scholarly journals Modeling and Mapping Forest Fire Occurrence from Aboveground Carbon Density in Mexico

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 402 ◽  
Author(s):  
Carlos Ivan Briones-Herrera ◽  
Daniel José Vega-Nieva ◽  
Norma Angélica Monjarás-Vega ◽  
Favian Flores-Medina ◽  
Pablito Marcelo Lopez-Serrano ◽  
...  
Keyword(s):  
Forest Fires ◽  
Aboveground Biomass ◽  
Fire Suppression ◽  
Weibull Model ◽  
Management Decision ◽  
Fire Occurrence ◽  
Carbon Density ◽  
Aboveground Carbon ◽  
Fire Activity ◽  
Aboveground Carbon Density

Understanding the spatial patterns of fire occurrence is key for improved forest fires management, particularly under global change scenarios. Very few studies have attempted to relate satellite-based aboveground biomass maps of moderate spatial resolution to spatial fire occurrence under a variety of climatic and vegetation conditions. This study focuses on modeling and mapping fire occurrence based on fire suppression data from 2005–2015 from aboveground biomass—expressed as aboveground carbon density (AGCD)—for the main ecoregions in Mexico. Our results showed that at each ecoregion, unimodal or humped relationships were found between AGCD and fire occurrence, which might be explained by varying constraints of fuel and climate limitation to fire activity. Weibull equations successfully fitted the fire occurrence distributions from AGCD, with the lowest fit for the desert shrub-dominated north region that had the lowest number of observed fires. The models for predicting fire occurrence from AGCD were significantly different by region, with the exception of the temperate forest in the northwest and northeast regions that could be modeled with a single Weibull model. Our results suggest that AGCD could be used to estimate spatial fire occurrence maps; those estimates could be integrated into operational GIS tools for assistance in fire danger mapping and fire and fuel management decision-making. Further investigation of anthropogenic drivers of fire occurrence and fuel characteristics should be considered for improving the operational spatial planning of fire management. The modeling strategy presented here could be replicated in other countries or regions, based on remote-sensed measurements of aboveground biomass and fire activity or fire suppression records.

scholarly journals Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion

2019 ◽  
Vol 11 (8) ◽  
pp. 928 ◽  
Author(s):  
Tom Swinfield ◽  
Jeremy A. Lindsell ◽  
Jonathan V. Williams ◽  
Rhett D. Harrison ◽  
Agustiono ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Canopy Height ◽  
Training Data ◽  
Small Scale ◽  
Model Parameters ◽  
Carbon Density ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density

Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (>10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location ( R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.

scholarly journals Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3330
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Emilio Moran ◽  
Mateus Batistella
Keyword(s):  
Carbon Stock ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Coefficient Of Determination ◽  
Lidar Data ◽  
Carbon Density ◽  
Modis Data ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density ◽  
Airborne Lidar Data

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

scholarly journals Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR

2015 ◽  
Vol 8 (1) ◽  
pp. 9 ◽  
Author(s):  
Patricio Molina ◽  
Gregory Asner ◽  
Mercedes Farjas Abadía ◽  
Juan Ojeda Manrique ◽  
Luis Sánchez Diez ◽  
...  
Keyword(s):  
Density Estimation ◽  
Airborne Lidar ◽  
Spatially Explicit ◽  
Carbon Density ◽  
Estimation Model ◽  
Amazonian Forest ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density

Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR

2018 ◽  
Vol 424 ◽  
pp. 323-337 ◽  
Author(s):  
R. Flint Hughes ◽  
Gregory P. Asner ◽  
James A. Baldwin ◽  
Joseph Mascaro ◽  
Lori K.K. Bufil ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Carbon Density ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density

scholarly journals Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Forests ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 5
Author(s):  
Slobodan Milanović ◽  
Nenad Marković ◽  
Dragan Pamučar ◽  
Ljubomir Gigović ◽  
Pavle Kostić ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Suppression ◽  
Standard Procedure ◽  
Predictive Ability ◽  
Fire Risk ◽  
Fire Season ◽  
Fire Occurrence ◽  
Probability Mapping

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area.

scholarly journals A 500-year history of forest fires in Sala area, central Sweden, shows the earliest known onset of fire suppression in Scandinavia

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Guilherme Alexandre Stecher Justini Pinto ◽  
Mats Niklasson ◽  
Nina Ryzhkova ◽  
Igor Drobyshev
Keyword(s):  
Forest Fires ◽  
Fire History ◽  
Fire Suppression ◽  
Fire Cycle ◽  
Superposed Epoch Analysis ◽  
Burned Areas ◽  
Fire Activity ◽  
History Of ◽  
Epoch Analysis ◽  
In Fire

AbstractThe Sala fire in the Västmanland County of central Sweden that burned about 14,000 ha in 2014 has been the largest fire recorded in the modern history of Sweden. To understand the long-term fire history of this area, we dendrochronologically dated fire scars on Scots pine (Pinus sylvestris L.) trees (live and deadwood) to reconstruct the fire cycle and fire occurrence in the area affected by the 2014 fire. We identified 64 fire years, using a total of 378 pine samples. The earliest reconstructed fire dated back to 1113 AD. The spatial reconstruction extended over the period of 1480–2018 AD. Lower levels of fire activity (fire cycle, FC = 43 years, with the central 90% of the distribution limited by 35 to 57 years) dominated in the earlier period (1480–1690 AD) that was followed by a strong decrease in fire activity since 1700 (FC = 403 years, with 90% of the distribution being within 149 to 7308 years), with a fire-free period between 1756 and 2014. Sala area, therefore, features the earliest known onset of fire suppression in Scandinavia. The high demand for timber during the peak in mining activities in the study area around the 1700–1800s, accompanied by passive fire suppression policies, were possibly the main drivers of the decline in fire activity. Superposed epoch analysis (SEA) did not show significant departures in the drought proxy during the ten years with the largest area burned between 1480 and1690. It is unclear whether the result is due to the relatively small area sampled or an indication that human controls of fires dominated during that period. However, significant departures during the following period with low fire activity (1700–1756), which just preceded the last fire-free period, suggested that the climate became an increasingly important driver of fire during the onset of the suppression period. We speculate that the lack of major firebreaks, the homogenization of forests, and the lack of burned areas with low fuel loads might contribute to the occurrence of the exceptionally large 2014 fire in Sala.

Forest fire occurrence and climate change in Canada

2010 ◽  
Vol 19 (3) ◽  
pp. 253 ◽  
Author(s):  
B. M. Wotton ◽  
C. A. Nock ◽  
M. D. Flannigan
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Forest Fire ◽  
Forest Fires ◽  
General Circulation ◽  
Fire Regime ◽  
General Circulation Models ◽  
Fire Occurrence ◽  
Fire Activity ◽  
In Fire

The structure and function of the boreal forest are significantly influenced by forest fires. The ignition and growth of fires depend quite strongly on weather; thus, climate change can be expected to have a considerable impact on forest fire activity and hence the structure of the boreal forest. Forest fire occurrence is an extremely important element of fire activity as it defines the load on suppression resources a fire management agency will face. We used two general circulation models (GCMs) to develop projections of future fire occurrence across Canada. While fire numbers are projected to increase across all forested regions studied, the relative increase in number of fires varies regionally. Overall across Canada, our results from the Canadian Climate Centre GCM scenarios suggest an increase in fire occurrence of 25% by 2030 and 75% by the end of the 21st century. Results projected from fire climate scenarios derived from the Hadley Centre GCM suggest fire occurrence will increase by 140% by the end of this century. These general increases in fire occurrence across Canada agree with other regional and national studies of the impacts of climate change on fire activity. Thus, in the absence of large changes to current climatic trends, significant fire regime induced changes in the boreal forest ecosystem are likely.

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