Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal

Karna, Yogendra K.; Hussin, Y.A.; Gilani, Hammad; Bronsveld, M.C.; Murthy, M. S. R.; Qamer, Faisal Mueen; Karky, Bhaskar Singh; Bhattarai, Tara Nidhi; Xu, Aigong; Baniya, Chitra Bahadur

doi:10.1016/j.jag.2015.01.011

Cited by 51 publications

(33 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The combination of LiDAR and Very High Resolution Multispectral Imagery, WV-3 has demonstrated a promising capability to model the aboveground biomass and carbon stocks needed for forest biomass estimation for lowland Dipterocarp forest. Results indicate that the relationship between carbon stocks with LiDAR and CPA obtained in this study are similar in terms of correlation produce and the levels of variances with other studies that have been done previously (Karna et al, 2013). The output MLR shown that there is non-linear equation of the predicted model where the natural logarithmic had been used and get the better prediction.…”

Section: Conclusion and Recommendationsupporting

confidence: 84%

“…Figure 9. Carbon stocks of the study area (Karna (2013) found out that a significant correlation coefficient (r) between (CPA -Carbon), (height -Carbon), and (CPA -Height) is 0.73, 0.76 and 0.63 respectively. Similar to this relationship, this study produced 0.671, 0.709 and 0.549 for the type of Lowland Dipterocarp forest which mainly focuses on tropical rain forest.…”

Section: Carbon Stocks Mapmentioning

confidence: 99%

See 1 more Smart Citation

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

Zaki¹,

Latif²,

Suratman³

et al. 2016

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

View full text Add to dashboard Cite

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.

show abstract

Section: Conclusion and Recommendationsupporting

confidence: 84%

Section: Carbon Stocks Mapmentioning

confidence: 99%

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

Zaki¹,

Latif²,

Suratman³

et al. 2016

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

View full text Add to dashboard Cite

show abstract

“…It now seems that the combination of these two data types may be able to simultaneously help identify tree species, thereby opening up the possibility of generating species-specific carbon estimates with a similar combined dataset. Other researchers looking to the future of remote sensing also highlighted the utility of LiDAR data in addressing large-scale questions like deforestation and carbon sequestration in whole forests on a species-specific basis [1,31].…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the structural information offered by LiDAR datasets, remote tree species classifications may take advantage of the differential reflectance of different wavelengths of light within heterogeneous forest canopies. Multispectral [29,30] and optical [31,32] datasets were previously used in combination with LiDAR for species-level classifications, and hyperspectral data in particular were used for tree species classification because of the differences in light reflectance off leaves with species-specific pigment concentrations [33]. Using a similar principle, recently developed multispectral LiDAR systems can be used to gather wavelength-dependent structural information, and were used for tree species identification with higher accuracies than single-wavelength LiDAR data [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

Marrs

Ni‐Meister

2019

Remote Sensing

View full text Add to dashboard Cite

The use of light detection and ranging (LiDAR) techniques for recording and analyzing tree and forest structural variables shows strong promise for improving established hyperspectral-based tree species classifications; however, previous multi-sensoral projects were often limited by error resulting from seasonal or flight path differences. The National Aeronautics and Space Administration (NASA) Goddard's LiDAR, hyperspectral, and thermal imager (G-LiHT) is now providing co-registered data on experimental forests in the United States, which are associated with established ground truths from existing forest plots. Free, user-friendly machine learning applications like the Orange Data Mining Extension for Python recently simplified the process of combining datasets, handling variable redundancy and noise, and reducing dimensionality in remotely sensed datasets. Neural networks, CN2 rules, and support vector machine methods are used here to achieve a final classification accuracy of 67% for dominant tree species in experimental plots of Howland Experimental Forest, a mixed coniferous-deciduous forest with ten dominant tree species, and 59% for plots in Penobscot Experimental Forest, a mixed coniferous-deciduous forest with 15 dominant tree species. These accuracies are higher than those produced using LiDAR or hyperspectral datasets separately, suggesting that combined spectral and structural data have a greater richness of complementary information than either dataset alone. Using greatly simplified datasets created by our dimensionality reduction methodology, machine learner performance remains comparable or higher to that using the full dataset. Across forests, the identification of shared structural and spectral variables suggests that this methodology can successfully identify parameters with high explanatory power for differentiating among tree species, and opens the possibility of addressing large-scale forestry questions using optimized remote sensing workflows.

show abstract

“…Ramoelo et al [24] monitored leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and the random forest technique. Karna et al [25] integrated WorldView-2 satellite images with small footprint airborne LiDAR data for estimation of tree carbon at the species level. Mutanga et al [26] demonstrated the utility of WorldView-2 imagery and random forest regression in estimating and mapping vegetation biomass at high density.…”

Section: Introductionmentioning

confidence: 99%

Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets

et al. 2016

View full text Add to dashboard Cite

Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June-August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m × 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy (R 2 = 0.75, RMSE = 9.94) than the single image upscaling (R 2 = 0.63, RMSE = 12.05) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model (R 2 = 0.61, RMSE = 13.78) than only using the original 48 plots for the phenology model (R 2 = 0.23, RMSE = 17.49). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems.

show abstract

Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal

Cited by 51 publications

References 54 publications

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

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

Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets

Contact Info

Product

Resources

About