Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Robert N Masolele"'
Autor:
Karimon Nesha, Martin Herold, Johannes Reiche, Robert N Masolele, Kristell Hergoualc’h, Erin Swails, Daniel Murdiyarso, Corneille E N Ewango
Publikováno v:
Environmental Research Letters, Vol 19, Iss 10, p 104031 (2024)
The largest tropical peatland complex in the Cuvette Centrale is marked by persistent knowledge gaps. We assessed recent peat forest disturbances and their direct drivers from 2019 to 2021 in Cuvette Centrale, spanning the Republic of Congo (ROC) and
Externí odkaz:
https://doaj.org/article/720a876fd41a4caea59f4bd4d11cab7e
Autor:
Johannes Reiche, Johannes Balling, Amy Hudson Pickens, Robert N Masolele, Anika Berger, Mikaela J Weisse, Daniel Mannarino, Yaqing Gou, Bart Slagter, Gennadii Donchyts, Sarah Carter
Publikováno v:
Environmental Research Letters, Vol 19, Iss 5, p 054011 (2024)
Satellite-based near-real-time forest disturbance alerting systems have been widely used to support law enforcement actions against illegal and unsustainable human activities in tropical forests. The availability of multiple optical and radar-based f
Externí odkaz:
https://doaj.org/article/a415d09ce4ac4e15b08777badbcbec55
Publikováno v:
Remote Sensing, Vol 16, Iss 22, p 4261 (2024)
Species-level phenology models are essential for predicting shifts in tree species under climate change. This study quantified phenological differences among dominant miombo tree species and modeled seasonal variability using climate variables. We us
Externí odkaz:
https://doaj.org/article/883186b66c254f8bb8aa0bd94cd28c27
Autor:
Martin Herold, Robert N. Masolele, Jan Verbesselt, Christopher Martius, Veronique De Sy, Diego Marcos, Adugna G. Mullissa, Fabian Gieseke
Publikováno v:
Remote Sensing of Environment, 264
Remote Sensing of Environment
Remote Sensing of Environment 264 (2021)
Remote Sensing of Environment
Remote Sensing of Environment 264 (2021)
Assessing land-use following deforestation is vital for reducing emissions from deforestation and forest degradation. In this paper, for the first time, we assess the potential of spatial, temporal and spatio-temporal deep learning methods for large-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43597cf741c0f886e6247fd43e0aae2e
https://research.wur.nl/en/publications/spatial-and-temporal-deep-learning-methods-for-deriving-land-use-
https://research.wur.nl/en/publications/spatial-and-temporal-deep-learning-methods-for-deriving-land-use-
Publikováno v:
Discover Applied Sciences, Vol 6, Iss 10, Pp 1-22 (2024)
Abstract Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe For
Externí odkaz:
https://doaj.org/article/f456e17ddb0b4f7ebe510aad284a8d1f
Autor:
Robert N. Masolele, Diego Marcos, Veronique De Sy, Itohan-Osa Abu, Jan Verbesselt, Johannes Reiche, Martin Herold
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spa
Externí odkaz:
https://doaj.org/article/596a7313fac049f79f393996c2119f80
Autor:
Robert N. Masolele, Veronique De Sy, Diego Marcos, Jan Verbesselt, Fabian Gieseke, Kalkidan Ayele Mulatu, Yitebitu Moges, Heiru Sebrala, Christopher Martius, Martin Herold
Publikováno v:
GIScience & Remote Sensing, Vol 59, Iss 1, Pp 1446-1472 (2022)
National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, mu
Externí odkaz:
https://doaj.org/article/6cb1292b53a14e15820b61b833fbaceb