Assessing woody biomass in African tropical savannahs by multiscale remote sensing
(2013) In International Journal of Remote Sensing 34(13). p.4525-4549- Abstract
- Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from... (More)
- Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R-2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example. The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250m in tropical savannah woodlands of any month or season. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3843116
- author
- Wu, Weicheng ; De Pauw, Eddy and Helldén, Ulf LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- in
- International Journal of Remote Sensing
- volume
- 34
- issue
- 13
- pages
- 4525 - 4549
- publisher
- Taylor & Francis
- external identifiers
-
- wos:000317840200002
- scopus:84876429162
- ISSN
- 1366-5901
- DOI
- 10.1080/01431161.2013.777487
- language
- English
- LU publication?
- yes
- id
- 629863bb-dd75-49db-9d2b-f5cdb5eaa331 (old id 3843116)
- date added to LUP
- 2016-04-01 10:16:57
- date last changed
- 2022-04-12 03:57:41
@article{629863bb-dd75-49db-9d2b-f5cdb5eaa331,
abstract = {{Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R-2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example. The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250m in tropical savannah woodlands of any month or season.}},
author = {{Wu, Weicheng and De Pauw, Eddy and Helldén, Ulf}},
issn = {{1366-5901}},
language = {{eng}},
number = {{13}},
pages = {{4525--4549}},
publisher = {{Taylor & Francis}},
series = {{International Journal of Remote Sensing}},
title = {{Assessing woody biomass in African tropical savannahs by multiscale remote sensing}},
url = {{http://dx.doi.org/10.1080/01431161.2013.777487}},
doi = {{10.1080/01431161.2013.777487}},
volume = {{34}},
year = {{2013}},
}