scholarly journals Modern calibration and historical testing of small-area, fire-interval reconstruction methods

2014 ◽  
Vol 23 (1) ◽  
pp. 58 ◽  
Author(s):  
Alexa J. Dugan ◽  
William L. Baker
Keyword(s):  
Ponderosa Pine ◽  
Small Area ◽  
Grand Canyon ◽  
Estimation Methods ◽  
Interval Methods ◽  
Interval Method ◽  
Population Mean ◽  
Ponderosa Pine Forests ◽  
Reconstruction Methods ◽  
Fire Intervals

Accuracy of small-area, fire-interval estimation methods has been inadequately assessed, thus we conducted modern calibration and historical testing of the traditional composite-fire-interval and a newer all-tree-fire-interval method for estimating population mean fire intervals. We tested in eight areas, at four scales, using 30 small plots across ponderosa pine forests on the South Rim of Grand Canyon National Park. In modern calibration, individual-plot all-tree-fire-intervals were equal to population mean fire intervals in all plots. Across the eight areas, a mean-plot version of the all-tree-fire-interval method never failed, whereas mean-plot versions of composite-fire-intervals failed in 37.5–100% of areas. Pooled composite-fire-intervals, the traditional method, failed in all subareas. In historical testing, pooled and mean-plot all-tree-fire-interval methods and two variations of a mean-plot composite-fire-interval method had the lowest mean relative errors. Again, pooled composite-fire-intervals performed poorly across the eight areas. Overall, in modern and historical tests, the mean-plot all-tree-fire-interval method outperformed all others, but highly filtered mean-plot composite-fire-intervals were fairly accurate in historical tests. Both could be reliable methods, if replicated in small plots averaged over 600–1000-ha landscapes, but for small areas, the all-tree-fire-interval method outperformed others. However, for general use, there may be more value in spatially explicit, landscape-scale methods, rather than any small-area method.

Fire histories in ponderosa pine forests of Grand Canyon are well supported: reply to Baker

2006 ◽  
Vol 15 (3) ◽  
pp. 439 ◽  
Author(s):  
Peter Z. Fulé ◽  
Thomas A. Heinlein ◽  
W. Wallace Covington
Keyword(s):  
Ponderosa Pine ◽  
Grand Canyon ◽  
Pine Forests ◽  
Fire Scars ◽  
Surface Fire ◽  
Ponderosa Pine Forests ◽  
Fire Scar ◽  
Study Sites ◽  
Fire Intervals ◽  
Old Trees

Fire scars and other paleoecological methods are imperfect proxies for detecting past patterns of fire events. However, calculations of long fire rotations in Grand Canyon ponderosa pine forests by Baker are not convincing in methodology or assumptions compared with fire-scar evidence of frequent surface fires. Patches of severe disturbance are a possible hypothesis to explain the relatively short age structure at the park, where ~12% fewer trees were older than 300 years compared with another unharvested northern Arizona site. However, mapped patterns of old trees as well as the evidence for frequent surface fire from fire scars, charcoal deposition studies, and evolutionary history are more consistent with the dominance of surface fire prior to c. 1880. The most relevant available evidence of fire recurrence at a given point, mean point fire intervals, had median values <16 years at all five study sites, close to filtered composite fire interval statistics (~6–10 years), but much lower than Baker’s calculated fire rotation values (55–110 years). The composite fire interval is not a uniquely important statistic or a numerical guideline for management, but one of many lines of evidence underscoring the ecological role of frequent surface fire in ponderosa pine forests.

scholarly journals Uncertainty in surface-fire history: the case of ponderosa pine forests in the western United States

2001 ◽  
Vol 31 (7) ◽  
pp. 1205-1226 ◽  
Author(s):  
William L Baker ◽  
Donna Ehle
Keyword(s):  
United States ◽  
Forest Structure ◽  
Ponderosa Pine ◽  
Fire History ◽  
Pine Forests ◽  
Western United States ◽  
Surface Fire ◽  
Population Mean ◽  
Ponderosa Pine Forests ◽  
Fire Scar

Present understanding of fire ecology in forests subject to surface fires is based on fire-scar evidence. We present theory and empirical results that suggest that fire-history data have uncertainties and biases when used to estimate the population mean fire interval (FI) or other parameters of the fire regime. First, the population mean FI is difficult to estimate precisely because of unrecorded fires and can only be shown to lie in a broad range. Second, the interval between tree origin and first fire scar estimates a real fire-free interval that warrants inclusion in mean-FI calculations. Finally, inadequate sampling and targeting of multiple-scarred trees and high scar densities bias mean FIs toward shorter intervals. In ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) forests of the western United States, these uncertainties and biases suggest that reported mean FIs of 2-25 years significantly underestimate population mean FIs, which instead may be between 22 and 308 years. We suggest that uncertainty be explicitly stated in fire-history results by bracketing the range of possible population mean FIs. Research and improved methods may narrow the range, but there is no statistical or other method that can eliminate all uncertainty. Longer mean FIs in ponderosa pine forests suggest that (i) surface fire is still important, but less so in maintaining forest structure, and (ii) some dense patches of trees may have occurred in the pre-Euro-American landscape. Creation of low-density forest structure across all parts of ponderosa pine landscapes, particularly in valuable parks and reserves, is not supported by these results.

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

scholarly journals Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

2021 ◽  
Vol 10 (3) ◽  
pp. 157
Author(s):  
Paul-Mark DiFrancesco ◽  
David A. Bonneau ◽  
D. Jean Hutchinson
Keyword(s):  
Computational Geometry ◽  
Surface Reconstruction ◽  
Power Law ◽  
Point Cloud ◽  
Point Clouds ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Small Scale ◽  
Reconstruction Methods ◽  
3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

Fire-induced erosion and millennial-scale climate change in northern ponderosa pine forests

Nature ◽  
2004 ◽  
Vol 432 (7013) ◽  
pp. 87-90 ◽  
Author(s):  
Jennifer L. Pierce ◽  
Grant A. Meyer ◽  
A. J. Timothy Jull
Keyword(s):  
Climate Change ◽  
Ponderosa Pine ◽  
Pine Forests ◽  
Ponderosa Pine Forests ◽  
Millennial Scale

Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

2021 ◽  
Author(s):  
Matthew B. Creasy ◽  
Wade Travis Tinkham ◽  
Chad M. Hoffman ◽  
Jody C. Vogeler
Keyword(s):  
Forest Structure ◽  
Ponderosa Pine ◽  
Laser Scanning ◽  
Point Clouds ◽  
System Structure ◽  
Detection Methods ◽  
Unmanned Aerial System ◽  
Individual Tree ◽  
Detection Rates ◽  
Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

BREEDING BIOLOGY OF THE CHIPPING SPARROW IN PONDEROSA PINE FORESTS OF THE COLORADO FRONT RANGE

2004 ◽  
Vol 116 (3) ◽  
pp. 246-251 ◽  
Author(s):  
HEATHER M. SWANSON ◽  
BREANNA KINNEY ◽  
ALEXANDER CRUZ
Keyword(s):  
Ponderosa Pine ◽  
Breeding Biology ◽  
Pine Forests ◽  
Colorado Front Range ◽  
Front Range ◽  
Ponderosa Pine Forests

scholarly journals District level child nutrition status in Bangladesh: An application of small area estimation method

2019 ◽  
Vol 53 (1) ◽  
pp. 45-61
Author(s):  
Mossamet Kamrun Nesa
Keyword(s):  
Small Area ◽  
Child Nutrition ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Nutrition Status ◽  
Estimation Methods ◽  
Small Scale ◽  
Demographic Health Survey ◽  
Area Estimation ◽  
National Surveys

National level indicators of child undernutrition often hide the real scenario across a country. In order to construct a child nutrition map, accurate estimates of undernutrition are required at very small spatial scales, typically the administrative units of a country or a region within a country. Although comprehensive data on child nutrition are collected in national surveys, the small scale estimates cannot be calculated using the standard estimation methods employed in national surveys, since such methods are designed to produce national or regional level estimates, and assume large samples. Small area estimation method has been widely used to find such micro-level estimates. Due to lack of unit level data, area level small area estimation methods (e.g., Fay-Herriot method) are widely used to calculate small-scale estimates. In Bangladesh, a few works have been done to estimate district level child nutrition status. The Bangladesh Demographic Health Survey covers all districts but district wise sample sizes are very small to get consistent estimates. In this paper, Fay-Herriot Model has been developed to calculate district wise estimates with efficient mean squared error. The Bangladesh Demographic Health Survey 2011 and Population Census 2011 are utilized for this study.

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