Landsat TM Derived Forest Covertypes for Modelling Net Primary Production

RESUMELes modeles regionaux actuels de production primaire nette (PPN) requierent des estimations precises de la surface foliaire du couvert et des classifications precises des couverts forestiers. Ces deux intrants aux modeles peuvent etre difficiles ou impossibles a extraire a partir de bases de donnees SIG mais peuvent etre obtenus par teledetection. Dans le cas des couverts forestiers, une base de donnees SIG d'inventaire forestier type contiendra generalement, sur une base polygonale ou au niveau du peuplement, certaines informations comme par exemple, la description des especes dominantes-codominantes. La structure polygonale est generalement deduite par photointerpretation aerienne et permet d'organiser le paysage en fonction de plusieurs activites de gestion, telles que la coupe d'arbres ou les operations sylvicoles. Toutefois, cette structure polygonale peut s'averer moins utile pour d'autres applications telles que l'estimation de la croissance forestiere, notamment dans le cas des petites surfa...

[1]  Gérard Dedieu,et al.  Methodology for the estimation of terrestrial net primary production from remotely sensed data , 1994 .

[2]  Richard H. Waring,et al.  Environmental Limits on Net Primary Production and Light‐Use Efficiency Across the Oregon Transect , 1994 .

[3]  Gordon B. Bonan,et al.  Importance of leaf area index and forest type when estimating photosynthesis in boreal forests , 1993 .

[4]  D. Leckie,et al.  Forest inventory in Canada with emphasis on map production , 1995 .

[5]  S. Franklin,et al.  High Spatial Resolution Optical Image Texture for Improved Estimation of Forest Stand Leaf Area Index , 1996 .

[6]  S. Running,et al.  Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates , 1993 .

[7]  Steven W. Running,et al.  A biophysical soil–site model for estimating potential productivity of forested landscapes , 1996 .

[8]  Steven E. Franklin,et al.  Radiometric processing of aerial and satellite remote-sensing imagery , 1995 .

[9]  Ramakrishna R. Nemani,et al.  Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation , 1989 .

[10]  Comparing branch biomass prediction equations for Abiesbaisamea , 1996 .

[11]  S. Running,et al.  Simulated dry matter yields for aspen and spruce stands in the North American boreal forest , 1992 .

[12]  J. Chen,et al.  Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .

[13]  Ronald J. Hall,et al.  Variability of Landsat Thematic Mapper data in boreal deciduous and mixed wood stands with conifer understory , 1995 .

[14]  T. M. Lillesand,et al.  Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper , 1997 .

[15]  P. Gong,et al.  Remote Sensing of Seasonal Leaf Area Index Across the Oregon Transect , 1994 .

[16]  Michael Jasinski,et al.  Estimation of subpixel vegetation density of natural regions using satellite multispectral imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[17]  G. Edwards,et al.  Modeling heterogeneity and change in natural forests : Special issue on temporal GIS , 1996 .

[18]  J. Beaubien,et al.  Landsat TM satellite images of forests: from enhancement to classification , 1994 .

[19]  J. Hunt,et al.  Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDVI , 1994 .

[20]  S. Running,et al.  8 – Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models , 1993 .