Forest chemistry mapping with hyperspectral data

Goodenough, D.G.; Han, Tong; Pearlman, Jay; Dyk, A.; McDonald, S. E.

doi:10.1109/warsd.2003.1295220

Cited by 5 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the GVWD study site, Goodenough and colleagues [5][6][7][8][30][31][32] have conducted extensive studies on forest species classification, biochemical mapping, and biomass estimation using hyperspectral data. The GVWD study site is located on Vancouver Island, British Columbia, Canada.…”

Section: Land Ecosystem Information Productsmentioning

confidence: 99%

“…This makes it possible to estimate foliar chemical concentrations, such as chlorophylla and -b, nitrogen, lignin, and water content. For example, at the GVWD study site, foliar nitrogen concentration has been mapped successfully with hyperspectral data by Goodenough et al [32], where the coefficients of determination were over 0.8 between measured and predicted nitrogen concentrations (map in Figure 2) and 0.79 for chlorophyll content. In these studies the hyperspectral data were converted to absorbance and transformed to derivatives for use in the PLS regression.…”

Section: Estimation Of Forest Biochemical Propertiesmentioning

confidence: 99%

“…Nitrogen concentration map for GVWD derived by PLS using the 2 nd derivative absorbance. Modified from [32].…”

Section: Estimation Of Forest Biochemical Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

Combining Hyperspectral Remote Sensing and Physical Modeling for Applications in Land Ecosystems

Goodenough

Asner

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

Self Cite

View full text Add to dashboard Cite

Land ecosystems, in particular forest ecosystems, are under increasing pressure from environmental changes such as population growth, global warming, wildfires, forest insects, and diseases. Data from hyperspectral sensors can be used to map forest species and determine biophysical and biochemical properties. Modeling plays an important role in accurate determination of ecosystem properties. Radiative transfer models are used to understand how radiation interacts with the atmosphere and the Earth's terrestrial surface and to correct observed radiances to surface reflectance. Canopy models are used to infer through inversion quantitative information from hyperspectral data on canopy structure and foliage biochemistry. This article presents an overview on combining hyperspectral sensing with canopy radiative transfer models to derive ecosystem information products.

show abstract

Section: Land Ecosystem Information Productsmentioning

confidence: 99%

Section: Estimation Of Forest Biochemical Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Combining Hyperspectral Remote Sensing and Physical Modeling for Applications in Land Ecosystems

Goodenough

Asner

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

A Method Creating Different Packet Loss Membership Function in Heterogeneous Network

Yanxiang

Huikan

2009

2009 WRI Global Congress on Intelligent Systems

View full text Add to dashboard Cite

1The packet loss differentiating mechanism of TCP for heterogeneous network has the important influence to the stability of wireless network, relative one-way delay (ROD) of Loss Pair is adopted as the differentiating parameter to distinguish the packet loss pattern in reference [1]. The accuracy of this algorithm depends on ROD membership function. In order to differentiate packet loss pattern more accurately, the initialization method based on potential function is proposed by EM algorithm analysis, and the simulation result shows it is superior in convergence speed. Then the modified EM algorithm is applied in creating different packet loss membership function which congestion and wireless error coexist in the network. From the results of simulation, this algorithm has well distinguishing effects in the membership function of different packet loss pattern and practical application value.

show abstract

Forest information from hyperspectral sensing

Goodenough

Pearlman²,

Chen³

et al.

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings

Self Cite

View full text Add to dashboard Cite

Remote sensing of forests is a major application for Canada's new hyperspectral satellite (HERO). Hyperspectral remote sensing can provide forest information products for applications in forest inventory, forest chemistry, and for some Kyoto Protocol information products. Through several projects, we have demonstrated that airborne and satellite hyperspectral sensing can provide accurate maps of west coast forest species. High correlations have been demonstrated between ground measurements of foliar nitrogen and estimates derived from hyperspectral sensing. Data for these experiments have included airborne AVIRIS and casi, and satellite Hyperion data. To achieve operational accuracies of 80% or better for forest species classification, it is essential that the hyperspectral data are well calibrated. Also, sensor artifacts, such as smile, keystone, and striping, must be corrected.Methods have also been developed for mapping forest health status bioindicators: leaf chlorophyll, nitrogen, and water content. Methods have also been developed for determining canopy fractions to levels as low as 20%. If HERO generates a 1% improvement in forest product sales, this would amount to a benefit of $700 million annually in Canada alone. This paper will report on the current state of hyperspectral sensing of forests, and present the hyperspectral sensor specifications needed for forest applications.

show abstract

Forest chemistry mapping with hyperspectral data

Cited by 5 publications

References 7 publications

Combining Hyperspectral Remote Sensing and Physical Modeling for Applications in Land Ecosystems

Combining Hyperspectral Remote Sensing and Physical Modeling for Applications in Land Ecosystems

A Method Creating Different Packet Loss Membership Function in Heterogeneous Network

Forest information from hyperspectral sensing

Contact Info

Product

Resources

About