Below there is a summary of the tasks performed across the four objectives of TREES4CLIMA:
A. Classify and represent agroforestry systems with remote sensing and deep learning
Downloading and processing satellite data. I downloaded high-resolution PlanetScope imagery via the NICFI programme. A standardized preprocessing workflow—mosaicking and normalization— was applied based on established protocols to produce 3 m resolution PlanetScope mosaics.
Obtaining training and validation data. During field campaigns, drone images and orthophotos were acquired across hedgerows, silvopastures, multistrata, shaded systems, and fallows. Farm geolocations were collected, as well as polygons under the Peruvian platform for EUDR verification
Designing a deep learning framework. I developed a dual-branch model, agroforestry_detection, which ingests satellite imagery and forest-loss layers in parallel, learns high-level semantic features, and fuses them for agroforestry classification.
B. Advance methods for rapid quantification of on-farm biomass carbon
Imaging and acquiring data. Across orchards and agroforestry sites, I collected UAV-mounted RGB imagery plus ground-level captures from smartphone cameras and smartphone LiDAR. Data were acquired in orchards in California, silvopastoral systems in Colombia, and fallows and multistrata systems in Peru.
Reconstructing 3D models. I generated agroforestry 3D models using a hybrid pipeline that combines structure-from-motion with state-of-the-art neural methods. These approaches were tested first in California orchards, then applied across hedgerows, silvopastures, multistrata systems, and shaded coffee in Latin America.
Estimating and modeling biomass. In parallel, I collected biomass and conducted forest inventories to train and evaluate machine-learning models. I derived plot and tree-level structural metrics and trained/evaluated supervised models to estimate biomass and benchmark predictions against field observations.
C. Quantification of carbon in soils
Field sampling and soil processing. Fieldwork covered soil sampling in georeferenced plots in the Maya Biosphere Reserve across protected forest, managed forest, and grassland.
Laboratory analyses. Soils were analyzed for SOC using Walkley–Black wet oxidation.
Predicting SOC. Linear regressions for SOC prediction—aimed at explaining carbon variation across land transitions.
D. Innovative solutions to link individual trees and climate finance
Obtaining high-resolution orthophotos. I downloaded 25 cm GSD orthophotos from Spain’s PNOA program and co-registered airborne LiDAR.
Leveraging National Forest Inventory (NFI) data. NFI data were obtained from the 4th Spanish National Forest Inventory. I developed matching_trees to address the misalignment between NFI-recorded tree positions and aerial-image features.
Implementing a deep learning pipeline to detect and classify individual trees. We introduced a pipeline that automatically matches field observations with predictions on aerial photography from an ensemble of pretrained models for individual-tree detection and segmentation.
Training undertaken
TREES4CLIMA allowed me to acquire training on: Project management; data management; advanced statistics and data science at UC Davis; deep learning courses at the University of Copenhagen; summer schools on 3D modeling, field and lab methods; pedagogics and research-based teaching at UC Davis and the University of Copenhagen; advancing communication skills inside and outside academia; and networking and proposal writing, among others.
Exploitation and dissemination of results
TREES4CLIMA’s results have been disseminated at AGU, IUFRO, ECCV, and ESA BioSpace25 and ESA Living Planet conferences, and within partner networks at CIFOR-ICRAF and CIAT. One scientific article has already been published in Ecological Informatics, with additional manuscripts in preparation. Community feedback sessions were held in San Martin for non-academic audiences. Results also informed research-based teaching and courses at the University of Copenhagen, and a short-scientific mission at the University College London (Department of Geography).
