Research Lines

  • Land use/Land cover geodatabase updating from high-resolution remote sensing images and LiDAR data using object-based classification techniques
  • Analysis of forest structure and fuel mapping using discrete and full-waveform LiDAR data
  • Multitemporal urban fragmentation analysis and relations with socio-economic variables
  • Coastal areas and shoreline monitoring using remote sensing and LiDAR


Current projects



Analysis and assessment of forest structure parameters from LiDAR and other emergent techniques for modeling fuel potencial (FIRe MAnagement CARtographic TOols)) [CGL2016-80705-R]

  • Financed by: Ministerio de Economía y Competitividad and FEDER
  • Partners: Universidad Politécnica de Valencia (UPV-CGAT)
  • Principal Investigator: Luis A. Ruiz
  • Duration: 30/12/2016 – 29/12/2019

Wildfire prediction and behaviour models require more precise information concerning forest structure, in particular that concerning understory vegetation. New data acquisition techniques should be adapted to provide the right information at affordable costs, and allowing for the integration of data acquired at different scales. The main goal of this project is to explore methods to acquire and process LiDAR data and other emergent terrestrial and aerial remote sensing techniques to a higher level of detail, to develop methods for the estimation of forest fuel variables required in the new models of fire prediction and behaviour, and to integrate them for the design of efficient protocols, data processing and analysis, and their application to the characterization of fuel potential.

For this purpose, new methods for LiDAR full-waveform processing will be developed, and their potential for understory vegetation characterization in Mediterranean areas evaluated. The potential of terrestrial laser scanner as a reference measure will be analysed, as well as its complementarity with hemispheric photography systems. Cloud points generated by optical cameras and low-cost UAS-LiDAR systems will be analysed and processed for the generation of canopy structural variables. Parameters obtained using different technologies (aerial and UAS-LiDAR, TLS, hemispheric cameras, dense image matching-UAS) will be tested for fuel modelling and wildfire behavior.


  • Increase LiDAR full-waveform applicability by exploring its potential to characterize understory vegetation
  • Study the potential of TLS as reference and calibration of understory aerial measurements, and complementarity with hemispheric photography
  • Acquisition and processing of point clouds from UAV optical cameras (SfM) for using in fuel and fire behavior models
  • Analysis of potential of low-cost UAS-LiDAR sensors for high detail (hot spots) forest structure characterization:
  • Integration of metrics for using as input in fire behavior models


Ruiz, L.A., Crespo-Peremarch, P., 2017. Optimizing operational parameters in a full-waveform LiDAR processing tool for forestry. 17th Symposium on Systems Analysis in Forest Resources (SSAFR 2017), 27-30 Aug., Suquamish, WA, USA.

Crespo-Peremarch, P., Ruiz, L.A., 2017. Análisis comparativo del potencial del ALS y TLS en la caracterización estructural de la masa forestal basado en voxelización. Nuevas plataformas y sensores de teledetección, XVII Congreso de la Asociación Española de Teledetección., pp. 131-135, 4-7 Oct., Murcia.

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Characterization of forest structure by integrated analysis of methods based on LiDAR, terrestrial laser scanning and imagery [CGL2013-46387-C2-1-R]

  • Financed by: Ministerio de Economía y Competitividad and FEDER
  • Partners: Universidad Politécnica de Valencia (UPV-CGAT); Universidad Politécnica de Madrid (UPM-Silvanet)
  • Principal Investigator: Luis A. Ruiz
  • Duration: 01/01/2014 – 31/12/2016

The analysis of forest structure is crucial for biomass estimates and carbon flux monitoring, for forest inventories, and for forest fuel mapping as input in wildfire risk models. Methods using LIDAR (Light Detection and Ranging) for forest structural and fuel variables estimation have an operational use, however, some aspects must be solved: (i) the development of LiDAR full waveform processing methods, with more potential for structure study; (ii) methods for cartography of forest inventories and management, and stratification of structural types using LiDAR and imagery, (iii) the procedures to improve estimates of diameters at plot level by developing models, (iv) the estimation of fuel attributes using LiDAR full waveform; (v) the estimation od errors in prediction models at small stands levels; (vi) the acquisition configuration and possibilities of TLS; and (vii) the integration of data and models. As a result of these goals and actions, tools useful for forest management at national level are developed, to contribute for the improvement of wildfire prediction and fire behavior.


  • Development of specific tools for LiDAR full-waveform processing and analysis
  • Development and evaluation of methods for the stratification of structural fuel types from LiDAR, high and medium resolution satellite imagery using object-based analysis
  • Study and comparison of structure and fuel variables models from discrete and full-waveform LiDAR data, and analysis of methodological parameters
  • Analysis of field data acquisition with terrestrial laser scanner (TLS):
    • Analysis of spatial configuration and acquisition parameters
    • Study of methods for prediction of structure and fuel variables from TLS
  • Integration of data and methods and future research


Ruiz, L.A., Recio, J.A., Crespo-Peremarch, P., Sapena, M. An object-based approach for mapping forest structural types based on low density LiDAR and multispectral imagery. Geocarto International(in press)

Crespo-Peremarch, P., Ruiz, L.A., Balaguer-Beser, A., 2016. A comparative study of regression methods to predict forest structure and canopy fuel variables from LiDAR full-waveform data. Revista de Teledetección, Special issue: Active Remote Sensing in Forest Applications. pp. 27-40. doi:

Crespo-Peremarch, P., Ruiz, L.A., Balaguer-Beser, A., Estornell, J., 2016. Analysis of the side-lap effect on full-waveform LiDAR data acquisition for the estimation of forest structure variables. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 12-19 July, Prague, XLI-B8, pp. 603-610. doi:10.5194/isprs-archives-XLI-B8-603-2016

Crespo-Peremarch, P., Ruiz, L.A., Balaguer, A., Estornell, J., 2015. Análisis temporal de la estructura forestal mediante métricas derivadas de LiDAR full-waveform. Actas del XVI Congreso Nacional de la Asociación Española de Teledetección, 21-23 Oct., 2015, Sevilla.

Final dissemination workshop on LiDAR technologies for forest structure assessment

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Higher Education interdisciplinary Reform in Tourism management and Applied Geoinformation curricula [561555-EPP-1-2015-1-ES-EPPKA2-CBHE-JP]

  • Financed by: Education, Audiovisual and Culture Executive Agency – European Union
  • Partners: Universidad Politécnica de Valencia (coordinator) and 15 others
  • Principal Investigator: Luis A. Ruiz
  • Duration: 15/10/2015 – 14/10/2018

The project HERITAG aims to develop an interdisciplinary reform in higher education programmes at master level and continuing education integrating Geo-information Technologies (GIT) applied to cultural heritage documentation, tourism management and entrepreneurship. The project promotes the synergy of three main groups of stakeholders: Universities, industry and Administration. The curricular reform integrates the development of capacities in 3 main national and regional priorities in Georgia and Armenia: Geo-information technologies, cultural heritage preservation and documentation, and fostering tourism business and entrepreneurship.


  • To create interdisciplinary master specialities in GIT for cultural heritage and tourism, integrating marketing and entrepreneurship skills oriented to consolidate local industry
  • To establish Geoinformation and Tourism Technology centres (GTTC), new GIS laboratories and update existing equipment in partner country universities
  • To foster the continuous education in Higher Education institutions
  • To re-train academic staff in GIT, tourism management and entrepreneurship:
  • To introduce and improve good practices for quality assurance in partner countries universities
  • To establish organized links between universities, administration and society


  • Creation of 2 Geoinformation and Tourism Technology Centres for training, documentation and dissemination
  • Creation/improvement of GIS laboratories in 4 HEI’s
  • Acquisition of modern equipment for GIT in cultural heritage
  • 6 intensive training courses for partner country teachers
  • 2 Workshops in quality assurance and new teaching methods
  • Creation of new master specialities in cultural heritage and tourism
  • Development of new course contents and teaching materials
  • Creation of 2 continuing education modules per country devoted to update professionals
  • Creation of promotional websites
  • Celebration of labour market days at HEI’s
  • Celebration of a final dissemination conference in Tbilisi (Georgia)


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Remote sensing to coastal changes in order to mitigate climate change impacts [CGL2015-69906-R]

  • Financed by: Ministerio de Economía y Competitividad
  • Partners: Universidad Politécnica de Valencia
  • Principal Investigator: Josep E. Pardo-Pascual
  • Duration: 2016 – 2018

Beaches are an environmental, social and economic resource of high importance that can be threatened by sea level rise caused by climate change. For minimizing the impact of these changes it would be necessary to study the response of the beach to different factors. To obtain response models that consider sea rise level sea and coastal storm situations specific information is required being expensive and complex to get for all the beaches since this information (bathymetry, slope, beach material texture) changes over time and energy conditions to which they are subjected.

This project aims to use the massive data related to the large series of medium-resolution images (such as Landsat and Sentinel-2) to characterize the evolution of the beaches in large areas obtaining key parameters that allow to analyze how climate change affects to the beaches.

In this project we propose to apply an own algorithm -Shoreline Extraction from Landsat Imagery, SELI- already published and used in previous work as basis for deriving new information using multiple archive Landsat images (series 5, 7 and 8) and those that are being registered by active satellites such as Landsat (7 and 8) and the new European satellite Sentinel-2 (in orbit and operating since June 2015). This set of data will allow us to characterize the evolution of the beaches and to obtain relevant information being expensive for large areas such as the slope of the beachfront or beach material texture.

Another objective of this research is to take advantage of some improvements in Landsat 8 satellite (new band 1 for coastal applications) trying to obtain bathymetry of shallow water next to the coast. To do this the classic algorithms will be applied to Landsat 8 images in a more efficient and accurate way according to the already published results. The bathymetric information-and its variation due to the waves- is extremely important to predict the response of the beaches to rough weather or sea level rise. In addition, all this information may be important to understand how populations of some species living in these highly changing environments are evolving.

The results obtained in this project can be of great interest to environmental, social and economic management of Spanish beaches, particularly for the Mediterranean ones. If objectives were reached, key information for beaches will be obtained (evolution and morphosedimentary features) in an quick and global way with lower costs what could face new and more rigorous studies on areas not yet analyzed.

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Recent past projects



Development and Integration of Methods for Land Use/Land Cover Database Updating [CGL2010-19591/ BTE]

  • Financed by: Ministerio de Ciencia e Innovación and FEDER
  • Partners: Universidad Politécnica de Valencia (UPV-CGAT)
  • Principal Investigator: Luis A. Ruiz
  • Duration: 01/01/2011 – 31/12/2013

Land use/Land cover (LULC) databases are essential information for applications such as natural resources or territory management and landscaping. Their maintenance is expensive and the updating rate must be dynamic. New LULC database updating procedures based on object-oriented methods for feature extraction, classification and change detection, were developed and evaluated. Image analysis methods were focused on the extraction of information from cartographic objects (parcel, plot) at the spectral, textural, structural, shape, intra-object and inter-object levels. The use and quality requirements of LiDAR data was analysed for extraction of 3D structure of landscape elements. Object-oriented change detection and classification techniques from a multivariate and multiscale perspective were evaluated in three landscape strata: urban, agriculture-forestry, and mixed areas.


The goal of this project was to develop, integrate and evaluate new LULC database updating procedures, based on object-oriented methods for feature extraction, classification and change detection, integrating a wide spectrum of georreferenced data and facilitate the progressive automatization of the spatial database updating tasks. In addition, multiscale thematic generalization methods were studied from a multiscale perspective in order to optimize the use of available data and their mutual relationship.


Gil-Yepes, J.L., Ruiz, L.A., Recio, J.A., Balaguer-Beser, A., Hermosilla, T., 2016. Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection. ISPRS Journal of Photogrammetry and Remote Sensing , 121, pp. 77-91. doi:

Hermosilla, T., Palomar-Vázquez, J., Balaguer-Beser, A., Balsa-Barreiro, J., Ruiz, L.A., 2014. Using street based metrics to characterize urban typologies. Computers, Environment and Urban Systems, 44, pp. 68-79. doi:10.1016/j.compenvurbsys.2013.12.002

Dimov, D., Palomar, J., Ruiz, L.A. Automated generalization of land-use data with GIS-based programming. gis.SCIENCE – Journal for Geoinformatics. Wichmann, 3 (2014), pp. 109-120.

Recio, J.A., Hermosilla, T., Ruiz, L.A., Palomar-Vázquez, J., 2013. Automated extraction of tree and plot-based parameters in citrus orchards from aerial images. Computers and Electronics in Agriculture, 90, pp. 24-34. doi:10.1016/j.compag.2012.10.005

Balaguer-Beser, A., Ruiz, L.A., Hermosilla, T., Recio, J.A., 2013. Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images. Computers and Geosciences, 50, pp. 115–127. doi:10.1016/j.cageo.2012.08.001

Hermosilla, T., Ruiz, L.A., Gil-Yepes, J.L., Recio, J.A., Pardo-Pascual, J.E., 2013. Multi-level object-based urban mapping from remote sensing and GIS data. Symposium GIS Ostrava 2013 – Geoinformatics for City Transformation. 21–23 January, 2013, Ostrava, Czech Republic.

Gil-Yepes, J.L., Hermosilla, T., Ruiz, L.A., Recio, J.A., Balaguer-Beser, A., 2013. Estudio de técnicas de detección de cambios basadas en la clasificación directa de objetos para la actualización de bases de datos agrícolas. XV Congreso de la Asociación Española de Teledetección. 22-24 octubre, Madrid, Spain.

Hermosilla, T., Ruiz, L.A., Recio, J.A., Cambra-López, M., 2012. Assessing contextual descriptive features for plot-based classification of urban areas. Landscape and Urban Planning, 106, pp. 124-137. doi:10.1016/j.landurbplan.2012.02.008

Hermosilla, T., Gil-Yepes, J.L., Recio, J.A., Ruiz, L.A., 2012. Change detection in periurban areas based on contextual classification. Photogrammetrie Fernerkundung Geoinformation, 4, pp. 359-370. doi:

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Development of techniques and methods for a sustainable forest management from Earth observation data [TSI-020100-2009-815]

  • Financed by: Ministerio de Industria, Turismo y Comercio and FEDER
  • Partners:
    • COTESA (Centro de Observación y Teledetección Espacial S.A.U.) (coordinador)
    • Universidad de Castilla La Mancha (UCLM)
    • Universidad Politécnica de Madrid (UPM)
    • Universidad Politécnica de Valencia (UPV-CGAT)
  • Principal Investigator: Luis A. Ruiz
  • Duration: 01/06/2008 – 31/12/2010

Development of key information for forest management using Earth observation data at different scales: satellite images, aerial images and LiDAR data. Development of methods and tools for feature extraction, generation of indicators, visualization and management infrastructures.


Extraction of forest dasometric variables from LiDAR data; design and adaptation of algorithms and parameters to optimize prediction models for biomass, volume, basal area, canopy cover, etc., in the testing area.


Software: Lidex, Software tool for LiDAR data preprocessing, generation of terrain models, feature extraction, creation and testing of prediction models

Ruiz, L.A., Hermosilla, T., Mauro, F., Godino, M., 2014. Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates. Forests, 5(5), pp. 936-957. doi:10.3390/f5050936

Ruiz, L.A., Hermosilla, T., Godino, M., Almonacid, J., Fernández-Sarría, A., Recio, J.A., Gil-Yepes, J.L., Mauro, F., 2011. Procedimiento para la estimación de variables dasométricas a partir de datos LiDAR. Actas del XIV Congreso Nacional de la Asociación Española de Teledetección, 21-23 September, Mieres (Asturias), pp. 129-132.

Gil-Yepes, J.L., Ruiz, L.A., Fernández-Sarría, A., Hermosilla, T., 2012. Detección y localización de árboles en áreas forestales empleando datos LiDAR y ortofotografías. Mapping, 155, pp. 20-26. I.S.S.N.: 1131-9100


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Geographic Information Technology for sustainable Development in Eastern neighbouring Countries [511322-TEMPUS-1-2010-SE-JPCR]

  • Financed by: Education, Audiovisual and Culture Executive Agency – European Union
  • Partners: Universidad Politécnica de Valencia (UPV-CGAT) and 10 others
  • Principal Investigator: Luis A. Ruiz
  • Duration: 15/10/2010 – 14/10/2013
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