Ana Hernando

Quantitatively defining the conservation status of Natura 2000 forest habitats and improving management options for enhancing biodiversity

The main goal of Natura 2000 network is to guarantee the favourable conservation status of habitats and species ensuring European biodiversity. As a result, certain forest areas have been included in this network listed as 9230-Quercus pyrenaica habitat and 9340-Quercus ilex subsp. rotundifolia forest habitat. These areas were previously used for firewood extraction or livestock grazing and browsing. Nowadays these habitats are coppice forests with asexual regeneration, which is far from the desired conservation status. Traditional timber harvesting plans do not take account of the new objectives required for these Natura sites, which attempt to ensure biodiversity and recreational uses instead of simply focusing on timber production. This paper proposes a flexible methodology (applied to the study area “Dehesa Boyal” in Ávila, Spain) for managing Natura 2000 forest sites by stands for sustainable forest management and the new requirements. The methodology has two phases. The first, “Division of the forest area into stands”, defines homogeneous patches of vegetation distinct in species composition, physiognomic structure and future management. The second, “Conservation status assessment of stands”, quantifies the conservation status of each previously classified stand considering a series of factors such as: functional health, restoration, floral richness and structure. A total value integrating the conservation status of stands is then calculated for the habitat. Both phases use Geographic Information System tools for managing information and visualizing results. The proposed methodology provides forest managers with a good knowledge of the territory and subsequently enables them to take appropriate conservation measures to maintain biodiversity.

Spatial and thematic assessment of object-based forest stand delineation using an OFA-matrix

Validacion de la cartografia generada del terreno a partir de una nuevo sistema de validacion propuesto

Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management.

Natura 2000 is a European network of protected areas established under the Habitats Directive (92/43/EEC). According to the Habitats Directive, habitat maps must be periodically updated, which requires the development of cost- and time-efficient mapping practices. In this study, we propose a methodology for habitat mapping using very high spatial resolution (QuickBird) images with Object-Based Image Analysis (OBIA). We classified five segmentation levels: level 5 incorporated the prior knowledge of the study area into the analysis; level 4 and 1 were used to identify arable areas and land covers, respectively. The information contained in levels 1, 4, and 5 was then combined to classify plant species in level 2. Finally, habitats were classified in level 3 using level 2 class-related features. The habitat map obtained had an overall accuracy of 86.3 percent. Classification accuracies were higher for tree-and pasture-dominated habitats than for shrub-dominated habitats.

The impact of object size on the thematic accuracy of landcover maps

We recently completed the accuracy assessment of a Landsat-derived landcover polygon layer covering the entire province of Alberta (660,000 km2), Canada, for which we gathered reference information for nearly 5000 randomly selected polygons ranging from two hectares to thousands of hectares in size. This gave us the unique opportunity to quantify, for the first time, how the probability of correctly classifying a landcover object varies with its size. Irrespective of whether they are represented as polygons or as sets of connected pixels with the same label, the classification accuracy of landcover objects decreases as their size decreases, steadily for large and medium sizes, and more dramatically when they are within two orders of magnitude of the pixel size of the input image. We show that this size-dependency is bound to occur whenever the size distribution of landcover objects follows an inverse power law. Our results are consistent with previous studies on related issues, confirm the need to account for size when assessing the accuracy of object-based landcover maps, and cast doubts on the validity of (1) recently proposed object-based accuracy estimators, and (2) landscape pattern analyses where the minimum patch size is close to the pixel size.

Virtualization of residential IoT functionality by using NFV and SDN

We propose to leverage the virtualization possibilities of Network Functions Virtualization (NFV) together with the programmability of Software Defined Networking (SDN) in order to offer a portfolio of IoT-related functions to the residential users. The objectives are to reach economies of scale by offering a reasonably inexpensive customer premises equipment supporting most IoT physical communication options, whereas all self-discovery and the rest of vendor-specific functionality is externalized and implemented by the ISP (Internet Service Provider) or third parties.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

ABSTRACT In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.

POLS: A versatile tool for sampling polygon GIS layers☆

Abstract We introduce POLS (shorthand for POLygon Sampling), a versatile GIS tool for extracting a random subset of polygons from a vector layer. POLS enables users to optionally (i) set the sampling intensity in terms of percent area of the layer, number of polygons, or both; (ii) specify different strata to be sampled with equal or different intensity; (iii) preclude the occurrence of adjacent polygons in the sample; (iv) ensure that the output sample is spatially balanced; (v) estimate empirically (through simulation) the inclusion probability of each individual polygon; and (vi) compute the Horvitz–Thompson Estimator (HTE) and its confidence interval for target variables measured in the sample polygons. POLS is specially suited for accuracy assessments of thematic maps that use polygons as sampling units, but it can also be applied to any probability-based survey that relies on GIS polygons. The option enforcing non-adjacency potentially increases sampling efficiency by reducing the effect of spatial autocorrelation. The spatial balance option ensures that the polygons in the sample are well distributed across the extent of the layer. When the non-adjacency constraint is used, the tool applies a novel random-selection algorithm that is designed to reduce the impact of this constraint on both the inclusion probability and the spatial distribution of sample polygons. We describe the tool and the algorithm behind it, compare the latter with two other methods that we previously tested, study the impact of the non-adjacency constraint and the spatial balance on the inclusion probability, and demonstrate the estimation of both the HTE and its variance for a sample target variable. The tool is freely available on the internet.

Biodiversity and Conservation Status of a Beech (Fagus sylvatica) Habitat at the Southern Edge of Species´Distribution

© 2012 Tejera et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Biodiversity and Conservation Status of a Beech (Fagus sylvatica) Habitat at the Southern Edge of Species ́Distribution

Planning and selection of green roofs in large urban areas. Application to Madrid metropolitan area

Abstract Due to the numerous environmental problems facing todays society, and especially urban areas, green roofs are presented as an adequate technique to fight the consequences of pollution, traffic and lack of green areas. These green structures help to reduce the effects of Urban Heat Island, to decrease noise and atmospheric pollution, to protect homes from isolation and cold; they also capture rainwater and improve biodiversity. A new methodology is presented to select the best location of green roofs in large cities. In the first phase, this methodology helps to determine the most suitable neighborhoods, analyzing four main variables of interest in urban environs: pollution, traffic, green areas and population. In order to benefit a greater number of inhabitants, the neighborhoods with the worst air quality, more traffic, less green areas and higher population density, are selected. In the second phase, we used LIDAR technology to identify available roofs for the installation of the green roofs according to the height and roof typology of the buildings. To select the optimal roofs, connectivity analysis techniques were used. The results show that the most conflictive neighborhoods from the environmental point of view are those located in the city center, so they result the ideal places for the location of green roofs. In general, all neighborhoods except one presented high connectivity values. This methodology helps to improve the connectivity of the green spaces of Madrid, favoring the dispersion of plant and animal species, air quality and promoting sustainable and quality urban development.