GIS-enabled bioenergy potential mapping in India

1. GIS-enabled bioenergy potential
mapping in India
Yan Yan
May 2019
Page 1

2. Motivation for bioenergy potential
mapping and optimization
• India has a large biomass resource inventory
• The installed bioenergy capacity in India is 9.5 GW,
ranked fourth in the world [1]
• However, by 2015 more than 40% (1200 MW) of the
installed installed capacity is temporarily or
permanently closed due to reasons like unsecured
biomass, unorganized supply chain and etc. [2]
• Other countries and regions have dedicated great
research effort in developing tools and model to help
planning for bioenergy usage
Page 2

3. Motivation for bioenergy potential
mapping and optimization
Page 3

4. Mapping contents
• Burned agricultural residue
• Animal manure
• MSW
Page 4

5. Burned agricultural residue
• Methodology
– Start with NASA MCD64A1 burn area product
– Reprocess the spatial and temporal data in
MCD64A1 using collection 6 mapping algorithm
– Visualize data points in geographical information
system
Page 5
MCD64A1
Collection 6 burned area
mapping algorithm by Giglio
et al. [3]
GIS

6. Burned agricultural residue based
energy ArcGIS online mapping result
Page 6

7. Animal manure
• Methodology [4]
– Start with 2012 India livestock census statistics
– Create geospatial mask using MODIS land cover and
population density map to filter out areas that are unsuitable
for livestock to live in, e.g. permanent water body and
densely populated urban area
– Calculate suitability-corrected livestock density
– Randomly select sampling points that are sparsely
distributed in the geographical space of India
– Extract the predictor values at each sampling point
Page 7

8. Animal manure
Anthropogenic
Human population density (consensus
model between Worldpop, Landscan
and GPW4)
Spatial predictor and suitability mask
Travel time to cities of 50,000 people Spatial predictor
Topography Elevation (GTOPO30) Spatial predictor
Slope (GTOPO30) Spatial predictor
Vegetation
10 Fourier-derived variables from
Normalized Difference Vegetation
Index from MODIS (MODIS)∗
Spatial predictor
Length of growing period Spatial predictor
Green-up and senescence (annual cycle
1 and 2)
Spatial predictor
Cropping intensity Spatial predictor
Forest cover Spatial predictor
Climatic
14 Fourier-derived variables from Day
Land Surface Temperature (MODIS)
Spatial predictor
Precipitations Spatial predictor
Page 8

9. Animal Manure
Page 9
Final prediction: majority voting for
classification or averaging for regression

10. Animal manure
• Methodology (cont. )
– 70% of the sampling points are selected as training data,
while the rest are testing data
– Build random forest models to predict livestock population
at each pixel (100 km2)
Page 10
Census data Apply suitability map Calculate density
Randomly select
sampling points
Train random forest
models with training
sampling points
Predict livestock
population at each
pixel

11. Different types of manure
distribution
Page 11

12. Animal manure based energy
ArcGIS online mapping result
Page 12

13. Municipal solid waste
• Methodology
– Start with WorldPop population density map
– Multiply population at each grid with municipal
solid waste production per day per capita
Page 13

14. Municipal solid waste mapping
result
Page 14

15. Comparative bioenergy distribution
map
Page 15

16. References
• [1] IRENA Report 2017
• [2] Natarajan, K., & Pelkonen, P. (n.d.). Exploiting the Unexploited Biomass Energy
in India through ... Retrieved from http://www.ipcbee.com/vol82/001-IEEA2015-
C003.pdf
• [3] Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O., 2018, The
Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of
Environment, in press.
• [4] Stevens, Forrest R., et al. “Disaggregating Census Data for Population Mapping
Using Random Forests with Remotely-Sensed and Ancillary Data.” PLoS ONE, vol.
10, no. 2, 2015, pp. 1–22, doi:10.1371/journal.pone.0107042.
Page 16