Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Leandro Parente"'
Autor:
Julia Hackländer, Leandro Parente, Yu-Feng Ho, Tomislav Hengl, Rolf Simoes, Davide Consoli, Murat Şahin, Xuemeng Tian, Martin Jung, Martin Herold, Gregory Duveiller, Melanie Weynants, Ichsani Wheeler
Publikováno v:
PeerJ, Vol 12, p e16972 (2024)
The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refer
Externí odkaz:
https://doaj.org/article/920c52b186804b4389f2d3bb70c52a6c
Autor:
Tomislav Hengl, Preston Sorenson, Leandro Parente, Kimberly Cornish, Jeffrey Battigelli, Carmelo Bonannella, Monika Gorzelak, Kris Nichols
Publikováno v:
FACETS, Vol 8, Iss , Pp 1-17 (2023)
A three-dimensional predictive soil mapping approach for predicting soil organic carbon (SOC) stocks (t/ha) at high spatial resolution (30 m) for Alberta for 2020–2021 is described. A remote sensing data stack was first prepared covering Alberta’
Externí odkaz:
https://doaj.org/article/6266db4823d849169bf72bd38a670f3f
Publikováno v:
PeerJ, Vol 11, p e15593 (2023)
The global potential distribution of biomes (natural vegetation) was modelled using 8,959 training points from the BIOME 6000 dataset and a stack of 72 environmental covariates representing terrain and the current climatic conditions based on histori
Externí odkaz:
https://doaj.org/article/8873706227a141a0a44ed0c743a2945e
Publikováno v:
PeerJ, Vol 11, p e15478 (2023)
The article describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000–2020+ Landsat data, 2017–2021+ Sentinel-2 data and a 30 m resolution digital terrain model (DTM). The main
Externí odkaz:
https://doaj.org/article/5ed66c72619742488caff66760ab3f86
Autor:
Martijn Witjes, Leandro Parente, Chris J. van Diemen, Tomislav Hengl, Martin Landa, Lukáš Brodský, Lena Halounova, Josip Križan, Luka Antonić, Codrina Maria Ilie, Vasile Craciunescu, Milan Kilibarda, Ognjen Antonijević, Luka Glušica
Publikováno v:
PeerJ, Vol 10, p e13573 (2022)
A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD L
Externí odkaz:
https://doaj.org/article/050241afd79e456c81a73bbd00714e2c
Autor:
Carmelo Bonannella, Tomislav Hengl, Johannes Heisig, Leandro Parente, Marvin N. Wright, Martin Herold, Sytze de Bruin
Publikováno v:
PeerJ, Vol 10, p e13728 (2022)
This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies
Externí odkaz:
https://doaj.org/article/4481f2208a564da9ad54213643b093e1
Autor:
Tomislav Hengl, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijević, Luka Glušica, Achim Dobermann, Stephan M. Haefele, Steve P. McGrath, Gifty E. Acquah, Jamie Collinson, Leandro Parente, Mohammadreza Sheykhmousa, Kazuki Saito, Jean-Martial Johnson, Jordan Chamberlin, Francis B. T. Silatsa, Martin Yemefack, John Wendt, Robert A. MacMillan, Ichsani Wheeler, Jonathan Crouch
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
Abstract Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point
Externí odkaz:
https://doaj.org/article/99f484c0771a491480592db97c08eade
Autor:
Leandro Parente, Laerte Ferreira
Publikováno v:
Remote Sensing, Vol 10, Iss 4, p 606 (2018)
The pasturelands areas of Brazil constitute an important asset for the country, as the main food source for the world’s largest commercial herd, representing the largest stock of open land in the country, occupying ~21% of the national territory. U
Externí odkaz:
https://doaj.org/article/9846121ca098440db02f9b176cdfeb01
Autor:
Hugo N. Bendini, Raian V. Maretto, Thales Sehn Körting, Nathan Jacobs, Leandro Parente, Leila Maria Garcia Fonseca
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 18:771-775
We address the task of mapping deforested areas in the Brazilian Amazon. Accurate maps are an important tool for informing effective deforestation containment policies. The main existing approaches to this task are largely manual, requiring significa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3851251cfc0b8caae9c7357f6ddc2402
https://doi.org/10.1109/lgrs.2020.2986407
https://doi.org/10.1109/lgrs.2020.2986407
Autor:
Adriano Rodrigues de Oliveira Oliveira, Leandro Parente, Nilson Clementino Ferreira, Marcelo Scolari Gosch, Laerte Guimarães Ferreira
Publikováno v:
REVISTA CAMPO-TERRITÓRIO. 15:202-229
O presente artigo está pautado em dois objetivos: I) verificar a predominância de pastagens degradadas nos imóveis rurais desapropriados por meio da política de reforma agrária no estado de Goiás; e II) avaliar o potencial dos dados satelitári
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fadc4d3dd8d4e32b02437be541b8d013
https://doi.org/10.14393/rct153508
https://doi.org/10.14393/rct153508