Aboveground biomass estimation using remote sensing imagery with allometric model
Also available in printed version : G70.4 M644 2010 raf
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| Format: | Bachelor thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/113662 |
| Abstract | Abstract here |
| _version_ | 1854975081488318464 |
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| author | Lau Pen Chow |
| author2 | Alvin Lau Meng Shin, supervisor |
| author_facet | Alvin Lau Meng Shin, supervisor Lau Pen Chow |
| author_sort | Lau Pen Chow |
| description | Also available in printed version : G70.4 M644 2010 raf |
| format | Bachelor thesis |
| id | utm-123456789-113662 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-1136622025-08-21T03:33:49Z Aboveground biomass estimation using remote sensing imagery with allometric model Lau Pen Chow Alvin Lau Meng Shin, supervisor Remote sensing Image processing--Digital techniques Also available in printed version : G70.4 M644 2010 raf Vegetation index algorithm can be used to quantify the concentrations of green leaf vegetation on the earth by using the wavelengths and intensity of visible and near-infrared light that reflected by the land surface back up into space. In addition, a vegetation index also called as a vegetative index which is a single number that quantifies vegetation biomass for each pixel in a remote sensing image. The index is computed using several spectral bands that are sensitive to plant biomass. This study used to calculate vegetation indices based on hyperspectral data automatically using Matlab where Graphical User Interface (GUI) technique was built to make user easy to calculate the vegetation indices. This automated vegetation indices is build on ten selected indices such as Normalized Difference Vegetation Index (NDVI), Infrared Index (II), Simple Ratio (SR), Soil and Atmospherically Resistant Vegetation Index (SARVI), MidIR Index, Moisture Stress Index (MSI), Soil Adjusted Vegetation Index (SAVI), Aerosol Free Vegetation Index (AFRI), Triangular Vegetation Index (TVI), and Normalized Difference Built-up Index (NDBI). The results of calculated vegetation index calculation automatically by using Matlab are similar to calculation by using Erdas Imagine. This is based on the regression value and RMSE for each vegetation index, which respectively to 1.0 and 0.0. Result of this study is an automatic vegetation indices that will be used as reference in future. asyik UTM 89 p. Project Paper (Sarjana Muda Sains (Remote Sensing)) - Universiti Teknologi Malaysia, 2010 2025-04-18T07:14:13Z 2025-04-18T07:14:13Z 2010-04 Bachelor thesis https://utmik.utm.my/handle/123456789/113662 valet-20150427-085934 vital:76044 ENG Closed Access UTM Complete Completion Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Remote sensing Image processing--Digital techniques Lau Pen Chow Aboveground biomass estimation using remote sensing imagery with allometric model |
| thesis_level | Other |
| title | Aboveground biomass estimation using remote sensing imagery with allometric model |
| title_full | Aboveground biomass estimation using remote sensing imagery with allometric model |
| title_fullStr | Aboveground biomass estimation using remote sensing imagery with allometric model |
| title_full_unstemmed | Aboveground biomass estimation using remote sensing imagery with allometric model |
| title_short | Aboveground biomass estimation using remote sensing imagery with allometric model |
| title_sort | aboveground biomass estimation using remote sensing imagery with allometric model |
| topic | Remote sensing Image processing--Digital techniques |
| url | https://utmik.utm.my/handle/123456789/113662 |
| work_keys_str_mv | AT laupenchow abovegroundbiomassestimationusingremotesensingimagerywithallometricmodel |