ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 187-194, 2016
https://doi.org/10.5194/isprs-annals-III-7-187-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA
N. A. M. Zaki1,2, Z. A. Latif2, M. N. Suratman3, and M. Z. Zainal4 1Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture Planning and Surveying, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
2Applied Remote Sensing & Geospatial Research Group (ARSG), Green Technology & Sustainable Development (GTSD) Community Research, Universiti Teknologi MARA (UiTM),Shah Alam, Malaysia
3Centre for Biodiversity & Sustainable Development, University Teknologi MARA (UiTM), Shah Alam, Malaysia
4Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture Planning and Surveying, Universiti Teknologi MARA, Arau, Perlis, Malaysia
Keywords: Tropical Rain Forest; Crown Biomass; LiDAR; Aboveground Biomass; Carbon stocks 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 regression (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 respectively, 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.
Conference paper (PDF, 1504 KB)


Citation: Zaki, N. A. M., Latif, Z. A., Suratman, M. N., and Zainal, M. Z.: MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 187-194, https://doi.org/10.5194/isprs-annals-III-7-187-2016, 2016.

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