Presentation on theme: "Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,"— Presentation transcript:
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T, University of Nairobi,
Problem Monitoring biophysical features of sugarcane using ground-based crop cut techniques is not accurate and requires immense resource inputs in terms of time, manpower and equipment.
Partial Solution Remotely sensed data could be used to estimate productivity of growing and mature cane, sugarcane health and vigor monitoring, and to understand sugarcane ecological processes.
The Study Pertinent crop biophysical parameters that characterize biomass in representative sugarcane fields and used satellite image and spectral reflectance values in TM combinations to calculate vegetative indices.
Study Area Mumias Nucleus Estate With 43000 ha of sugarcane Fields ideal as they measure at least a pixel each 35 34 Mumias- Butere Location of study siteLocation of study site K en ya
Study Methods Data Collection –Biophysical data Crop cuts ( dry matter wt. height, brix GPS positions
Study Methods Contd. Satellite Image –LANDSAT ETM + Path 169 Row 70 of 22 Feb 2003, cloud free –Geo referenced and geometric correction Gave a RMS error of 0.15 Extract spectral values using ILWIS
Results Strong relationship between biomass and most spectral variables. The ETM+ visible bands strongly correlated with biomass, Band 1 (blue-green), band 3, and band 4 (near infra-red) stronger predictors (r 2 = 0.93, 0.85, and 0.91, respectively). Band 6 was a good predictor of biomass (r 2 =0.86).
Results Pixel-by-pixel gave the best prediction of r 2 of 0.98. In comparison, when the sample points where averaged to cover half a pixel, the prediction was slightly lower (r 2 =0.92). The lowest prediction was given when the points were considered point by point (r 2 = 0.81).
Results Model used to map sugarcane biomass Validation at r 2 for 0.82 point to point and 0.98 for normalized HighMediumLow
Limitations Dependent on date of satellite Accuracy of GPS Spatial resolution
Conclusion Results from the study show that the LANDSAT ETM+ crop reflectance can be used to predict biomass yields for large sugarcane fields. Mature sugarcane biophysical parameters were well predicted in spectral reflectance