Miguel Marchamalo

Seasonal and temporal changes in species use of the landscape: how do they impact the inferences from multi-scale habitat modeling?

ContextMulti-scale approaches to habitat modeling have been shown to provide more accurate understanding and predictions of species-habitat associations. It remains however unexplored how spatial and temporal variations in habitat use may affect multi-scale habitat modeling.ObjectivesWe aimed at assessing how seasonal and temporal differences in species habitat use and distribution impact operational scales, variable influence, habitat suitability spatial patterns, and performance of multi-scale models.MethodsWe evaluated the environmental factors driving brown bear habitat relationships in the Cantabrian Range (Spain) based on species presence records (ground observations) for the period 2000–2010, LiDAR data on forest structure, and seasonal estimates of foraging resources. We separately developed multi-scale habitat models for (i) each season (spring, summer, fall and winter) (ii) two sub-periods with different population status: 2000–2004 (with brown bear distribution restricted to the main population nuclei) and 2005–2010 (with expanding bear population and range); and (iii) the entire 2000–2010 period.ResultsScales of effect remained considerably stable across seasonal and temporal variations, but not the influence of certain environmental variables. The predictive ability of multi-scale models was lower in the seasons or periods in which populations used larger areas and a broader variety of environmental conditions. Seasonal estimates of foraging resources, together with LiDAR data, appeared to improve the performance of multi-scale habitat models.ConclusionsWe highlight that the understanding of multi-scale behavioral responses of species to spatial patterns that continually shift over time may be essential to unravel habitat relationships and produce reliable estimates of species distributions.

Organization environmental footprint applying a multi-regional input-output analysis: A case study of a wood parquet company in Spain

Wood has been presented as a carbon-neutral material capable of significantly contribute to climate change mitigation and has become an appealing option for the building sector. This paper presents the quantification of the organization environmental footprint of a wood parquet company. The multi-regional input-output (MRIO) database EXIOBASE was used with a further structural path analysis decomposition. The application of the proposed method quantifies 14 environmental impacts. Highly influential sectors and regions responsible for these impacts are assessed to propose efficient measures. For the parquet company studied, the highest impact category once normalized was ozone depletion and the dominant sector responsible for this impact was the chemical industry from Spain and China. The structural path decomposition related to ozone loss revealed that the indirect impacts embedded in the supply chain are higher than the direct impacts. It can be concluded that the assessment of the organizational environmental footprint can be carried out applying this well-structured and robust method. Its implementation will enable tracking of the environmental burdens through a companys supply chain at a global scale and provide information for the adoption of environmental strategies.

Understanding ‘saturation’ of radar signals over forests

There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain ‘saturation’ point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points.

High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar

ABSTRACT Many countries have employed recently developed technologies, such as airborne lidar, to capture nationwide three-dimensional information over the past few years. In Spain, a huge volume of lidar information is available for the majority of the territory in the Spanish National Plan of Aerial Orthophotography. In this article, a multi-source approach is taken, integrating available databases, such as nationwide lidar flights, Landsat imagery and permanent field plots from the Spanish National Forest Inventory, with good results in the generation of wall-to-wall forest inventories. Volume and basal area errors are similar to those obtained by other authors (using specific lidar flights and field plots) for similar species. Errors in stem number estimates are larger than the values found in the literature as a consequence of the great influence of variable-radius plots, as used in the Spanish National Forest Inventory, on this variable.

A scenario approach to assess stakeholder preferences for ecosystem services in agricultural landscapes of Costa Rica

In many developing rural areas, efforts to avoid degradation of soil regulation services (SRS) face significant challenges especially in steep slopes due to the combined effect of climate change-related extreme precipitation and inadequate soil management practices in agriculture and grazing. In order to design socially desirable alternatives to the status quo, it is important to identify and engage relevant stakeholders to discuss the evaluation of land use and management alternative scenarios. However, innovative methods are needed to ensure the best use of available knowledge and often scarce data. We use structured value referendum (SVR) poll-type voting which is a value-focused decision-making process that can be used to create land use scenarios combining expert knowledge, modeling and stakeholders’ perspectives. We applied this approach to a Costa Rican watershed affected by heavy soil erosion. We engaged actors directly concerned with the on- and off-site effects of SRS degradation such as upstream farmers, a downstream hydropower facility affected by siltation and watershed planners. Results from preference elicitation regarding watershed land use scenarios showed that actors preferred alternatives to the status quo. They supported win–win land use strategies that protect SRS through soil conservation practices to be implemented in critical agricultural plots. Along with other land use scenario approaches that engage stakeholders, application of SVR can promote discussion and learning among interested parties and, at least in watershed conservation initiatives, can lead to identification of opportunities for joint gains among upstream land users and downstream users of soil- and water-related ecosystem services.

An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica

REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.

Evolution of urban monitoring with radar interferometry in Madrid City: performance of ERS-1/ERS-2, ENVISAT, COSMO-SkyMed, and Sentinel-1 products

ABSTRACT During the last decades, synthetic aperture radar (SAR) image exploitation has matured with the launch of different satellite missions and the development of different techniques, which allow exploiting the capabilities of the radar images. Among these techniques, persistent scatterer interferometry (PSI) has proven to be a powerful tool to derive terrain deformations over urban areas. It is based on the use of a large number of images over wide areas in order to obtain terrain displacements time series. The imagery from the different SAR missions has led to an archive with data that covers up to 30 years in the past. Moreover, different methods and algorithms have been proposed in order to perform this complex task. In this line, this work aims at identifying if data from different missions and processed by different techniques can be combined in order to study the evolution of urban monitoring. Three different PSI techniques are used in order to process data from four SAR missions: European Remote Sensing (ERS)-1/2, Environmental Satellite, COSMO-SkyMed, and the recent Sentinel-1 A/B. The rapidly evolving urban area of Madrid, where numerous undergrounding works have been carried out in the last decade, has been chosen as the testing environment. The density of persistent scatterers, the deformation accuracy validated with GPS displacements and deformation trends are used as the key performance items for the assessment.

Mechanisms of Degradation and Identification of Connectivity and Erosion Hotspots

The context of processes and characteristics of soil erosion and land degradation in Mediterranean lands is outlined. The concept of connectivity is explained. The remainder of the chapter demonstrates development of methods of mapping, analysis and modelling of connectivity to produce a spatial framework for development of strategies of use of vegetation to reduce soil erosion and land degradation. The approach is applied in a range of typical land use types and at a hierarchy of scale from land unit to catchment. Patterns of connectivity and factors influencing the location and intensity of processes are identified, including the influence of topography, structures such as agricultural terraces and check dams, and past land uses. Functioning of connectivity pathways in various rainstorms is assessed. Modes of terrace construction and extent of maintenance, as well as presence of tracks and steep gradients are found to be of importance. A method of connectivity modelling that incorporates effects of structure and vegetation was developed and has been widely applied subsequently.

Spectral Analysis for Anomaly Detection in the Central Volcanic Range, Costa Rica. Implications for Planetary Geology

The aim of this work is to study the IR spectra (reflectance and absorbance) of samples from the Central Volcanic Range (CVR) of Costa Rica and their relationship as outliers in anomaly detection. We investigate the spectral characteristics of variable reflectance in the pattern recognition of geological materials in several single hyperspectral scenes. It is assumed no prior knowledge of the targets in anomaly detection. Thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by the image segmentation. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of HyMAP and MASTER have been used. Several experiments on natural scenarios of the CVR and semi-urban of different complexity, have been designed, analyzing the behavior of the standard RX anomaly detector and different approaches based on image projection and dimensionality reduction. First results and their consequences as terrestrial analogs in planetary exploration are discussed.

Fish habitat characterization and quantification using lidar and conventional topographic information in river survey

This study presents the application of LIDAR data to the evaluation and quantification of fluvial habitat in river systems, coupling remote sensing techniques with hydrological modeling and ecohydraulics. Fish habitat studies depend on the quality and continuity of the input topographic data. Conventional fish habitat studies are limited by the feasibility of field survey in time and budget. This limitation results in differences between the level of river management and the level of models. In order to facilitate upscaling processes from modeling to management units, meso-scale methods were developed (Maddock & Bird, 1996; Parasiewicz, 2001). LIDAR data of regulated River Cinca (Ebro Basin, Spain) were acquired in the low flow season, maximizing the recorded instream area. DTM meshes obtained from LIDAR were used as the input for hydraulic simulation for a range of flows using GUAD2D software. Velocity and depth outputs were combined with gradient data to produce maps reflecting the availability of each mesohabitat unit type for each modeled flow. Fish habitat was then estimated and quantified according to the preferences of main target species as brown trout (Salmo trutta). LIDAR data combined with hydraulic modeling allowed the analysis of fluvial habitat in long fluvial segments which would be time-consuming with traditional survey. LIDAR habitat assessment at mesoscale level avoids the problems of time efficiency and upscaling and is a recommended approach for large river basin management.