TY - JOUR
T1 - Multispectral drone imagery dataset for plus and non-plus Neltuma pallida trees in northern Peru
AU - Castro, Wilson
AU - Seminario, Roberto
AU - Nauray, William
AU - Acevedo-Juárez, Brenda
AU - De-la-Torre, Miguel
AU - Avila-George, Himer
N1 - Publisher Copyright:
© 2025
PY - 2025/6
Y1 - 2025/6
N2 - Neltuma pallida (“Algarrobo”) is an endangered species native to seasonally dry forests. Preserving this species necessitates creating spatially explicit records of specimens with superior phenological traits, commonly referred to as “plus” trees, which serve as a foundation for reforestation programs. This dataset article describes a collection of multispectral images of Algarrobo trees classified as plus and non-plus, captured between January and September 2023 across the Lambayeque, Piura, and Tumbes departments in northern Peru. Sampling was conducted within forest management zones supervised by the Servicio Nacional Forestal y de Fauna Silvestre. Fieldwork included in-situ evaluation, classification, and georeferencing of specimens, followed by image acquisition using a multispectral drone at a flight altitude of 70 m. Subsequent processing isolated regions of interest corresponding to tree crowns, from which morpho-geometric parameters were extracted, summarized, and compared between tree classes. The dataset contains 500 images per class, geographically distributed across the study area, with plus trees predominantly located in Piura (70 %), Tumbes (20 %), and Lambayeque (10 %). Plus trees exhibit larger mean values for area, perimeter, and major and minor axes. The distributions of major and minor axes and equivalent diameter approximate normal distributions, differing in central tendency and dispersion between classes. However, no significant differences in roundness were observed. This database provides a foundational resource for developing classification models to distinguish between plus and non-plus Neltuma pallida trees, supporting conservation and reforestation efforts.
AB - Neltuma pallida (“Algarrobo”) is an endangered species native to seasonally dry forests. Preserving this species necessitates creating spatially explicit records of specimens with superior phenological traits, commonly referred to as “plus” trees, which serve as a foundation for reforestation programs. This dataset article describes a collection of multispectral images of Algarrobo trees classified as plus and non-plus, captured between January and September 2023 across the Lambayeque, Piura, and Tumbes departments in northern Peru. Sampling was conducted within forest management zones supervised by the Servicio Nacional Forestal y de Fauna Silvestre. Fieldwork included in-situ evaluation, classification, and georeferencing of specimens, followed by image acquisition using a multispectral drone at a flight altitude of 70 m. Subsequent processing isolated regions of interest corresponding to tree crowns, from which morpho-geometric parameters were extracted, summarized, and compared between tree classes. The dataset contains 500 images per class, geographically distributed across the study area, with plus trees predominantly located in Piura (70 %), Tumbes (20 %), and Lambayeque (10 %). Plus trees exhibit larger mean values for area, perimeter, and major and minor axes. The distributions of major and minor axes and equivalent diameter approximate normal distributions, differing in central tendency and dispersion between classes. However, no significant differences in roundness were observed. This database provides a foundational resource for developing classification models to distinguish between plus and non-plus Neltuma pallida trees, supporting conservation and reforestation efforts.
KW - Algarrobo
KW - Deforestation
KW - Multi-spectral image
KW - Neltuma pallida
KW - Plus tree
KW - Seasonally dryed forest
UR - https://www.scopus.com/pages/publications/105005264647
U2 - 10.1016/j.dib.2025.111645
DO - 10.1016/j.dib.2025.111645
M3 - Artículo
AN - SCOPUS:105005264647
SN - 2352-3409
VL - 60
JO - Data in Brief
JF - Data in Brief
M1 - 111645
ER -