@article {aAlbors25, title = {BiomSHARP: Biomass Super-Resolution for High Accuracy Prediction}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {63}, year = {2025}, month = {11/2025}, pages = {1-18}, chapter = {1}, abstract = {

Accurate estimation of above-ground biomass (AGB) is essential to understanding carbon stocks and flows, monitoring forest health, assessing biodiversity, and tracking ecological disturbances, which together help to inform climate policies. Imminent global satellite biomass missions (such as ESA{\textquoteright}s BIOMASS and NASA-ISRO{\textquoteright}s NISAR satellites) will offer valuable environmental monitoring, but their low spatial resolution limits their application in detailed local assessments. In this study, we present biomass super-resolution for high accuracy prediction (BiomSHARP), a deep learning (DL) model that extends the hierarchical attention Transformer (HAT) architecture, adapting it to enhance coarse-resolution biomass maps by fusing them with high-resolution (HR) multispectral data from sensors such as Sentinel-2 or Landsat. BiomSHARP achieves 25-m biomass predictions{\textemdash}four times the spatial resolution of the input{\textemdash}bridging the gap between global-scale monitoring and local-scale applications. In a first set of experiments, conducted in a local area in Europe, we demonstrate that BiomSHARP outperforms both traditional interpolation methods and state-of-the-art (SOTA) DL interpolation and prediction approaches for HR AGB estimation across all evaluated metrics [mean absolute error (MAE), mean squared error (mse), root mse (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM)], while using a comparable/lower number of parameters. Moreover, the model exhibits strong global-scale generalization, as demonstrated by its ability to accurately estimate biomass across diverse climatic regions despite being trained on a limited subset of data. Furthermore, the model presents strong temporal generalization, achieving improved performance in estimating AGB from 2020 data even when trained solely on 2010 data. We also analyze the impact of different combinations of spectral bands on biomass estimation, identifying optimal subsets that reduce redundancy and improve computational efficiency. BiomSHARP represents a promising approach to advance global environmental assessments and support improved climate strategies. The code and models are publicly available at: https://github.com/laiaalbors/biomsharp

}, keywords = {Above-Ground Biomass (AGB), Biomass super-resolution for high accuracy prediction (BiomSHARP), Climate monitoring, Deep Learning (DL), Guided biomass super-resolution (SR), Multispectral imagery, Remote Sensing (RS), Satellite biomass estimation}, issn = {1558-0644}, doi = {10.1109/TGRS.2025.3636434}, url = {https://ieeexplore.ieee.org/abstract/document/11265727}, author = {Laia Albors and Javier Marcello and Ferran Marques} } @conference {cMarcello25, title = {Comparative Analysis of Tree Segmentation Techniques on High- and Low-Density LiDAR data}, booktitle = {International Conference on Advanced Remote Sensing (ICARS 2025)}, year = {2025}, month = {03/2025}, publisher = {MDPI}, organization = {MDPI}, address = {Basel, Switzerland}, abstract = {

LiDAR systems are powerful tools for sustainable forest management and ecological research, offering the capability to extract detailed information about canopy structure, tree height, biomass, carbon storage, and biodiversity. Unmanned Aerial Systems (ULSs) mounted on drones provide high spatial resolution, density, and flexibility for capturing detailed forest metrics. Their ability to fly at low altitudes enables the collection of fine-scale details. Conversely, Airborne Laser Scanning (ALS) systems, mounted on aircraft, are ideal for large-scale assessments, despite providing a lower point-cloud density compared to ULSs. This study evaluates individual tree segmentation algorithms in a coniferous forest ecosystem using two data sources: (1) high-density ULS LiDAR data collected with the Zenmuse L1 sensor on a DJI Matrice 300RTK drone, and (2) lower-density ALS LiDAR data from the third coverage of Spain{\textquoteright}s PNOA-LiDAR project (Plan Nacional de Ortofotograf{\'\i}a A{\'e}rea). The general methodology for processing LiDAR data involves preliminary steps to generate Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and Canopy Height Models (CHMs). Subsequently, segmentation techniques are applied to assist tree-level forest analysis. Segmentation is critical for understanding forest structure; however, selecting the most suitable segmentation technique remains an active area of research. To address this issue, a comparative assessment of four commonly used segmentation algorithms (Watershed, Dalponte2016, Silva2016 and Li2012) was conducted using CHMs and normalized 3D point clouds derived from both high- and low-density LiDAR data. Ground-truth reference data were generated by the manual segmentation of individual trees in a representative plot. The results revealed that the Li2012 algorithm demonstrated the best performance in properly segmenting trees in both types of datasets.

}, keywords = {ALS, CHM, forest, LiDAR, tree segmentation, ULS}, url = {https://sciforum.net/paper/view/21619}, author = {Javier Marcello and Mar{\'\i}a Sp{\'\i}nola and Laia Albors and Ferran Marques and Dionisio Rodr{\'\i}guez-Esparrag{\'o}n and Francisco Eugenio} } @article {aMederos-Barrera25, title = {Comparison of Conventional Machine Learning and Convolutional Deep Learning models for Seagrass Mapping using Satellite Imagery}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, year = {2025}, month = {12/2025}, pages = {1-19}, chapter = {1}, abstract = {

Benthic communities, such as seagrass meadows, play a crucial environmental role in marine ecosystems and provide socio-economic benefits. Satellite remote sensing is currently used for their monitoring, and Deep Learning (DL) techniques offer improvements in mapping quality compared to traditional Machine Learning (ML). This study compares conventional ML and convolutional DL models for mapping Cymodocea nodosa meadows in El R{\'\i}o, Canary Islands, using WorldView-2 satellite imagery. An in-situ measurement campaign was conducted to generate an open dataset for segmentation. Evaluated models include Decision Trees, Gaussian Na{\"\i}ve Bayes, Support Vector Machines, K-Nearest Neighbors, Subspace KNN, Feedforward Neural Networks, U-Net, Attention U-Net, and Pix2Pix models. Results show that DL models significantly outperform conventional ML models in detecting Cymodocea nodosa. The best model (U-net) achieved an Intersection over Union (IoU) of 83\% overall and 74\% for Cymodocea nodosa, while the best ML model (FNN) only reached 62\% and 23\%, respectively. IoU was highlighted for its sensitivity to minor mapping changes. Additionally, a temporal analysis revealed a dramatic 96\% reduction in Cymodocea nodosa coverage over 21 years, from 245.32 ha in 2001 to 9.31 ha in 2022. This study not only compares conventional ML and convolutional DL techniques for benthic habitat mapping but also provides a valuable methodology and dataset for future marine ecosystem monitoring research.

}, keywords = {Cymodocea nodosa, deep learning, machine learning, seagrass mapping}, issn = {2151-1535}, doi = {10.1109/JSTARS.2025.3642923}, url = {https://ieeexplore.ieee.org/document/11297818}, author = {Antonio Mederos-Barrera and Laia Albors and Ferran Marques and Javier Marcello and Gerard Martinez and Francisco Eugenio} } @conference {cAlbors24, title = {An{\'a}lisis multiresoluci{\'o}n espacial del estado de conservaci{\'o}n de un bosque de laurisilvas con sensores pasivos y activos de teledetecci{\'o}n}, booktitle = {XX Congreso de la Asociaci{\'o}n Espa{\~n}ola de Teledetecci{\'o}n}, year = {2024}, month = {06/2024}, publisher = {Universidad de C{\'a}diz}, organization = {Universidad de C{\'a}diz}, address = {C{\'a}diz, Espa{\~n}a}, abstract = {

La conservaci{\'o}n de las zonas naturales es esencial para mantener el equilibrio ecol{\'o}gico de nuestro planeta y preservar la biodiversidad. Estas {\'a}reas desempe{\~n}an un papel fundamental en la regulaci{\'o}n del clima y la provisi{\'o}n de h{\'a}bitats para innumerables especies. En este contexto, la teledetecci{\'o}n se ha convertido en una herramienta valiosa para la gesti{\'o}n sostenible de estos espacios. Espec{\'\i}ficamente, este trabajo aborda la evaluaci{\'o}n del estado de conservaci{\'o}n de un bosque de laurisilvas a partir de datos multiplataforma (sat{\'e}lite, avi{\'o}n y dron) de alta y muy alta resoluci{\'o}n. Se ha realizado un detallado estudio y evaluaci{\'o}n de {\'\i}ndices de vegetaci{\'o}n, se han aplicado los correspondientes procesados, usando modelos avanzados de correcci{\'o}n y clasificaci{\'o}n y se ha generado informaci{\'o}n relativa al estado de salud del ecosistema, a la estructura del dosel arb{\'o}reo y a la distribuci{\'o}n de los sistemas naturales de vegetaci{\'o}n predominantes (bosques de laurisilva y de fayal-brezal). Los resultados de este trabajo est{\'a}n siendo utilizados para el seguimiento ecol{\'o}gico del Parque y para la identificaci{\'o}n de las zonas de desvitalizaci{\'o}n y de p{\'e}rdida de vigor vegetal.

}, keywords = {dron, {\'\i}ndices de vegetaci{\'o}n, laurisilva, LiDAR, salud vegetal, WorldView-2}, url = {http://eo.csic.es/images/yootheme/aet24/Libro\%20ACTAS\%20XX_AET.pdf}, author = {Laia Albors and Mar{\'\i}a Sp{\'\i}nola and Javier Marcello and Ferran Marques and Dionisio Rodr{\'\i}guez-Esparrag{\'o}n and Francisco Eugenio} } @conference {cMederos-Barrera24, title = {Generaci{\'o}n de mapas de comunidades y h{\'a}bitats bent{\'o}nicos mediante el modelo Deep Learning U-Net utilizando im{\'a}genes satelitales multiespectrales de muy alta resoluci{\'o}n}, booktitle = {XX Congreso de la Asociaci{\'o}n Espa{\~n}ola de Teledetecci{\'o}n}, year = {2024}, month = {06/2024}, publisher = {Universidad de C{\'a}diz}, organization = {Universidad de C{\'a}diz}, address = {C{\'a}diz, Espa{\~n}a}, abstract = {

La flora marina, en especial las praderas marinas y praderas de algas, posee un rol esencial en la sostenibilidad de los ecosistemas costeros. Por esto, existe una elevada necesidad en desarrollar herramientas que permitan monitorizar de forma precisa la flora marina. La teledetecci{\'o}n por sat{\'e}lite es una tecnolog{\'\i}a eficiente y econ{\'o}mica para poder elaborar mapas de h{\'a}bitats bent{\'o}nicos o de tipos de fondos, permitiendo caracterizar los fondos a lo largo del tiempo. Para la generaci{\'o}n de estos mapas, en la actualidad t{\'\i}picamente se implementan t{\'e}cnicas basadas en p{\'\i}xel, como modelos Machine Learning convencionales, y otras t{\'e}cnicas basadas en objetos, como el OBIA. No obstante, el avance de las t{\'e}cnicas Deep Learning permite entrenar modelos que aprovechen mejor las caracter{\'\i}sticas de los fondos marinos para la generaci{\'o}n de mapas de elevada precisi{\'o}n. En este trabajo se expone el uso del modelo U-Net, basado en Redes Neuronales Convoluciones (CNN), para la generaci{\'o}n de mapas de h{\'a}bitats bent{\'o}nicos a partir de im{\'a}genes satelitales multiespectrales. La zona de estudio es El R{\'\i}o, estrecho entre las islas de La Graciosa y Lanzarote, islas Canarias, donde existe una elevada biodiversidad con la presencia de multitud de especies como praderas de Cymodocea nodosa, Caulerpa prolifera y algas rojas filamentosas, entre otras especies.

}, keywords = {deep learning, mapas de h{\'a}bitats bent{\'o}nicos, praderas marinas y de algas, U-Net, WorldView-2}, url = {http://eo.csic.es/images/yootheme/aet24/Libro\%20ACTAS\%20XX_AET.pdf}, author = {Antonio Mederos-Barrera and Laia Albors and Gerard Martinez and Javier Marcello and Francisco Eugenio and Ferran Marques} } @article {aMarcello24, title = {Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data}, journal = {Drones}, volume = {8}, year = {2024}, month = {12/2024}, abstract = {

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91\% and an IoU of 83\%.

}, keywords = {CHM, Dalponte, DEM, forest, Li, LiDAR, Silva, tree segmentation, ULS, watershed}, issn = {2504-446X}, doi = {10.3390/drones8120772}, url = {https://www.mdpi.com/2504-446X/8/12/772}, author = {Javier Marcello and Mar{\'\i}a Sp{\'\i}nola and Laia Albors and Ferran Marques and Dionisio Rodr{\'\i}guez-Esparrag{\'o}n and Francisco Eugenio} }