Canadian Forest Service Publications

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production. 1997. Franklin, S.E.; Lavigne, M.B.; Deuling, M.J.; Wulder, M.A.; Hunt, E.R., Jr. International Journal of Remote Sensing 18(16): 3459-3471 .

Year: 1997

Available from: Pacific Forestry Centre

Catalog ID: 30102

Language: English

CFS Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1080/014311697216973

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Abstract

Ecosystem models can be used to estimate potential net primary production (pNPP) using GIS data, and remote sensing input of actual forest leaf area to such models can provide estimates of current actual net primary production (aNPP) . Comparisons of pNPP and aNPP for a given site or regional landscape can be used to identify forest stands for different management treatments, and may provide new information on wildlife habitat, forest diversity and growth characteristics. Leaf area estimates may be obtained from satellite imagery through correlation with physiologically-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI). However, in areas with high Leaf Area Index (LAI), vegetation indices usually saturate at leaf areas greater than about 4. In predominantly deciduous (hardwood) and mixedwood stands remote sensing estimates may be influenced by understory and other factors. We examined digital Landsat TM imagery and GIS data in the Fundy Model Forest of southeastern New Brunswick to determine relations to forest leaf area index within different stand structures or covertypes. The image data were stratified using GIS covertype information prior to development of LAI predictive equations using spectral reflectance, and the prediction of LAI from Landsat TM imagery was improved with reference to estimates of stem density which are standard forest inventory information contained in GIS databases. Actual stand LAI was compared to assumed maximum LAI values for several species and sites using an ecosystem process model (BIOME-BGC) which relies on climate, soils and topographic information also obtained from the GIS. Subsequent comparison of pNPP and aNPP revealed that even disturbed sites in this environment can reach close to maximum site potential. Specific sites with suboptimal species composition were identified. A future refinement of this approach is to classify the imagery independently of the GIS, which assumes a homogeneous covertype for each polygon in the system, and thus improve still further the aNPP estimates through higher covertype and LAI estimation accuracy.

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