Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park

Kane, Van R.; North, Malcolm P.; Lutz, James A.; Churchill, Derek J.; Roberts, Susan L.; Smith, Douglas; McGaughey, Robert J.; Kane, Jonathan T.; Brooks, Matthew L.

doi:10.1016/j.rse.2013.07.041

Cited by 124 publications

(98 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Subplot variability suggests that upscaling plot-based measures to the stand scale (as done to populate a landscape from plots for a 2-dimensional fire model) may be inaccurate and points to the necessity for a remote-sensing derived, landscape-scale ladder fuel metric that quantifies heterogeneity within stands. Our work demonstrates the potential application of LiDAR for quantifying ladder fuels, which has already been used to describe forest structure beneath the canopy [37,39,43]. Despite our fairly successful findings, questions concerning appropriate measurement scale and ladder fuel variability and clumpiness still need to be addressed.…”

Section: Discussionmentioning

confidence: 86%

See 1 more Smart Citation

Quantifying Ladder Fuels: A New Approach Using LiDAR

Kramer

Collins

Kelly

et al. 2014

Forests

View full text Add to dashboard Cite

Abstract:We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance, ladder fuels are difficult to quantify. One common approach is to calculate canopy base height, but this has many potential sources of error. LiDAR may be a way forward to better characterize ladder fuels, but has only been used to address this question peripherally and in only a few instances. After establishing that landscape fuel treatments reduced canopy and ladder fuels at our site, we tested which LiDAR-derived metrics best differentiated treated from untreated areas. The percent cover between 2 and 4 m had the most explanatory power to distinguish treated from untreated pixels across a range of spatial scales. When compared to independent plot-based measures of ladder fuel classes, this metric differentiated between high and low levels of ladder fuels. These findings point to several immediate applications for land managers and suggest new avenues of study that could lead to possible improvements in the way that we model wildfire behavior across forested landscapes in the US. OPEN ACCESSForests 2014, 5 1433

show abstract

Section: Discussionmentioning

confidence: 86%

“…Light Detection and Ranging (LiDAR) is a remote sensing technology that has proven well-suited to account for forest structure when collected from the ground or the air [37][38][39][40][41][42][43][44]. Terrestrial LiDAR has been used to estimate canopy height, canopy cover, CBH, and fuel strata gap on the plot scale [44].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Ladder Fuels: A New Approach Using LiDAR

Kramer

Collins

Kelly

et al. 2014

Forests

View full text Add to dashboard Cite

show abstract

“…Por ejemplo, para el análisis de los cambios rápidos y graduales en la cobertura vegetal, en el seguimiento de procesos ambientales; como la degradación y la desertificación (Almeida-Filho y Shimabukuro, 2002), la deforestación (Huang et al, 2007(Huang et al, , 2008(Huang et al, , 2010, la recuperación de la vegetación después de perturbaciones naturales (Lawrence y Ripple, 1999) e incendios forestales (Viedma et al, 1997;Roder et al, 2008;Jong et al, 2010;Houghton, 2012;Vasconcelos et al, 2013;Goodwin y Collett, 2014;Kane et al, 2013).…”

Section: Calibración Radiométrica De Imágenes De Satélite Landsatunclassified

Implementación y evaluación del modelo Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS): estudio de caso en los Andes colombianos

2016

View full text Add to dashboard Cite

Resumen: En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesadas con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos.Palabras clave: LEDAPS, Landsat, corrección atmosférica, prueba exacta de Wilcoxon-Mann-Whitney, modelo de Huber, regresión por mínimas desviaciones absolutas, mínimos cuadrados recortados, Bootstrap. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian AndesAbstract: This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1-5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling.

show abstract

“…Despite its success in identifying downed logs, the OBIA method requires significant analyst interpretation for classification, and so it can be considered a complement to fieldbased methods but not a replacement for them. Kane, North et al (2013) used Lidar combined with Landsat data to examine the ecological relationships between differences in fire severity and the spatial structures of forests, defined as tree clumps and openings, for three forest types (ponderosa pine, white fir and sugar pine, and red fir). A complementary analysis was performed by , focusing on changes in canopy profiles after fires.…”

Section: Sierra Nevada Adaptive Management Projectmentioning

confidence: 99%