Sahyadri Conservation Series - 53 ENVIS Technical Report: 100,  April 2016

Time-series MODIS NDVI based Vegetation Change Analysis with Land

Surface Temperature and Rainfall in Western Ghats, India
Ramachandra TV           Uttam Kumar          Anindita Dasgupta

METHODS

The steps involved are listed below and shown in figure 2.

  1. Creation of base layers: Base layers like district boundary, district with taluk and village boundaries, road network, drainage network, mapping of water bodies, etc. were generated from SOI topographic maps of scale 1:250000 and 1:50000.
  2. Geo-referencing of MODIS NDVI data: MODIS NDVI data were geo-corrected with known ground control points (GCP’s), projected to Polyconic (latitude-longitude coordinate system) with Evrst 56 datum, and resampled to 250 m × 250 m grid cell, multilayer image stack followed by masking and cropping of the study area.
  3. Geo-referencing of MODIS LST data: MODIS LST data were geo-corrected with known GCP’s and projected to Polyconic system, Evrst 56 datum, followed by masking and cropping of the study area. These 1 km bands were resampled to 250 m using nearest neighbourhood technique to be consistent with MODIS NDVI bands.
  4. Computation of LST from MODIS LST bands: MODIS Land Surface Temperature/Emissivity (LST/E) data with 1 km spatial resolution with a data type of 16-bit unsigned integer were multiplied by a scale factor of 0.02 (http://lpdaac.usgs.gov/modis/dataproducts.asp#mod11). The corresponding temperatures for all data were converted to degree Celsius (°C).
  5. Generation of rainfall maps: Interpolation of rainfall data points were performed by krigging to obtain rainfall raster maps at 250 m spatial resolution so as to maintain consistency of spatial resolutions between datasets.
  6. NDVI thresholding: NDVI data were thresholded empirically with field data to segregate NDVI values into four LC classes – dense vegetation, agricultural/farmland/grassland, settlement/barren land/soil and water bodies through training data, boxplot, Google Earth and field knowledge.
  7. Validation of classified maps: The LC maps obtained by thresholding NDVI maps were validated using test data collected from ground and other sources discussed later. 
  8. LC Change detection: LC change detection was performed by comparing the LC area per class during 10 years.
  9. Seasonal pattern/trend analysis: NDVI, LST and rainfall data of vegetation class (forest and agricultural) were analysed to understand their variations in different seasons (summer, monsoon and winter) during 10 years.
  10. Statistical analysis: Relationship between time-series NDVI, LST and rainfall data were analysed using statistical methods.
  11. Trend analysis and modelling: The rainfall patterns in forest and agriculture/grassland areas were modelled and forecasted using autoregressive integrated moving average (ARIMA).


Figure 2: Flowchart of the overall method.