Regional rates of young US forest growth estimated from annual Landsat disturbance history and IKONOS stereo imagery

Neigh, C. S. R.; Masek, Jeffrey G.; Bourget, Paul; Rishmawi, Khaldoun; Zhao, Feng; Huang, Chengquan; Cook, Bruce D.; Nelson, Ross

doi:10.1016/j.rse.2015.09.007

Cited by 19 publications

(5 citation statements)

References 43 publications

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“…One use of HRSI is the application of stereogrammetry to estimate surface elevations. Recently, work with this HRSI application has involved detailed surface elevation mapping, characterizing canopy surface elevations, and quantifying height and biomass density in a variety of forests (Baltsavias, Gruen, Eisenbeiss, Zhang, & Waser, 2008;Lagomasino, Fatoyinbo, Lee, & Simard, 2015;Montesano, Sun, Dubayah, & Ranson, 2014;Neigh et al, 2016;2014;Persson, Wallerman, & Olsson, 2013;Poon, Fraser, & Zhang, 2007;Shean et al, 2016;Vega & St-Onge, 2008). The pointing capabilities of HRSI platforms (e.g., QuickBird, IKONOS, GeoEye-1, WorldView-1, -2, -3, & -4) provide along-track (i.e.…”

Section: Hrsi Stereogrammetric Estimates Of Forest Canopy Surfacesmentioning

confidence: 99%

The use of sun elevation angle for stereogrammetric boreal forest height in open canopies

Montesano

Neigh

Sun

et al. 2017

Remote Sensing of Environment

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Section: Hrsi Stereogrammetric Estimates Of Forest Canopy Surfacesmentioning

confidence: 99%

The use of sun elevation angle for stereogrammetric boreal forest height in open canopies

Montesano

Neigh

Sun

et al. 2017

Remote Sensing of Environment

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“…Remote sensing data have the advantage of wide coverage, long time series, and low cost. With the improvement of spatial, temporal, and spectral resolutions, it provides an effective solution for accurate vegetation classification and altitudinal belt analysis [11][12][13]. However, previous studies often relied on medium-or low-resolution remote sensing data [12,14], treating forests as one category [8].…”

Section: Introductionmentioning

confidence: 99%

Vertical Characteristics of Vegetation Distribution in Wuyishan National Park Based on Multi-Source High-Resolution Remotely Sensed Data

Ye,

Lu,

et al. 2023

Remote Sensing

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Identifying vertical characteristics of mountainous vegetation distribution is necessary for studying the ecological environment quality and biodiversity and for evaluating its responses to climate change. However, producing fine vegetation distribution in a complex mountainous area remains a huge challenge. This study developed a framework based on multi-source high-resolution satellite images to strengthen the understanding of vertical features of vegetation distribution. We fused GaoFen-6 and Sentinel-2 data to produce 2 m multispectral data, combined with ALOS PALSAR digital elevation model (DEM) data, and used an object-based method to extract variables for establishing a classification model. The spatial distribution of vegetation types in Wuyishan National Park (WNP) was then obtained using a hierarchical random forest classifier. The characteristics of different vegetation types along the elevation gradient and their distribution patterns under different human protection levels were finally examined. The results show that (1) An overall accuracy of 87.11% and a Kappa coefficient of 0.85 for vegetation classification was achieved. (2) WNP exhibits obviously vertical differentiation of vegetation types, showing four compound dominant zone groups and five dominant belts. (3) The composition of vegetation types in the scenic area differs significantly from other regions. The proportions of Masson pine and Chinese fir exhibit a noticeably decreasing trend as the distance increases away from roads, while the changes in broadleaf forest and bamboo forest are less pronounced.

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“…At the local level, some authors performed spatially explicit biomass estimations with high spatial resolution remotely sensed data, i.e., Ikonos, Quickbird, Geoeye, Worldview. Algorithms used in this context include k-nearest-neighbor (kNN) (Breidenbach et al, 2012;Sun et al, 2015), linear regression modeling (Neigh et al, 2016), and linear regression following Fourier-based textural ordination (Proisy et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…, Ikonos, Quickbird, Geoeye, Worldview. Algorithms used in this context include k -nearest-neighbor (kNN) (Breidenbach et al, 2012; Sun et al, 2015), linear regression modeling (Neigh et al, 2016), and linear regression following Fourier-based textural ordination (Proisy et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Mapping Potential Areas for Conservation Under Forest Carbon Credit Eligibility in a Natural Protected Area in Northern Mexico

Orta-Salazar

Aguirre-Salado

Hernández

et al. 2021

Tropical Conservation Science

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Emerging carbon markets are posing new challenges for stakeholders. In order to operationalize the selection of feasible areas to promote above-ground carbon capture and quantify its potential as claimed in the norm NMX-AA-173-SCFI-2015, a methodology may be proposed. Above-ground tree biomass geospatial estimates at the pixel level, neighboring land cover, and legal information were combined in order to obtain the potential for above-ground carbon capture. Forest above-ground tree carbon capture estimates were conducted under the assumption that forest cover in bare soil land will be recovered to the most conserved neighboring vegetation type. This approach may be used in other situations around the world where potential carbon estimates are required to design forest carbon capture projects considering native vegetation type.

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Regional rates of young US forest growth estimated from annual Landsat disturbance history and IKONOS stereo imagery

Cited by 19 publications

References 43 publications

The use of sun elevation angle for stereogrammetric boreal forest height in open canopies

The use of sun elevation angle for stereogrammetric boreal forest height in open canopies

Vertical Characteristics of Vegetation Distribution in Wuyishan National Park Based on Multi-Source High-Resolution Remotely Sensed Data

Mapping Potential Areas for Conservation Under Forest Carbon Credit Eligibility in a Natural Protected Area in Northern Mexico

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