Diagnosing pristine pine forest development through pansharpened-surface-reflectance Landsat image derived aboveground biomass productivity

Lin, Chinsu; Lee, Young Jin; Lumbres, Roscinto Ian C.; Daipan, Bernard Peter O.; Bayer, Derek C.; Parian, Christine P.

doi:10.1016/j.foreco.2021.119011

Cited by 10 publications

(8 citation statements)

References 75 publications

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“…The amount of fine surface fuels of the 120 level-2 subplots in an area of 10 × 10 m 2 is shown in Table 2 (hereafter a pixel) and was aggregated from the cokriging-derived level-1 SFL map. As can be seen, the pine stands had fSFL values that ranged from 73.71 kg/pixel to 149.75 kg/pixel, with an average of 107.32 ± 24.56 kg/pixel greater than 99.58 ± 56.11 Pine Model * Gaus (1) Exp (2) Exp (3) Exp (3) Exp (4) Spher (1) Exp ( 4) Exp ( 4 (4) Exp (4) Exp (4) Exp (1) Exp (1) Exp (1) Exp (2) Exp (4)…”

Section: The Level-2 Subplot-based Fsfl-biotopo Models and Their Performance In Generating The Fsfl Map Of The Whole Forestmentioning

confidence: 99%

“…Model * Gaus (1) Exp (2) Exp (3) Exp (3) Exp (4) Spher (1) Exp (4) Exp (4) (4) Exp (4) Exp (4) Exp (1) Exp (1) Exp (1) Exp (2) Exp (4) Model * Exp (3) Exp (5) Exp (2) Cir (4) Exp (4) Exp (3) Exp (6) x Model * Exp (2) Exp (4) Exp (1) Exp (5) Exp (4) Exp (4) Exp (2)…”

Section: Pineunclassified

“…Model * Exp (3) Exp (5) Exp (2) Cir (4) Exp (4) Exp (3) Exp (6) x Model * Exp (2) Exp (4) Exp (1) Exp (5) Exp (4) Exp (4) Exp (2) x 2), ( 3), ( 4), ( 5), and ( 6) after the type of semivariogram model indicate a combination of secondary variables used in deriving that semivariogram model. Correspondingly, the codes represent slope, slope-aspect, slopefuel bed depth, fuel bed depth, slope-aspect-fuel bed depth, and aspect-fuel bed depth, respectively.…”

Section: Mixed Xmentioning

confidence: 99%

“…Gaus (1) Exp (2) Exp (3) Exp (3) Exp (4) Spher (1) Exp ( 4) Exp ( 4 Pine Model * Gaus (1) Exp (2) Exp (3) Exp (3) Exp (4) Spher (1) Exp ( 4) Exp ( 4 (4) Exp (4) Exp (4) Exp (1) Exp (1) Exp (1) Exp (2) Exp (4)…”

Section: Model *mentioning

confidence: 99%

“…Naturally occurring wildfires caused by lightning or extreme climate events (a long dry season or drought and high temperature) are generally inevitable. Fires are also frequently used to clear forest for large-scale plantation establishment [1] and small-scale agricultural uses [2][3][4]. Although rapid warming has recently resulted in more wildfires worldwide, human activities have been recognized as the major causes of wildfire [5].…”

Section: Introductionmentioning

confidence: 99%

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Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Lin

Huang

et al. 2021

Remote Sensing

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Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.

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