Carbon sinks and tropical forest biomass estimation: a review on role of remote sensing in aboveground-biomass modelling

Zaki, Nurul Ain Mohd; Latif, Zulkiflee Abd

doi:10.1080/10106049.2016.1178814

Cited by 57 publications

(24 citation statements)

References 95 publications

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“…On the other hand, the model (7) HV + CHM5-DTM10 showed a weak reproduction of the high AGB values, and there was also the area in the south (shown in a circle) where the AGB value was much lower than the (A) LiDAR AGB. This indicates that the model (5) . The maps and the histograms of (A) LiDAR AGB, (B) HV AGB and the AGB estimated by the top six HV-TDX models of (1)-(3), (7), (10) and (11) at 9.0 ha scale.…”

Section: Agb Retrieval At Higher Agb Rangesmentioning

confidence: 85%

“…The R 2 values increased from 0.62 to 0.71 for (5), 0.63 to 0.71 for (6), and 0.68 to 0.76 for (7) at the 9.0 ha scale. We repeated the procedure at the 1.0 ha scale; the R 2 increased from 0.56 to 0.66 for (5), from 0.53 to 0.60 for (6) and from 0.63 to 0.73 for (7). Non-DTM10 models did not show any large model improvement at either the 1.0 or 9.0 ha scale.…”

Section: Low Agb Estimation Accuracies Of Dtm10 Models and Height Devmentioning

confidence: 98%

“…Considering that other DTM10 models, (5) HV + CHM12-DTM10, (6) HV + CHM90-DTM10 and (8) HV + Model10-DTM10, showed much lower R 2 and larger R 2 decrease, the model (7) performed very well. The models (5) and (6) were derived from the TDX 12 m DSM and TDX 90 m DSM respectively, which were developed using multiple TDX images [37]. However, they showed a lower model performance across the slope classes than the model (7), which was derived from the DSM we developed from two TDX-TSX image pairs for reference (Table 3).…”

Section: Slope-class Analysismentioning

confidence: 99%

“…The accurate estimation of forest above-ground biomass (AGB) has become an imperative task for us in a time of the increasing global temperature [1], as it directly relates to the carbon amounts stored in terrestrial ecosystems [2,3]. Among several techniques for estimating AGB (e.g., destructive field sampling, stereo aerial photogrammetry, and airborne laser scanning (ALS) surveys), satellite-based remote sensing is expected to produce a large-scale AGB estimation map for its wide-spatial and continuous-time coverage and its increasing data availability [4,5]. In particular, microwave synthetic aperture radar (SAR) satellites have high potential, due to their all-weather, day and night observation capabilities and their intrinsic ability to penetrate through tree canopies [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping

et al. 2020

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In this study, we compared the accuracies of above-ground biomass (AGB) estimated by integrating ALOS (Advanced Land Observing Satellite) PALSAR (Phased-Array-Type L-Band Synthetic Aperture Radar) data and TanDEM-X-derived forest heights (TDX heights) at four scales from 1/4 to 25 ha in a hemi-boreal forest in Japan. The TDX heights developed in this study included nine canopy height models (CHMs) and three model-based forest heights (ModelHs); the nine CHMs were derived from the three digital surface models (DSMs) of (I) TDX 12 m DEM (digital elevation model) product, (II) TDX 90 m DEM product and (III) TDX 5 m DSM, which we developed from two TDX–TSX (TerraSAR-X) image pairs for reference, and the three digital terrain models (DTMs) of (i) an airborne Light Detection and Ranging (LiDAR)-based DTM (LiDAR DTM), (ii) a topography-based DTM and (iii) the Shuttle Radar Topography Mission (SRTM) DEM; the three ModelHs were developed from the two TDX-TSX image pairs used in (III) and the three DTMs (i to iii) with the Sinc inversion model. In total, 12 AGB estimation models were developed for comparison. In this study, we included the C-band SRTM DEM as one of the DTMs. According to Walker et al. (2007), the SRTM DEM serves as a DTM for most of the Earth’s surface, except for the areas with extensive tree and/or shrub coverage, e.g., the boreal and Amazon regions. As our test site is located in a hemi-boreal zone with medium forest cover, we tested the ability of the SRTM DEM to serve as a DTM in our test site. This study especially aimed to analyze the capability of the two TDX DEM products (I and II) to estimate AGB in practice in the hemi-boreal region, and to examine how the different forest height creation methods (the simple DSM and DTM subtraction for the nine CHMs and the Sinc inversion model-based approach for the three ModelHs) and the different spatial resolutions of the three DSMs and three DTMs affected the AGB estimation results. We also conducted the slope-class analysis to see how the varying slopes influenced the AGB estimation accuracies. The results show that the combined use of the PALSAR data and the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM achieved the highest AGB estimation accuracies across the scales (R2 ranged from 0.82 to 0.97), but the CHMs derived from (I) TDX 12 m DEM and another two DTMs, (ii) and (iii), showed low R2 values at any scales. In contrast, the two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed high R2 values > 0.87 and 0.78, respectively, at the scales > 9.0 ha, but they yielded much lower R2 values at smaller scales. The three ModelHs gave the lowest R2 values across the scales (R2 ranged from 0.39 to 0.60). Analyzed by slope class at the 1.0 ha scale, however, all the 12 AGB estimation models yielded high R2 values > 0.66 at the lowest slope class (0° to 9.9°), including the three ModelHs (R2 ranged between 0.68 to 0.69). The two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed R2 values of 0.80 and 0.71, respectively, at the lowest slope class, while the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM showed high R2 values across the slope classes (R2 > 0.82). The results show that (I) TDX 12 m DEM had a high capability to estimate AGB, with a high accuracy across the scales and the slope classes in the form of CHM, but the use of (i) LiDAR DTM was required. On the other hand, (II) TDX 90 m DEM was able to achieve high AGB estimation accuracies not only with (i) LiDAR DTM, but also with (iii) SRTM DEM in the form of CHM, but it was limited to large scales > 9.0 ha; however, all the models developed in this study have the possibility to achieve higher AGB estimation accuracies at the 1.0 ha scale in flat terrains with slope < 10°. The analysis showed the strengths and limitations of each model, and it also indicates that the data creation methods, the spatial resolutions of datasets and topographic features affects the effective spatial scales for AGB mapping, and the optimal combinations of these features should be chosen to obtain high AGB estimation accuracies.

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Section: Agb Retrieval At Higher Agb Rangesmentioning

confidence: 85%

Section: Low Agb Estimation Accuracies Of Dtm10 Models and Height Devmentioning

confidence: 98%

Section: Slope-class Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping

et al. 2020

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“…Estimating the aboveground biomass of the forest, and investigating the carbon stock for the tropical rainforest are not easy tasks due to the complex architecture of trees and plants that vary in sizes Mohd Zaki & Latif, 2016). In order to predict the tree structure parameter using integration of field data and remote sensing technology, models are normally developed by previous researchers (Babcock et al, 2015;Gonzalez et al, 2010;Li et al, 2015;Saeidi et al, 2014).…”

Section: Aboveground Biomass For Tropical Rain Forestmentioning

confidence: 99%

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using Lidar and Remotely Sensed Data

Zaki

Latif

Suratman³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear re gression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland Dipterocarp forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respecti vely, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland Dipterocarp forest.

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Biofertilizer Utilization in Forestry

Liu

Poobathy

2021

Biofertilizers

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Carbon sinks and tropical forest biomass estimation: a review on role of remote sensing in aboveground-biomass modelling

Cited by 57 publications

References 95 publications

Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping

Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using Lidar and Remotely Sensed Data

Biofertilizer Utilization in Forestry

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