Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Lwando Royimani"'
Publikováno v:
Smart Agricultural Technology, Vol 6, Iss , Pp 100377- (2023)
While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass
Externí odkaz:
https://doaj.org/article/e19f9d8d7f054295886c5db75f1b805b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 7714-7723 (2021)
Grass senescence estimation in rangeland is particularly important for monitoring the conditions of forage quality and quantity. During senescence, grasses lose their nutrients from the leaves to the root and thereby affecting forage productivity. St
Externí odkaz:
https://doaj.org/article/7ae0b0bf74fc44dabf264c67c2f95eb8
Publikováno v:
Land, Vol 12, Iss 1, p 183 (2023)
Climate and topography are influential variables in the autumn senescence of grassland ecosystems. For instance, extreme weather can lead to earlier or later senescence than normal, while higher altitudes often favor early grass senescence. However,
Externí odkaz:
https://doaj.org/article/74541e8d1c87412e8a8df7cbdae59dc1
Publikováno v:
Physics and Chemistry of the Earth, Parts A/B/C. 112:237-245
Detecting and mapping the occurrence, spatial distribution and abundance of Alien Invasive Plants (AIPs) have recently gained substantial attention, globally. This work, therefore, provides an overview of advancements in satellite remote sensing for
Autor:
John Odindi, Kiala Serge Zolo, Mbulisi Sibanda, Lwando Royimani, Timothy Dube, Onisimo Mutanga
Publikováno v:
Remote Sensing Applications: Society and Environment. 13:215-223
Detecting the spatial and temporal distribution of weeds such as parthenium (Parthenium hysterophorus L.) is crucial to facilitate the management and mitigation of their spread. The availability of historical remotely sensed data with fine spatial re
Publikováno v:
Ecological Informatics. 69:101651