Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data

Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Zahabu, Eliakimu; Næsset, Erik

doi:10.3390/rs70100788

Cited by 70 publications

(57 citation statements)

References 57 publications

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“…The final model was based on percentile height (H.p25) and canopy cover (CC.all.first). The variables are in line with the previous studies in Africa, which have used, for example, mean canopy height, height percentiles and canopy density for modelling [6,[10][11][12]. Both modelling accuracy and explanatory variables are similar to the model used for the hills in the study by [15], who developed separate models for hills and lowlands using different ALS data.…”

Section: Als-based Aboveground Biomass Mapsupporting

confidence: 63%

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

et al. 2017

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Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600-2200 m. The field measurements were carried out in 215 plots in 2013-2015 and ALS flights conducted in 2014-2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R 2 = 0.88, RMSE = 52.9 Mg ha −1 ). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region.

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Section: Als-based Aboveground Biomass Mapsupporting

confidence: 63%

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

et al. 2017

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“…Taking the design-based approach to variance estimation d'Oliveira et al [49], reported a relative efficiency of 3.4 in a study utilising 50 plots of ~0.25 ha in the Brazilian Amazon. We can similarly compute the relative efficiency from the variance estimates reported by Hansen et al [10]. For a plot size of ~0.1 ha the relative efficiency was 2.1.…”

Section: Resultsmentioning

confidence: 97%

“…Modelling of biomass using image matching requires a high quality digital terrain model (DTM) as reference surface, usually derived from airborne laser scanning (ALS). ALS is itself a remote sensing technology that provides three-dimensional data of the forest vegetation and has been used successfully for biomass estimation, even in tropical areas [9,10]. Another technology that provides three-dimensional data is synthetic aperture radio detection and ranging (SAR).…”

Section: Introductionmentioning

confidence: 99%

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Hansen

Gobakken

Solberg³

et al. 2015

Remote Sensing

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Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, …, 1900 m 2 , sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5-6.0 times as many observations for plot sizes of 700-1900 m 2 to achieve the same precision as an inventory supported by ALS data. OPEN ACCESSRemote Sens. 2015, 7 9866

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“…2017, 9, 18 3 of 16 pine woodlands using field data collected at 0.25, 0.5, and 1 ha, and a weak dependence using data at 0.1 ha. However, due to the high amount of resources needed to set up and monitor forest plots, it is difficult to establish large sampling areas and obtain field datasets based on large plots [28]; thus, small plots are much more used than large ones in biomass research and mapping activities.…”

Section: Introductionmentioning

confidence: 99%

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

et al. 2016

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Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R 2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R 2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

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Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data

Cited by 70 publications

References 57 publications

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

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