Forest Structure Classification in the North Columbia Mountains Using the Landsat TM Tasseled Cap Wetness Component

Hansen, M.; Franklin, Steven E.; Woudsma, Clarence; Peterson, Marc

doi:10.1080/07038992.2001.10854916

Cited by 60 publications

(32 citation statements)

References 23 publications

(32 reference statements)

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“…We also derived Tasseled-cap greenness and wetness from Landsat 5 imagery as an additional variable (software: ERDAS 2008 Leica geosystems geospatial imaging, Atlanta, GA). Tasseled-cap transformation variables represent a continuous environmental gradient that is highly correlated with stand age and structural complexity (Hansen et al 2001). Pixels containing high greenness and wetness values are associated with dense vege tation having high leaf area index, while lower values indicate sparsely vegetated areas such as barren areas or regions of snow and ice (White et al 1997, Waring & Running 1998.…”

Section: Methodsmentioning

confidence: 99%

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Cleve¹,

Perrine

Holzman³

et al. 2011

Endang. Species. Res.

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The Sierra Nevada red fox Vulpes vulpes necator is listed as a threatened species under the California Endangered Species Act. It originally occurred throughout California's Cascade and Sierra Nevada mountain regions. Its current distribution is unknown but should be determined in order to guide management actions. We used occurrence data from the only known population, in the Lassen Peak region of northern California, combined with climatic and remotely sensed variables, to predict the species' potential distribution throughout its historic range. These model predictions can guide future surveys to locate additional fox populations. Moreover, they allow us to compare the relative performances of presence-absence (logistic regression) and presence-only (maximum entropy, or Maxent) modeling approaches using occurrence data with potential false absences and geographical biases. We also evaluated the recently revised Maxent algorithm that reduces the effect of geographically biased occurrence data by subsetting background pixels to match biases in the occurrence data. Within the Lassen Peak region, all models had good fit to the test data, with high values for the true skill statistic (76-83%), percent correctly classified (86-92%), and area under the curve (0.94-0.96), with Maxent models yielding slightly higher values. Outside the Lassen Peak region, the logistic regression model yielded the highest predictive performance, providing the closest match to the fox's historic range and also predicting a site where red foxes were subsequently detected in autumn 2010. Subsetting background pixels in Maxent reduced but did not eliminate the effect that geographically biased occurrence data had on prediction results relative to the Maxent model using full background pixels.

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Section: Methodsmentioning

confidence: 99%

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Cleve¹,

Perrine

Holzman³

et al. 2011

Endang. Species. Res.

View full text Add to dashboard Cite

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“…The Tasseled Cap Transformation (TCT) was also calculated [42] using the coefficients derived by Baig et al [43] and then the Tasseled Cap Angle (TCA) and Tasseled Cap Distance (TCD) [44] were computed from the TCT Greenness and Brightness components. These components are informative for describing forest structure [45][46][47][48]. TCA is responsive to vegetation cover and its gradient, and TCD is related to vegetation composition and structure [49].…”

Section: Landsat Data and Processingmentioning

confidence: 99%

“…The models used to estimate the canopy fuel properties also included the NDII as well as the brightness and wetness components of the TCT. Spectral indices including the shortwave infrared have been proved to have higher sensitivity to high LAI values [63,64], whereas TCT components have shown their suitability to infer information on stand age and forest structure [45,46,48], particularly for mature and old growth stands.…”

Section: Canopy Fuel Properties Estimation From Landsat Datamentioning

confidence: 99%

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

et al. 2017

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Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R 2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg· ha −1 , 0.09, and 0.02 kg·m −3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.

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“…e evolution of land cover over time is a topic of extensive research . Habitat suitability and change is an important application of optical imagery in the field of ecology (e.g., Hansen et al, 2001). Natural hazards and geohazards, such as landslides and earthquakes, can also be mapped from satellite imagery (Kargel et al, 2016).…”

Section: Land Covermentioning

confidence: 99%

A Review of Free Optical Satellite Imagery for Watershed-Scale Landscape Analysis

Bevington

Gleason²,

Giroux-Bougard³

et al. 2018

JWSM

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Tyler de Jong is a researcher based in Whitehorse, Yukon with Global Water Futures at McMaster University. Email: tyler.dejong@mcmaster.caWatershed-scale landscape analysis includes many disciplines, including ecological, hydrological, and geographical sciences. e recent proliferation of free optical satellite imagery (FOSI) has changed the possibilities for the monitoring of environmental change at local and global scales. Many reviews exist for discipline-specific remote sensing applications; however, this article seeks to highlight the rapidly growing archive of FOSI and applied tools that can be used by all levels of users. Herein, ten techniques and eight applications of FOSI are reviewed, along with the specifications and limitations of various sources of FOSI. Although this review focuses on Western Canada, the democratization of FOSI is globally relevant, and the objective is to explain basic concepts via figures and reference materials to help summarize this rapidly changing field. KEYWORDS free optical satellite imagery; remote sensing; watershed science; environmental monitoring; open dataRemote sensing is the science of obtaining information about an object from a distance. is information can be collected from terrestrial, aquatic, aerial, or satellite platforms. Optical imaging sensors aboard these platforms record image data consisting of emitted or reflected electromagnetic energy. Since 2008, the availability of free optical satellite imagery (FOSI) has grown exponentially since the United States Geological Survey (USGS) first opened its image archives (Woodcock et al., 2008). As such, the Earth is currently being imaged many times per day at a variety of spatial resolutions (Malenovský et al., 2012;Roy et al., 2014). e availability of FOSI has enabled the development of free online visualization tools (see Appendix A), free open source remote sensing soware (see Appendix B), and free analysis-ready remote sensing products (see Appendix C). e abundance of FOSI has empowered natural resource managers, researchers, and others to increase, and ultimately improve, the monitoring and quantification of watershed-scale environmental change.Review articles have addressed vegetation mapping (Xie et al., 2008), land cover classification (Phiri & Morgenroth, 2017), forest inventories , and large area mapping techniques (Gómez et al., 2016; Hansen & Loveland, 2012 IntroductionAbstract of: 1) fundamental concepts; 2) common datasets; 3) data portals; 4) processing techniques; and 5) applied examples of watershed-scale remote sensing. e objective is to provide readers new to satellite remote sensing with a comprehensive reference guide explained in plain language, and to provide advanced users with a helpful compilation of reference materials.is section provides a synthesis of fundamental concepts in Earth observation (EO) remote sensing science, with a focus on passive optical sensors that measure and record energy emitted from external sources (typically the sun). Earth observation satellites have ne...

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Forest Structure Classification in the North Columbia Mountains Using the Landsat TM Tasseled Cap Wetness Component

Cited by 60 publications

References 23 publications

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

A Review of Free Optical Satellite Imagery for Watershed-Scale Landscape Analysis

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