Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

Englhart, Sandra; Keuck, Vanessa; Siegert, Florian

doi:10.1016/j.rse.2011.01.008

Cited by 250 publications

(190 citation statements)

References 38 publications

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“…Our study provides specific estimates for upper dipterocarp forests (273.8 t/ha) and mountain forests (249.1 t/ha). Our MEAN_DF estimate for heath forests and forests on ultrabasic soils (250.9 t/ha) is slightly higher as compared to AGB values between 196 t/ha and 235 t/ha for heath forests [19]. A literature overview of AGB estimates for mangrove ecosystems reported a range of values between 93.7 t/ha and 202.4 t/ha for Indonesia and Malaysia respectively [20] which encompass our AGB MEAN_DF estimate of 136.5 t/ha.…”

Section: Analysis Of Agb Results For Borneo and Comparison With Litercontrasting

confidence: 69%

“…A literature overview of AGB estimates for mangrove ecosystems reported a range of values between 93.7 t/ha and 202.4 t/ha for Indonesia and Malaysia respectively [20] which encompass our AGB MEAN_DF estimate of 136.5 t/ha. For peat swamp forests literature values cover a wide range: while [21] reports values between 252.5 t/ha and 313.9 t/ha for low and high biomass stands, reference [19] provides lower ranges between 83 t/ha and 214 t/ha or between 101 t/ha and 249 t/ha depending on the methodology. Our results for peat swamp forests are consistent with the literature with an average AGB MEAN_DF estimate of 235.1 t/ha.…”

Section: Analysis Of Agb Results For Borneo and Comparison With Litermentioning

confidence: 99%

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Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

Langner

Achard

Vancutsem

et al. 2015

Land

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This study investigates how two existing pan-tropical above-ground biomass (AGB) maps (Saatchi 2011 can be combined to derive forest ecosystem specific carbon estimates. Several data-fusion models which combine these AGB maps according to their local correlations with independent datasets such as the spectral bands of SPOT VEGETATION imagery are analyzed. Indeed these spectral bands convey information about vegetation type and structure which can be related to biomass values. Our study area is the island of Borneo. The data-fusion models are evaluated against a reference AGB map available for two forest concessions in Sabah. The highest accuracy was achieved by a model which combines the AGB maps according to the mean of the local correlation coefficients calculated over different kernel sizes. Combining the resulting AGB map with a new Borneo land cover map (whose overall accuracy has been estimated at 86.5%) leads to average AGB estimates of 279.8 t/ha and 233.1 t/ha for forests and degraded forests respectively. Lowland dipterocarp and mangrove forests have the highest OPEN ACCESSLand 2015, 4 657 and lowest AGB values (305.8 t/ha and 136.5 t/ha respectively). The AGB of all natural forests amounts to 10.8 Gt mainly stemming from lowland dipterocarp (66.4%), upper dipterocarp (10.9%) and peat swamp forests (10.2%). Degraded forests account for another 2.1 Gt of AGB. One main advantage of our approach is that, once the best fitting data-fusion model is selected, no further AGB reference dataset is required for implementing the data-fusion process. Furthermore, the local harmonization of AGB datasets leads to more spatially precise maps. This approach can easily be extended to other areas in Southeast Asia which are dominated by lowland dipterocarp forest, and can be repeated when newer or more accurate AGB maps become available.

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Section: Analysis Of Agb Results For Borneo and Comparison With Litercontrasting

confidence: 69%

Section: Analysis Of Agb Results For Borneo and Comparison With Litermentioning

confidence: 99%

Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

Langner

Achard

Vancutsem

et al. 2015

Land

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“…By SAR data biomass over a large area is calculated from the backscattering value. Previous studies calculate the biomass from SAR data (ALOS PALSAR-1 [8], RADARSAT [9] [10], TERRASAR-X [11], ENVISAT [12], ALOS-2 [13]). Compared to all SAR data ALOS PALSAR is the most commonly used because it has higher wavelength L band that penetrates more through the canopy of trees and produce high accuracy biomass.…”

Section: Introductionmentioning

confidence: 99%

Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

Kumar¹,

Nagai²,

Witayangkurn³

et al. 2016

JGIS

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Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% -80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. K. K. Kumar et al.507

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“…Figure 9 shows the vegetation moisture content and Figure 10 shows soil moisture retrievals where vegetation contribution has been considered. This vegetation moisture content can be related to biomass although not in a linear trend [40].…”

Section: Oh Soil Moisture Retrievalmentioning

confidence: 89%

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Kirimi¹,

Kuria²,

Thonfeld³

et al. 2016

ARS

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Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

Cited by 250 publications

References 38 publications

Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

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