Natural reforestation on a mile-square clearcut in southeast Alaska / by A.S. Harris

Harris, A. S.

doi:10.5962/bhl.title.88012

Cited by 10 publications

(23 citation statements)

References 1 publication

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“…An optimal neighbourhood for KED-LN is found using a cross-validation procedure that is analogous to that used in GWR for finding its bandwidth (but where the root mean squared error, RMSE, is reported instead). Further details on the calibration of all five study models can be found in Harris et al [37,57]; Harris and Juggins [38].…”

Section: Prediction Modelsmentioning

confidence: 99%

“…The other two predictors stem from the kriging with an external drift (KED) model (e.g., [39]), which, provided it is calibrated within a local neighbourhood (i.e. KED-LN), similarly captures both spatial effects; whilst KED within a global neighbourhood (i.e., KED-GN) only caters for stationary relationships, as with MLR [37,40]. Although this particular model comparison is not novel in the wider literature (e.g., see [37,38]), its use in this study's context is.…”

Section: Introductionmentioning

confidence: 99%

“…However, as autocorrelation effects are also present, we aim to demonstrate a possible improvement to the study by Su et al, (2014) [34] above, by expanding the model comparison from two to five models. One predictor is a hybrid between GWR and kriging (GWRK) [37,38] that captures both spatial effects. The other two predictors stem from the kriging with an external drift (KED) model (e.g., [39]), which, provided it is calibrated within a local neighbourhood (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

et al. 2015

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Abstract:The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other OPEN ACCESSRemote Sens. 2015, 7 13783 applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management.

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Section: Prediction Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

et al. 2015

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“…It is more tolerant of ocean spray than the associated trees species and often occupies positions on exposed headlands and beaches along the outer coast [32,34]. Sitka spruce, a prolific seed producer [35,36], can regenerate on a variety of seedbeds. It is commonly established on mixed mineral soil, e.g., exposed by disturbances such as landslides or logging; it also occurs in riparian zones [37][38][39].…”

Section: Range and Habitat Of Sitka Sprucementioning

confidence: 99%

“…Western hemlock thrives in a mild, humid climate where frequent fog and precipitation occur during the growing season [33,40]. Western hemlock is a prolific seed producer [35,36] and natural regeneration is often dense after logging [36][37][38]41]. Western hemlock is a very shade tolerant species [42] and regenerates abundantly as an understory tree and in organic soils and on woody debris and logs [43][44][45][46].…”

Section: Range and Habitat Of Western Hemlockmentioning

confidence: 99%

Red Alder-Conifer Stands in Alaska: An Example of Mixed Species Management to Enhance Structural and Biological Complexity

Deal

Orlikowska

D’Amore

et al. 2017

Forests

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Abstract:There is worldwide interest in managing forests to improve biodiversity, enhance ecosystem services and assure long-term sustainability of forest resources. An increasingly important goal of forest management is to increase stand diversity and improve wildlife and aquatic habitat. Well-planned silvicultural systems containing a mixture of broadleaf-conifer species have potential to enhance stand diversity and provide other ecosystem services earlier than typical even-aged conifer plantations. Here, we use the example of mixed Sitka spruce/western hemlock and red alder in young, managed stands in southeast Alaska to achieve these goals. We briefly describe the silvics of Sitka spruce, western hemlock and red alder plantations as pure conifer stands or pure broadleaf stands. Then, we synthesize studies of mixed red alder-Sitka spruce/western hemlock stands in southeast Alaska and present their potential for improving stand structural complexity, biodiversity and other ecosystem services over pure conifer forests. Finally, we discuss some of the opportunities and potential tradeoffs for managing mixed broadleaf-conifer stands for providing a number of natural resources and the influence of these broadleaf-conifer forests on ecosystem linkages and processes.

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Influence of forest harvesting activities on debris avalanches and flows

Sidle¹

Springer Praxis Books

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Natural reforestation on a mile-square clearcut in southeast Alaska / by A.S. Harris

Cited by 10 publications

References 1 publication

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

Red Alder-Conifer Stands in Alaska: An Example of Mixed Species Management to Enhance Structural and Biological Complexity

Influence of forest harvesting activities on debris avalanches and flows

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