Remotely sensing primary production recovery following bushfire

Vegetation growth is the key process driving landscape dynamics and carbon flux. Fire disturbs gross primary productivity to varying degrees depending on fire effects and the ability of the landscape to absorb these. Simple remote sensing diagnosis can build a description of vegetation growth considering physiological drivers from the top down, which are related to fire disturbance through time. Analysis of these disturbances in terms of ecosystem processes at landscape scales are not common. This method used here produces results showing a near constant relationship between fire severity and vegetation type, and time to GPP recovery in a semi-arid shrub landscape. Other landscapes with structurally complex vegetation show a range of GPP values and recovery trajectories with time after fire. The balance of radiation and conductance model components’ response to fire disturbance needs to be analysed further. The work here highlights the opportunities in remote sensing available to analysis of landscape disturbance and the potential for integrating such fluctuation into landscape model

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research

Koalas going with the flow

The 2019 drought, and the hot smokey summer that followed, threatened many koala (Phascolarctos cinereus) populations with overheating and dehydration. We have seen pictures of koalas approaching cyclists for a drink of water from their water bottles, and Kulin people tell of the koala taking control of the water in the country when it is mismanaged. During the drought, some koalas were forced to leave their dry Eucalyptus trees to hydrate and cool themselves down by means other than sweating. Suppose you braved the forest during the drought; a unique behaviour in the journey of koala thermoregulation may have been witnessed. Suppose you did manage to spot the solitary animal without the help of Global Positioning System tracking. In that case, the koala may have been observed spreading its limbs and belly across a large cool branch close to the ground, allowing heat to be drawn away into the bark. ‘Tree-hugging’ is a behaviour used during high air temperatures, above approximately 30°C, along with increased panting and shade seeking to lower the body temperature

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through feld campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. GlobeLFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfre behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader feld of environmental and ecological research.info:eu-repo/semantics/publishedVersio

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research