Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data
Various measurements methods have been used to determine the validity of the information produced for carbon sequestration especially in tropical rainforests. Generally, these methods can be divided into two major categories which are the micrometeorological and biometric approaches. The former uses...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2014
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/77617/1/AbdWahidRasibPFGHT2014.pdf |
| _version_ | 1796862687915802624 |
|---|---|
| author | Rasib, Abd. Wahid |
| author_facet | Rasib, Abd. Wahid |
| author_sort | Rasib, Abd. Wahid |
| collection | ePrints |
| description | Various measurements methods have been used to determine the validity of the information produced for carbon sequestration especially in tropical rainforests. Generally, these methods can be divided into two major categories which are the micrometeorological and biometric approaches. The former uses remote sensing and tower flux and the latter refers to field direct measurement of biomass. Presently, use of a single measurement approach has sometimes caused uncertainty in the accuracy of carbon sequestration in terms of the source or sink of carbon in these forests. Thus, this study proposed and developed a new model for carbon sequestration generated from the integration of remote sensing and biometric approach. This study was carried out in Pasoh Forest Reserve and the model was used for up-scaling to estimate the carbon concentration of the entire forest. Data for remote sensing were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data and the biometric approach was based on tree census and litterfall observations. The results for the years 2000 until 2009 based on the new model showed that the carbon sequestration was a carbon source with increments ranging between -1.421 t ha-1yr-1 to -16.573 t ha-1yr-1, a mean value of -8.526 t ha-1yr-1 and Root Mean Square Error (RMSE) 2.916. The use of the new model revealed that there is a 6% accuracy improvement in the results as compared to a single-based remote sensing model. As a conclusion, the integration of approaches for a new model for carbon sequestration is more efficient than the use of a single approach. Furthermore, the new model is suitable for validating and calibrating global automatic climate products. |
| first_indexed | 2024-03-05T20:15:24Z |
| format | Thesis |
| id | utm.eprints-77617 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:15:24Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | utm.eprints-776172018-06-25T08:57:31Z http://eprints.utm.my/77617/ Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data Rasib, Abd. Wahid G70.39-70.6 Remote sensing Various measurements methods have been used to determine the validity of the information produced for carbon sequestration especially in tropical rainforests. Generally, these methods can be divided into two major categories which are the micrometeorological and biometric approaches. The former uses remote sensing and tower flux and the latter refers to field direct measurement of biomass. Presently, use of a single measurement approach has sometimes caused uncertainty in the accuracy of carbon sequestration in terms of the source or sink of carbon in these forests. Thus, this study proposed and developed a new model for carbon sequestration generated from the integration of remote sensing and biometric approach. This study was carried out in Pasoh Forest Reserve and the model was used for up-scaling to estimate the carbon concentration of the entire forest. Data for remote sensing were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data and the biometric approach was based on tree census and litterfall observations. The results for the years 2000 until 2009 based on the new model showed that the carbon sequestration was a carbon source with increments ranging between -1.421 t ha-1yr-1 to -16.573 t ha-1yr-1, a mean value of -8.526 t ha-1yr-1 and Root Mean Square Error (RMSE) 2.916. The use of the new model revealed that there is a 6% accuracy improvement in the results as compared to a single-based remote sensing model. As a conclusion, the integration of approaches for a new model for carbon sequestration is more efficient than the use of a single approach. Furthermore, the new model is suitable for validating and calibrating global automatic climate products. 2014-12 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/77617/1/AbdWahidRasibPFGHT2014.pdf Rasib, Abd. Wahid (2014) Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data. PhD thesis, Universiti Teknologi Malaysia, Faculty of Geoinformation and real estate. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:98587 |
| spellingShingle | G70.39-70.6 Remote sensing Rasib, Abd. Wahid Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data |
| title | Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data |
| title_full | Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data |
| title_fullStr | Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data |
| title_full_unstemmed | Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data |
| title_short | Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data |
| title_sort | carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data |
| topic | G70.39-70.6 Remote sensing |
| url | http://eprints.utm.my/77617/1/AbdWahidRasibPFGHT2014.pdf |
| work_keys_str_mv | AT rasibabdwahid carbonsequestrationmodeloftropicalrainforestecosystemusingsatelliteremotesensingdata |