Use of geographically weighted regression to enhance the spatial features of forest attribute maps

Abstract Geographically weighted regression (GWR) procedures can be adapted to enhance the spatial features of low spatial resolution maps based on higher resolution remotely sensed imagery. This operation relies on the assumption that the GWR models developed at low resolution can be proficiently applied to higher resolution data. An example of such an application is presented for downscaling a forest growing stock map which has been recently produced over the Italian national territory. GWR was applied to a Landsat Thematic Mapper image of Tuscany (Central Italy) for downscaling the growing stock predictions from a 1-km to a 100-m resolution. The accuracy of the experiment was assessed versus the measurements of a regional forest inventory. The results obtained indicate that GWR can enhance the spatial features of the original map depending on the spatially variable correlation existing between the forest attribute and the ancillary data used. A final ecosystem modeling exercise demonstrates the utility of the spatially enhanced growing stock predictions to drive the simulation of the main forest processes.

[1]  Piermaria Corona,et al.  Prediction of forest NPP in Italy by the combination of ground and remote sensing data , 2015, European Journal of Forest Research.

[2]  Sandra Englhart,et al.  Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use , 2011 .

[3]  Giles M. Foody,et al.  Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna , 2004 .

[4]  A. Barbati,et al.  Contribution of large-scale forest inventories to biodiversity assessment and monitoring , 2011 .

[5]  W. Cleveland,et al.  Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .

[6]  P. Corona,et al.  Mapping by spatial predictors exploiting remotely sensed and ground data: A comparative design-based perspective , 2014 .

[7]  Marco Bindi,et al.  Modelling the forest carbon budget of a Mediterranean region through the integration of ground and satellite data , 2009 .

[8]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[9]  Piermaria Corona,et al.  Large-scale monitoring of coppice forest clearcuts by multitemporal very high resolution satellite imagery. A case study from central Italy , 2011 .

[10]  Anna Barbati,et al.  Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests , 2009, Remote. Sens..

[11]  Fabio Maselli,et al.  Improved Estimation of Environmental Parameters through Locally Calibrated Multivariate Regression Analyses , 2002 .

[12]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[13]  Erkki Tomppo,et al.  Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach , 2004 .

[14]  Fabio Maselli,et al.  Integration of high- and low-resolution satellite data to estimate pine forest productivity in a Mediterranean coastal area , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Geoffrey J. Hay,et al.  The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data , 2012 .

[16]  P. Corona.  Integration of forest mapping and inventory to support forest management , 2010 .

[17]  R. McRoberts,et al.  Remote sensing support for national forest inventories , 2007 .

[18]  Pavel Propastin,et al.  Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia. , 2009 .

[19]  Piermaria Corona,et al.  Monitoring and assessing old‐growth forest stands by plot sampling , 2010 .

[20]  Piermaria Corona,et al.  Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems , 2009 .

[21]  Piermaria Corona,et al.  Modeling primary production using a 1 km daily meteorological data set , 2012 .

[22]  Peter E. Thornton,et al.  Parameterization and Sensitivity Analysis of the BIOME–BGC Terrestrial Ecosystem Model: Net Primary Production Controls , 2000 .

[23]  M. Bauer,et al.  Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method , 2001 .

[24]  Anna Barbati,et al.  Use of BIOME-BGG to simulate Mediterranean forest carbon stocks , 2011 .

[25]  R. De Lauretis,et al.  An approach to estimate carbon stocks change in forest carbon pools under the UNFCCC: the Italian case , 2008 .

[26]  Piermaria Corona,et al.  European Forest Types and Forest Europe SFM indicators: Tools for monitoring progress on forest biodiversity conservation , 2014 .

[27]  J. Hyyppä,et al.  Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests , 2008 .

[28]  Ivo Trinajstić.  La vegetazione forestale dell’ isola di Krk (Veglia) , 1964 .

[29]  Gherardo Chirici,et al.  Combination of optical and LiDAR satellite imagery with forest inventory data to improve wall-to-wall assessment of growing stock in Italy , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[30]  Piermaria Corona,et al.  Area-based lidar-assisted estimation of forest standing volume , 2008 .

[31]  Mathias Schardt,et al.  EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements , 2010 .

[32]  S. Franklin.  Remote Sensing for Sustainable Forest Management , 2001 .