New methodological approach for biomass resource assessment in India using GIS application and land use/land cover (LULC) maps

Natarajan, Karthikeyan; Latva-Käyrä, Petri; Zyadin, Anas; Pelkonen, Paavo

doi:10.1016/j.rser.2016.05.070

Cited by 36 publications

(17 citation statements)

References 28 publications

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“…For Supervised classification, representative pixels or training data [46] for each land cover classes to be segmented were selected and utilized in the classification algorithm. Ten (10) training points per land cover class were identified and randomly selected from Google Earth [47] online [48,49] for Landsat 8 ( Table 3). The land cover classes of these training points were determined using the Food and Agriculture Organization (FAO) land cover classes and description [50].…”

Section: Classification and Analysismentioning

confidence: 99%

“…Kappa coefficient was also used to measure the classification accuracy. Kappa coefficient (K) is a discrete multivariate technique commonly used in accuracy assessment that provides a more unbiased estimate of overall agreement [48,54]. The K interpretation values range from poor to excellent agreement ranging from 0 to 1.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…Due to the time and resources constraints, the accuracy of the classified image was based on the available higher resolution online maps (e.g., Google Earth) [44,52,55] and other relevant secondary information available such as land cover maps from National Mapping and Resource Information Authority (NAMRIA) [56]. The matrix (Table 6) tallied the correctness of the classification generated through the application of sampling points [44][45][46]48,70,74]. The matrix was constructed based on the reference data as represented in the columns and compared against to the classified data in rows ( Table 6).…”

mentioning

confidence: 99%

“…Kappa Coefficient (K)-provides a more unbiased estimate of the overall agreement [48,54]. The K interpretation values range from poor to excellent agreement ranging from 0 to 1.…”

mentioning

confidence: 99%

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Land Use/Land Cover Change Detection and Urban Sprawl Analysis in the Mount Makiling Forest Reserve Watersheds and Buffer Zone, Philippines

et al. 2019

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Ecologically Valuable Areas play an important role in providing ecosystem services, however, human activities such as land conversion and urban sprawl pose pressures and threats to these areas. The study assessed the land use/land cover and urban sprawl in the Mount Makiling Forest Reserve (MMFR) Watersheds and Buffer Zone from 1992 to 2015 using remote sensing and Geographic Information System (GIS). Results showed that the land use/cover within the MMFR buffer zone has changed from 1992 to 2015 with built-up areas increasing by 117% despite Proclamation 1257, s. 1998 which regulates human activities in the zone. Based on the Shannon entropy analysis the land development in the MMFR buffer zone tends to be dispersed and sprawling. However, when the magnitude of change of urban sprawl in the buffer zone from 2002 to 2015 was calculated, a decrease in the entropy value was observed which implies a compacting pattern as the human settlement in the buffer zone increases over time. Proclamation 1257, s. 1998 needs to be strengthened to protect MMFR and its buffer zone from further encroachment and pressure. Moreover, remote sensing and GIS proved to be useful tools for assessing urban sprawl in ecologically valuable areas such as MMFR.

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Section: Classification and Analysismentioning

confidence: 99%

Section: Accuracy Assessmentmentioning

confidence: 99%

mentioning

confidence: 99%

“…Kappa Coefficient (K)-provides a more unbiased estimate of the overall agreement [48,54]. The K interpretation values range from poor to excellent agreement ranging from 0 to 1.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Land Use/Land Cover Change Detection and Urban Sprawl Analysis in the Mount Makiling Forest Reserve Watersheds and Buffer Zone, Philippines

et al. 2019

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“…Moreover, remotesensing information, such as satellite images, is applied for assessing or monitoring the field-based biomass residues by image interpretation together with a geographic information system program (GIS) [12]. Medium-resolution images, such as Landsat 8 images, can be used to assess the spatial allocation of biomass resources [13]. The routine of satellite orbits can be used to monitor the crop growth and the temporal allocation of the biomass generated.…”

Section: Assessment Of Rice Straw Potentialmentioning

confidence: 99%

Study on the Potential of Rice Straws as a Supplementary Fuel in Very Small Power Plants in Thailand

et al. 2018

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Agricultural residue is a major raw material for renewable energy production, particularly heat production, in Thailand. Meanwhile, the process-based residue, such as bagasse, rice husk, wood residue, palm fiber, palm shell, and saw dust, is used as a fuel for energy production in the agro-industry. Hence, this study is intended to assess the net potential and capacity of alternative agricultural residues, specifically rice straws, to serve as the supplementary fuel for very small power plants (VSPPs) in Thailand. According to the results obtained during the crop season of 2015/2016, approximately 26 Mt of rice straws were generated upon the harvesting process. The net potential of rice straws, including those that were burned and those that were left in the fields, was only about 15% or 3.85 Mt, which could be used for heat and electricity production at 1331 kilotons of oil equivalent (ktoe) or 457 MWe. As agro-residues vary by seasonality, the peak season of rice straws was in November, where approximately 1.64 Mt (43%) were generated, followed by December, at 1.32 Mt (34%). On the basis of the results, rice straw has the potential to serve as a fuel supply for VSPPs at 14.2%, 21.6%, 26.3%, and 29.0% for the radii of compilation at 24, 36, 48 km and 60 km, respectively.

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Lignocellulosic Biomass Availability Map: A GIS‐Based Approach for Assessing Production Statistics of Lignocellulosics and its Application in Biorefinery

Kumar

Srinivas

Banerjee

2017

Lignocellulosic Biomass Production and Industrial Applications

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New methodological approach for biomass resource assessment in India using GIS application and land use/land cover (LULC) maps

Cited by 36 publications

References 28 publications

Land Use/Land Cover Change Detection and Urban Sprawl Analysis in the Mount Makiling Forest Reserve Watersheds and Buffer Zone, Philippines

Land Use/Land Cover Change Detection and Urban Sprawl Analysis in the Mount Makiling Forest Reserve Watersheds and Buffer Zone, Philippines

Study on the Potential of Rice Straws as a Supplementary Fuel in Very Small Power Plants in Thailand

Lignocellulosic Biomass Availability Map: A GIS‐Based Approach for Assessing Production Statistics of Lignocellulosics and its Application in Biorefinery

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