ALOS-2 L-band SAR backscatter data improves the estimation and temporal transferability of wildfire effects on soil properties under different post-fire vegetation responses

Remote sensing techniques are of particular interest for monitoring wildfire effects on soil properties, which may be highly context-dependent in large and heterogeneous burned landscapes. Despite the physical sense of synthetic aperture radar (SAR) backscatter data for characterizing soil spatial variability in burned areas, this approach remains completely unexplored. This study aimed to evaluate the performance of SAR backscatter data in C-band (Sentinel-1) and L-band (ALOS-2) for monitoring fire effects on soil organic carbon and nutrients (total nitrogen and available phosphorous) at short term in a heterogeneous Mediterranean landscape mosaic made of shrublands and forests that was affected by a large wildfire. The ability of SAR backscatter coefficients and several band transformations of both sensors for retrieving soil properties measured in the field in immediate post-fire situation (one month after fire) was tested through a model averaging approach. The temporal transferability of SAR-based models from one month to one year after wildfire was also evaluated, which allowed to assess short-term changes in soil properties at large scale as a function of pre-fire plant community type. The retrieval of soil properties in immediate post-fire conditions featured a higher overall fit and predictive capacity from ALOS-2 L-band SAR backscatter data than from Sentinel-1 C-band SAR data, with the absence of noticeable under and overestimation effects. The transferability of the ALOS-2 based model to one year after wildfire exhibited similar performance to that of the model calibration scenario (immediate post-fire conditions). Soil organic carbon and available phosphorous content was significantly higher one year after wildfire than immediately after the fire disturbance. Conversely, the short-term change in soil total nitrogen was ecosystem-dependent. Our results support the applicability of L-band SAR backscatter data for monitoring short-term variability of fire effects on soil properties, reducing data gathering costs within large and heterogeneous burned landscapesS

An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa

The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019

Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impactFunding for this study was provided by CONAFOR/CONACYT Projects “CO2-2014-3-252620” and “CO-2018-2-A3-S-131553” for the development and enhancement of a Forest Fire Danger Prediction System for Mexico, funded by the Sectorial Fund for forest research, development and technological innovation “Fondo Sectorial para la investigación, el desarrollo y la innovación tecnológica forestal”S

Application of satellite image time series and texture information in land cover characterization and burned area detection

Land cover is critical information to various land management and scientific applications, including biogeochemical and climate modeling. In addition, fire is an essential factor in shaping of vegetation structures, as well as for the functioning of savanna ecosystems. Remote sensing has long been an important and effective means of mapping and monitoring land cover and burned area over large areas in a consistent and robust way. Owing to the free and open Landsat archive and the increasing availability of high spatial resolution imagery, seasonal features from the temporal domain and the use of texture features from the spatial domain create new opportunities for land cover characterization and burned area detection. This thesis examined the application of satellite image time series and texture information in land cover characterization and burned area detection. First, the utility of seasonal features derived from Landsat time series (LTS) in improving accuracies of land cover classification and attribute prediction in a savanna area in southern Burkina Faso was studied. Then, the temporal profiles from LTS were explored for mapping burned areas over a 16 year period, and MODIS burned area product was used for comparison. Finally, the application of texture features derived from high spatial resolution data in land cover classification and attribute predictions was investigated in a savanna area of Burkina Faso and an urban fringe area in Beijing. According to the results, firstly, seasonal features from LTS based on all available imagery during one year as input led to a significant increase in land cover classification accuracy in comparison to the dry and wet season single date imagery. The harmonic model used for time series modeling provided a robust method for extracting seasonal features, and the influence of burned pixels on seasonal features could be considered simultaneously. Secondly, the annual burned area mapping based on a harmonic model and breakpoint identification with LTS was capable of detecting small and patchy burn scars with higher accuracy than MODIS burned area product. The approach demonstrated the potential of LTS for improving burned area detection in savannas, and was robust against data gaps caused by clouds and Landsat 7 missing lines. Thirdly, predictive models of tree crown cover (CC) using RapidEye and LTS imagery achieved similar accuracy, indicating the importance of texture and seasonal features from RapidEye and LTS imagery, respectively. Predictions of aboveground carbon and tree species richness, which were strongly correlated with CC, were promising using RapidEye and LTS imagery. Finally, the optimized window size texture classification improved classification accuracy in comparison to the classifications with single window size texture features and multiple window size texture features in an urban fringe area in Beijing, indicating the importance of multiscale texture information. Keywords: Landsat time series, texture, land cover classification, burned area, savanna, tree crown cove

Using New and Long-Term Multi-Scale Remotely Sensed Data to Detect Recurrent Fires and Quantify Their Relationship to Land Cover/Use in Indonesian Peatlands

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. A further study was conducted using additional moderate spatial resolution data to compare two years of different severity (2014 and 2015) (Landsat, Sentinel 2, Sentinel 1, VIIRS 375 m). The results showed that MODIS BA products poorly discriminated small fires and failed to detect many burned areas due to persistent interference from clouds and smoke that often worsens as fire seasons progress. Although there are unique fire detection capabilities associated with each sensor (MODIS, VIIRS, Landsat, Sentinel 2, Sentinel 1), no single sensor was ideal for accurate detection of peatland fires under all conditions. Multisensor approaches could advance biomass-burning detection in peatlands, improving the accuracy and comprehensive coverage of burned area maps, thereby enabling better estimation of associated fire emissions. Despite missing many burned areas, MODIS BA (MCD64A1 C6) provides the best available data for evaluating longer term (2001-2018) associations between the frequency of fire occurrence and land use/cover change across large areas. Results showed that Sumatra and Kalimantan have both experienced frequent fires since 2001. Although extensive burning was present across the entire landscape, burning in peatlands was ~5- times more frequent and strongly associated with changes of forest to other land use/cover classes. If fire frequencies since 2001 remain unchanged, remnant peat swamp forests of Sumatra and Kalimantan will likely disappear over the next few decades. The findings reported in this dissertation provide critical insights for Indonesian stakeholders that can help them to minimize impacts of environmental change, manage ecological restoration efforts, and improve fire monitoring systems within Indonesia

Remote Sensing and GIS Applications in Wildfires

Wildfires are closely associated with human activities and global climate change, but they also affect human health, safety, and the eco-environment. The ability of understanding wildfire dynamics is important for managing the effects of wildfires on infrastructures and natural environments. Geospatial technologies (remote sensing and GIS) provide a means to study wildfires at multiple temporal and spatial scales using an efficient and quantitative method. This chapter presents an overview of the applications of geospatial technologies in wildfire management. Applications related to pre-fire conditions management (fire hazard mapping, fire risk mapping, fuel mapping), monitoring fire conditions (fire detection, detection of hot-spots, fire thermal parameters, etc.) and post-fire condition management (burnt area mapping, burn severity, soil erosion assessments, post-fire vegetation recovery assessments and monitoring) are discussed. Emphasis is given to the roles of multispectral sensors, lidar and evolving UAV/drone technologies in mapping, processing, combining and monitoring various environmental characteristics related to wildfires. Current and previous researches are presented, and future research trends are discussed. It is wildly accepted that geospatial technologies provide a low-cost, multi-temporal means for conducting local, regional and global-scale wildfire research, and assessments

The global tree carrying capacity (keynote)

editorial reviewe

Above-ground carbon stocks, species diversity and fire dynamics in the Bateke Plateau

Savannas are heterogeneous systems characterised by a high spatial and temporal variation in ecosystem structure. Savannas dominate the tropics, with important ecological functions, and play a prominent role in the global carbon cycle, in particular responsible for much of its inter-annual variability. They are shaped by resource availability, soil characteristics and disturbance events, particularly fire. Understanding and predicting the demographic structure and woody cover of savannas remains a challenge, as it is currently poorly understood due to the complex interactions and processes that determine them. A predictive understanding of savanna ecosystems is critical in the context of land use management and global change. Fire is an essential ecological disturbance in savannas, and forest-savanna mosaics are maintained by fire-mediated positive feedbacks. Over half of the world’s savannas are found in Africa, and over a quarter Africa’s surface burns every year, with fires occurring principally in the savanna biome. These have strong environmental and social impacts. Most fires in Africa are anthropogenic and occur during the late dry season, but their dynamics and effects remain understudied. The main objective of this research is to understand the floristic composition, carbon storage, woody cover and fire regime of the mesic savannas of the Bateke Plateau. The Bateke Plateau is savanna-forest mosaic ecosystem, situated mainly in the Republic of Congo, with sandy Kalahari soils and enough precipitation for potential forest establishment (1600 mm/yr). Despite occupying 89,800 km2, its ecology and ecosystem functions are poorly understood. This study combines two approaches: firstly experimental, setting up long term field experiments where the fire regime is manipulated, and then observational, using remote sensing to estimate the carbon storage and study the past history of the fire regime in the region. I established four large (25 ha) plots at two savanna sites, measured their carbon stocks, spatial structure and floristic composition, and applied different annual fire treatments (early and late dry season burns). These treatments were applied annually during 3 years (2015, 2016 and 2017), and the plots were re-measured every year to estimate tree demographic rates and the identification of the key processes that impact them, including fire and competition. Field data were combined with satellite radar data from ALOS PALSAR, and the fire products of the MODIS satellites, to estimate carbon stocks and fire regimes for the entire Bateke Plateau. I also analyse the underlying biophysical and anthropogenic processes that influence the patterns in Above-Ground Woody Biomass (AGWB) and their spatial variability in the Bateke landscape. The total plant carbon stocks (above-ground and below-ground) were low, averaging only 6.5 ± 0.3 MgC/ha, with grass representing over half the biomass. Soil organic matter dominate the ecosystem carbon stocks, with 16.7 ± 0.9 Mg/ha found in the top 20 cm alone. We identified 49 plant species (4 trees, 13 shrubs, 4 sedges, 17 forbs and 11 grass species), with a tree hyperdominance of Hymenocardia acida, and a richer herbaceous species composition. These savannas showed evidence of tree clustering, and also indications of tree-tree competition. Trees had low growth rates (averaging 1.21 mm/yr), and mortality was relatively low (3.24 %/yr) across all plots. The experiment showed that late dry season fires significantly reduced tree growth compared to early dry season fires, but also reduced stem mortality rates. Results show that these mesic savannas had very low tree biomass, with tree cover held far below its climate potential closed-canopy maximum, likely due to nutrient poor sandy soils and frequent fires. Results from the remote sensing analysis indicated that multiple explanatory variables had a significant effect on AGWB in the Bateke Plateau. Overall, the frequency of fire had the largest impact on AGWB (with higher fire frequency resulting in lower AGWB), with sand content the next most important explanatory variable (with more sand reducing AGWB). Fires in the Bateke are very frequent, and show high seasonality. The proportion of fires that occurred in the late dry season, though smaller predictor, was also more important than other factors (including soil carbon proportion, whether or not the savanna area was in a protected area, annual rainfall, or distance to the nearest town, river or road), with a larger proportion of late dry season fires associated with a small increase in AGWB. The results give pointers for management of the savannas of the Bateke Plateau, as well as improving our understanding of vegetation dynamics in this understudied ecosystem and help orient policy and conservation

Quantifying the aboveground biomass stock changes associated with oil palm expansion on tropical peatlands using plot-based methods and L-band radar

The recent rapid expansion of oil palm (OP, Elaeis guineensis) plantations into tropical forest peatlands has resulted in net ecosystem carbon emissions. However, quantifications of the net carbon flux from biomass changes require accurate estimates of the above ground biomass (AGB) accumulation rate of OP on peat in working plantations. Current efforts that aim to reduce the emissions from OP expansion would also benefit from the development of economically viable remote sensing approaches that enable the detection of OP plantation expansion and monitoring of AGB stocks across at a fine spatial and temporal resolution. Here, destructive harvest and non-destructive plot inventories are conducted across a chronosequence of OP planting blocks (3 to 12 years after planting (YAP)) in plantations on drained peat in Sarawak, Malaysia. The effectiveness of using a timeseries of L-band synthetic aperture radar (SAR) scenes (ALOS PALSAR-1/2) and a novel ‘biomass matching’ approach to detect, quantify and map the AGB stock changes associated with OP establishment and growth was then assessed. Peat specific allometric equations for palm (9 palms, R2 = 0.92) and frond biomass are developed and upscaled to estimate AGB at the plantation block-level (902 palms). Aboveground biomass stocks on peat accumulated at ~6.39 ± 1.12 Mg ha-1 per year in the first 12 years after planting. However, high inter-palm and inter-block AGB variability was observed in mature classes as a result of variations in palm leaning and mortality. The ‘biomass matching’ approach detected statistically significant deforestation associated with OP establishment. OP growth was well estimated between 4 and 10 YAP, however sensitivity to increases in AGB was lost at ~ 45 - 60 Mg ha. Validation of the allometric equations defined and expansion of non-destructive inventories across alternative plantations and age classes on peat would further strengthen our understanding of OP AGB accumulation rates. With further investigation into the relationship between OP structural characteristics and L-band radar cross section (RCS) in the HV and HH polarisations, ‘biomass matching’ could be a feasible tool for monitoring AGB stock changes to inform carbon emission mitigation strategies