Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data

Thenkabail, Prasad S.; Stucky, N.; Griscom, Bronson W.; Ashton, Mark S.; Diels, Jan; Meer, B. van der; Enclona, Eden A

doi:10.1080/01431160412331291279

Cited by 156 publications

(110 citation statements)

References 27 publications

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“…For the normalized difference greenness index (NDGI)-the only index exploiting the Green band-no evident improvement in model goodness was noticeable among the data groups. The importance of the red wavelength as a most critical band in providing vegetation biophysical information is well-supported in the literature (e.g., [44]). Nevertheless, NDGI shows the best R 2 in the radiance and TOA data groups (A, B).…”

Section: Resultsmentioning

confidence: 52%

“…Previous work with VHR imagery includes the use of IKONOS by Thenkabail et al [44] to estimate AGB and carbon stock of oil palm plantations in African savannas. Zhou and colleagues [45] exploited VIs and texture images derived from Quickbird and topographic information to derive AGB of black locust plantations in the Loess Plateau of China.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Clerici

Rubiano

Abd-Elrahman

et al. 2016

Forests

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Periurban forests are key to offsetting anthropogenic carbon emissions, but they are under constant threat from urbanization. In particular, secondary Neotropical forest types in Andean periurban areas have a high potential to store carbon, but are currently poorly characterized. To address this lack of information, we developed a method to estimate periurban aboveground biomass (AGB)-a proxy for multiple ecosystem services-of secondary Andean forests near Bogotá, Colombia, based on very high resolution (VHR) GeoEye-1, Pleiades-1A imagery and field-measured plot data. Specifically, we tested a series of different pre-processing workflows to derive six vegetation indices that were regressed against in situ estimates of AGB. Overall, the coupling of linear models and the Ratio Vegetation Index produced the most satisfactory results. Atmospheric and topographic correction proved to be key in improving model fit, especially in high aerosol and rugged terrain such as the Andes. Methods and findings provide baseline AGB and carbon stock information for little studied periurban Andean secondary forests. The methodological approach can also be used for integrating limited forest monitoring plot AGB data with very high resolution imagery for cost-effective modelling of ecosystem service provision from forests, monitoring reforestation and forest cover change, and for carbon offset assessments.

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Section: Resultsmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Clerici

Rubiano

Abd-Elrahman

et al. 2016

Forests

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“…It has a potential benefit in biomass estimation ranging from medium to large scales. High spatial resolution data like IKONOS [6] and WORLDVIEW-2 [7] provide the accurate biomass at local scales. For regional scales a large volume of data like LANDSAT which is medium spatial resolution data is used.…”

Section: Introductionmentioning

confidence: 99%

Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

Kumar¹,

Nagai²,

Witayangkurn³

et al. 2016

JGIS

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Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% -80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. K. K. Kumar et al.507

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Section: Role Of Remote Sensing In Mapping Above-ground Biomassmentioning

confidence: 99%

“…The spatial resolutions vary from less than one metre to hundreds of metres. Optical Remote Sensing data has been used by numerous researchers for biomass estimation [16][17][18][19][20][21][22][23][24][25][26][27].Radar Remote Sensing has gained prominence for above-ground biomass estimation in recent years due to its cloud penetration ability as well as detailed vegetation structural information [15]. While airborne Synthetic Aperture Radar (SAR) systems have been operating for many years, space-borne systems such as Terra-SAR, ALOS and PALSAR have become available since 2000.…”

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confidence: 99%

Remote Sensing of Above-Ground Biomass

2017

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Accurate measurement and mapping of biomass is a critical component of carbon stock quantification, climate change impact assessment, suitability and location of bio-energy processing plants, assessing fuel for forest fires, and assessing merchandisable timber. While above-ground biomass includes both live and dead plant material, most of the recent research effort on biomass estimation has focussed on the 'live' component (live trees) due to the prominence of this component. Accurate estimates of biomass is a prerequisite for better understanding of the impacts of deforestation and environmental degradation on climate change.The Intergovernmental Panel on Climate Change (IPCC) [1] has listed five terrestrial ecosystem carbon pools involving biomass: above-ground biomass, below-ground biomass, litter, woody debris and soil organic matter. Of these five, above-ground biomass is the most visible, dominant, dynamic and important pool of the terrestrial ecosystem, constituting around 30% of the total terrestrial ecosystem carbon pool. Above-ground biomass estimation, and especially forest biomass, has received considerable attention over the last few decades because of increased awareness of climate warming and the role forest biomass plays in carbon sequestration and release of greenhouse gases due to deforestation.Above-ground biomass estimates are the central basis for carbon inventories and most international negotiations in carbon trading schemes. Carbon trading markets require long-term information on carbon stocks, particularly on the above-ground 'live' biomass component as this is the most dynamic, changing and manipulable component of all the biomass pools. This is the 'merchantable' component of biomass.Above-ground forest biomass accounts for between 70% to 90% of total forest biomass [2]. While soil organic matter holds two to three times more carbon than biomass on a global scale, much of the soil carbon is more protected and not easily oxidised [3]. On the other hand, above-ground biomass is in a continuous state of flux due to fire, logging, storms, landuse changes, etc., and thus contributes to atmospheric carbon fluxes to a much greater extent and so is of much greater interest. Due to this dynamism of above-ground biomass, it is necessary to monitor it continuously and not measure once and forget.While detailed estimations of biomass is necessary for accurate carbon accounting, reliable estimation methods are few. Accurate estimates of stored carbon (biomass as dry weight is 50% carbon [4] and understanding sources and sinks can improve the accuracy of carbon flux models and thus lead to better projections of climate change and impacts. Initiatives such as Reducing Emissions from Deforestation and Forest Degradation (REDD) and REDD+ also rely heavily on above-ground biomass estimates [5,6]. REDD+ includes financing schemes and incentives with the aim of mitigating climate change by reducing deforestation and forest degradation through sustainable forest management and conservation, and enhancement...

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Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data

Cited by 156 publications

References 27 publications

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

Remote Sensing of Above-Ground Biomass

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