scholarly journals Evaluating Model Predictions of Fire Induced Tree Mortality Using Wildfire-Affected Forest Inventory Measurements

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 958 ◽  
Author(s):  
Jason S. Barker ◽  
Jeremy S. Fried ◽  
Andrew N. Gray
Keyword(s):  
Wind Speed ◽  
Forest Inventory ◽  
Tree Mortality ◽  
Forest Type ◽  
Flame Length ◽  
Weather Data ◽  
Fuel Moisture ◽  
Forest Types ◽  
Mean Error ◽  
Wide Range

Forest land managers rely on predictions of tree mortality generated from fire behavior models to identify stands for post-fire salvage and to design fuel reduction treatments that reduce mortality. A key challenge in improving the accuracy of these predictions is selecting appropriate wind and fuel moisture inputs. Our objective was to evaluate postfire mortality predictions using the Forest Vegetation Simulator Fire and Fuels Extension (FVS-FFE) to determine if using representative fire-weather data would improve prediction accuracy over two default weather scenarios. We used pre- and post-fire measurements from 342 stands on forest inventory plots, representing a wide range of vegetation types affected by wildfire in California, Oregon, and Washington. Our representative weather scenarios were created by using data from local weather stations for the time each stand was believed to have burned. The accuracy of predicted mortality (percent basal area) with different weather scenarios was evaluated for all stands, by forest type group, and by major tree species using mean error, mean absolute error (MAE), and root mean square error (RMSE). One of the representative weather scenarios, Mean Wind, had the lowest mean error (4%) in predicted mortality, but performed poorly in some forest types, which contributed to a relatively high RMSE of 48% across all stands. Driven in large part by over-prediction of modelled flame length on steeper slopes, the greatest over-prediction mortality errors arose in the scenarios with higher winds and lower fuel moisture. Our results also indicated that fuel moisture was a stronger influence on post-fire mortality than wind speed. Our results suggest that using representative weather can improve accuracy of mortality predictions when attempting to model over a wide range of forest types. Focusing simulations exclusively on extreme conditions, especially with regard to wind speed, may lead to over-prediction of tree mortality from fire.

scholarly journals Comparison of Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning across Multiple Forest Types in California, USA

2021 ◽  
Vol 13 (21) ◽  
pp. 4292
Author(s):  
James E. Lamping ◽  
Harold S. J. Zald ◽  
Buddhika D. Madurapperuma ◽  
Jim Graham
Keyword(s):  
Forest Inventory ◽  
Low Cost ◽  
Forest Types ◽  
Canopy Density ◽  
Positioning Systems ◽  
Aerial Photogrammetry ◽  
3D Data ◽  
Wide Range ◽  
Forest Attributes ◽  
Forest Conditions

Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74–0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17–0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53–0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions.

scholarly journals A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

2020 ◽  
Author(s):  
Ben. G. Weinstein ◽  
Sarah J. Graves ◽  
Sergio Marconi ◽  
Aditya Singh ◽  
Alina Zare ◽  
...  
Keyword(s):  
Forest Type ◽  
R Package ◽  
Benchmark Dataset ◽  
Sensor Data ◽  
Evaluation Metrics ◽  
Forest Types ◽  
Individual Tree ◽  
Standard Data ◽  
Wide Range ◽  
Crown Delineation

AbstractBroad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Network’s Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.

Assessment of Disturbances across Forest Inventory Plots in the Southeastern United States for the Period 1995–2018

2019 ◽  
Vol 66 (2) ◽  
pp. 242-255 ◽  
Author(s):  
Santosh K Ojha ◽  
Kozma Naka ◽  
Luben D Dimov
Keyword(s):  
United States ◽  
Coastal Plain ◽  
Forest Inventory ◽  
Stand Structure ◽  
Forest Type ◽  
Southeastern United States ◽  
Forest Types ◽  
Forest Inventory And Analysis ◽  
Silvicultural Treatments ◽  
Structure And Functions

Abstract Disturbances of varying frequency and intensity shape the species composition, stand structure, and functions of forests. This study assessed the frequency and distribution of disturbances caused by eight agents (insects, diseases, fire, animals, weather, other vegetation, human, and unknown) in the forests of the southeastern United States from 1995 to 2018. We used data from 88,722 inventory measurements of 33,531 plots from the USDA Forest Inventory and Analysis database to assess disturbance among different forest types and to different canopy strata. Disturbances were detected in approximately 14 percent of the plots, located mostly in pine-dominated forest types. Fire was the most frequent disturbance agent (occurring 6 percent of the time), followed by weather and animal agents. The agents that caused the highest mortality rate during the period for saplings were silvicultural treatments (8.6 percent), other vegetation (5.6 percent), and fire (4.4 percent), whereas for trees they were silvicultural treatments (9.8 percent), weather (1.9 percent) and insects (1.7 percent). The forest type that appeared to have been most affected by disturbances was longleaf–slash pine of the Coastal Plain. These results are useful for understanding the spatiotemporal distribution of disturbance events in different southeastern forest types and locations and for guiding forest management activities to mitigate potential impacts.

scholarly journals Prototipe Sistem Peringatan Dini Kebakaran Hutan Berbasis Parameter Cuaca

Wahana Fisika ◽  
2017 ◽  
Vol 2 (2) ◽  
pp. 65
Author(s):  
Hapsoro Agung Nugroho ◽  
Chinthya Margaretta S
Keyword(s):  
Wind Speed ◽  
Early Warning ◽  
Forest Fires ◽  
Fire Danger ◽  
World Meteorological Organization ◽  
Rating System ◽  
Weather Data ◽  
Fuel Moisture ◽  
Fire Danger Rating System ◽  
Sd Card

Sistem peringatan dini kebakaran hutan memiliki peranan penting untuk mengendalikan secara dini kerusakan hutan. Badan Meteorologi Klimatologi dan Geofisika mempunyai tugas pokok, salah satunya yaitu memberikan peringatan dini kebakaran hutan menggunakan metode Fire Danger Rating System (FDRS) dimana data parameter cuaca sebagai masukan, masih terbatas pada lokasi tertentu. Penelitian ini merancang dan membangun prototipe yang menghasilkan skala Fine Fuel Moisture Code (FFMC) sebagai tingkat kemudahan terjadinya kebakaran di suatu lokasi. Perancangan prototipe ini menggunakan mikrokontroler ATMega328, sensor suhu dan kelembaban udara DHT22, penakar hujan jenis tipping bucket, sensor arah dan kecepatan angin JL_FS2, dan micro SD Card sebagai penyimpan data. Hasil kalibrasi sensor menunjukkan adanya selisih nilai sensor yang telah memenuhi nilai toleransi dari World Meteorological Organization (WMO). Pengujian setiap sensor menghasilkan nilai standar deviasi kurang dari 2.5 dengan rata- rata selisih pada sensor suhu +0.5oC, kelembaban relatif +6%, dan kecepatan angin +2 m/s. Setiap data yang diolah dapat ditampilkan dan disimpan secara otomatis oleh sistem. Sistem menampilkan secara realtime dan memberikan informasi peringatan dini kebakaran hutan. Kata Kunci   :  Kebakaran Hutan; FDRS; FFMC; Tipping BucketForest fire early warning system has an important role for the control of early damage to the forest. Indonesia Agency of Meteorology Climatology and Geophysics had a duty, one that is giving early warning forest fires using the method of Fire Danger Rating System (FDRS) where weather data as the input parameters, are still limited on site certain. The study design and build a prototype that generates scale Fine Fuel Moisture Code (FFMC) as the level of ease the onset of fire in any given location. This prototype design using the ATMega328 microcontroller, sensor temperature and humidity DHT22, tipping bucket type of rain gauge, direction and wind speed sensor JL_FS2, and micro SD Card as the data storage. The results showed a difference in sensor calibration value of sensor meets the tolerance values of the World Meteorological Organization (WMO). Test each sensor shows a value less than 2.5 standard deviation by the average difference in temperature sensors + 0.5 oC, + 6% relative humidity, and wind speed + 2 m/s. Data can be displayed and stored automatically by the system. The system displays in realtime and provide early warning information forest fires.           Keywords  :  Forest Fire; FDRS; FFMC; Tipping Bucket

Fuel moisture, forest type, and lightning-caused fire in Yellowstone National Park

1992 ◽  
Vol 22 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Roy A. Renkin ◽  
Don G. Despain
Keyword(s):  
Yellowstone National Park ◽  
National Park ◽  
Forest Cover ◽  
Forest Type ◽  
Fire Season ◽  
Fuel Moisture ◽  
Forest Types ◽  
Forest Cover Type ◽  
Fire Activity ◽  
Moisture Conditions

The occurrence and behavior of lightning-caused fires in Yellowstone National Park were summarized for 17 years (1972–1988) during a prescribed natural fire program. Both ignition (occurrence) and spread (stand replacing fire activity) of fires were strongly influenced by fuel moisture and forest cover type. Fuel moisture estimates of 13% for large (>7.6 cm) dead and downed fuels indicated a threshold below which proportionately more fire starts and increased stand replacing fire activity were observed. During periods of suitable fuel moisture conditions, fire occurrence and activity were significantly greater than expected in old-growth, mixed-canopy lodgepole pine (Pinuscontorta Dougl. var. latifolia) and Engelmann spruce–subalpine fir (Piceaengelmannii Parry–Abieslasiocarpa (Hook.) Nutt.) forest types, and significantly less than expected in the successional lodgepole pine forest types. During periods of extended low fuel moisture conditions (drought), sustained high winds significantly reduced the influence of forest cover type on stand replacing fire activity. These extreme weather conditions were observed during the later stages of the 1988 fire season, and to a lesser extent, for a short duration during the 1981 fire season. The Douglas-fir (Pseudotsugamenziesii (Mirb.) Franco) forest type typically supported little stand replacing fire activity, even though a preponderance of fire starts was observed.

scholarly journals Impacts of Forest Thinning on Wildland Fire Behavior

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 918 ◽  
Author(s):  
Tirtha Banerjee
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Severity ◽  
Fire Behavior ◽  
Fire Intensity ◽  
Fuel Moisture ◽  
Forest Thinning ◽  
Rate Of Spread ◽  
Wide Range ◽  
Actual Fire

Key message: We have explored the impacts of forest thinning on wildland fire behavior using a process based model. Simulating different degrees of thinning, we found out that forest thinning should be conducted cautiously as there could be a wide range of outcomes depending upon the post-thinning states of fuel availability, fuel connectivity, fuel moisture and micrometeorological features such as wind speed. Context: There are conflicting reports in the literature regarding the effectiveness of forest thinning. Some studies have found that thinning reduces fire severity, while some studies have found that thinning might lead to enhanced fire severity. Aims: Our goal was to evaluate if both of these outcomes are possible post thinning operations and what are the limiting conditions for post thinning fire behavior. Methods: We used a process based model to simulate different degrees of thinning systematically, under two different conditions, where the canopy fuel moisture was unchanged and when the canopy fuel moisture was also depleted post thinning. Both of these scenarios are reported in the literature. Results: We found out that a low degree of thinning can indeed increase fire intensity, especially if the canopy fuel moisture is low. A high degree of thinning was effective in reducing fire intensity. However, thinning also increased rate of spread under some conditions. Interestingly, both intensity and rate of spread were dependent on the competing effects of increased wind speed, fuel loading and canopy fuel moisture. Conclusion: We were able to find the limits of fire behavior post thinning and actual fire behavior is likely to be somewhere in the middle of the theoretical extremes explored in this work. The actual fire behavior post thinning should depend on the site specific conditions which would determine the outcome of the interplay among the aforementioned conditions. The work also highlights that policymakers should be careful about fine scale canopy architectural attributes and micrometeorological aspects when planning fuel treatment operations.

scholarly journals Carbon estimation using sampling to correct LiDAR-assisted enhanced forest inventory estimates

2020 ◽  
Vol 96 (01) ◽  
pp. 9-19 ◽  
Author(s):  
Yingbing Chen ◽  
John A. Kershaw ◽  
Yung-Han Hsu ◽  
Ting-Ru Yang
Keyword(s):  
Forest Inventory ◽  
Carbon Budget ◽  
Forest Type ◽  
Woody Debris ◽  
Light Detection And Ranging ◽  
Forest Sector ◽  
Ratio Estimator ◽  
Standing Tree ◽  
Forest Types ◽  
Carbon Budget Model

Light Detection and Ranging (LiDAR) scanning has been increasingly applied in forest ecosystem surveys. Data from LiDAR describe forest structure and provide attribute information for forest inventory. These attributes can potentially aid in the estimation of biomass and carbon by providing sampling covariates. Therefore, this study explored the accuracy of estimating carbon storage by calibrating LiDAR attributes using list sampling with a ratio estimator. Standing tree carbon and down woody debris carbon were estimated across 10 broad forest types. LiDAR-derived gross total volume was used as a listing factor and big BAF samples to collect field data. Gross total volumes were “corrected” using a ratio estimator. The results show that standing tree carbon was 58.5 Mg C × ha-1 (± 2.9% SE), and dead woody debris carbon 1.8 Mg C × ha-1 (± 7.2% SE). With the exception of one forest type, these estimates were comparable to those derived from the carbon budget model of the Canadian forest sector (CBM-CFS3).

Using a Geographic Information System to Plan a Forest Inventory that Respects the Spatial Distribution of Forest Strata

1993 ◽  
Vol 10 (4) ◽  
pp. 161-165
Author(s):  
Jean-Marie Bilodeau ◽  
Yvan Bédard ◽  
Kim Lowell
Keyword(s):  
Information System ◽  
Spatial Distribution ◽  
Geographic Information System ◽  
Sample Size ◽  
Forest Inventory ◽  
Forest Type ◽  
Field Work ◽  
Geographic Information ◽  
Forest Types ◽  
Forest Strata

Abstract Existing forest inventory techniques stratify a territory into forest types, determine a global sample size for the entire territory, and then partition this sample size by forest type. Sampling units are then located within the appropriate stratum in a fashion that will allow the field work to be conducted "efficiently." This final step inevitably leads to adjustments in the number of units per stratum and also does not account for the spatial distribution of a given stratum over an area. This paper demonstrates how, with the aid of a geographic information system, one can obtain a sample that is "adequately distributed" spatially in addition to being statistically representative. North. J. Appl. For. 10(4):161-165.

Fire modelling in Tasmanian buttongrass moorlands. IV. Sustaining versus non-sustaining fires

2001 ◽  
Vol 10 (2) ◽  
pp. 255 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole ◽  
Adrian Pyrke
Keyword(s):  
Logistic Regression ◽  
Wind Speed ◽  
Marginal Utility ◽  
Classification Tree ◽  
Fuel Moisture ◽  
Site Productivity ◽  
Fire Modelling ◽  
Wide Range ◽  
Water Courses ◽  
Will Self

Buttongrass moorlands are widespread in western Tasmania. In these moorlands, the ability to conduct burning without having to rely on hard fuel boundaries (e.g. vegetation which is too wet to burn, water courses, mineral earth breaks and/or roads) would be a major advantage to land managers. Such burning relies on fires self-extinguishing and is normally referred to as unbounded burning. The aim of this project was to model the probability of fires extinguishing using the data from 156 buttongrass moorland fires. The variables used were wind speed, dead fuel moisture and site productivity. The model, derived from a combination of logistic regression and classification tree modelling, predicts that fires will self-extinguish over a wide range of conditions in low productivity moorlands but, in medium productivity moorlands, the conditions within which fires will self-extinguish will be much more restrictive. As a result, the technique of unbounded burning should be widely applicable in low productivity moorlands, but will be of marginal utility in medium productivity moorlands.

Are Current Seedling Demographics Poised to Regenerate Northern US Forests?

2019 ◽  
Vol 117 (6) ◽  
pp. 592-612 ◽  
Author(s):  
Lance A Vickers ◽  
William H McWilliams ◽  
Benjamin O Knapp ◽  
Anthony W D’Amato ◽  
Daniel C Dey ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Tree Mortality ◽  
Sustainable Forest Management ◽  
Mortality Rates ◽  
Lower Proportion ◽  
Forest Types ◽  
Small Tree ◽  
Us States ◽  
Canopy Removal ◽  
Canopy Species

Abstract Securing desirable regeneration is essential to sustainable forest management, yet failures are common. Detailed seedling measurements from a forest inventory across 24 northern US states were examined for plausible regeneration outcomes following overstory removal. The examination included two fundamental regeneration objectives: 1) stand replacement- securing future forest and 2) species maintenance- securing upper canopy species. Almost half the plots lacked adequate seedlings to regenerate a stand after canopy removal and over half risked compositional shifts. Based on those advance reproduction demographics, regeneration difficulties could occur on two-thirds of the plots examined. The remaining one-third were regeneration-ready. However, compared to historical norms, increased small-tree mortality rates reduces that proportion. Not all forest types rely on advance reproduction and results varied among the forest types examined. Some variability was associated with browsing intensity, as areas of high deer browsing had a lower proportion of regeneration-ready plots.

Sign in / Sign up

Export Citation Format

Share Document