Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.foreco.2017.10.006 |
Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA | |
Parresol, B. R.1; Scott, D. A.2; Zarnoch, S. J.3; Edwards, L. A.4; Blake, J. I.5 | |
2017-12-15 | |
发表期刊 | FOREST ECOLOGY AND MANAGEMENT |
ISSN | 0378-1127 |
EISSN | 1872-7042 |
出版年 | 2017 |
卷号 | 406 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Spatially explicit mapping of forest productivity is important to assess many forest management alternatives. We assessed the relationship between mapped variables and site index of forests ranging from southern pine plantations to natural hardwoods on a 74,000-ha landscape in South Carolina, USA. Mapped features used in the analysis were soil association, land use condition in 1951, depth to groundwater, slope and aspect. Basal area, species composition, age and height were the tree variables measured. Linear modelling identified that plot basal area, depth to groundwater, soils association and the interactions between depth to groundwater and forest group, and between land use in 1951 and forest group were related to site index (SI) (R-2 = 0.37), but this model had regression attenuation. We then used structural equation modeling to incorporate error-in-measurement corrections for basal area and groundwater to remove bias in the model. We validated this model using 89 independent observations and found the 95% confidence intervals for the slope and intercept of an observed vs. predicted site index error-corrected regression included zero and one, respectively, indicating a good fit. With error in measurement incorporated, only basal area, soil association, and the interaction between forest groups and land use were important predictors (R-2 = 0.57). Thus, we were able to develop an unbiased model of SI that could be applied to create a spatially explicit map based primarily on soils as modified by past (land use and forest type) and recent forest management (basal area). |
英文关键词 | Regression attenuation Site index Spatial analysis Structural equation modeling |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000416395800020 |
WOS关键词 | LONGLEAF PINE PLANTATIONS ; LOBLOLLY-PINE ; SOUTHEASTERN USA ; SOIL PROPERTIES ; INDEX ; GROWTH ; VARIABLES ; RESPONSES ; CLIMATE ; HEIGHT |
WOS类目 | Forestry |
WOS研究方向 | Forestry |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/24028 |
专题 | 气候变化 |
作者单位 | 1.US Forest Serv, USDA, Pacific Northwest Res Stn, Portland, OR USA; 2.US Forest Serv, USDA, Southern Res Stn, Normal, AL 35762 USA; 3.US Forest Serv, USDA, Southern Res Stn, Clemson, SC 29634 USA; 4.US Forest Serv, USDA, Southern Res Stn, Asheville, NC 28804 USA; 5.US Forest Serv, USDA, Savannah River Site, New Ellenton, SC 29809 USA |
推荐引用方式 GB/T 7714 | Parresol, B. R.,Scott, D. A.,Zarnoch, S. J.,et al. Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA[J]. FOREST ECOLOGY AND MANAGEMENT,2017,406. |
APA | Parresol, B. R.,Scott, D. A.,Zarnoch, S. J.,Edwards, L. A.,&Blake, J. I..(2017).Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA.FOREST ECOLOGY AND MANAGEMENT,406. |
MLA | Parresol, B. R.,et al."Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA".FOREST ECOLOGY AND MANAGEMENT 406(2017). |
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