Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania
Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of esti...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243420304232 |
| _version_ | 1828826588641230848 |
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| author | Erik Næsset Ronald E. McRoberts Anssi Pekkarinen Sassan Saatchi Maurizio Santoro Øivind D. Trier Eliakimu Zahabu Terje Gobakken |
| author_facet | Erik Næsset Ronald E. McRoberts Anssi Pekkarinen Sassan Saatchi Maurizio Santoro Øivind D. Trier Eliakimu Zahabu Terje Gobakken |
| author_sort | Erik Næsset |
| collection | DOAJ |
| description | Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. After being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions. |
| first_indexed | 2024-12-12T14:43:15Z |
| format | Article |
| id | doaj.art-82cd3683b18e4670953d7370e1f4d9bb |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-12-12T14:43:15Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-82cd3683b18e4670953d7370e1f4d9bb2022-12-22T00:21:10ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322020-12-0193102138Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in TanzaniaErik Næsset0Ronald E. McRoberts1Anssi Pekkarinen2Sassan Saatchi3Maurizio Santoro4Øivind D. Trier5Eliakimu Zahabu6Terje Gobakken7Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway; Corresponding author.Department of Forest Resources, University of Minnesota, Saint Paul, MN, 55108, USAThe Food and Agriculture Organization of the United Nations (FAO), 00152 Rome, ItalyNASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USAGamma Remote Sensing, Worbstrasse 225, Gümligen, SwitzerlandNorwegian Computing Center, Gaustadalléen 23A, P.O. Box 114, NO-0314 Oslo, NorwayDepartment of Forest Resources Assessment and Management, Sokoine University of Agriculture, P.O. Box 3013, Chuo Kikuu, Morogoro, United Republic of TanzaniaFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, NorwayField surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. After being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions.http://www.sciencedirect.com/science/article/pii/S0303243420304232Biomass mapsModel-assisted estimationSystematic map errorsDry tropical forests |
| spellingShingle | Erik Næsset Ronald E. McRoberts Anssi Pekkarinen Sassan Saatchi Maurizio Santoro Øivind D. Trier Eliakimu Zahabu Terje Gobakken Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania International Journal of Applied Earth Observations and Geoinformation Biomass maps Model-assisted estimation Systematic map errors Dry tropical forests |
| title | Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania |
| title_full | Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania |
| title_fullStr | Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania |
| title_full_unstemmed | Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania |
| title_short | Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania |
| title_sort | use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in tanzania |
| topic | Biomass maps Model-assisted estimation Systematic map errors Dry tropical forests |
| url | http://www.sciencedirect.com/science/article/pii/S0303243420304232 |
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