Application of QuickBird imagery in fuel load estimation in the Daxinganling region, China

Jin, Sen; Chen, Shyh-Chin

doi:10.1071/wf11018

Cited by 16 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuels characteristics can be accurately estimated through extensive field surveying methods such as fixed-area plots, planar intersect, or photo loads [39,65]. Although these techniques are successful, their implementation is impractical for large-scale areas, because they are quite labor-intensive and expensive [19,67].…”

Section: Discussionmentioning

confidence: 99%

“…In most cases, vegetation mapping is performed first, and then each vegetation class is assigned to a fuel model using a look-up table (e.g., [12]). Relatively few studies have been conducted to estimate surface fuel load directly [17][18][19]. Reich et al [17] developed a linear equation for estimating SFL using Landsat TM reflectance data, and biophysical variables as predictors for the Black Hills National Forest, South Dakota.…”

Section: Introductionmentioning

confidence: 99%

“…Brandis and Jacobson [18] indirectly estimated SFL from stand characteristics extracted from Landsat TM data in New South Wales (Australia). Jin and Chen [19] tested both spectral reflectance-based and stand characteristics-based models for estimating fuel loads in the Daxinganling region (northeast China) using Landsat TM and QuickBird imageries.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

et al. 2018

View full text Add to dashboard Cite

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

et al. 2018

View full text Add to dashboard Cite

show abstract

“…So far, several studies have examined the contribution of remote sensing to fuel properties, such as fuel type, fuel load and structure, and fuel condition mapping on global, regional, and local scales [8,11]. Yet, most studies have focused on forest fuel classification [18][19][20][21], whereas relatively few studies have been conducted to estimate fuel load [22,23]. At the regional level, medium to high optical spatial resolution sensors have been used for the development of empirical regression equations between fuel load parameters and recorded reflectance values [24,25].…”

mentioning

confidence: 99%

“…At the local level, airborne LIDAR efficiency in estimating canopy fuels has been well proven [17,[26][27][28][29]; however, there are limitations associated with using airborne laser (as well as microwave) products and their platforms, including the high costs associated with data acquisition and limited availability for wildfire management agencies across the world [30]. Regarding the use of passive, high to very high spatial resolution imagery, Jin and Chen [22] recently used Quickbird satellite images to develop linear regression equations to estimate surface fuel loads at the plot level over central China. The authors stated that although their approach gave good results for fine fuels, there is still room for improvement, particularly for the relationship between coarse fuels and stand characteristics.…”

mentioning

confidence: 99%

Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

et al. 2013

View full text Add to dashboard Cite

This study presents an individual tree-crown-based approach for canopy fuel load estimation and mapping in two Mediterranean pine stands. Based on destructive sampling, an allometric equation was developed for the estimation of crown fuel weight considering only pine crown width, a tree characteristic that can be estimated from passive imagery. Two high resolution images were used originally for discriminating Aleppo and Calabrian pines crown regions through a geographic object based image analysis approach. Subsequently, the crown region images were segmented using a watershed segmentation algorithm and crown width was extracted. The overall accuracy of the tree crown isolation expressed through a perfect match between the reference and the delineated crowns was 34.00% for the Kassandra site and 48.11% for the Thessaloniki site, while the coefficient of determination between the ground measured and the satellite extracted crown width was 0.5. Canopy fuel load values estimated in the current study presented mean values from 1.29 ± 0.6 to 1.65 ± 0.7 kg/m 2 similar to other conifers worldwide. Despite the modest accuracies attained in this first study of individual tree crown fuel load mapping, the OPEN ACCESSRemote Sens. 2013, 5 6462 combination of the allometric equations with satellite-based extracted crown width information, can contribute to the spatially explicit mapping of canopy fuel load in Mediterranean areas. These maps can be used among others in fire behavior prediction, in fuel reduction treatments prioritization and during active fire suppression.

show abstract

Fire and Smoke Remote Sensing and Modeling Uncertainties

Ichoku

Ellison

Yue

et al. 2016

Natural Hazard Uncertainty Assessment

View full text Add to dashboard Cite

Significant uncertainties are incurred in deriving various quantities related to biomass burning from satellite measurements at different scales, and, in general, the coarser the resolution of observation the larger the uncer tainty. WRF-Chem model simulations of smoke over the northern sub-Saharan African (NSSA) region for January-February 2010, using fire energetics and emissions research version 1.0 (FEERv1) aerosol emissions derived from MODIS measurements of fire radiative power (FRP) and aerosol optical depth (AOD), resulted in a severe model underestimation of AOD compared with satellite retrievals. Such uncertainties are attributable to three major factors: limitations in the spatial and temporal resolutions of the satellite observations used to quantify emissions, modeling parameters and assumptions, and the unique geographic characteristics of NSSA. It is recommended that field campaigns involving synergistic coordination of ground-based, airborne, and satellite measurements with modeling be conducted in major and complex biomass burning regions such as the NSSA, and that significant improvements in the spatial and temporal resolutions of observation systems needed to reduce uncertainties in biomass burning characterization be seriously considered in future satellite missions.

show abstract

Application of QuickBird imagery in fuel load estimation in the Daxinganling region, China

Cited by 16 publications

References 18 publications

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

Fire and Smoke Remote Sensing and Modeling Uncertainties

Contact Info

Product

Resources

About