Cesar Ivan Alvites Diaz | Remote Sensing & GeoAI

ABOUT

About.

Remote sensing scientist with a background in agriculture and forest science, specializing in Earth observation, forest monitoring and open scientific software. Research experience includes terrestrial, airborne and spaceborne Light Detection and Ranging (LiDAR), radar, optical imagery, vegetation mapping, forest structure, biomass and carbon dynamics, and machine learning, deep learning and artificial intelligence algorithms.

BSc in Agricultural Sciences from Catholic University Sedes Sapientiae, Peru (2013). Awarded a merit-based scholarship through the university to pursue graduate studies in Italy. MSc in Forest and Environmental Sciences and Technologies from the University of Molise (2016), with a final grade of 110/110. PhD in Sciences, Technologies, and Biotechnologies for Sustainability from the University of Molise and the University of Tuscia (2021). Doctoral dissertation recognized with the Michele Stanca Prize for the best PhD thesis at the XVIII AISSA Annual Conference (2022).

Current postdoctoral research at the University of Florida and previous postdoctoral experience at the University of Molise. Teaching experience includes university courses and practical workshops on remote sensing, Google Earth Engine and LiDAR systems, together with mentoring and co-supervision of master’s students.

Fieldwork qualifications and training

Remote Pilot Certificate, UAS Open Category A1/A3, obtained in Italy through ENAC in 2022, under the EASA framework.

Specialization in Forest Inventories, Modules I–II: Design, Planning and Field Data Collection. National Forestry and Wildlife Service (SERFOR, Ministry of Agriculture) and National University of the Peruvian Amazon, Loreto, Peru, 2018.

Cesar Ivan Alvites Diaz at the SilviLaser 2023 poster session

CESAR IVAN ALVITES DIAZ, PhD

Earth observation and forest monitoring.
Understanding natural and human-driven change.

Research focused on monitoring forest ecosystems and their responses to natural and human-driven land change. Multisensor observations, including LiDAR, radar and optical imagery, are combined with geospatial foundation-model representations, machine learning and deep learning to study forest structure, biomass and carbon dynamics over time.

Postdoctoral Researcher
University of Florida · Gainesville, USA

Explore my research ↗ GitHub ↗

Actual screenshot of Canopy Height Mapper · Google Earth Engine — **CH-GEE**Canopy Height Mapper · Google Earth EngineOpen application ↗

Actual screenshot of MichiChat · GALS DownloadeR Agent — **MichiChat**MichiChat · GALS DownloadeR AgentOpen application ↗

Screenshot of GEDI Forest Structure Maps — **GEDI Forest Structure Maps**Multisensor mapping of forest structureOpen application ↗

Screenshot of CSF-Ind — **CSF-Ind — Climate-Smart Forestry Indicators Generator**Climate-smart forestry · mitigation & adaptationOpen application ↗

RESEARCH

Research experience & projects.

March 2025–present

University of Florida · Postdoctoral Research Fellow · OpenForest4D

Postdoctoral Researcher in the Forest Biometrics, Remote Sensing and AI Laboratory (SilvaLab), led by Dr. Carlos Alberto Silva. Research supported by the NSF OpenForest4D project, integrating multisensor Earth observations for forest structure and carbon mapping.

February 2024–February 2025

University of Molise · Postdoctoral Research Fellow · FORWARDS

Postdoctoral Research Fellow working on ecological restoration planning and monitoring forest ecosystems in mountain environments. Software development included CSF-Ind for climate-smart forestry indicators related to mitigation and adaptation.

2021–2022; July 2023–February 2024

University of Molise · Postdoctoral Research Fellow · EUFORICC

Postdoctoral Research Fellow applying remote sensing to urban forests, forest attributes and ecosystem services within the project Establishing Urban Forest-based Solutions in Changing Cities.

02 / PUBLICATIONS

Peer-reviewed research.

Journal articles from my academic CV. My name is highlighted in each author list.

2026
Fareed, N., Silva, C. A., Gaskins, A. J., Prichard, S. J., Hudak, A. T., Xia, J., & Alvites Diaz, C. I. (2026). From wood–foliar semantics to 3D fuel characterization: Transferable deep learning for terrestrial LiDAR across global forest ecosystems. Remote Sensing, 18(18), 3158. DOI ↗
2026
Silva, C. A., Neuenschwander, A., Hamamura, C., Godinho, S., Brock, K. M., Malambo, L., Narine, L., Atkins, J. W., Borak, J. S., Popescu, S., Bueno, I. T., Rezende, L. B., Santoro, G. B., Dalla Corte, A. P., Alvites Diaz, C. I., Mashhadi, N., Cardil, A., Hudak, A. T., Korhonen, L., Sharma, A., Cannon, J. B., Mohan, M., Wan Mohd Jaafar, W. S., Liesenberg, V., & Klauberg, C. (2026). Beyond ice: NASA’s ICESat-2 spaceborne lidar mission for land and vegetation applications. IEEE Geoscience and Remote Sensing Magazine, 14(3), 442–473. DOI ↗
2026
Petti, B., D’Amico, G., Alvites, C., Parisi, F., Bambagioni, E., Bruno, R., Santopuoli, G., Lasserre, B., Chirici, G., Ottaviano, M., Marchetti, M., & Francini, S. (2026). Remote sensing across scales and platforms: Monitoring Castelporziano nature reserve forest insect outbreaks. Rendiconti Lincei. Scienze Fisiche e Naturali, 37(Suppl. 1), 75–87. DOI ↗
2025
Puletti, N., Innocenti, S., Guasti, M., Alvites, C., & Ferrara, C. (2025). Improving aboveground biomass estimation in beech forests with 3D tree crown parameters derived from UAV-LS. Remote Sensing, 17(9), 1497. DOI ↗
2025
Alvites, C., O’Sullivan, H., Francini, S., Marchetti, M., Santopuoli, G., Chirici, G., Lasserre, B., Marignani, M., & Bazzato, E. (2025). Canopy height Mapper: A Google Earth Engine application for predicting global canopy heights combining GEDI with multi-source data. Environmental Modelling & Software, 183, 106268. DOI ↗
2024
Alvites, C., O’Sullivan, H., Francini, S., Marchetti, M., Santopuoli, G., Chirici, G., Lasserre, B., Marignani, M., & Bazzato, E. (2024). High-resolution canopy height mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with multi-source remote sensing data. Remote Sensing, 16(7), 1281. DOI ↗
2023
Alvites, C., Maesano, M., Molina-Valero, J. A., Lasserre, B., Marchetti, M., & Santopuoli, G. (2023). Discrimination of leaves in a multi-layered Mediterranean forest through machine learning algorithms. Remote Sensing, 15(18), 4450. DOI ↗
2022
Alvites, C., Marchetti, M., Lasserre, B., & Santopuoli, G. (2022). LiDAR as a tool for assessing timber assortments: A systematic literature review. Remote Sensing, 14(18), 4466. DOI ↗
2021
Alvites, C., Santopuoli, G., Hollaus, M., Pfeifer, N., Maesano, M., Moresi, F. V., Marchetti, M., & Lasserre, B. (2021). Terrestrial laser scanning for quantifying timber assortments from standing trees in a mixed and multi-layered Mediterranean forest. Remote Sensing, 13(21), 4265. DOI ↗
2021
Alvites, C., Santopuoli, G., Maesano, M., Chirici, G., Moresi, F. V., Tognetti, R., Marchetti, M., & Lasserre, B. (2021). Unsupervised algorithms to detect single trees in a mixed-species and multilayered Mediterranean forest using LiDAR data. Canadian Journal of Forest Research, 51(12), 1766–1780. DOI ↗
2019
Alvites, C., Battipaglia, G., Santopuoli, G., Hampel, H., Vázquez, R. F., Matteucci, G., & Tognetti, R. (2019). Dendrochronological analysis and growth patterns of Polylepis reticulata (Rosaceae) in the Ecuadorian Andes. IAWA Journal, 40(2), 331–S5. DOI ↗

03 / SOFTWARE & APPLICATIONS

Geospatial and data tools.

Open tools for accessing observations, processing data and mapping forest attributes.

R / ShinyDeveloper

MichiChat — GALS DownloadeR

An R package and application for discovering, inspecting and downloading airborne laser scanning data, with the MichiChat agent.

GitHub profile ↗

PythonDeveloper

pyNISAR

Search, screening, processing and download workflows for NASA–ISRO NISAR data. Developed using CryoCloud (now StratusGeo).

Source code ↗

Google Earth EngineDeveloper

GEDI Forest Structure Maps

Global mapping of canopy height, plant area index, foliage height diversity, canopy cover and aboveground biomass.

Source code ↗ Open application ↗

Google Earth EngineLead developer

Canopy Height Mapper

An application for predicting canopy heights by combining GEDI observations with multisource data.

Source code ↗

RCo-author / contributor

ICESat2VegR

Access, handling and analysis of ICESat-2 observations for land and vegetation applications.

Source code ↗

R / ShinyWeb application

CSF-Ind — Climate-Smart Forestry Indicators Generator

An application for estimating climate-smart forestry (CSF) indicators related to climate change mitigation and adaptation, supporting the assessment of forest management outcomes.

Open application ↗

04 / TEACHING & MENTORING

Teaching, mentoring
& sharing knowledge.

Sharing scientific knowledge with students, workshop participants and stakeholders is central to my academic work. Through university teaching, practical workshops and graduate mentoring, I connect scientific questions with hands-on geospatial analysis and reproducible research.

University teaching

Course Instructor, Forest Monitoring Using Google Earth Engine and LiDAR (2023–2025), University of Molise. Teaching Assistant in Cartography, Remote Sensing, and GIS (2021–2024).

ForestSAT workshops

Organized the ForestSAT 2026 Virtual Workshop Series and taught four workshops, totaling 16 hours, on Earth Engine, ICESat-2, airborne laser scanning and forest biomass mapping.

Graduate mentoring

Co-supervised four master's theses at the University of Molise.

Hands-on teaching resources

Materials from practical workshops on forest mapping and Earth observation.

Mapping forest attributes using airborne laser scanning and artificial intelligence ↗
Guided exercises combining ALS data and field measurements to map forest attributes, including biomass and carbon stock.
Wall-to-wall forest biomass estimation using Google Earth Engine ↗
A documented workflow covering GEDI reference data, biomass mapping, accuracy assessment, spatial interpretation and output download.

OpenForest4D teaching resources ↗

05 / IN THE FIELD & AT CONFERENCES