Mapping land cover and green vegetation abundance using MODIS-like data: A case study of New England

Xiaoyang Zhang; Crystal B. Schaaf; Feng Gao; Mark A. Friedl; Alan H. Strahler; John C.F. Hodges

Mapping land cover and green vegetation abundance using MODIS-like data: A case study of New England

Xiaoyang Zhang
, Crystal B. Schaaf
, Feng Gao
, Mark A. Friedl
, Alan H. Strahler
, John C.F. Hodges

SFE - Dean's Office

Boston University

Research output: Contribution to conference › Paper › peer-review

Abstract

To reduce the uncertainty of many environmental models it is necessary to both classify land-cover type accurately and to compute dynamic changes in green vegetation abundance of each land-cover type. To meet these needs, a case study is carried out in New England. A univariate decision tree is implemented to classify IGBP land cover using MODIS-like data. Classification results are presented using different combinations of input features including NDVI, NBAR (nadir BRDF-adjusted reflectances), and NDVI+NBAR. It is shown that NBAR is the best input feature for this classification problem. The abundance of green vegetation is calculated using a constrained linear mixture modeling approach based on NBARs. Variation in green vegetation abundance within each land-cover type is analyzed at different phenological stages from July to October 1996.

Original language	English
Pages	2005-2007
Number of pages	3
State	Published - 2000
Event	2000 Intenational Geoscience and Remote Sensing Symposium (IGARSS 2000) - Honolulu, HI, USA Duration: Jul 24 2000 → Jul 28 2000

Conference

Conference	2000 Intenational Geoscience and Remote Sensing Symposium (IGARSS 2000)
City	Honolulu, HI, USA
Period	7/24/00 → 7/28/00

ASJC Scopus Subject Areas

Computer Science Applications
General Earth and Planetary Sciences

Cite this

@conference{4c91186dbd2b4e18b40e77c03ac4a2ea,

title = "Mapping land cover and green vegetation abundance using MODIS-like data: A case study of New England",

abstract = "To reduce the uncertainty of many environmental models it is necessary to both classify land-cover type accurately and to compute dynamic changes in green vegetation abundance of each land-cover type. To meet these needs, a case study is carried out in New England. A univariate decision tree is implemented to classify IGBP land cover using MODIS-like data. Classification results are presented using different combinations of input features including NDVI, NBAR (nadir BRDF-adjusted reflectances), and NDVI+NBAR. It is shown that NBAR is the best input feature for this classification problem. The abundance of green vegetation is calculated using a constrained linear mixture modeling approach based on NBARs. Variation in green vegetation abundance within each land-cover type is analyzed at different phenological stages from July to October 1996.",

author = "Xiaoyang Zhang and Schaaf, \{Crystal B.\} and Feng Gao and Friedl, \{Mark A.\} and Strahler, \{Alan H.\} and Hodges, \{John C.F.\}",

year = "2000",

language = "English",

pages = "2005--2007",

note = "2000 Intenational Geoscience and Remote Sensing Symposium (IGARSS 2000) ; Conference date: 24-07-2000 Through 28-07-2000",

}

TY - CONF

T1 - Mapping land cover and green vegetation abundance using MODIS-like data

T2 - 2000 Intenational Geoscience and Remote Sensing Symposium (IGARSS 2000)

AU - Zhang, Xiaoyang

AU - Schaaf, Crystal B.

AU - Gao, Feng

AU - Friedl, Mark A.

AU - Strahler, Alan H.

AU - Hodges, John C.F.

PY - 2000

Y1 - 2000

N2 - To reduce the uncertainty of many environmental models it is necessary to both classify land-cover type accurately and to compute dynamic changes in green vegetation abundance of each land-cover type. To meet these needs, a case study is carried out in New England. A univariate decision tree is implemented to classify IGBP land cover using MODIS-like data. Classification results are presented using different combinations of input features including NDVI, NBAR (nadir BRDF-adjusted reflectances), and NDVI+NBAR. It is shown that NBAR is the best input feature for this classification problem. The abundance of green vegetation is calculated using a constrained linear mixture modeling approach based on NBARs. Variation in green vegetation abundance within each land-cover type is analyzed at different phenological stages from July to October 1996.

AB - To reduce the uncertainty of many environmental models it is necessary to both classify land-cover type accurately and to compute dynamic changes in green vegetation abundance of each land-cover type. To meet these needs, a case study is carried out in New England. A univariate decision tree is implemented to classify IGBP land cover using MODIS-like data. Classification results are presented using different combinations of input features including NDVI, NBAR (nadir BRDF-adjusted reflectances), and NDVI+NBAR. It is shown that NBAR is the best input feature for this classification problem. The abundance of green vegetation is calculated using a constrained linear mixture modeling approach based on NBARs. Variation in green vegetation abundance within each land-cover type is analyzed at different phenological stages from July to October 1996.

UR - https://www.scopus.com/pages/publications/0034539354

UR - https://www.scopus.com/pages/publications/0034539354#tab=citedBy

M3 - Paper

AN - SCOPUS:0034539354

SP - 2005

EP - 2007

Y2 - 24 July 2000 through 28 July 2000

ER -

Mapping land cover and green vegetation abundance using MODIS-like data: A case study of New England

Abstract

Conference

ASJC Scopus Subject Areas

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