Original research papers:
Jacon, AD, Galvão, LS, Martins-Neto, RP , Crespo-Peremarch, P., Aragão, LE, Ometto, JP, ... & Dalagnol, R. (2024). Characterizing Canopy Structure Variability in Amazonian Secondary Successions with Full-Waveform Airborne LiDAR. Remote Sensing, 16(12), 2085. https://doi.org/10.3390/rs16122085
Ziegelmaier Neto, BH, Schimalski, MB, Liesenberg, V., Sothe, C., Martins-Neto, RP, & Floriani, MMP (2024). Combining LiDAR and Spaceborne Multispectral Data for Mapping Successional Forest Stages in Subtropical Forests. Remote Sensing, 16 (9), 1523. https://doi.org/10.3390/rs16091523
Pereira Martins-Neto, R. , Garcia Tommaselli, AM, Imai, NN, Honkavaara, E., Miltiadou, M., Saito Moriya, EA, & David, HC (2023). Tree Species Classification in a Complex Brazilian Tropical Forest Using Hyperspectral and LiDAR Data. Forests , 14 (5), 945. https://doi.org/10.3390/f14050945
Neto, RPM, Tommaselli, AM, Imai, NN, Berveglieri, A., Thomaz, MB, Miyoshi, GT, ... & David, HC (2022). Structure and tree diversity of an inland Atlantic Forest–A case study of Ponte Branca Forest Remnant, Brazil. The Indonesian Journal of Geography , 54 (1), 112-122. https://doi.org/10.22146/ijg.61120
Berveglieri, A., Imai, NN, Tommaselli, AM, Martins-Neto, RP , Miyoshi, GT, & Honkavaara, E. (2021). Forest cover change analysis based on temporal gradients of the vertical structure and density. Ecological Indicators , 126 , 107597. https://doi.org/10.1016/j.ecolind.2021.107597
Martins-Neto, RP , Tommaselli, AMG, Imai, NN, David, HC, Miltiadou, M., & Honkavaara, E. (2021). Identification of significant LiDAR metrics and comparison of machine learning approaches for estimating stand and diversity variables in heterogeneous Brazilian Atlantic forest. Remote Sensing, 13 (13), 2444. https://doi.org/10.3390/rs13132444
Galvan, KA, Medeiros, RC, Neto, RPM , Liberalesso, T., Golombieski, JI, & Zanella, R. (2020). Análise ambiental macroscopic ea qualidade da água de nascentes na bacia do Rio São Domingos/SC, Brasil. Revista Ibero-Americana de Ciências Ambientais , 11 (1), 165-176.
David, HC, MacFarlane, DW, Péllico Netto, S., Corte, APD, Piotto, D., de Oliveira, YM, ... & Neto, RPM (2019). Exploring coarse-to fine-scale approaches for mapping and estimating forest volume from Brazilian National Forest Inventory data. Forestry: An International Journal of Forest Research, 92 (5), 577-590. ttps://doi.org/10.1093/forestry/cpz030
Machado, MV, Tommaselli, AMG, Tachibana, VM, Martins-Neto, RP , & Campos, MB (2019). Evaluation of multiple linear regression model to obtain DBH of trees using data from a lightweight laser scanning system on-board a UAV. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 42 , 449-454. https://doi.org/10.5194/isprs-archives-XLII-2-W13-449-2019
Buck, ALB, Lingnau, C., Péllico Neto, S., Machado, Á. ML, & Martins-Neto, RP (2019). Stem modeling of Eucalyptus by terrestrial laser scanning. Floresta e Ambiente , 26 , e20160125. https://doi.org/10.1590/2179-8087.012516
