Modeling Percent Tree Canopy Cover

Coulston, John W.; Moisen, Gretchen G.; Wilson, Barry T.; Finco, Mark; Cohen, Warren B.; Brewer, C. Kenneth

doi:10.14358/pers.78.7.715

Cited by 230 publications

(106 citation statements)

References 23 publications

Supporting

Mentioning

104

Contrasting

Unclassified

Order By: Relevance

“…Nonetheless, decision trees are used more frequently in classification than in regression applications. Only lately the random forests approach gains popularity in applications with mapping of a diverse range of vegetation attributes e.g., biomass (Le Maire et al, 2011;Mutanga et al, 2012;Adam et al, 2014;Vaglio Laurin et al, 2014), canopy cover (Coulston et al, 2012;Gessner et al, 2013), LAI (Vuolo et al, 2013) and canopy nitrogen (Li et al, 2014). These studies typically demonstrate the higher efficiency of the random forests method compared with the more conventional parametric and linear non-parametric methods.…”

Section: Decision Tree Learningmentioning

confidence: 99%

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Verrelst

Camps‐Valls

Muñoz-Marí

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

543

428

View full text Add to dashboard Cite

Section: Decision Tree Learningmentioning

confidence: 99%

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Verrelst

Camps‐Valls

Muñoz-Marí

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

543

428

View full text Add to dashboard Cite

“…Validation of the NLCD canopy cover model with canopy cover values from field data by Coulston et al . () showed an R 2 value of 0.8 in the southeastern United States. LANDFIRE s‐class data are based on LANDFIRE vegetation height data, which have a spatial bias of 3.8%, and LANDFIRE cover data had an overall agreement of 74% when compared with field data across the U.S. (Toney et al ., ).…”

Section: Methodsmentioning

confidence: 99%

Projected gains and losses of wildlife habitat from bioenergy‐induced landscape change

et al. 2016

View full text Add to dashboard Cite

Domestic and foreign renewable energy targets and financial incentives have increased demand for woody biomass and bioenergy in the southeastern United States. This demand is expected to be met through purposegrown agricultural bioenergy crops, short-rotation tree plantations, thinning and harvest of planted and natural forests, and forest harvest residues. With results from a forest economics model, spatially explicit state-and-transition simulation models, and species-habitat models, we projected change in habitat amount for 16 wildlife species caused by meeting a renewable fuel target and expected demand for wood pellets in North Carolina, USA. We projected changes over 40 years under a baseline 'business-as-usual' scenario without bioenergy production and five scenarios with unique feedstock portfolios. Bioenergy demand had potential to influence trends in habitat availability for some species in our study area. We found variation in impacts among species, and no scenario was the 'best' or 'worst' across all species. Our models projected that shrub-associated species would gain habitat under some scenarios because of increases in the amount of regenerating forests on the landscape, while species restricted to mature forests would lose habitat. Some forest species could also lose habitat from the conversion of forests on marginal soils to purpose-grown feedstocks. The conversion of agricultural lands on marginal soils to purpose-grown feedstocks increased habitat losses for one species with strong associations with pasture, which is being lost to urbanization in our study region. Our results indicate that landscape-scale impacts on wildlife habitat will vary among species and depend upon the bioenergy feedstock portfolio. Therefore, decisions about bioenergy and wildlife will likely involve trade-offs among wildlife species, and the choice of focal species is likely to affect the results of landscape-scale assessments. We offer general principals to consider when crafting lists of focal species for bioenergy impact assessments at the landscape scale.

show abstract

“…The overall NLCD 2011 database philosophy and methodology is presented in Homer et al (2015). Coulston et al (2012) describe the methodology used to map canopy density for TCC 2011. Data are free to download and are available at: http://www.mrlc.gov/nlcd2011.php.…”

Section: Stratification At Nrs-fiamentioning

confidence: 99%

Statistics and quality assurance for the Northern Research Station Forest Inventory and Analysis Program, 2016

Gormanson¹,

Pugh²,

Barnett³

et al. 2017

View full text Add to dashboard Cite

The U.S. Forest Service Forest Inventory and Analysis (FIA) program collects sample plot data on all forest ownerships across the United States. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the Nation's forest land through a comprehensive inventory and analysis of the Nation's forest resources. The FIA program strives for transparency by making the methods and results of the inventory and analysis available to the public. The standard for distributing FIA data is the FIADatabase (FIADB). FIADB data for individual states can be downloaded from the FIA DataMart at https://www. fia.fs.fed.us/tools-data/ as Microsoft Access© databases, which can be used to generate estimates of forest area, number of trees, volume, biomass, growth, removals, and mortality. This report complements the Northern Research Station's FIA 5-year state reports beginning with the 2014 inventory and includes detailed information on forest inventory methods, important resource statistics, quality of estimates, and key references.

show abstract

Modeling Percent Tree Canopy Cover

Cited by 230 publications

References 23 publications

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Projected gains and losses of wildlife habitat from bioenergy‐induced landscape change

Statistics and quality assurance for the Northern Research Station Forest Inventory and Analysis Program, 2016

Contact Info

Product

Resources

About