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Spatial analysis of area and carbon stocks under Populus deltoides based agroforestry systems in Punjab and Haryana states of Indo-Gangetic Plains
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Spatial analysis of area and carbon stocks under Populus deltoides based agroforestry systems in Punjab and Haryana states of Indo-Gangetic Plains
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Journal Article
Spatial analysis of area and carbon stocks under Populus deltoides based agroforestry systems in Punjab and Haryana states of Indo-Gangetic Plains
2020
Overview
Various agroforestry systems are prevalent in different agro-climatic regions of India and occupy sizeable area. Populus deltoides (poplar) based agroforestry systems are very predominant in Indo-Gangetic Plains of northern India. These systems are not only meeting wood demand of wood-based industries but also contributing significantly in generating employment, reducing atmospheric CO2 vis-à-vis mitigating climate change. The present study aimed at estimating area under poplar plantations and assessment of biomass production and carbon stock at district level for selected districts of Punjab and Haryana states. High resolution multispectral remote sensing data (LISS-IV, spatial resolution- 5.8 m) have been used for mapping poplar area in these districts. Growth data (tree density, diameter at breast height) from farmers’ fields were collected for assessment of aboveground and total biomass. Poplar area in Hoshiarpur, Rupnagar, Ludhiana, Shahid Bhagat Singh Nagar, Yamunanagar, Kurukshetra and Karnal districts accounted for 3.19, 4.09, 1.02, 0.43, 7.25, 1.58 and 0.97%, respectively, of their geographical area. As percentage of agroforestry area, poplar area occupied 43.8% in four districts of Punjab and 53.4% in three districts of Haryana. Estimated carbon stock in total biomass of poplar plantations was 0.931, 0.481, 0.287, 0.040, 0.949, 0.256 and 0.229 million tonnes, respectively. CO2 equivalent C in aboveground and total biomass was estimated to be 7.854 and 9.946 million t, respectively, in all these seven districts. The proposed methodology based on object-oriented image classification showed promising results as far as mapping of agroforestry systems/species is concerned.
Publisher
Springer Nature B.V
Subject
