scholarly journals Correction to: A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Willem A. Nieman ◽  
Brian W. van Wilgen ◽  
Alison J. Leslie
Keyword(s):  
Remote Sensing ◽  
Fire Regimes ◽  
Wildlife Reserve

scholarly journals A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Willem A. Nieman ◽  
Brian W. van Wilgen ◽  
Alison J. Leslie
Keyword(s):  
Remote Sensing ◽  
Fire Management ◽  
Fire Regime ◽  
Fire Regimes ◽  
Dry Season ◽  
Annual Rainfall ◽  
Vegetation Types ◽  
Local Evidence ◽  
Hot Dry Season ◽  
Wildlife Reserve

Abstract Background Fire is an important process that shapes the structure and functioning of African savanna ecosystems, and managers of savanna protected areas use fire to achieve ecosystem goals. Developing appropriate fire management policies should be based on an understanding of the determinants, features, and effects of prevailing fire regimes, but this information is rarely available. In this study, we report on the use of remote sensing to develop a spatially explicit dataset on past fire regimes in Majete Wildlife Reserve, Malawi, between 2001 and 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) images were used to evaluate the recent fire regime for two distinct vegetation types in Majete Wildlife Reserve, namely savanna and miombo. Additionally, a comparison was made between MODIS and Visible Infrared Imager Radiometer Suite (VIIRS) images by separately evaluating selected aspects of the fire regime between 2012 and 2019. Results Mean fire return intervals were four and six years for miombo and savanna vegetation, respectively, but the distribution of fire return intervals was skewed, with a large proportion of the area burning annually or biennially, and a smaller proportion experiencing much longer fire return intervals. Variation in inter-annual rainfall also resulted in longer fire return intervals during cycles of below-average rainfall. Fires were concentrated in the hot-dry season despite a management intent to restrict burning to the cool-dry season. Mean fire intensities were generally low, but many individual fires had intensities of 14 to 18 times higher than the mean, especially in the hot-dry season. The VIIRS sensors detected many fires that were overlooked by the MODIS sensors, as images were collected at a finer scale. Conclusions Remote sensing has provided a useful basis for reconstructing the recent fire regime of Majete Wildlife Reserve, and has highlighted a current mismatch between intended fire management goals and actual trends. Managers should re-evaluate fire policies based on our findings, setting clearly defined targets for the different vegetation types and introducing flexibility to accommodate natural variation in rainfall cycles. Local evidence of the links between fires and ecological outcomes will require further research to improve fire planning.

scholarly journals Drivers of fire occurrence in a mountainous Brazilian cerrado savanna: Tracking long-term fire regimes using remote sensing

2017 ◽  
Vol 78 ◽  
pp. 270-281 ◽  
Author(s):  
Swanni T. Alvarado ◽  
Tamires Fornazari ◽  
Andresa Cóstola ◽  
Leonor Patricia Cerdeira Morellato ◽  
Thiago Sanna Freire Silva
Keyword(s):  
Remote Sensing ◽  
Fire Regimes ◽  
Fire Occurrence ◽  
Brazilian Cerrado ◽  
Cerrado Savanna

scholarly journals Invasive grasses in South Texas rangelands: historical perspectives and future directions

2020 ◽  
Vol 13 (2) ◽  
pp. 41-58
Author(s):  
Justin P. Wied ◽  
Humberto L. Perotto-Baldivieso ◽  
April A. T. Conkey ◽  
Leonard A. Brennan ◽  
José M. Mata
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Fire Regimes ◽  
South Texas ◽  
Grass Species ◽  
Forage Production ◽  
Bothriochloa Ischaemum ◽  
Heteropogon Contortus ◽  
Historical Perspectives ◽  
Sensing Applications

AbstractSouth Texas is home to a high diversity of species due to its location at the confluence of subtropical, desert, and coastal ecoregions. Historical overgrazing of South Texas rangelands transformed the savanna and prairie to a landscape dominated by woody plants and shrubs interspersed with low seral grass species and bare ground. During the first half of the 20th century, exotic grass species, coupled with the application of industrial agricultural practices appeared to be the future of forage production in South Texas and elsewhere. Several of these exotic species, namely King Ranch bluestem [Bothriochloa ischaemum (L.) Keng], Kleberg bluestem [Dichanthium annulatum (Forssk.) Stapf], Angelton bluestem [Dichanthium aristatum (Poir.) C.E. Hubbard], buffelgrass [Pennisetum ciliare (L.) Link], guineagrass [Urochloa maxima (Jacq.) R. Webster], Lehmann lovegrass (Eragrostis lehmanniana Nees), and Bermudagrass [Cynodon dactylon (L.) Pers.], have escaped pasture cultivation. Additionally, the native grass tanglehead [Heteropogon contortus (L.) P. Beauv. ex Roem. & Schult.] has begun displaying invasive behaviors. The monoculture growth habit of these species simplifies vegetation structure, reduces biodiversity, and decreases habitat for many species of wildlife. These grasses also alter natural fire regimes and nutrient cycling. This landscape-level transformation of vegetation composition and structure requires monitoring to quantify and assess the spatial and temporal distributions of invasive species as a basis to inform management practices. Current advances in remote sensing technologies, such as very high spatial resolution coupled with daily satellite imagery and unmanned aerial vehicles, are providing tools for invasive vegetation monitoring. We provide a synthesis of the natural history of these grasses, including their introductions, an overview of remote sensing applications in South Texas, and recommendations for future management practices.

scholarly journals Effects of large fires on boreal forests of China : historical reconstruction and future prediction through landscape modeling

2020 ◽  
Author(s):  
◽  
Wenru Xu
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Climate Warming ◽  
Boreal Forests ◽  
Fire Regimes ◽  
Forest Recovery ◽  
Burn Severity ◽  
Inventory Data ◽  
Large Fires ◽  
Species Composition And Distribution

Boreal forests of China store about 350 Tg tree biomass carbon, which is approximately 24â€"31 [percent] of the total forest carbon storage in China, and thus, play an important role in maintain national carbon balance. Long-term fire exclusion and climate warming have foster larger and more severe fires. On 1987 May 6, a catastrophic fire, known as the Black Dragon Fire, occurred in this region, and burned 1.3 million ha. This fire is among the top five of such megafires ever recorded in the world, resulting in high degree of tree mortality and reset forest succession stage for most burned stands. Forests have grown back since, with much more homogeneous age classes and composition, which post new ecological risks and challenges. It is predicted that the warming will continue in the next century, and thus uncertainties exist in future fire regimes and vegetation response under novel climate. Chapter II estimate the burn severity and carbon emissions from the Black Dragon fire. I combined field and remote sensing data to map four burn severity classes and calculated combustion efficiency in terms of the biomass immediately consumed in the fire. Results of this chapter showed that 1.30 million hectares burned and 52 [percent] of that area burned with high severity. The emitted carbon dioxide equivalents (CO2e), accounted for approximately 10 [percent] of total fossil fuel emissions from China in 1987, along with CO (2 [percent] - 3 [percent] of annual anthropogenic CO emissions from China) and non-methane hydrocarbons (NMHC) contributing to the atmospheric pollutants. This study provides an important basis for carbon emission estimation and understanding the impacts of megafires. Chapter III developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. I derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. I simulated the megafire and the post-megafire forest recovery from 1985-2015 using the LANDIS model. I calibrated the model and validated the simulation results using inventory data. I demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances. Chapter IV investigated the effects of future fire regimes on boreal forests of China under a warming climate. I simulated species composition and distribution changes to the year 2100 using a coupled forest dynamic model (LANDIS PRO) and ecosystem process model (LINKAGES). I focused on two possible fire regimes (frequent small fires and infrequent large fires). Results of this chapter showed that climate warming and fires strongly affected tree species composition and distribution in the boreal forests of China. Climate warming promoted transitions from boreal species to pioneer and temperate species. Fire effects acted in the same direction as climate change effects on species occurrences, thereby catalyzing climate-induced transitions. Frequent small fires exerted stronger effects on the species composition shifts than infrequent large fires. The combined effects of climate warming and fire on the shifts in species composition will accumulate through time and space and can induce a complete transition of forest type, and alter forest dynamics and functions.

Satellite remote sensing of biomass burning with optical and thermal sensors

2000 ◽  
Vol 24 (4) ◽  
pp. 543-561 ◽  
Author(s):  
Douglas O. Fuller
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Satellite Remote Sensing ◽  
Field Experiments ◽  
Temporal Distribution ◽  
Fire Detection ◽  
Fire Regimes ◽  
Fire Monitoring ◽  
Fire Scar ◽  
In Fire

A major goal in satellite remote sensing of fire is to derive globally accurate measurements of the spatial and temporal distribution of burning. To date, the main sensor employed in fire and fire-scar detection has been the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA polar-orbiting platforms. Other sources supporting fire observation over large areas include the Defense Meteorological Satellite Program -Optical Linescan (DMSP-OLS), the Geostationary Operational Environmental Satellite - 8 (GOES-8) and the Along Track Scanning Radiometer (ATSR). These sources have often been used in conjunction with high spatial-resolution imagery provided by the Landsat Thematic Mapper and SPOT to assess the accuracy of proposed fire and fire-scar retrieval algorithms. Although a range of fire detection algorithms have been proposed based on more than a decade of research on the AVHRR data, it remains to be seen whether variations in land-cover type, surface temperature and fire regimes will permit application of global thresholds of temperature and reflectance. Moreover, the emerging set of satellite sensors with demonstrated utility in fire monitoring indicates substantial possibilities for greater synergy of current and future remote-sensing systems leading to improved monitoring of fire extent and frequency. As a more complete global picture of biomass burning emerges with the launch of new sensors for fire monitoring (e.g., MODIS), this information, combined with detailed data from field experiments, can help provide reliable budgets of trace gases and particulate species that affect global energy balance and climate.

Fire regimes and post-fire evolution of burned areas in selected plant biomes of the planet studying the phenology of the landscape with time series of satellite images

2021 ◽  
Author(s):  
Nikos Koutsias ◽  
Anastasia Karamitsou ◽  
Foula Nioti ◽  
Frank Coutelieris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Fire History ◽  
Fire Regime ◽  
Fire Regimes ◽  
Burned Areas ◽  
History Of ◽  
Sentinel 2

<p>Plant biomes and climatic zones are characterized by a specific type of fire regime which can be determined from the history of fires in the area and it is a synergy mainly of the climatic conditions and the functional characteristics of the types of vegetation. They correspond also to specific phenology types, a feature that can be useful for various applications related to vegetation monitoring, especially when remote sensing methods are used. Both the assessment of fire regime from the reconstruction of fire history and the monitoring of post-fire evolution of the burned areas can be studied with satellite remote sensing based on satellite time series images. The free availability of (i) Landsat satellite imagery by US Geological Survey (USGS, (ii) Sentinel-2 satellite imagery by ESA and (iii) MODIS satellite imagery by NASA / USGS allow low-cost data acquisition and processing (eg 1984-present) which otherwise would require very high costs. The purpose of this work is to determine the fire regime as well as the patterns of post-fire evolution of burned areas in selected vegetation/climate zones for the entire planet by studying the phenology of the landscape with time series of satellite images. More specifically, the three research questions we are negotiating are: (i) the reconstruction of the history of fires in the period 1984-2017 and the determination of fire regimes with Landsat and Sentinel-2 satellite data , (ii) the assessment of pre-fire phenological pattern of vegetation and (iii) the monitoring and comparative evaluation of post-fire evolution patterns of the burned areas.</p><p><strong>Acknowledgements</strong></p><p>This research has been co-financed by the Operational Program "Human Resources Development, Education and Lifelong Learning" and is co-financed by the European Union (European Social Fund) and Greek national funds.</p><p> </p>

Remote sensing of fire regimes in semi-arid Nusa Tenggara Timur, eastern Indonesia: current patterns, future prospects

2006 ◽  
Vol 15 (3) ◽  
pp. 307 ◽  
Author(s):  
Rohan Fisher ◽  
Wilfrida E. Bobanuba ◽  
Agus Rawambaku ◽  
Greg J. E. Hill ◽  
Jeremy Russell-Smith
Keyword(s):  
Remote Sensing ◽  
Prescribed Burning ◽  
Landsat Imagery ◽  
Great Majority ◽  
Fire Regimes ◽  
Dry Season ◽  
Hotspot Detection ◽  
Fire Policy ◽  
Eastern Indonesia ◽  
Semi Arid

Substantial areas of eastern Indonesia are semi-arid (with a pronounced dry season extending from April to November) with extensive areas of uncultivated vegetation dominated by savanna grasslands and woodlands. These are highly fire-prone, despite high population densities reliant on intensive subsistence agriculture and an official national fire policy that prohibits all burning. To date, no regional studies have been undertaken that reliably assess the seasonal extent and patterning of prescribed burning and wildfire. Focusing on two case studies in east Sumba (7000 km2) and central Flores (3000 km2) in the eastern Indonesian province of Nusa Tenggara Timur, the present paper addresses: (1) the efficacy of applying standard remote sensing and geographic information system tools as developed for monitoring fire patterns in savanna landscapes of adjacent northern Australia, for (2) describing the seasonal patterning of burning at village and broader regional scales in 2003 and 2004. Despite recurring cloudiness, which significantly affected daily fire detection of ‘hotspots’ from Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer sensors, fire mapping from Landsat imagery was undertaken successfully to reveal: (1) fires burnt an annual average of 29% of eastern Sumba (comprising mostly grassland savanna), and 11% of central Flores (with large forested areas); (2) most fire extent occurred in savanna grassland areas, and significantly also in cultivated lands and small remnant patches of forest; (3) most fire activity occurred under harsh, late dry season conditions; and (4) while the great majority of individual fires were less than 5 ha, some late dry season fires were hundreds of hectares in extent. The potential routine application of different image sensors for fire mapping and hotspot detection is considered in discussion.

scholarly journals Application of remote sensing to understanding fire regimes and biomass burning emissions of the tropical Andes

2014 ◽  
Vol 28 (4) ◽  
pp. 480-496 ◽  
Author(s):  
Immaculada Oliveras ◽  
Liana O. Anderson ◽  
Yadvinder Malhi
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Fire Regimes ◽  
Tropical Andes

MAPPING FUELS AND FIRE REGIMES USING REMOTE SENSING, ECOSYSTEM SIMULATION, AND GRADIENT MODELING

2004 ◽  
Vol 14 (1) ◽  
pp. 75-95 ◽  
Author(s):  
Matthew G. Rollins ◽  
Robert E. Keane ◽  
Russell A. Parsons
Keyword(s):  
Remote Sensing ◽  
Fire Regimes ◽  
Ecosystem Simulation ◽  
Gradient Modeling

scholarly journals Structure and composition of the woody plant communities of Majete Wildlife Reserve, Malawi

Bothalia ◽  
2021 ◽  
Vol 51 (2) ◽  
Author(s):  
Willem Nieman ◽  
Brian Van Wilgen ◽  
Alison Leslie
Keyword(s):  
Remote Sensing ◽  
Plant Communities ◽  
Policy Development ◽  
Woody Plant ◽  
Human Populations ◽  
Miombo Woodlands ◽  
Mammal Species ◽  
Shrub Species ◽  
Lower Altitude ◽  
Wildlife Reserve

Background: The role of protected areas as sanctuaries for indigenous vegetation in Malawi, particularly miombo woodlands, will become increasingly important in the face of global change and rising human populations. Accurate knowledge of the extent and composition of woody components of plant communities will therefore play a vital part in informing conservation and management initiatives.Objectives: The aims of this study were to (1) classify, describe and map thewoody plant communities of the Majete Wildlife Reserve (MWR) using a combination of remote sensing and on-the-ground surveys, and (2) to compile an inventory of the tree and shrub species present in MWR.Methods: A combination of remote sensing and on-the-ground surveys was used to classify, describe and map the woody plant communities of MWR. Additionally, an inventory of the tree and shrub species in each delineated woody plant community was made.Results: Five distinct woody plant communities, two of which were subdivided into three sub-communities each, were recognised in MWR, and a total of 118 woody plant species within 31 families were identified. A description of the location, structure and species composition of each community is provided. Miombo was the most widespread community (covering 35.9% of the area), while the lower-altitude shrublands and woodlands were the richest floristically.Conclusion: This information is intended to provide a basis for improved management planning and policy development, including fire management, the placement of infrastructure, and the re-introduction of extirpated mammal species, as well as providing a baseline against which to monitor change. Additionally, this study provided an example of how the combination of remote sensing and ground surveys can provide a rapid and relatively inexpensive method for classifying the woody components of communities at a relatively fine scale over large areas, which may become particularly relevant for developing countries and regions that undergo rapid and constant change.

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