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

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.

[1]  Michael Clutter,et al.  Multivariate Multilevel Nonlinear Mixed Effects Models for Timber Yield Predictions , 2004, Biometrics.

[2]  Sakari Tuominen,et al.  Mapping biomass variables with a multi-source forest inventory technique , 2010 .

[3]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[4]  S. Tuominen,et al.  Data combination and feature selection for multi-source forest inventory , 2008 .

[5]  Håkan Olsson,et al.  Regression functions for multitemporal relative calibration of Thematic Mapper data over boreal forest , 1993 .

[6]  Ronald E. McRoberts,et al.  Estimating forest attribute parameters for small areas using nearest neighbors techniques , 2012 .

[7]  Timo Tokola,et al.  Relative Calibration of Multitemporal Landsat Data for Forest Cover Change Detection , 1999 .

[8]  Margarida Tomé,et al.  A generalized nonlinear mixed-effects height-diameter model for Eucalyptus globulus L. in northwestern Spain. , 2010 .

[9]  J. Lappi.  Calibration of Height and Volume Equations with Random Parameters , 1991, Forest Science.

[10]  Markus Neteler,et al.  Open Source GIS: A GRASS GIS Approach , 2007 .

[11]  Ronald E. McRoberts,et al.  Diagnostic tools for nearest neighbors techniques when used with satellite imagery , 2009 .

[12]  Kalle Eerikäinen,et al.  A multivariate linear mixed-effects model for the generalization of sample tree heights and crown ratios in the Finnish National Forest Inventory. , 2009 .

[13]  E. Vonesh,et al.  Linear and Nonlinear Models for the Analysis of Repeated Measurements , 1996 .

[14]  J. Lappi,et al.  A Longitudinal Analysis of Height/Diameter Curves , 1997, Forest Science.

[15]  Frédéric Achard,et al.  Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics , 2011 .

[16]  E. Tomppo,et al.  Selecting estimation parameters for the Finnish multisource National Forest Inventory , 2001 .

[17]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[18]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[19]  Jari Varjo.  Change detection and controlling forest information using multi-temporal Landsat TM imagery. , 1997 .

[20]  K. Eerikäinen,et al.  A calibrateable site index model for Pinus kesiya plantations in southeastern Africa , 2002 .

[21]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[22]  A. Pekkarinen,et al.  Local radiometric correction of digital aerial photographs for multi source forest inventory , 2004 .

[23]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[24]  P. Anttila,et al.  Change detection in boreal forests using bi-temporal aerial photographs , 2006 .

[25]  C. Wirth,et al.  Generic biomass functions for Common beech (Fagus sylvatica) in Central Europe: predictions and components of uncertainty , 2008 .

[26]  E. Tomppo.  The Finnish multisource National Forest Inventory: Small-area estimation and map production , 2006 .

[27]  T. Tokola,et al.  Use of topographic correction in Landsat TM-based forest interpretation in Nepal , 2001 .

[28]  Timo Tokola,et al.  The Influence of Field Sample Data Location on Growing Stock Volume Estimation in Landsat TM-Based Forest Inventory in Eastern Finland , 2000 .

[29]  Erkki Tomppo,et al.  A report to the food and agriculture organization of the united nations (FAO) in support of sampling study for National Forestry Resources Monitoring and Assessment (NAFORMA) in Tanzania , 2010 .