David Miranda

An analysis of the effect of the stochastic component of urban cellular automata models

Urban cellular automata models have proved useful tools in urban growth prediction because of their simplicity and their ability to reproduce complex emergent dynamics. Complex emergent dynamic systems involve processes that are difficult to predict, in which randomness plays a key role. In view of the fact that randomness is particularly relevant to complex processes, the aim of this paper is to analyze the sensitivity of the results of urban cellular automata models to the different methods used to incorporate the stochastic component in the models. The urban growth patterns obtained using different stochastic components are analyzed and compared using a number of spatial metrics. The results show that the differences observed in the simulated patterns are sufficiently relevant to justify the need for this type of analysis, which allows for the selection of the stochastic component that best suits the dynamics of the area.

Assessing the attributes of high-density Eucalyptus globulus stands using airborne laser scanner data

This article presents an airborne Light Detection and Ranging (LiDAR)-based method to extract interesting stand attributes for forest management in high-density Eucalyptus globulus Labill. plantations. An adaptive morphological filter (AMF) for classifying terrain LiDAR points in forested areas is used to classify LiDAR points; canopy cover (CC), number of LiDAR-detected trees per hectare (N LD) and individual tree height (h tree) were calculated using the canopy height model (CHM); and several statistics and metrics extracted from the CHM and the normalized height of the LiDAR data cloud (NHD) were incorporated into the linear and multiplicative models for estimating mean height (H m), dominant height (H d), mean diameter (d m), quadratic mean diameter (d g), number of stems per hectare (N), basal area (G) and volume (V). The height accuracy results of the LiDAR-derived digital terrain model (DTM), root mean square error (RMSE) = 0.303 m, revealed that the developed filter behaved well. The values of the RMSE for CC, N LD and h tree were 13.2%, 733.3 stems ha–1 and 1.91 m, respectively. The regressions explained 78% of the variance in ground-truth values for H m (RMSE = 1.33 m); 92% for H d (RMSE = 1.18 m); 71% for d m (RMSE = 1.68 cm); 73% for d g (RMSE = 1.66 cm); 49% for N (RMSE = 667 stems ha–1); 78% for G (RMSE = 5.30 m2 ha–1); and 81% for V (RMSE = 53.6 m3 ha–1).

Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data

Crown fire initiation and spread are key elements in gauging fire behaviour potential in conifer forests. Crown fire initiation and spread models implemented in widely used fire behaviour simulation systems such as FARSITE and FlamMap require accurate spatially explicit estimation of canopy fuel complex characteristics. In the present study, we evaluated the potential use of very low-density airborne LiDAR (light detection and ranging) data (0.5 first returns m-2) - which is freely available for most of the Spanish territory - to estimate canopy fuel characteristics in Pinus radiata D. Don stands in north-western Spain. Regression analysis indicated strong relationships (R2 = 0.82-0.98) between LiDAR-derived metrics and field-based fuel estimates for stand height, canopy fuel load, and average and effective canopy base height Average and effective canopy bulk density (R2 = 0.59-0.70) were estimated indirectly from a set of previously modelled forest variables. The LiDAR-based models developed can be used to elaborate geo-referenced raster files to describe fuel characteristics. These files can be generated periodically, whenever new freely available airborne LiDAR data are released by the Spanish National Plan of Aerial Orthophotography, and can be used as inputs in fire behaviour simulation systems.

Evolutionary feature selection to estimate forest stand variables using LiDAR

Abstract Light detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed by means of multiple linear regression (MLR) after stepwise selection of predictors. An increasing interest in machine learning and evolutionary computation has recently arisen to improve regression use in LiDAR data processing. Although evolutionary machine learning has already proven to be suitable for regression, evolutionary computation may also be applied to improve parametric models such as MLR. This paper provides a hybrid approach based on joint use of MLR and a novel genetic algorithm for the estimation of the main forest stand variables. We show a comparison between our genetic approach and other common methods of selecting predictors. The results obtained from several LiDAR datasets with different pulse densities in two areas of the Iberian Peninsula indicate that genetic algorithms perform better than the other methods statistically. Preliminary studies suggest that a lack of parametric conditions in field data and possible misuse of parametric tests may be the main reasons for the better performance of the genetic algorithm. This research confirms the findings of previous studies that outline the importance of evolutionary computation in the context of LiDAR analisys of forest data, especially when the size of fieldwork datatasets is reduced.

A mixed pixel-and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations

The aim of this study was to evaluate the use of high-resolution airborne laser scanner (ALS) data to detect and measure individual trees. We developed and tested a new mixed pixel- and region-based algorithm (using Definiens Developer 7.0) for locating individual tree positions and estimating their total heights. We computed a canopy height model (CHM) of pixel size 0.25 m from dense first-pulse point data (8 pulses m−2) acquired with a small-footprint discrete-return lidar sensor. We validated the results of individual tree segmentation with accurate field measurements made in 37 plots of Monterey pine (Pinus radiata D. Don) distributed over an area of 36 km2. Fieldwork consisted of labelling all of the trees in each plot and measuring their height and position, for posterior integration of the data from both sources (field and lidar). The proposed algorithm correctly detected and linked 59.8% of the trees in the 37 sample plots. We also manually located the trees by using FUSION software to visualize the raw lidar data cloud. However, because the latter method is extremely time-consuming, we only considered 10 randomly selected plots. Manual location correctly detected and linked 71.9% of the trees (in this subsample the algorithm correctly detected and measured 63.5% of the trees). The R2 values for the linear model relating field- and lidar-measured heights of the linked trees located manually and with the automatic location algorithm were 0.90 and 0.88, respectively.

A comparative study between two regression methods on LiDAR data: a case study

Airborne LiDAR (Light Detection and Ranging) has become an excellent tool for accurately assessing vegetation characteristics in forest environments. Previous studies showed empirical relationships between LiDAR and field-measured biophysical variables. Multiple linear regression (MLR) with stepwise feature selection is the most common method for building estimation models. Although this technique has provided very interesting results, many other data mining techniques may be applied. The overall goal of this study is to compare different methodologies for assessing biomass fractions at stand level using airborne Li-DAR data in forest settings. In order to choose the best methodology, a comparison between two different feature selection techniques (stepwise selection vs. genetic-based selection) is presented. In addition, classical MLR is also compared with regression trees (M5P). The results when each methodology is applied to estimate stand biomass fractions from an area of northern Spain show that genetically-selected M5P obtains the best results.

Classification of rural landscapes from low-density lidar data: is it theoretically possible?

Lidar technology has become an important data source in 3D terrain modelling. In Spain, the National Plan for Aerial Orthophotography will soon release public low-density lidar data (0.5–1 pulses/m2) for most of the country territory. Taking advantage of this fact, this article experimentally assesses the possibility of classifying a rural landscape into eight classes using multitemporal and multidensity lidar data and analyses the effect of point density on classification accuracy. Two statistical methods (transformed divergence and the Jeffries–Matusita distance) were used to assess the possibility of discriminating the eight classes and to determine which data layers were best suited for classification purposes. The results showed that ‘dirt road’ cannot be discriminated from ‘bare earth’ and that the possibility of discriminating ‘bare earth’, ‘pavement’, and ‘low vegetation’ decreases when using densities below 4 pulses/m2. Two non-parametric tests, the Kruskal–Wallis test and the Friedman test, were used to strengthen the results by assessing their statistical significance. According to the results of the Kruskal–Wallis test, lidar point density does not significantly affect the classification, whereas the results of the Friedman test show that bands could be considered as the only parameter affecting the possibility of discriminating some of the classes, such as ‘high vegetation’. Finally, the J48 algorithm was used to perform cross-validation in order to obtain the most familiar quantitative values in the international literature (e.g. overall accuracy). Mean overall accuracy was around 85% when the eight classes were considered and increased up to 95% when ‘dirt road’ was disregarded.

Web-GIS tool for the management of rural land markets: Application to the Land Bank of Galicia (NWSpain)

Land abandonment and stagnation of rural markets in the last few years have become one of the main concerns of rural administrations. The use of Web and GIS (Geographic Information System) technologies can help to mitigate the effects of these problems. This paper pro-poses a novel Web-GIS tool with spatial capabilities for the dynamization of rural land markets by encouraging the transfer of land from owners to farmers through the leasing of plots. The system, based on open source software, offers information about the properties, their environment and their owners. It uses standards for handling the geographic information and for communicating with external data sources. This system was used as the basis for the development of SITEGAL, the tool for the management of the Land Bank of Galicia (www.bantegal.com/sitegal). SITEGAL has been operational since 2007 obtaining benefits for both administration and users (farmers and land owners), and promoting the e-Government.

An open source GIS-based Planning Support System: Application to the land use plan of La Troncal, Ecuador

Planning Support Systems (PSS) comprise a wide variety of geo-technological tools related to GIS and spatial modeling aimed at addressing land planning processes. This article describes the OpenRules system, a PSS based on a previous system called RULES. Among OpenRules new features are its architecture, based exclusively on free and open source software, and its applicability to all land use types, including rural and urban uses. In addition, OpenRules incorporates an unlimited number of land evaluation factors and a new objective in land use spatial allocation. OpenRules has been programmed in Java and implemented as a module of the free GIS software gvSIG, with full integration between the GIS and the decision support tools. Decision support tools include multicriteria evaluation, multiobjective linear programming and heuristic techniques, which support three basic stages of land use planning processes, namely land suitability evaluation, land use area optimization and land use spatial allocation. The application of OpenRules to the region of La Troncal, Ecuador, demonstrates its capability to generate alternative and coherent solutions through a scientific and justified procedure at low cost in terms of time and resources.

Land-Development Dynamics by Morphological Areas: A Case Study of Ribadeo, Northwest Spain

The need to understand land-development processes in order to address the problem derived from urban growth led to the implementation of a number of scientific methods aimed at explaining urban-growth patterns. The dynamics that originate these patterns are complex and may vary across space, such that in small areas there may be various processes operating and producing different kinds of growth. Our aim is to use cluster-analysis techniques to identify zones with similar urban-growth patterns in a coastal rural municipality of the northwest of Spain. Then, the processes that originated the different growth patterns identified from the cluster analysis are characterized using logistic regression techniques. The methodology differentiated three clusters (an urban cluster, a rural cluster, and a rural cluster with urban influence) and characterized the underlying dynamics. This proves that the techniques used in this study constitute a straightforward tool to identify and analyze areas with uniform land-development patterns in order to gain deeper knowledge and produce better regulations and zoning for each area.