MODELING VEGETATION PATTERN USING DIGITAL TERRAIN DATA

被引：120

作者：

DAVIS, FW ^{[1
]}

GOETZ, S ^{[1
]}

机构：

[1] SCI APPLICAT RES, GREENBELT, MD 20771 USA

来源：

LANDSCAPE ECOLOGY | 1990年 / 4卷 / 01期

关键词：

California; geographic information system; oak forest; patch size; predictive mapping; remote sensing;

D O I：

10.1007/BF02573952

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

摘要：

Using a geographic information system (GIS), digital maps of environmental variables including geology, topography and calculated clear-sky solar radiation, were weighted and overlaid to predict the distribution of coast live oak (Ouercus agrifolia) forest in a 72 km2 region near Lompoc, California. The predicted distribution of oak forest was overlaid on a map of actual oak forest distribution produced from remotely sensed data, and residuals were analyzed to distinguish prediction errors due to alteration of the vegetation cover from those due to defects of the statistical predictive model and due to cartographic errors. Vegetation pattern in the study area was associated most strongly with geologic substrate. Vegetation pattern was also significantly associated with slope, exposure and calculated monthlysolar radiation. The proportion of observed oak forest occurring on predicted oak forest sites was 40% overall, but varied substantially between substrates and also depended strongly on forest patch size, with a much higher rate of success for larger forest patches. Only 21% of predicted oak forest sites supported oak forest, and proportions of observed vegetation on predicted oak forest sites varied significantly between substrates. The non-random patterns of disagreement between maps of predicted and observed forest indicated additional variables that could be included to improve the predictive model, as well as the possible magnitude of forest loss due to disturbances in different parts of the landscape. © 1990 SPB Academic Publishing bv.

引用

页码：69 / 80

页数：12

共 35 条

[1]

Band L.E., Topographic partition of watersheds with digital elevation models, Wat. Resour. Res., 22, pp. 15-23, (1986)

[2]

Box E.O., Macroclimate and plant forms: an introduction to predictive modeling in phytogeography, (1981)

[3]

Burrough P.A., Principles of geographic information systems for land resource assessment, (1986)

[4]

Card D.H., Using known map category marginal frequencies to improve estimates of thematic map accuracy, Photogramm. Engng. and Rem. Sens., 48, pp. 431-439, (1982)

[5]

Causton D.R., Introduction to vegetation analysis, (1988)

[6]

Chambers J.M., Cleveland W.B., Kleiner B., Tukey P.A., Graphical methods for data analysis, (1983)

[7]

Cibula W.G., Nyquist M.O., Use of topographic and climatological models in a geographical data base to improve Landsat MSS classification for Olympic National Park, Photogramm. Engng. and Rem. Sens., 53, pp. 67-76, (1987)

[8]

Cliff A.D., Ord J.K., Spatial processes, (1981)

[9]

Cole K., Geologic control of vegetation in the Purisima Hills, California, Madrono, 27, pp. 79-89, (1980)

[10]

Costanzo C.M., Halperin W.C., Gale N.D., Richardson G.D., An alternative method for assessing goodness-offit for logit models, Environ. and Plann. A, 14, pp. 963-971, (1982)

← 1 2 3 4 →