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Utilizing a Multi-Source Forest Inventory Technique, MODIS Data and Landsat TM Images in the Production of Forest Cover and Volume Maps for the Terai Physiographic Zone in Nepal

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Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland
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Department of Forest Research and Survey, G.P.O. 3339, Babarmahal, Kathmandu, Nepal
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Author to whom correspondence should be addressed.
Remote Sens. 2012, 4(12), 3920-3947; https://doi.org/10.3390/rs4123920
Received: 15 October 2012 / Revised: 4 December 2012 / Accepted: 5 December 2012 / Published: 10 December 2012
An approach based on the nearest neighbors techniques is presented for producing thematic maps of forest cover (forest/non-forest) and total stand volume for the Terai region in southern Nepal. To create the forest cover map, we used a combination of Landsat TM satellite data and visual interpretation data, i.e., a sample grid of visual interpretation plots for which we obtained the land use classification according to the FAO standard. These visual interpretation plots together with the field plots for volume mapping originate from an operative forest inventory project, i.e., the Forest Resource Assessment of Nepal (FRA Nepal) project. The field plots were also used in checking the classification accuracy. MODIS satellite data were used as a reference in a local correction approach conducted for the relative calibration of Landsat TM images. This study applied a non-parametric k-nearest neighbor technique (k-NN) to the forest cover and volume mapping. A tree height prediction approach based on a nonlinear, mixed-effects (NLME) modeling procedure is presented in the Appendix. The MODIS image data performed well as reference data for the calibration approach applied to make the Landsat image mosaic. The agreement between the forest cover map and the field observed values of forest cover was substantial in Western Terai (KHAT 0.745) and strong in Eastern Terai (KHAT 0.825). The forest cover and volume maps that were estimated using the k-NN method and the inventory data from the FRA Nepal project are already appropriate and valuable data for research purposes and for the planning of forthcoming forest inventories. Adaptation of the methods and techniques was carried out using Open Source software tools. View Full-Text
Keywords: data imputation; GIS; k-nearest neighbors; mapping forest variables; nonlinear mixed-effects model; Open Source software; Landsat; MODIS data imputation; GIS; k-nearest neighbors; mapping forest variables; nonlinear mixed-effects model; Open Source software; Landsat; MODIS
MDPI and ACS Style

Muinonen, E.; Parikka, H.; Pokharel, Y.P.; Shrestha, S.M.; Eerikäinen, K. Utilizing a Multi-Source Forest Inventory Technique, MODIS Data and Landsat TM Images in the Production of Forest Cover and Volume Maps for the Terai Physiographic Zone in Nepal. Remote Sens. 2012, 4, 3920-3947.

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