The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLII-3/W6
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 369–375, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-369-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 369–375, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-369-2019

  26 Jul 2019

26 Jul 2019

SPATIAL DISAGGREGATION OF THE BIOENERGY POTENTIAL FROM CROP RESIDUES USING GEOSPATIAL TECHNIQUE

A. Chakraborty, A. Biswal, V. Pandey, C. S. Murthy, P. V. N. Rao, and S. Chowdhury A. Chakraborty et al.
  • Agricultural Sciences & Applications, Remote Sensing Applications Area, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India

Keywords: crop residue, biomass, bioenergy, spatial disaggregation, random forest, NPP

Abstract. Limited national fossil fuel resources and sustained increases in energy prices have resulted in nationwide efforts to study and deploy alternative energy sources. Despite high prospect, biomass resources has not been effectively utilized in India due to the lack of information on amount, type and time of its availability on a geospatial frame work to facilitate its transportability, establishment of bio-fuel plants tailor made for specific crop residues. Hence, a comprehensive approach towards geospatial mapping of bio-energy potential from surplus crop residues of selected crops (rice. wheat, cotton and sugarcane) over the Haryana state of India is implemented by utilizing a hybrid model combining both statistical and remote sensing technique. Bioenergy potential was calculated from crop production statistics collected at district level. The grain production data were converted into gross residue potential using residue production ratio. The crop residue was further converted into collectable crop residue using collectable coefficient. To generate the spatial map of the selected crops, potential crop masks were prepared using multi-temporal satellite data. These crop masks were then converted to crop fraction at 1 km grid level. MODIS NPP data product was then processed and converted into same 1 km to account the spatial variability of biomass potential. Using these crop fractions as independent variables, relationship was established with NPP as dependent variable using a machine learning technique (Random Forest algorithm). These crop specific response curves (crop fraction vs NPP) were utilized as a weight to disaggregate district level gross biomass potential to 1 km grid level. The spatial map thus generated provided spatial details of the type and amount surplus crop residues and could be vital input for planning and policy making of utilization of the surplus biomass resources of India.