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Holl A.,Fundacion de Estudios de Economia Aplicada FEDEA | Pardo R.,Fundacion BBVA | Rama R.,Institute of Economics and Geography
Regional Studies

HOLL A., PARDO R. and RAMA R. Just-in-Time manufacturing systems, subcontracting and geographic proximity, Regional Studies. The paper studies the spatial extent of subcontracting linkages for a sample of medium-sized and large Spanish manufacturing firms operating in the automotive and electronics industries. In particular, it analyses how Just-in-Time (JIT) organization of production is related to the spatial pattern of these sourcing relationships when contractors' structural and organizational characteristics, as well as contract characteristics, are taken into account. It is found that firms that implement new technologies and manufacturing systems at the plant level tend to prefer regional to extra-regional outsourcing. This is consistent with Justin-Time's reliance on flexibility in ordering and quick and frequent deliveries, as well as reliable arrival times, to guarantee the disruption-free production which proximity can facilitate. The results support the view that Just-in-Time, in the context of production subcontracting, increases the importance of proximity. © 2010 Regional Studies Association. Source

Garcia M.,University of Alcala | Riano D.,Institute of Economics and Geography | Riano D.,University of California at Davis | Chuvieco E.,University of Alcala | Danson F.M.,University of Salford
Remote Sensing of Environment

Biomass fractions (total aboveground, branches and foliage) were estimated from a small footprint discrete-return LiDAR system in an unmanaged Mediterranean forest in central Spain. Several biomass estimation models based on LiDAR height, intensity or height combined with intensity data were explored. Raw intensity data were normalized to a standard range in order to remove the range dependence of the intensity signal. In general terms, intensity-based models provided more accurate predictions of the biomass fractions. Height models selected were mainly based on a percentile of the height distribution. Intensity models selected included variables that consider the percentage of the intensity accumulated at different height percentiles, which implicitly take into account the height distribution. The general models derived considering all species together were based on height combined with intensity data. These models yielded R2 values greater than 0.58 for the different biomass fractions considered and RMSE values of 28.89, 18.28 and 1.51 Mg ha-1 for aboveground, branch and foliage biomass, respectively. Results greatly improved for species-specific models using the main species present in each plot, with R2 values greater than 0.85, 0.70 and 0.90 for black pine, Spanish juniper and Holm oak, respectively, and with lower RMSE for the biomass fractions. Reductions in LiDAR point density had only a small effect on the results obtained, except for those models based on a variation of the Canopy Reflection Sum, which was weighted by the mean point density. Based on the species-specific equations derived, Holm oak dominated plots showed the highest average carbon contained by aboveground biomass and branch biomass 44.66 and 31.42 Mg ha- 1 respectively, while for foliage biomass carbon, Spanish juniper showed the highest average value (3.04 Mg ha- 1). © 2009 Elsevier Inc. Source

Garcia M.,University of Alcala | Popescu S.,Texas A&M University | Riano D.,Institute of Economics and Geography | Riano D.,University of California at Davis | And 4 more authors.
Remote Sensing of Environment

This study aimed to estimate canopy fuel properties relevant for crown fire behavior using ICESat/GLAS satellite LiDAR data. GLAS estimates were compared to canopy fuel products generated from airborne LiDAR data, which had been previously validated against field data. The geolocation accuracy of the data was evaluated by comparing ground elevation on both datasets, showing an offset of 1 pixel (20m). Canopy cover (CC) was estimated as the ratio of the canopy energy to the total energy of the waveform. Application of a canopy base height threshold (CBH) to compute the canopy energy increased the accuracy of CC estimates (R 2=0.89; RMSE=16.12%) and yielded a linear relationship with airborne LiDAR estimates. In addition, better agreement was obtained when the CC derived from airborne LiDAR data was estimated using the intensity of the returns. An empirical model, based on the CC and the leading edge (LE), was derived to estimate leaf area index (LAI) using stepwise regression providing good agreement with the reference data (R 2=0.9, RMSE=0.15). Canopy bulk density (CBD) was estimated using an approach based on the method developed by Sando and Wick (1972) to derive CBD from field measurements, and adapted to GLAS data. Thus, foliage biomass was distributed vertically throughout the canopy extent based on the distribution of canopy material and CBD was estimated as the maximum 3m-deep running mean considering layers with a thickness of 15cm, which is the vertical resolution of the GLAS data. This approach gave a coefficient of determination of 0.78 and an RMSE of 0.02kgm -3. © 2012 Elsevier Inc. Source

Garcia M.,University of Alcala | Riano D.,Institute of Economics and Geography | Riano D.,University of California at Davis | Chuvieco E.,University of Alcala | And 2 more authors.
Remote Sensing of Environment

This paper presents a method for mapping fuel types using LiDAR and multispectral data. A two-phase classification method is proposed to discriminate the fuel classes of the Prometheus classification system, which is adapted to the ecological characteristics of the European Mediterranean basin. The first step mapped the main fuel groups, namely grass, shrub and tree, as well as non-fuel classes. This phase was carried out using a Support Vector Machine (SVM) classification combining LiDAR and multispectral data. The overall accuracy of this classification was 92.8% with a kappa coefficient of 0.9. The second phase of the proposed method focused on discriminating additional fuel categories based on vertical information provided by the LiDAR measurements. Decision rules were applied to the output of the SVM classification based on the mean height of LiDAR returns and the vertical distribution of fuels, described by the relative LiDAR point density in different height intervals. The final fuel type classification yielded an overall accuracy of 88.24% with a kappa coefficient of 0.86. Some confusion was observed between fuel types 7 (dense tree cover presenting vertical continuity with understory vegetation) and 5 (trees with less than 30% of shrub cover) in some areas covered by Holm oak, which showed low LiDAR pulses penetration so that the understory vegetation was not correctly sampled. © 2011 Elsevier Inc. Source

Garcia M.,University of Alcala | Danson F.M.,University of Salford | Riano D.,Institute of Economics and Geography | Riano D.,University of California at Davis | And 3 more authors.
International Journal of Applied Earth Observation and Geoinformation

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R 2= 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R 2 = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R 2 = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R 2 = 0.87. © 2011 Elsevier B.V. Source

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