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Context - Prioritising is likely to differ depending on the species considered for connectivity assessments, leading to a lack of consensual decisions for territorial planning. Objectives - The objective was to assess the relevance of identifying priority areas for connectivity for groups of species based on common dispersal abilities. We aimed to assess the impact of target groups choices on predicted priority areas. Method - The study was located at the Thau Lagoon territory to demonstrate the methodological approach. Ecological niche modelling was used to quantify species resistance and to identify suitable habitat patches. We coupled the least-cost path methodology with circuit theory to assess species connectivity. We classified connectivity from high to low levels and averaged the results by dispersal groups. Results - We found important differences in identified priority areas between groups with dissimilar dispersal abilities, with little overlap between highly connected areas. We identified a gap between the level of protection of low dispersal species and highly connected areas. We found mismatches between existing corridors and connectivity in low dispersal species, and a greater impact in areas of expected urban sprawl projects on favourably connected areas for species with high dispersal capabilities. Conclusion - We have demonstrated that a diversity of dispersal capacity ranges must be accounted for in order to identify ecological corridors in programmes that aim to restore habitat connectivity at territorial levels. Our findings are oriented to support the decisions of planning initiatives, at both local and regional scale., 18 pages, 7 figures + Appendix

Monitoring semi-natural grasslands is difficult and costly because they occur in highly dynamic and extremely complex habitat mosaics. We combined the use of a low-cost unmanned aerial vehicle (UAV) and Structure from Motion (SfM) photogrammetry to acquire high spatial resolution (∼1.5 cm pixel) RGB imagery. After image classification through Object-Based Image Analysis (OBIA), we accurately were able to distinguish three semi-natural grassland types, one of which is a habitat of conservation concern. The use of orthomosaics, digital elevation models (DEMs), and canopy height models (CHMs) yielded excellent overall classification accuracies (89%) assessed through both remotely validated and ground-truthed points. We identified two layers of woody vegetation with a user's (UA) and producer's (PA) accuracies73% and three grassland types: closed grassland (UA = 94%; PA = 97%), open grassland habitat (UA = 97%; PA = 93%) and open grasslands with soil erosion (UA = 96%; PA = 98%). The grassland types differed substantially in the cover of vegetation, rocks, stones, and bare soil measured in the field, as well as in the number and relative cover of the habitat diagnostic species. The proposed methodology is highly promising for mapping and monitoring semi-natural grassland of conservation concern in support of tailored management actions.

The prevention of biodiversity loss in agricultural landscapes to protect ecosystem stability and functions is of major importance in itself and for the maintenance of associated ecosystem services. Intense agriculture leads to a loss in species richness and homogenization of species pools as well as the fragmentation of natural habitats and groundwater pollution. Constructed wetlands stand as nature-based solutions (NBS) to buffer the degradation of water quality by intercepting the transfer of particles, nutrients and pesticides between crops and surface waters. In karstic watersheds where sinkholes short-cut surface water directly to groundwater increasing water resource vulnerability, constructed wetlands are recommended to mitigate agricultural pollutants. Constructed wetlands also have the potential to improve landscape connectivity by providing refuge and breeding sites for wildlife, especially for amphibians. We propose here a methodology to identify optimal locations for water pollution mitigation using constructed wetlands from the perspective of habitat connectivity. We use ecological niche modelling at the regional scale to model the potential of habitat suitability for nine amphibian species, and to infer how the landscape impedes species movements. We combine those results to graph theory to identify connectivity priorities at the operational scale of an agricultural catchment area. Our framework allowed us to identify optimal areas from the point of view of the species, to analyze the effect of multifunctional constructed wetlands aiming to both reduce water pollution and to improve amphibian species habitat overall connectivity. More generally, we show the potential of habitat connectivity assessment to improve multifunctionality of NBS for pollution mitigation., Comment: 26 pages, 4 figures

International audience; Wetlands conservation and resilience capacities are key issues in many places over the globe. Understanding these issues will benefit from a precise knowledge of seagrass species occupancy and coverage over time and over space. Such information can be obtained from remote sensing images and their classification thanks to a vegetation index, to be used in a complementary manner to field work inventories. Sentinel-2 data, which are available with a frequent revisit time (

We evaluated the skills of different palaeofire reconstruction techniques to reconstruct the fire history of a boreal landscape (Russian Karelia) affected by surface fires. The analysis of dated lacustrine sediments from two nearby lakes was compared with independent dendrochronological dating of fire scars, methods which have rarely been used in context of surface fires. We used two sediment sub-sampling volumes (1 and 3.5 cm3, wet volumes) and three methods of calculating the Charcoal Accumulation Rate to reconstruct fire histories: CHAR number, charcoal surface area and estimated charcoal volume. The results show that palaeofire reconstructions obtained with fossil charcoal data from lake sediments and dendrochronology are similar and complementary. Dendrochronological reconstruction of fire scars established 12 fire dates over the past 500 years, and paleo-data from lake sediments identified between 7 and 13 fire events. Several ‘false fire events’ were also recorded in the charcoal chronologies, likely because of errors associated with the estimation of the sediment accumulation rate in the unconsolidated part of the sediment. The number of replicates, that is, number of sub-samples and lakes analyzed, had an effect on the number of identified fire events, whereas no effect was seen in the variation in the analyzed sediment volume or the choice of the charcoal-based metric. Whenever possible, we suggest the use of the dendrochronological data as an independent control for the calibration of charcoal peak series, which helps provide more realistic millennia-long reconstruction of past fire activity. We also argue for the use of 1 cm3sample volume, a sampling protocol involving sampling of more than one lake, and sufficient number of intra-sample replicates to achieve skilful reconstructions of past fire activity.

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation; International audience; In many boreal regions of Russia the past natural variability of forest fire activity remains largely undocumented, preventing accurate assessment of the impact of current climate warming on forest ecosystem dynamics. This study aims to reconstruct the Holocene fire history of the northern Ural mountain foothills, in the Komi Republic, based on analyses of charcoal particles from peatland deposits and coupled with dendrochronological investigations. The results show that there was a gradual increase in forest fire activity during the past 11,000 years. Between 11,000 and 5100 cal. yr BP, the mean fire return interval (FRI) oscillated between 600 and 200 years. During this period, regional data showed that cold temperature, humid climatic conditions, combined with steppe vegetation between 11,000'9000 cal. BP, and then the development of spruce-dominated forest between 9300 and 4600 cal. yr BP, were less conducive to fires. After 5100 cal. yr BP, a gradual increase in drought conditions through reduced precipitations, associated with the establishment of a Scots pine forest favored fire frequency, with a mean FRI under 200 years (range, 200'40 years). Nowadays (since CE 1500), human activity induces an unprecedented fire activity with a mean FRI below 100 years (range, 100'40 years).

The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pl&eacute, iades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics, related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.

International audience; The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.

The delimitation of bioregions helps to understand historical and ecological drivers of species distribution. In this work, we performed a network analysis of the spatial distribution patterns of plants in south of France (Languedoc-Roussillon and Provence-Alpes-C\^ote d'Azur) to analyze the biogeographical structure of the French Mediterranean flora at different scales. We used a network approach to identify and characterize biogeographical regions, based on a large database containing 2.5 million of geolocalized plant records corresponding to more than 3500 plant species. This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of biogeographical regions under the form of spatial network communities, the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions. First, we identified two sub-networks that distinguish Mediterranean and temperate biota. Then, we separated eight statistically significant bioregions that present a complex spatial structure. Some of them are spatially well delimited, and match with particular geological entities. On the other hand fuzzy transitions arise between adjacent bioregions that share a common geological setting, but are spread along a climatic gradient. The proposed network approach illustrates the biogeographical structure of the flora in southern France, and provides precise insights into the relationships between bioregions. This approach sheds light on ecological drivers shaping the distribution of Mediterranean biota: the interplay between a climatic gradient and geological substrate shapes biodiversity patterns. Finally this work exemplifies why fragmented distributions are common in the Mediterranean region, isolating groups of species that share a similar eco-evolutionary history., Comment: 16 pages, 7 figures + Appendix

International audience; The Mediterranean forests have been used for millennia and are organized according to a heterogeneous and complex landscape particularly beneficial to biological diversity. The forests have been degraded by overgrazing and exploitation for firewood, but also as a result of fires. Such forest areas may become open, secondary forests with several understories, but if not properly managed they may turn into varied types of high or low matorral (dry shrubland) or in some areas to heathlands that could be degraded into sparsely vegetated areas. As a result, there is an urgent need for monitoring tools to inform changes in this valuable ecosystem to support public policies to protect a sustainable management of these biodiversity hotspots. In the framework of the Essential Biodiversity Variables (EBV) developed by the GEO-BON, six classes of variables have been defined to cover different key indicators, including ecosystem structure, which is intimately linked to the fauna and flora richness. Remotely-sensed earth observation (RS) has become essential to provide a rapid, repeated and synoptic access of these EBVs (i.e., RS-enabled EBVs): the increasing availability of open access satellite data provides enhanced possibilities to monitor this natural landscape under increasing anthropic pressures. Mediterranean ecosystem could be monitor with various types of sensors, providing information on key indicators like structure, function and composition at frequent revisit times and high to very high spatial resolution. The support of innovative descriptors involving tools such as light sensors embedded on drones, spectro-radiometers or terrestrial Lidar are also needed.

International audience; Landscape heterogeneity is a key factor for understanding ecosystems function and is often associated to high biodiversity. Yet, its characterization for biodiversity monitoring still remains challenging, especially for very heterogeneous landscapes mosaics. In this work, we study the potential of Fourier-based Textural Ordination (FOTO) applied on high resolution airborne optical images to provide synthetic information about landscape fragmentation of Mediterranean heterogeneous landscapes. The ability of texture indices resulting from FOTO combined with vegetation indices to predict landscape metrics was also tested using non linear SVM regression. Our research showed that FOTO methods had great potential to finely characterize different vegetation strata organization at large scale with only few synthetic indices. Best regression results were obtained for ligneous and ground strata fragmentation with coefficient of determination greater than 0.7.

International audience; Nowadays, satellite images are widely exploited in many fields including agriculture, cartography, regional planning, etc. The various advancement in satellite technologies have made satellite images abundantly available. In addition to the specificity and complexity of remote sensing data (spatial and temporal dimensions), the huge amount of this data makes its analysis challenging. Hence, data mining approaches are more adapted to satellite image analysis. In this article, we propose an unsupervised method that combines an object-oriented analysis with a clustering approach to characterize the evolutions in satellite image time series. Our objective is to track the evolutions of land use from satellite image time series. Our framework takes as input raw satellite image time series and their segmentation to highlight spatio-temporal entities that evolve similarly. The method first analyses the segments to detect reference objects among them. The reference objects are meaningful structures of the landscape we want to track. Once they are selected, their evolution over time is then described using a graph-based representation. The spatial extent of the reference object is used to describe its evolution over time. For each reference object, we select the image segments it overlaps and use them to create a graph, named evolution graph. The idea is to represent the state of the reference object on each image in the time series. Finally, the resulting evolution graphs are transformed to synopsis before clustering. The synopsis aims to synthesize the radiometric information contained in the evolution graphs. While the clustering algorithm organizes them in homogenous groups according to their evolution type. The choice of the clustering algorithm and the distance metric is crucial to make the method suitable for handling satellite images time series with their specificity. To evaluate the proposed framework, the experimentations were carried on two study areas: (i) the Lower Aude Valley and (ii) the Pic Saint Loup. These areas are a Natura 2000 sites located in the south of France (https://inpn.mnhn.fr/site/natura2000/listeSites). To track the evolution on these areas, we exploit different satellite images: Spot-2, Spot-4, Spot-5 and Landsat-8. These satellite images are available in the context of the SpotWorld Heritage program (https://www.theia-land.fr/fr/projets/spot-world-heritage), they constitute a multi-annual time series over twenty-six years from 1990 to 2016. Our aim is to perform an inter-site analysis on multi-annual satellite images time series using an object-oriented approach. The results have shown that the proposed method highlights different evolution types according to the different land cover. In our study, the results obtained when gathering the two sites are more accurate than the one obtained analyzing each site independently. The obtained clusters have been compared to an expert classification using a clustering validity index. The identified clusters have also been described and discussed to understand and analyze the behaviour of each group.

International audience; Safeguarding the diversity of natural and semi-natural habitats in Europe is one of the aims set out by the Habitats Directive (Council Directive 92/43/EEC on the conservation of natural habitats and of wild fauna and flora) and one of the targets of the European 2020 Biodiversity Strategy, and is to be accomplished by maintaining a favourable conservation status. To reach this aim a high-level understanding of the distribution and conditions of these habitats is needed. Remote sensing can considerably contribute to habitat mapping and their observation over time. Several European projects and a large number of scientific studies have addressed the issue of mapping and monitoring natural habitats via remote sensing and the deriving of indicators on their conservation status. The multitude of utilized remote sensing sensors and applied methods used in these studies, however, impede a common understanding of what is achievable with current state-of-the-art technologies. The aim of this paper is to provide a synthesis on what is currently feasible in terms of detection and monitoring of natural and semi-natural habitats with remote sensing. To focus this endeavour, we concentrate on those studies aimed at direct mapping of individual habitat types or discriminating between different types of habitats occurring in relatively large, spatially contiguous units. By this we uncover the potential of remote sensing to better understand the distribution of habitats and the assessment of their conservation status in Europe.

Oleaster and olive, a long history of man-forest interactions An average of 2.5 million tons of olive oil is produced annually at global level (COI 2015). Mediterranean countries from the EU and from southern and eastern Mediterranean regions still represent the major exporters. The amounts consumed in source areas are difficult to evaluate, although olive oil is a major element of local diets and health. Understanding the historical and cultural bonds and associated empirical knowledge devel...

International audience; Remote sensing tools (RS) can contribute to a better understanding of the diversity of natural and semi-naturals habitats, their spatial distribution, and their conservation status. RS can also provide a generic set of derived indicators to support local to regional habitat monitoring. Here we propose a set of synthetic continuous textural indices computed from high spatial resolution airborne images for the characterization of vegetation structure in very heterogeneous landscape mosaics. These indices are based on Fourier-based textural ordination (FOTO) of very-high-resolution images. We investigate the relationship between textural indices and a set of common landscape metrics derived from vegetation maps, identifying four strata of interest: bare soil, herbs, low ligneous, and high ligneous. We identify two continuous textural indices, the first one being related to vegetation strata fragmentation and the second being related to the dominance of high ligneous. The combination of these two textural indices with the Normalized Difference Vegetation Index (NDVI) provides a synoptic and accurate overview of the spatial organization of the different vegetation strata. The methodological approach presented herein has a generic value in response to national conservation targets in the context of mapping relevant habitat indicators.This article belongs to the Section Remote Sensing in Agriculture and Vegetation.

International audience; Due to their high degree of vegetation heterogeneity, fragmentation, and biodiversity, Mediterranean natural habitats are difficult to assess and monitor with in situ observations solely. Together with standardized ground plots and regular in situ measurements, remote sensing contributes to better understand the diversity of these habitats and their phenology. We used field spectroradiometry to simulate the radiometric signal corresponding to six multispectral satellites: 1) IKONOS, 2) Landsat 5 TM, 3) Landsat 8, 4) Pleiades, 5) Sentinel-2, and 6) WorldView-2. We compared the suitability of each sensor for the estimation of the cover fraction of photosynthetic vegetation (PV) observed for five types of habitats during a vegetation cycle from February to October 2013. We also analyzed the contribution of multiseasonal satellite acquisitions for habitat discrimination. We showed that multivariate regression applied to Worldview-2 reflectance produces the most accurate PV. This was explained by the higher number of spectral bands in the visible domain. Habitat discrimination based on monotemporal acquisitions showed better performances when PV was higher. Sentinel-2 and WorldView-2 outperformed other sensors for each individual date. Multitemporal acquisitions outperformed monotemporal acquisition for habitat discrimination. However, selecting all reflectance data acquired during the season resulted in suboptimal performances compared to more parsimonious combinations. Finally, all of them ranged between 86.6% and 89.2% classification accuracy with multiseasonal acquisitions. New strategies need to be designed to identify individual habitats of particular interest. Defining optimal multiseasonal remote-sensing acquisitions specific to each habitat and appropriate spectral and spatial resolution will contribute to improved discrimination of Mediterranean natural habitats.

Aim Africa is identified by the Inter-governmental Panel on Climate Change (IPCC) as the least studied continent in terms of ecosystem dynamics and climate variability. The aim of this study was (1) to adapt the Lund-Postdam-Jena-GUESS (LPJ-GUESS) ecological modelling framework to Africa by providing new parameter values for tropical plant functional types (PFT), and (2) to assess the sensitivity of some African biomes to changes in precipitation regime. Location The study area was a representative transect (0-22 degrees N and 7-18 degrees E) through the transition from equatorial evergreen forests to savannas, steppes and desert northwards. The transect showed large latitudinal variation in precipitation (mean rainfall ranged from 50 to 2300 mm year(-1)). Methods New PFT parameters used to calibrate LPJ-GUESS were based on modern pollen PFTs and remote sensed leaf area index (LAI). The model was validated using independent modern pollen assemblages, LAI and through comparison with White's modern potential vegetation map. Several scenarios were developed by combining changes in total rainfall amount with variation in the length of the dry season in order to test the sensitivity of African biomes. Results Simulated vegetation compared well to observed data at local and regional scales, in terms of ecosystem functioning (LAI), and composition (pollen and White's vegetation map). The assessment of the sensitivity of biomes to changes in precipitation showed that none of the ecosystems would shift towards a new type under the range of precipitation increases suggested by the IPCC (increases from 5 to 20%). However, deciduous and semi-deciduous forests may be very sensitive to small reductions in both the amount and seasonality of precipitation. Main conclusions This version of LPJ-GUESS parameterized for Africa simulated correctly the vegetation present over a wide precipitation gradient. The biome sensitivity assessment showed that, compared with savannas and grasslands, closed canopy forests may be more sensitive to change in precipitation regime due to the synergetic effects of changed rainfall amounts and seasonality on vegetation functioning.

High repetitivity remote sensing could substantially improve natural habitats monitoring and mapping in the next years. However, dense time series of satellite images require new processing methodologies. In this paper we proposed an approach which combines Object Based Image Analysis (OBIA) and k-partite graphs for detecting spatiotemporal evolutions in a Mediterranean protected site composed of several types of natural and semi-natural habitats. The method was applied over a recent dataset (SPOT4 Take-5) specially conceived to simulate the acquisition frequency of the future Sentinel-2 satellites. The results indicate our method is capable to synthesize complex spatiotemporal evolutions in a semi-automatic way, therefore offering a new tool to analyze high repetitivity satellite time series.

Due to their high degree of vegetation heterogeneity, fragmentation and biodiversity, Mediterranean natural habitats are difficult to assess and monitor with in-situ observations solely. Together with standardized ground plots and regular in-situ measurements, remote sensing is a powerful monitoring device that can contribute to a better understanding of the diversity of natural and semi-natural habitats and to monitor their phenology. In this paper, we implemented a systematic test of the suitability of multiseasonal remote sensing data for monitoring the phenological variations of natural habitats in a Mediterranean landscape. Six multispectral sensor signals were simulated for comparison based on their spectral response curves and in-situ averaged spectra collected at monthly intervals between February and October 2013 (IKONOS, Landsat 5 TM, Landsat 8, Pleiades, Sentinel-2, and Worldview-2). The simulations and comparisons performed in this test showed that Sentinel-2 sensor has the higher sensitivity to the variations in the coverage of photosynthetic vegetation thus offering interesting perspectives for operational monitoring of natural habitats.

Pour aider les opérationnels à traiter l'ensemble des informations relatives à la définition de l'état des eaux au sens de la DCE, l'ONEMA développe un projet informatique de grande envergure applicable pour toutes les catégories de masses d'eau. Nous présentons ici les opérations effectuées en appui à l'Onema pour la mise en place de cet outil sur les plans d'eau.

International audience; A 2.5 x 2.5 degrees gridded summer (April-September) drought reconstruction over the larger Mediterranean land area (32.5 degrees/47.5 degrees N, 10 degrees W/50 degrees E; 152 grid points) is described, based on a network of 165 tree-ring series. The drought index used is the self-calibrated Palmer Drought Severity Index, and the period considered is 1500-2000. The reconstruction technique combines an analogue technique for the estimation of missing tree-ring data with an artificial neural network for optimal non-linear calibration, including a bootstrap error assessment. Tests were carried out on the various sources of error in the reconstructions. Errors related to the temporal variations of the number of proxies were tested by comparing four reconstructions calibrated with four different sized regressor datasets, representing the decrease in the number of available proxies over time. Errors related to the heterogeneous spatial density of predictors were tested using pseudo-proxies, provided by the global climate model ECHO-G. Finally the errors related to the imperfect climate signal recorded by tree-ring series were tested by adding white noise to the pseudo-proxies. Reconstructions pass standard cross-validation tests. Nevertheless tests using pseudo-proxies show that the reconstructions are less good in areas where proxies are rare, but that the average reconstruction curve is robust. Finally, the noise added to proxies, which is by definition a high frequency component, has a major effect on the low frequency signal, but not on the medium frequencies. The comparison of the low frequency trends of our mean reconstruction and the GCM simulation indicates that the detrending method used is able to preserve the long-term variations of reconstructed PDSI. The results also highlight similar multi-decadal PDSI variations in the central and western parts of the Mediterranean basin and less clear low frequency changes in the east. The sixteenth and the first part of the seventeenth centuries are characterized by marked dry episodes in the west similar to those observed in the end of the twentieth century. In contrast, the eighteenth and nineteenth centuries (Little Ice Age) are characterized by dominant wet periods. In the eastern part of the Mediterranean basin the observed strong drought period of the end of the twentieth century seems to be the strongest of the last 500 years.

We present a regional fuel load model (1 km 2 spatial resolution) applied in the southern African savanna region. The model is based on a patchscale production efficiency model (PEM) scaled up to the regional level using empirical relationships between patch-scale behavior and multi-source remote sensing data (spatio-temporal variability of vegetation and climatic variables). The model requires the spatial distribution of woody vegetation cover, which is used to determine separate respiration rates for tree and grass. Net primary production, grass and tree leaf death, and herbivory are also taken into account in this mechanistic modeling approach. The fuel load model has been calibrated and validated from independent measurements taken from savanna vegetation in Africa southward from the equator. A sensitivity analysis on the effect of climate variables (incoming radiation, air temperature, and precipitation) has been conducted to demonstrate the strong role that water availability has in determining productivity and subsequent fuel load over the southern African region. The model performance has been tested in four different areas representative of a regional increasing rainfall gradient—Etosha National Park, Namibia, Mongu and Kasama, Zambia, as well as in Kruger National Park, South Africa. Within each area, we analyze model output from three different magnitudes of canopy coverage (

As part of SAFARI 2000, this study investigated fire severity associated with, and emissions released from, a wildfire that burned a total area of approximately 3200 km2 of semi‐arid savannahs in the region of Etosha National Park, Namibia. Percent tree cover derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to scale up from site‐level field measurements to landscape‐level emission fields. Empirical relationships relating fuel load and combustion completeness to tree cover were developed from field observations. These relationships were coupled to the remotely sensed data to determine the emissions released over the entire area burned. Emissions from this single fire event were estimated to be 1.4×1012 g of CO2, 52.4×109 g of CO, 1.5×109 g of CH4, 1.85×109 g of non‐methane hydrocarbons (NMHC), and 2.4×109 g of particles less than 2.5 µm (PM2.5). A Normalized Burned Index difference (NBI diff), representative of fire severity and modified for MODIS data was used to assess the...

This study investigates whether increases in elephant populations may influence the structure of African savannas, and consequently may affect other herbivores through changes in habitats. Two contrasting periods in terms of elephant population densities were compared in Hwange National Park, Zimbabwe: the early 1980s and the late 1990s. Elephant population density and other ungulate population densities were estimated for a c. 400-km2 area from road counts. Vegetation structure at the landscape scale was assessed using aerial photographs for the same area. All browsers and grazers declined between the early 1980s and the late 1990s, whereas elephants experienced a 16-fold increase. At the landscape scale, vegetation structure changed little with no evidence of an opening of the habitats. These results do not support any kind of medium-term facilitation between elephants and other herbivores. They rather suggest a negative effect of elephants on other herbivore species when elephants are present at high densities. This study rules out a scenario where the decrease of the different herbivore populations was caused by large changes in vegetation structure due to elephant activity.

Dryland regions are climatically defined as having low annual precipitation and dry season periods that can span over several months and take place once or twice a year. Dryland ecosystems (e.g., grasslands, savannas, or dry forests) that experience recurrent fires often exhibit fire-adapted (or “pyrophytic”) vegetation (Trabaud 1981; Scholes 1997; van Wilgen and Scholes 1997; Mistry 1998; Roques et al. 2001; Nicholas et al. 2011; Blackhall et al. 2017; Linder et al. 2017). Fire affects ecosystem dynamics in terms of species selection, regeneration, structure, nutrient cycling, and mortality. While this chapter is devoted to fire regimes, we will also summarize ecological impacts and feedbacks of fire on the environment and in particular on plant communities. Other impacts, such as the effects of fires on soil moisture dynamics, infiltration, and runoff production, are discussed in Chap. 2, while the effects on soil nutrient cycling and soil gas emissions are briefly analyzed in Chaps. 11 and 13. Additional discussion on the role of fire dynamics on different biomes, e.g., grasslands, shrublands, dry forests, and savannas, can be found in Chaps. 16 and 17, while the effects of fire on land–atmosphere interactions are discussed more in detail in Chap. 7.

[1] Available fuel loads for burning in savanna ecosystems in the southern African region have been estimated using a new Fuel load-Net Primary Production model based on ecophysiological processes such as respiration and Potential Evapotranspiration. The model outputs 15-day standing available fuel load layers for an entire year (a total of 24 layers). Published data from the Southern African Fire-Atmosphere Research Initiative (SAFARI-92) project and from the Southern African Regional Science Initiative (SAFARI 2000) field campaigns were generally in agreement with the estimations. Consistently with previous studies, precipitation was recognized to be the major climatic driver for fuel production. As a consequence, even though there was a regional increase in precipitation in 1999–2000 as compared to the 1991–1992 periods, the temporal and spatial variability in precipitation at fine scales (site level) was important enough to restrict generalities over the entire region for fuel load production. Four areas of interest, Etosha National Park (Namibia), Mongu (Zambia), Kasama (Zambia), and Kruger National Park (South Africa), were selected to reconstruct an aridity gradient and analyze their fuel load variability over the two years. These areas presented contrasting fuel load distributions for the two very different studied periods with arid areas producing heavier fuel loads in 1999–2000, and the more humid areas producing heavier fuel loads in 1991–1992. The consequences of such fuel load variability and the use of such results are discussed. INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 1615 Global Change: Biogeochemical processes (4805); 1640 Global Change: Remote sensing; 3354 Meteorology and Atmospheric Dynamics: Precipitation (1854); 9305 Information Related to Geographic Region: Africa; KEYWORDS: climate, precipitation, net primary production, Fire, NOVI, tree cover

[1] Source strengths to the atmosphere for different forms of carbon (CO2, CO, CH4, nonmethane hydrocarbons (NMHC), and PM2.5) produced by biomass burning were calculated during the Southern African Regional Science Initiative (SAFARI 2000) intensive study period (August and September 2000) for Africa south of the equator. For a given pixel burned, these calculations are a product of the aboveground biomass before a fire, the proportion of biomass that is burned by the fire, and the emission factor for a given carbon compound based on the combustion efficiency. The total emission for the region is the summation of such calculations for all the burned pixels. Products used for these calculations are a prototype satellite-based burned area based on the SPOT-VGT-S1 satellite product, a fuel load map produced by a simulation model developed and applied to southern Africa at 1 km2 resolution, information from fire experiments from our own research in southern Africa to compute combustion completeness, and emission factors published for the region. Over August and September 2000, 31,067 fires detected by the SPOT satellite are calculated to have emitted 96.9 × 1012 g CO2, 4.6 × 1012 g CO, and lesser amounts of CH4, NMHC, and PM2.5. These calculations are in the range of previous estimates of the emissions of these compounds for southern Africa. Along with documenting the estimates of the emissions during the SAFARI 2000 campaign, regional emission estimates are very strongly controlled by the burned area of fires due to a correlation between the cumulative distribution of percent burned area and the cumulative distribution of percent emissions.