Monitoring Vegetation Recovery Research Articles

Utilization of geospatial technology to monitor vegetation recovery after forest and land fires in 2023 in the Bromo Volcano Area, Indonesia

Abstract This study was conducted to identify the condition of savannah fields after forest and land fires in parts of the Bromo Volcano area, East Java, Indonesia, in September 2023. The research was conducted through remote sensing technology and integrated geographic information systems. The data used is remote sensing multitemporal data in the form of Sentinel 2 imagery. Sentinel 2 imagery is remote sensing data generated from Sentinel satellite recording. Sentinel 2 imagery was used in this study because it has excellent spatial and temporal resolution. The spatial resolution of Citra Sentinel is 10 meters, and its temporal resolution is ten daily. The data were analyzed using spatial and temporal approaches to determine the condition of the savanna before and after forest and land fires. The software used to analyze the data and visualize spatial information is Google Earth Engine (GEE) and Arc Map Version 10.6. The data analysis methods used include changes in the detection of burned areas using the normalized burn ratio (NBR) method, making fire class classifications based on the U.S. Geological Survey (USGS), post-fire monitoring using the normalized difference vegetation index (NDVI), and monitoring vegetation recovery from post-NDVI values—fires by comparing in different years in the same month using the fishnet method. The temporal months chosen to analyze the recovery process are September, October, November, and December 2023. Based on the study’s results, it is known that in September and October 2023, there has been no vegetation recovery. Vegetation began to look restored in November and December 2023.

Seasonal Effects of Wildfires on the Physical and Chemical Properties of Soil in Andean Grassland Ecosystems in Cusco, Peru: Pending Challenges

Soils are a valuable renewable resource on human timescales, and they interact with distinctive grassland ecosystems characterized by unique biodiversity and essential provision of ecosystem services, such as water supply and carbon sequestration. However, knowledge of the effects of wildfires on soil properties and nutrient availability in the Andes remains limited. Andean grasslands are currently one of the ecosystems of the Peruvian Andes most affected by wildfires. Our objective is to analyze the effect of fire activity on the physicochemical properties of soil and analyze its social context in Cusco, in the southern Andes of Peru. Soil samples were collected during five periods, spanning both the dry and rainy seasons, to characterize changes in soil properties and monitor vegetation recovery post-fire in two local communities dedicated to livestock activities. The vegetation restored after the wildfire was measured by the “step transect” method. Post-fire changes in soil properties indicate slight increases in pH, electrical conductivity, organic matter, nitrogen, phosphorus, and potassium during the onset of the rainy season; thereafter, a gradual reduction in these values was observed. This reduction can be attributed to leaching associated with the seasonal rainfall and runoff regime. Our findings indicate that one-year post-fire, the biomass in burned areas is reduced to 30–46% of the biomass in unburned areas. A complete regeneration is likely to occur in up to 4 years; this assertion is supported by the perceptions of the affected population, as expressed in interviews conducted in the two farming communities. These results are significant for decision-makers formulation of policies and regulations regarding grasslands and their seasonal restoration.

NBR Index-Based Fire Detection Using Sentinel-2 Images and GIS: A Case Study in Mosul Park, Iraq

Forest fires lead to severe damage to the environment and human health. Monitoring can be applied using remotely sensed data and in combination with Geographical Information Systems (GIS) based spatial analysis. Lately, Iraq subjected to many forest fires. In this study, the aim was to monitor and detect the burned areas in Mosul Park during the latest period which happened in June 2022. The hypothesis of the study was based on using Sentinel-2 images and the Normalized Burn Ratio (NBR) index. Two images have been used to compare burned areas; one during the fire events and another postfire. as well as, Normalized Difference Vegetation Index (NDVI) map has been used to identify the Park's characteristics. Moreover, Pearson's correlation (r) with Air Quality Index (AQI) was determined during the burning period. GIS-based processes resulted in detecting the area of burning where the burned area was 16.76 hectares and lay in the eastern part of the study area. Pearson correlation with AQI has resulted in 0.92, while the collinearity between the burned areas and AQI was 0.84. Accurate and prompt planning for fire-affected regions is essential for supporting fire affect assessment, calculating environmental losses, determining planning strategies, and monitoring vegetation recovery.

Use of Mobile Laser Scanning (MLS) to Monitor Vegetation Recovery on Linear Disturbances

Seismic lines are narrow, linear corridors cleared through forests for oil and gas exploration. Their inconsistent recovery has led to Alberta’s forests being highly fragmented, resulting in the need for seismic line restoration programs and subsequent monitoring. Light detection and ranging (LiDAR) is becoming an increasingly popular technology for the fast and accurate measurement of forests. Mobile LiDAR scanners (MLS) are emerging as an alternative to traditional aerial LiDAR due to their increased point cloud density. To determine whether MLS could be effective for collecting vegetation data on seismic lines, we sampled 17 seismic lines using the Emesent Hovermap™ in leaf-on and leaf-off conditions. Processing the LiDAR data was conducted with GreenValley International’s LiDAR 360 software, and data derived from the point clouds were compared to physically measured field data. Overall, the tree detection algorithm was unsuccessful at accurately segmenting the point clouds. Complex vegetation environments on seismic lines, including small conifers with obscured stems or extremely dense and tall shrubs with overlapping canopies, posed a challenge for the software’s capacity to differentiate trees As a result, tree densities and diameters were overestimated, while tree heights were underestimated. Exploration of alternative algorithms and software is needed if measuring vegetation data on seismic lines with MLS is to be implemented.

Post-Fire Vegetation Recovery in Elwasita Area of Al-Jabal Al-Akhdar-Libya - Based on Remote Sensing Information

Satellite-based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Elwasita wildfire, which occurred during April and May in 2013 in Libya, was selected as a study site. This study aims to monitor vegetation recovery and investigate the relationship between vegetation recovery and topographic factors by using multi-temporal spectral indices together with topographical factors. Landsat 8 (OLI and TIRS) images from different data were obtained which were for four years; April 2013, June 2014, July 2015, and July 2016, to assess the related fire severity using the widely-used Normalized Burn Ratio (NBR). Normalized difference Vegetation Index (NDVI) was used to determine vegetation regeneration dynamics for four consecutive years. Also, the state of damage, vegetation recovery and, damage dimensions about the burned area were capable of being effectively detected using the result of supervised classification of Landsat satellite images. In addition, aspect, slope, and altitude images derived from Digital Elevation Model (DEM) were used to determine the fire severity of the study area. The results have found that it could be possible to figure out the degree of vegetation recovery by calculating the NDVI and NBR using Landsat 8 OLI and TIRS images. Analysis showed that it mainly oriented towards the northwest (47%), north (29%), and northeast (12%). The statistical analysis showed that fire was concentrated on the incline by 76%, and the most affected areas are those between 200 m-450 m above sea level, with a percentage of 80%. It is expected that the information can be acquired by various satellite data and digital forests. This study serves as a window to an understanding of the process of fire severity and vegetation recovery that is vital in wildfire management systems.

Using satellite data to assess management frequency and rate of regeneration on heather moorlands in England as a resilience indicator

Peatland resilience, defined here as the rate of recovery from perturbation, is crucial to our understanding of the impacts of climate change and land management on these unique ecosystems. Many peatland areas in the UK are managed as grouse moors using small burns (or increasingly, heather cutting) to encourage heather growth and limit fuel load. These small burns or cuts are distinct disturbance events which provide a useful means of assessing resilience. Until now, it has been difficult to monitor the area affected by management each season due to the remoteness and size of moorland sites. Newer satellite sensors such as those on Sentinel-2 are now collecting data at a spatial resolution that is fine enough to detect individual burns or cut areas, and at a temporal resolution which can be used to monitor occurrence and recovery each year. This study considered four areas of moorland; the North Pennines, Yorkshire Dales, North York Moors, and the Peak District. For each of these areas Sentinel-2 optical data was used to detect management areas using the dNBR (differenced Normalized Burn Ratio), and to monitor vegetation recovery using the NDVI (Normalised Difference Vegetation Index). Significant differences were found between the four selected sites in management repeat interval, with the North York Moors having the shortest repeat interval of 20 years on average (compared to 40–66 years across the other three study sites). Recovery times were found to be affected by burn size and severity, weather during the summer months, and altitude. This suggests that the interactions between peatland management and climate change may affect the future resilience of these areas, with hot, dry summers causing longer management recovery times.

Analysis of vegetation recovery in areas impacted by bauxite mining in the Amazon Forest

Mining activity is essential for the Brazilian economy, representing 21% of exports in 2018, but it causes several environmental impacts, including deforestation. To minimize these impacts, replanting techniques are applied for environmental recovery and regulatory compliance. In this context, this study aims to monitor vegetation recovery in decommissioned bauxite mining areas located in the Amazon rainforest. The case study was carried out in the municipality of Paragominas (state of Para). The monitoring used the series of a newly defined spectral index, called Biomass Composite Index (BCI), and the Enhanced Vegetation Index (EVI), both from Landsat images between 1986 and 2017. The analysis was complemented by interpretation of false-color images and by in situ photographs. The method was applied for periods before mining, during exploitation, and after decommissioning, when the traditional planting technique was implemented. BCI demonstrated greater sensitivity to forest disturbance in areas affected by forest degradation and less influence of canopy homogeneity where the forest was substituted by more open formations. The height homogeneity of recovered areas also affected the EVI data more than the BCI data, since these areas presented higher values of EVI, and some areas had index values close to those detected before mine operation. The analysis could have benefited from sampling intensification, with more images analyzed per year, to overcome the presence of clouds, which block the acquisition of surface data by the sensor. Nevertheless, the method proved to be very promising and can improve the evaluation of forest recomposition even in remote areas, besides facilitating large-scale monitoring of recovered forests.

Monitoring vegetation recovery in the early stages of the Dirk Hartog Island Restoration Programme using high temporal frequency Landsat imagery

SummaryDirk Hartog Island is the largest island off the West Australian coast. From the 1860s to 2008, the island was managed as a pastoral lease. In 2009, the island was gazetted as a National Park and the process of removing introduced animals, to allow for the reintroduction of a suite of 12 native mammal species, began. With the removal of high numbers of goats and sheep concerns were raised regarding the proliferation of weed species (which would no longer be controlled through grazing) and the ability of the island’s native vegetation to recover. A vegetation monitoring programme was developed which integrated detailed floristic surveys, repeated site photography and Landsat time series data to provide a comprehensive picture of how the island’s vegetation cover had changed since destocking. The integration of these elements has allowed present‐day observations to be put into context of longer term landscape dynamics. Through statistical analysis of temporal sequences of Landsat satellite imagery, the timing of changes to phenology and cover at monitoring sites was related directly to the management action of stock removal. With the use of field observations, the species responsible for the increase in cover were identified. These data sources, when analysed together, allowed management to have confidence that, following destocking, native vegetation cover is increasing.

Image Analysis to Monitor Experimental Trampling and Vegetation Recovery in Icelandic Plant Communities

With growing tourism in natural areas, monitoring recreational impacts is becoming increasingly important. This paper aims to evaluate how different trampling intensities affect some common Icelandic plant communities by using digital photographs to analyze and quantify vegetation in experimental plots and to monitor vegetation recovery rates over a consecutive three-year period. Additionally, it seeks to evaluate the use of image analysis for monitoring recreational impact in natural areas. Experimental trampling was conducted in two different sites representing the lowlands and the highlands in 2014, and the experimental plots were revisited in 2015, 2016, and 2017. The results show that moss has the highest sensitivity to trampling, and furthermore has a slow recovery rate. Moss-heaths in the highlands also show higher sensitivity and slower recovery rates than moss-heaths in the lowlands, and grasslands show the highest resistance to trampling. Both methods tested, i.e., Green Chromatic Coordinate (GCC) and Maximum Likelihood Classification (MLC), showed significant correlation with the trampling impact. Using image analysis to quantify the status and define limits of use will likely be a valuable and vital element in managing recreational areas. Unmanned aerial vehicles (UAVs) will add a robust way to collect photographic data that can be processed into vegetation parameters to monitor recreational impacts in natural areas.

A monitoring toolkit for banksia woodlands: comparison of different scale methods to measure recovery of vegetation after fire

AbstractHere, we compare the efficiency and accuracy of remote sensing and plot‐based methods for measuring vegetation cover for the understory and canopy of banksia woodland in an urban area of Western Australia. Methods compared were visual estimation, foliage cover computation from photographs, satellite imagery and aerial photographs. Observations and images from 1 m2, 100 m2 and 625 m2 quadrats measured cover of small plants, understory plants and trees respectively. Aerial photography and satellite imagery allowed the number, height and cover of trees to be estimated in 625 m2 and 1 ha plots. The accuracy of methods was compared using a 28 month time series commencing before and after an intense bushfire that removed all foliage cover. Directly comparable methods were in close agreement and in combination allowed plant recovery to be quantified in great detail. Visual estimation of cover in the field was time‐consuming but necessary to measure the contribution of individual species. Visual estimates from 1 m2 downward photos allowed functional groups of plants to be measured. The number of green pixels selected manually in photographs confirmed that cover calculated from ground‐based photographs using algorithms was accurate, except when cover was very low. We developed a new algorithm for computing cover from photographs that was accurate at low cover (Gperc). Canopy cover estimation by algorithm from upward photographs was subject to more errors, requiring exclusion of some images. Landsat satellite images allowed the impacts of severe drought and previous fires to be identified against a background of relatively consistent seasonal variations since 1988. Aerial photographs from 1953 onwards showed gradual recolonisation by banksia woodland trees over 60 years following tree felling. These methods provide a toolkit for monitoring vegetation recovery after disturbance and baseline data for monitoring banksia woodland. This toolkit should also be suitable for most other plant communities.

The spatial-temporal patterns of land cover changes due to mining activities in the Darling Range, Western Australia: A Visual Analytics Approach

The Darling Range in Western Australia is a major bauxite producing region. Clearing, excavation and rehabilitation activities related to bauxite mining have influenced land cover within this region since mining commenced in the 1960s. This paper presents a study that used machine learning and time series visualisation to analyse the land cover changes of the Darling Range using time-lapse multispectral images, with the aim of understanding the impact of the mining activities and land rehabilitation patterns of the region. Land cover changes were analysed using 14 Landsat Thematic Mapper (TM) images between 1988 and 2014. The spatial distribution of land cover was classified automatically using machine learning algorithms, and their temporal changes were visualized for analysis. Supervised classification was carried out based on the six spectral features of the images using three machine learning algorithms, namely support vector machine (SVM), random forest (RF) and Naïve Bayes (NB). The results showed that the RF algorithm achieved overall accuracy, (ratio of correctly classified samples divided by the total number of samples), greater than 95% for all years. The temporal changes of land cover distribution over the study period were visualized using a change map. These changes were compared with land clearing and rehabilitation records of a mining company operating in that region. A close correlation was observed between the automated analysis outputs and the company’s records. This work demonstrates the potential use of machine analysis in improving the accuracy of spatial data related to land clearing; and in monitoring vegetation recovery of closed and rehabilitated mines.

Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry

Monitoring vegetation recovery typically requires ground measurements of vegetation height, which is labor-intensive and time-consuming. Recently, unmanned aerial vehicles (UAVs) have shown great promise for characterizing vegetation in a cost-efficient way, but the literature on specific methods and cost savings is scant. In this study, we surveyed vegetation height on seismic lines in Alberta’s Boreal Forest using a point-intercept sampling strategy, and compared them to height estimates derived from UAV-based photogrammetric point clouds. In order to derive UAV-based vegetation height, we tested three different approaches to estimate terrain elevation: (1) UAV_RTK, where photogrammetric point clouds were normalized using terrain measurements obtained from a real-time kinematic global navigation satellite system (RTK GNSS) surveys; (2) UAV_LiDAR, where photogrammetric data were normalized using pre-existing LiDAR (Light Detection and Ranging) data; and (3) UAV_UAV, where UAV photogrammetry data were used alone. Comparisons were done at two scales: point level (n = 1743) and site level (n = 30). The point-level root-mean-square errors (RMSEs) of UAV_RTK, UAV_LiDAR, and UAV_UAV were 28 cm, 31 cm, and 30 cm, respectively. The site-level RMSEs were 11 cm, 15 cm, and 8 cm, respectively. At the aggregated site level, we found that UAV photogrammetry could replace traditional field-based vegetation surveys of mean vegetation height across the range of conditions assessed in this study, with an RMSE less than 10 cm. Cost analysis indicates that using UAV-based point clouds is more cost-effective than traditional field vegetation surveys.

ASSESSMENT OF FIRE SEVERITY AND POST-FIRE REGENERATION BASED ON TOPOGRAPHICAL FEATURES USING MULTITEMPORAL LANDSAT IMAGERY: A CASE STUDY in MERSIN, TURKEY

Satellite based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Mersin-Gülnar wildfire, which occurred in August 2008 in Turkey, selected as study site. The fire was devastating and continued 55 days. According to Turkish General Directorate of Forestry reports, it caused two deaths and left hundreds of people homeless. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors. Pre-fire and post-fire Landsat ETM+ images were obtained to assess the related fire severity with using the widely-used differenced Normalized Burn Ratio (dNBR) algorithm. Also, the Normalized Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to determine vegetation regeneration dynamics for a period of six consecutive years. In addition, aspect image derived from Aster Global Digital Elevation Model (GDEM) were used to determine vegetation regeneration regime of the study area. Results showed that 5388 ha of area burned with moderate to high severity damage. As expected, NDVI and SAVI values distinctly declined post-fire and then began to increase in the coming years. Mean NDVI value of burned area changed from 0.48 to 0.17 due to wildfire, whilst mean SAVI value changed from 0.61 to 0.26. Re-growth rates calculated for NDVI and SAVI 57% and 63% respectively, six years after the fire. Moreover, NDVI and SAVI were estimated six consecutive year period by taking into consideration east, south, north and west facing slopes. Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. This study serves as a window to an understanding of the process of fire severity and vegetation regeneration that is vital in wildfire management systems.

ASSESSMENT OF FIRE SEVERITY AND POST-FIRE REGENERATION BASED ON TOPOGRAPHICAL FEATURES USING MULTITEMPORAL LANDSAT IMAGERY: A CASE STUDY in MERSIN, TURKEY

Abstract. Satellite based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Mersin-Gülnar wildfire, which occurred in August 2008 in Turkey, selected as study site. The fire was devastating and continued 55 days. According to Turkish General Directorate of Forestry reports, it caused two deaths and left hundreds of people homeless. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors. Pre-fire and post-fire Landsat ETM+ images were obtained to assess the related fire severity with using the widely-used differenced Normalized Burn Ratio (dNBR) algorithm. Also, the Normalized Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to determine vegetation regeneration dynamics for a period of six consecutive years. In addition, aspect image derived from Aster Global Digital Elevation Model (GDEM) were used to determine vegetation regeneration regime of the study area. Results showed that 5388 ha of area burned with moderate to high severity damage. As expected, NDVI and SAVI values distinctly declined post-fire and then began to increase in the coming years. Mean NDVI value of burned area changed from 0.48 to 0.17 due to wildfire, whilst mean SAVI value changed from 0.61 to 0.26. Re-growth rates calculated for NDVI and SAVI 57% and 63% respectively, six years after the fire. Moreover, NDVI and SAVI were estimated six consecutive year period by taking into consideration east, south, north and west facing slopes. Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. This study serves as a window to an understanding of the process of fire severity and vegetation regeneration that is vital in wildfire management systems.

Fisher–Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance

The time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher–Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher–Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1km×1km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers.

Assessment of carbon storage and biomass on minelands reclaimed to grassland environments using Landsat spectral indices

This study investigated carbon (C) storage and biomass in grasslands of West Virginia reclaimed surface minesites. Mine-related disturbance and subsequent reclamation may be an important component of C cycling. Biomass and C storage generally increased for the first five years after reclamation, but then declined, suggesting a nonlinear pattern to vegetation recovery. Three 2007 Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus images were used to assess the potential to predict biomass from raw red and near infrared radiance, the tasseled cap transformation (TC), and four vegetation indices [normalized difference vegetation index, enhanced vegetation index (EVI), triangular vegetation index, and the soil adjusted vegetation index]. TC greenness and EVI were most strongly correlated with biomass and illustrate a modest potential for monitoring vegetation recovery in reclaimed minelands. Additionally, a number of regression models that included age since reclamation and spectral indices were statistically significant suggesting a temporal recovery pattern amongst minesites in this study.

Monitoring vegetation recovery after China's May 2008 Wenchuan earthquake using Landsat TM time‐series data: a case study in Mao County

AbstractThe Wenchuan earthquake (Richter scale 8) on 12 May 2008 in southwestern China caused widespread ecosystem damage in the Longmenshan area. It is important to evaluate natural vegetation recovery processes and provide basic information on ecological aspects of the recovering environment after the earthquake. To circumvent the weather limits of remote sensing in the Wenchuan earthquake‐hit areas, and to meet the need for regional observation analyses, three Landsat TM images pre‐ and post‐earthquake in Mao County were used for analysis. Post‐earthquake normalized difference vegetation index (NDVI) values were compared to pre‐earthquake values with an NDVI‐based index differencing method to determine the extent to which the vegetation was damaged in relation to the pre‐earthquake pattern, and the rate of recovery was evaluated. The spatial characteristics of vegetation loss and natural recovery patterns were analyzed in relation to elevation, slope and aspect. The results indicated that severely damaged sites occurred mainly in river valleys, within a range of 1,500–2,500 m elevation and on slopes of 25–55°. The distance from rivers, rather than the distance from active faults, controls the damage patterns. After 1 year of natural regeneration, 36 % of the destroyed areas showed a decrease in NDVI value, 28.8 % showed very little change, 19.1 % showed an increase, and 16.1 % also increased with a recovery rate greater than 100 %. Moreover, there is a good correlation between recovery rate and both slope and elevation, but recovery patterns in the damaged area are complicated. Our results indicate that natural recovery in this arid valley is a slow process.

Does fire regime affect both temporal patterns and drivers of vegetation recovery in a resilient Mediterranean landscape? A remote sensing approach at two observation levels

We investigate how fire regime may influence both temporal patterns and drivers of vegetation regeneration in a Natural Park (NW Spain) affected by a long history of fire events and human activities. To address this issue, we evaluate the suitability of five spectral indices derived from Landsat imagery (for the period 1992–2005) for estimating biophysical properties of vegetation and monitoring post-fire recovery. Complementarily, we assess the role of the observation level (pixel and patch) on the results, identifying which of them is more informative for land management. Tasselled Cap Wetness was the best-performing index and total cover was the vegetation property more closely related to spectral data. Most post-fire recovery occurred within the 2 years following the fire event. Fire recurrence did not influence patch extent or shape nor did it affect ecosystem resilience. However, patch extent and shape affected resilience. The relevance of the environmental drivers behind vegetation recovery was not related to fire recurrence and changed over time. Prior vegetation status and rainfall were the most important drivers, while topography and vegetation type had a more secondary role. Our results advocate the consideration of patches as the most appropriate organisational unit when monitoring vegetation recovery.

The effect of wildfires on vegetation cover and dune activity in Australia's desert dunes: a multisensor analysis

Most of Australia’s desert dune fields are stable; however, wildfires may reduce vegetation and biological soil crust cover so that sand movement may take place until vegetation recovers. In this study, we aimed to study the recovery rate of vegetation cover in spinifex (Triodia)-dominated desert dunes following wildfires using satellite imagery-derived spectral indices to: (1) determine for how long after fire these dunes may be active until critical levels of vegetation cover are attained; (2) determine which spectral index is the most suitable for monitoring vegetation recovery in this area. We have used a combination of MODIS, Landsat, Aster and QuickBird images to analyse vegetation cover following fire at various spatial and temporal scales, in the Great Victoria Desert, WA. The following spectral indices were compared: Brightness Index, Biological Soil Crust Index (BSCI), Crust Index, Enhanced Vegetation Index, Normalised Burn Ratio, Normalised Difference Vegetation Index, Soil Adjusted Vegetation Index and the Stress-related Vegetation Index. The BSCI was found to outperform the other spectral indices in monitoring vegetation cover in this area. Whereas full recovery of vegetation following wildfires in the study area was attained only after 25–30 years, critical thresholds of vegetation cover limiting sand movement were attained after just 1–5 years. The frequency and intensity of wildfires is therefore an important factor controlling dune activity in Australia’s deserts.

Damage of alpine vegetation by the 2009 fire on Mt. Shirouma, central Japan: comparison between herbaceous vegetation and Pinus pumila scrub

A fire occurred (0.59 ha) in an alpine fellfield (2600 m a.s.l.) on Mount Shirouma, central Japan, on 9 May 2009 before the start of the growing season. Herbaceous plants and dwarf pine Pinus pumila dominated the site. Plots were established in burned and unburned herb vegetation and P. pumila scrub just after the fire to monitor vegetation recovery. This study reports the short-term monitoring results 3 months after the fire. Burned herb vegetation mostly recovered by late August 2009. However, burned P. pumila did not recover, and other alpine plants were scarce in burned P. pumila scrub. The observed number of species in herb vegetation was 15–20 m−2 whereas it was only 1–6 m−2 in P. pumila scrub. The total cover of plants was 111–129% for burned herb vegetation but was only 8–31% for burned P. pumila scrub. Although the species composition in P. pumila scrub distinctly differed between burned and unburned plots, in herb vegetation it was similar between them. Therefore, P. pumila scrub was greatly damaged by the fire, whereas herb vegetation was not damaged. Rapid recovery of herbaceous plants was because winter buds in the soil were not damaged by the fire, but winter buds on shoots of P. pumila were burned. Therefore, the difference in winter bud location (above or belowground) may have resulted in the difference in damage between herbaceous plants and P. pumila.