Application of remote sensing, an artificial neural network leaf area model, and a process-based simulation model to estimate carbon storage in Florida slash pine plantations

被引：6

作者：

Shoemaker D.A. ^{[1
]}

Cropper W.P., Jr. ^{[2
]}

机构：

[1] The Center for Applied Geographical Information Science, McEniry 320, University of North Carolina-Charlotte, Charlotte, NC 28223

[2] School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611-0410

来源：

Journal of Forestry Research | 2010年 / 21卷 / 2期

关键词：

Artificial neural network; Carbon exchange; Forest carbon; Leaf area; NEE; Slash pine;

D O I：

10.1007/s11676-010-0027-x

中图分类号：

学科分类号：

摘要：

Carbon sequestration in forests is of great interest due to concerns about global climate change. Carbon storage rates depend on ecosystem fluxes (photosynthesis and ecosystem respiration), typically quantified as net ecosystem exchange (NEE). Methods to estimate forest NEE without intensive site sampling are needed to accurately assess rates of carbon sequestration at stand-level and larger scales. We produced spatially-explicit estimates of NEE for 9 770 ha of slash pine (Pinus elliottii) plantations in North-Central Florida for a single year by coupling remote sensing-based estimates of leaf area index (LAI) with a process-based growth simulation model. LAI estimates produced from a neural-network modeling of ground plot and Landsat TM satellite data had a mean of 1.06 (range 0-3.93, including forest edges). Using the neural network LAI values as inputs, the slash pine simulation model (SPM2) estimates of NEE ranged from -5.52 to 11.06 Mg·ha-1·a-1 with a mean of 3.47 Mg·ha-1·a-1. Total carbon storage for the year was 33 920 t, or about 3.5 tons per hectare. Both estimated LAI and NEE were highly sensitive to fertilization. © 2010 Northeast Forestry University and Springer-Verlag Berlin Heidelberg.

引用

页码：171 / 176

页数：5

共 28 条

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