Mapping the natural distribution of bamboo and related carbon stocks in the tropics using google earth engine, phenological behavior, landsat 8, and sentinel-2
(2020) In Remote Sensing 12(18).- Abstract
Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018.... (More)
Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.
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- author
- Venkatappa, Manjunatha ; Anantsuksomsri, Sutee ; Castillo, Jose Alan ; Smith, Benjamin LU and Sasaki, Nophea
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bamboo mapping, Carbon stocks, CDM, Google Earth Engine, Landsat 8 OLI, PBTC, REDD+, Sentinel-2, Threshold classification, Threshold values, Vegetation phenology
- in
- Remote Sensing
- volume
- 12
- issue
- 18
- article number
- 3109
- pages
- 23 pages
- publisher
- MDPI AG
- external identifiers
-
- scopus:85095437570
- ISSN
- 2072-4292
- DOI
- 10.3390/rs12183109
- language
- English
- LU publication?
- yes
- id
- 3ec2a1c6-ac7c-415c-b97b-5d7c044b8177
- date added to LUP
- 2020-11-17 11:08:52
- date last changed
- 2022-04-26 21:49:22
@article{3ec2a1c6-ac7c-415c-b97b-5d7c044b8177,
abstract = {{<p>Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.</p>}},
author = {{Venkatappa, Manjunatha and Anantsuksomsri, Sutee and Castillo, Jose Alan and Smith, Benjamin and Sasaki, Nophea}},
issn = {{2072-4292}},
keywords = {{Bamboo mapping; Carbon stocks; CDM; Google Earth Engine; Landsat 8 OLI; PBTC; REDD+; Sentinel-2; Threshold classification; Threshold values; Vegetation phenology}},
language = {{eng}},
number = {{18}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Mapping the natural distribution of bamboo and related carbon stocks in the tropics using google earth engine, phenological behavior, landsat 8, and sentinel-2}},
url = {{http://dx.doi.org/10.3390/rs12183109}},
doi = {{10.3390/rs12183109}},
volume = {{12}},
year = {{2020}},
}