Mapping above ground carbon using worldview satellite image and LiDAR data in relationship with tree diversity of forests

Yogendra K. Karna, Y.A. Hussin, M. C. Bronsveld, Bhaskar Singh Karky

Research output: Contribution to conferencePaperAcademicpeer-review

1 Citation (Scopus)

Abstract

Forests play a major role in global warming and climate change issues through its unique nature of carbon sinks and sources. Therefore, precise estimation of carbon stock is crucial for mitigation and adaptation of these issues through REDD+ carbon incentive program. This study aims to develop species specific regression model using canopy projection area (CPA) and LiDAR derived tree height for accurate estimation and mapping of carbon stock in tropical forests of Chitwan, Nepal. Pan-sharpened WorldView-2 image and canopy height models (CHM) were used for tree crown delineation to extract CPA and height of the individual trees. Species wise multiple regression models were developed using CPA, Lidar height and field measured carbon stock for carbon estimation of the study area. Shannon diversity index of each community forests (CF) was calculated to find out the relationship between tree species diversity and carbon stock of CF. LiDAR derived tree height was able to explain 76% of variability in field height measurement. Multi-resolution segmentation resulted with overall accuracy of 76% in 1:1 correspondence. Tree species classification resulted in overall accuracy of 58.06% and Kappa statistics 0.47 for classifying six tree species. On average correlation coefficient of CPA and carbon, height and carbon, and CPA and height was found to be 0.73, 0.76 and 0.63 respectively for five dominant tree species. Species wise multiple regression models were able to explain more than 75% of variation in carbon estimation using CPA and LiDAR height for each species. The relationship between tree diversity and carbon stock at CF level was not significant and indicated weak correlation. WorldView-2 satellite imagery and airborne LiDAR data are very promising remote-sensing sources for estimating and mapping species wise above ground carbon stock of tropical forests. Further research is suggested to explore the relationship between tree diversity and carbon stock at a broad scale of various forest types.

Original languageEnglish
Pages846-855
Number of pages10
Publication statusPublished - 1 Dec 2012

Fingerprint

Satellites
Carbon
Satellite imagery
Biodiversity
Optical radar
Global warming
Climate change
Remote sensing
Statistics

Keywords

  • Canopy projection area
  • Carbon stock
  • LiDAR derived tree height
  • Multi-resolution segmentation
  • Tree diversity

Cite this

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title = "Mapping above ground carbon using worldview satellite image and LiDAR data in relationship with tree diversity of forests",
abstract = "Forests play a major role in global warming and climate change issues through its unique nature of carbon sinks and sources. Therefore, precise estimation of carbon stock is crucial for mitigation and adaptation of these issues through REDD+ carbon incentive program. This study aims to develop species specific regression model using canopy projection area (CPA) and LiDAR derived tree height for accurate estimation and mapping of carbon stock in tropical forests of Chitwan, Nepal. Pan-sharpened WorldView-2 image and canopy height models (CHM) were used for tree crown delineation to extract CPA and height of the individual trees. Species wise multiple regression models were developed using CPA, Lidar height and field measured carbon stock for carbon estimation of the study area. Shannon diversity index of each community forests (CF) was calculated to find out the relationship between tree species diversity and carbon stock of CF. LiDAR derived tree height was able to explain 76{\%} of variability in field height measurement. Multi-resolution segmentation resulted with overall accuracy of 76{\%} in 1:1 correspondence. Tree species classification resulted in overall accuracy of 58.06{\%} and Kappa statistics 0.47 for classifying six tree species. On average correlation coefficient of CPA and carbon, height and carbon, and CPA and height was found to be 0.73, 0.76 and 0.63 respectively for five dominant tree species. Species wise multiple regression models were able to explain more than 75{\%} of variation in carbon estimation using CPA and LiDAR height for each species. The relationship between tree diversity and carbon stock at CF level was not significant and indicated weak correlation. WorldView-2 satellite imagery and airborne LiDAR data are very promising remote-sensing sources for estimating and mapping species wise above ground carbon stock of tropical forests. Further research is suggested to explore the relationship between tree diversity and carbon stock at a broad scale of various forest types.",
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Mapping above ground carbon using worldview satellite image and LiDAR data in relationship with tree diversity of forests. / Karna, Yogendra K.; Hussin, Y.A.; Bronsveld, M. C.; Karky, Bhaskar Singh.

2012. 846-855.

Research output: Contribution to conferencePaperAcademicpeer-review

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AU - Karky, Bhaskar Singh

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AB - Forests play a major role in global warming and climate change issues through its unique nature of carbon sinks and sources. Therefore, precise estimation of carbon stock is crucial for mitigation and adaptation of these issues through REDD+ carbon incentive program. This study aims to develop species specific regression model using canopy projection area (CPA) and LiDAR derived tree height for accurate estimation and mapping of carbon stock in tropical forests of Chitwan, Nepal. Pan-sharpened WorldView-2 image and canopy height models (CHM) were used for tree crown delineation to extract CPA and height of the individual trees. Species wise multiple regression models were developed using CPA, Lidar height and field measured carbon stock for carbon estimation of the study area. Shannon diversity index of each community forests (CF) was calculated to find out the relationship between tree species diversity and carbon stock of CF. LiDAR derived tree height was able to explain 76% of variability in field height measurement. Multi-resolution segmentation resulted with overall accuracy of 76% in 1:1 correspondence. Tree species classification resulted in overall accuracy of 58.06% and Kappa statistics 0.47 for classifying six tree species. On average correlation coefficient of CPA and carbon, height and carbon, and CPA and height was found to be 0.73, 0.76 and 0.63 respectively for five dominant tree species. Species wise multiple regression models were able to explain more than 75% of variation in carbon estimation using CPA and LiDAR height for each species. The relationship between tree diversity and carbon stock at CF level was not significant and indicated weak correlation. WorldView-2 satellite imagery and airborne LiDAR data are very promising remote-sensing sources for estimating and mapping species wise above ground carbon stock of tropical forests. Further research is suggested to explore the relationship between tree diversity and carbon stock at a broad scale of various forest types.

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