were seeded in all possible two-grass combinations with white clover in conventional and underseeded barley treatments using a split-plot design at the Western Agriculture Centre near Pynn’s Brook, NL. The objectives were: (1) to assess dry matter yield (DMY) of two binary grass species when sown with white clover in mixtures under a system with cuttings at similar crop growth stages as rotational grazing and to assess the effect of underseeding to barley on this system; (2) to identify mixtures that enhance herbage distribution throughout the grazing season; and (3) to assess the sward dynamics over successive cropping seasons. The composition of the binary grass mixtures with white clover affected seasonal DMY, seasonal herbage distribution, and sward dynamics over the production years. Orchardgrass in mixtures decreased DMY, shifted the herbage distribution toward early season, and competed with other species. Timothy composition of the stand showed the largest decline over the 3 production years, whereas white clover declined in mixtures with bluegrass, orchardgrass, or tall fescue. Meadow fescue and reed canarygrass with white clover was the most productive mixture with excellent persistence and good yield distribution over the growing season. Orchardgrass was the least compatible species in the mixtures; it dominated first growth and contributed the least to biomass production in later years. Both bluegrass and reed canarygrass performed well in mixtures over the 3 production years; bluegrass appeared to enhance the performance of the other species during summer regrowth whereas reed canarygrass was superior in the later part of the growing season. Underseeding with barley did not affect white clover yield in any production year but detrimentally affected the yield of orchardgrass and meadow fescue in mixtures, and their seasonal distribution. Key words: Bluegrass, orchardgrass, meadow fescue, tall fescue, timothy, reed canarygrass, repeated measurements, principal component analysis, herbage DM distribution, species competition

Fire weather index system components for large fires in the Canadian boreal forest

2004 ◽  
Vol 13 (4) ◽  
pp. 391 ◽  
Author(s):  
B. D. Amiro ◽  
K. A. Logan ◽  
B. M. Wotton ◽  
M. D. Flannigan ◽  
J. B. Todd ◽  
...  
Keyword(s):  
Weather Conditions ◽  
Fire Season ◽  
Fire Weather ◽  
Fuel Moisture ◽  
Weather Index ◽  
Fire Weather Index ◽  
System Parameters ◽  
System Components ◽  
Large Fires ◽  
Canadian Boreal Forest

Canadian Fire Weather Index (FWI) System components and head fire intensities were calculated for fires greater than 2 km2 in size for the boreal and taiga ecozones of Canada from 1959 to 1999. The highest noon-hour values were analysed that occurred during the first 21 days of each of 9333 fires. Depending on ecozone, the means of the FWI System parameters ranged from: fine fuel moisture code (FFMC), 90 to 92 (82 to 96 for individual fires); duff moisture code (DMC), 38 to 78 (10 to 140 for individual fires); drought code (DC), 210 to 372 (50 to 600 for individual fires); and fire weather index, 20 to 33 (5 to 60 for individual fires). Fine fuel moisture code decreased, DMC had a mid-season peak, and DC increased through the fire season. Mean head fire intensities ranged from 10 to 28 MW m−1 in the boreal spruce fuel type, showing that most large fires exhibit crown fire behaviour. Intensities of individual fires can exceed 60 MW m−1. Most FWI System parameters did not show trends over the 41-year period because of large inter-annual variability. A changing climate is expected to create future weather conditions more conducive to fire throughout much of Canada but clear changes have not yet occurred.

scholarly journals Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping

2021 ◽  
Author(s):  
Maria Polivova ◽  
Anna Brook
Keyword(s):  
State Estimation ◽  
Vegetation Indices ◽  
Principal Component ◽  
State Monitoring ◽  
Early Stress ◽  
Alternative Approach ◽  
Crop Development ◽  
Spectral Vegetation Indices ◽  
Data Source ◽  
Stress States

Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. These technologies have great importance for plant state monitoring, especially for agriculture. The main aim is to assess the performance level of the selected VIs calculated from space-borne multispectral imagery and point-based field spectroscopy in application to crop state estimation. The results obtained indicate that space-borne VIs react on phenology. This feature makes it an appropriate data source for monitoring crop development, crop water needs and yield prediction. Field spectrometer VIs were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability of the calculated values. The results obtained in the second part of the presented study were reported on crop state estimated by 17 VIs known as sensitive to plant drought. An alternative approach for identification early stress by VIs proposed in this study is Principal Component Analysis (PCA). The results show that PCA has identified the degree of similarity of the different states and together with reference stress states from the control plot clearly estimated stress in the actual irrigated field, which was hard to detect by VIs values only.

scholarly journals Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS

Minerals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1379
Author(s):  
Nina Rethfeldt ◽  
Pia Brinkmann ◽  
Daniel Riebe ◽  
Toralf Beitz ◽  
Nicole Köllner ◽  
...  
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Matrix Effects ◽  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Regression Methods ◽  
Breakdown Spectroscopy ◽  
Field Samples ◽  
Laser Induced Breakdown

The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples.

scholarly journals RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C3 and C4 Turfgrass under Different Water Conditions

2020 ◽  
Vol 12 (6) ◽  
pp. 2160 ◽  
Author(s):  
José Marín ◽  
Salima Yousfi ◽  
Pedro V. Mauri ◽  
Lorena Parra ◽  
Jaime Lloret ◽  
...  
Keyword(s):  
Festuca Arundinacea ◽  
Deficit Irrigation ◽  
Vegetation Index ◽  
Positive Impact ◽  
Poa Pratensis ◽  
Human Life ◽  
Brachypodium Distachyon ◽  
Vegetation Indices ◽  
Grass Species ◽  
Severe Condition

Grasslands have a natural capacity to decrease air pollution and a positive impact on human life. However, their maintenance requires adequate irrigation, which is difficult to apply in many regions where drought and high temperatures are frequent. Therefore, the selection of grass species more tolerant to a lack of irrigation is a fundamental criterion for green space planification. This study compared responses to deficit irrigation of different turfgrass mixtures: a C4 turfgrass mixture, Cynodon dactylon-Brachypodium distachyon (A), a C4 turfgrass mixture, Buchloe dactyloides-Brachypodium distachyon (B), and a standard C3 mixture formed by Lolium perenne-Festuca arundinacea-Poa pratensis (C). Three different irrigation regimes were assayed, full irrigated to 100% (FI-100), deficit irrigated to 75% (DI-75), and deficit irrigated to 50% (DI-50) of container capacity. Biomass, normalized difference vegetation index (NDVI), green area (GA), and greener area (GGA) vegetation indices were measured. Irrigation significantly affected the NDVI, biomass, GA, and GGA. The most severe condition in terms of decreasing biomass and vegetation indices was DI-50. Both mixtures (A) and (B) exhibited higher biomass, NDVI, GA, and GGA than the standard under deficit irrigation. This study highlights the superiority of (A) mixture under deficit irrigation, which showed similar values of biomass and vegetation indices under full irrigated and deficit irrigated (DI-75) container capacities.

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