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Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Floods in ideal landscapes follow a coherent pattern where single water‐covered areas expand and afterward recede following the inverse sequence, but deviate in real landscapes due to natural or human factors resulting in water coverage shifts. Using remote sensing, we introduced two indices to describe the discrepancies between spatially integrated versus pixel‐level frequency distributions under maximum inundated conditions (dext) and throughout all flooding conditions (dtot), expressed as the relative weight of shifts on each landscape's maximum registered coverage, theoretically ranging between no displacement (<20%) to maximum displacement (≪inf). Globally, over 36 years inundations exhibited redistributions representing, on average, 25% and 45% of their peak extents revealing previously unnoticed extra engaged areas and rotational movements within events, rising up to 500% in meandering rivers (South America) and irrigated croplands (Central Asia). We also assessed the influence of natural and human variables and discussed the indices' potential for advancing flood research. Plain Language Summary: While in ideal landscapes surface water should display the same spatial distribution across the expansion and recession stages of any flooding event, real dynamics may drift away from this expected pattern. We developed two indices based on remote sensing data to locate where these shifts are important and understand how they are influenced by nature and humans. By analyzing data from around the world, we discovered that thanks to the displacement from the ideal distributions, surface water covered globally an extra quarter of the area. Natural factors like low terrain ruggedness and high aridity foster much larger inundation displacement. In regions hosting rivers that carry large quantities of sediment and often change their course (e.g., India and Perú), displacement engages five times more area in floods than expected. We also found that water infrastructure like reservoirs and irrigation also influenced inundation displacement. For instance, displacement was very relevant in intensely irrigated regions like Central Asia and Australia, reflecting surface water deviation as needed for crop production. Because these variations scope water's spatiotemporal dynamics with important implications for the provision of many ecosystem services, their quantification and assessment allow us to monitor and understand our ongoing imprint on regional inundation dynamics. Key Points: We developed two complementary indices to describe water cover shifts between and within flooding eventsOver the last 36 years, shifts expanded the global flooded‐affected area by 25% with another 20% redistributing at intermediate stagesFlat topographies, arid climates, and irrigation favor this phenomenon while river dams and channels inhibit it over time [ABSTRACT FROM AUTHOR]

Until recently, the development of a global geography of floods was challenged by the fragmentation and heterogeneity of in situ data and the high costs of processing large amounts of remote sensing data. Such geography would facilitate the exploration of large‐scale drivers of flood extent and timing including wide latitudinal, climate, and topographic effects. Here we used a monthly data set spanning 30 years (Global Surface Water) to develop a worldwide geographical characterization of slow floods (1‐degree grid), weighting the relative contribution of seasonal, interannual, and long‐term fluctuations on overall variability, and quantified precipitation‐flooding delays where seasonality dominated. We explored the dominance of different flooding timings across five main Köppen‐Geiger climates and seven topography classes derived from modeled water table depths (i.e., hydro‐topography) to contribute top‐down insight about the most salient, cross‐regional flooding patterns and their likely large‐scale drivers. Our results showed that, globally, the mean extent of floods averaged 0.48% of the global land area, predominantly associated with hydro‐topography (>2× more extensive in flatter landscapes). Climate drove flood timings, with predictable, seasonally dominated fluctuations in cold regions, interannual, and mixed patterns in temperate climates, and more irregular (higher variability) and unpredictable (less seasonal) patterns in arid regions. Net gains of flooded area dominated temporal variability in 9% of the cells including boreal clusters likely affected by warming trends. We propose that this new geographical perspective of floods can aid different avenues of hydrological research in the upscaling and extrapolation of field studies and the parsimonious representation of floods in hydroclimatic models. Key Points: Topography is more important than climate in explaining the global distribution of floodsFloods are more seasonal in equatorial and boreal climates and less seasonal in arid lowlands and the subtropical and temperate beltOver the last 30 years, near a tenth of the Earth's land experienced long‐term flooding increases, while less than 2% saw net decreases [ABSTRACT FROM AUTHOR]

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The Joint Research Centre of the European Commission (JRC), in partnership with 30 institutions, has produced a global land cover map for the year 2000, the GLC 2000 map. The validation of the GLC2000 product has now been completed. The accuracy assessment relied on two methods: a confidence-building method (quality control based on a comparison with ancillary data) and a quantitative accuracy assessment based on a stratified random sampling of reference data. The sample site stratification used an underlying grid of Landsat data and was based on the proportion of priority land cover classes and on the landscape complexity. A total of 1265 sample sites have been interpreted. The first results indicate an overall accuracy of 68.6%. The GLC2000 validation exercise has provided important experiences. The design-based inference conforms to the CEOS Cal-Val recommendations and has proven to be successful. Both the GLC2000 legend development and reference data interpretations used the FAO Land Cover Classification System (LCCS). Problems in the validation process were identified for areas with heterogeneous land cover. This issue appears in both in the GLC2000 (neighborhood pixel variations) and in the reference data (cartographic and thematic mixed units). Another interesting outcome of the GLC2000 validation is the accuracy reporting. Error statistics are provided from both the producer and user perspective and incorporates measures of thematic similarity between land cover classes derived from LCCS. Index Terms--Quality control, statistics, vegetation mapping.

Forests have resistance that allows them to resist fires without changing to another state, and resilience that allows them to recover after disturbance. These properties are determined by many structural and functional determinants that interact between them. Despite the importance of structural resistance and functional resilience to wildland fires, few studies have evaluated the combined effect that structural and functional determinants have on them. Our goal was to assess the structural resistance and functional resilience to fire using remote sensing information. We specifically assessed the combined effect of pre‐fire vegetation characteristics, burn severity, and post‐fire precipitation on forest structural resistance and functional resilience to fire. Eighty‐five forest plots of 250 m × 250 m were selected in areas that burned in 2003. For each burned plot, a paired unburned control plot of 250 m × 250 m was selected outside the burned areas. We measured burn severity and post‐fire precipitations (2004–2011). We analysed MODIS time series in order to calculate the following pre‐ (2002) and post‐fire (2011) phenological parameters: minimum level of photosynthetic activity per year; maximum level of photosynthetic activity per year; length of growing season per year; integral of annual photosynthetic activity; relative seasonality of photosynthetic activity. Also we detected plots that changed into a shrubland eight years after the fire. Fifty three per cent of burned plots changed from forest into a shrubland state. Results show that the forest structural resistance to fire depends on the balance between the level of severity and the parameters related to pre‐fire aboveground net primary production. The impact of pre‐fire vegetation characteristics on functional resilience ability was driven by burn severity and it's interactions with pre‐fire productivity and seasonality. Results suggest that changes in forest species composition and aboveground net primary production reduced forest structural resistance and functional resilience to fire. [ABSTRACT FROM AUTHOR]

The neotropical-forest's northern and southern extremes, covering the Mexican Yucatán and the Argentine Chaco, have among the highest rates of recent tree-cover loss in the biome. This study contrasts the character of loss in these regions, estimating proportions of types of loss and subsequent land cover. It is based on two-stage probability sampling design and field and satellite-image surveys. All estimates include uncertainties, which could be further reduced via model-assisted estimation or additional sampling. This approach can be replicated in other regions to estimate types of loss and associated land cover from a definitive, in-situ perspective. The character of loss in the two areas differed greatly. That in the Yucatán was 54% temporary, mostly under fallow or selectively logged, while that in the Chaco was 85% permanent, split nearly equally between crops and pasture. These data contribute to a quantitative basis for studies of socio-economic drivers of neotropical deforestation. [ABSTRACT FROM AUTHOR]

Absorbed solar radiation and radiation use efficiency ( RUE) can be used to estimate net primary productivity of terrestrial ecosystems. In ecosystems dominated by grasses, belowground productivity cannot be neglected in terms of carbon balance because of the high proportion of biomass allocated to roots. The objective of this study was to quantify total RUE ( tRUE), which includes both below and aboveground biomass of two C3 ( Lolium perenne and Dactylis glomerata) and one C4 ( Cynodon dactylon) grass species, under four treatments with contrasting water and nitrogen availabilities. The ratios between tRUE and aboveground RUE ( aRUE) for species and treatments were analyzed. The tRUE was calculated from measurements of incoming photosynthetically active radiation ( PAR), the fraction of PAR intercepted and shoots and roots productivity in pot experiments. The highest tRUE values in the three species were found in the treatment without growth limitations (4.32-6.93 g MJ−1), while the lowest tRUE values were observed under water and nutrient deficits conditions (2.62-2.85 g MJ−1). Contrary to predictions from the optimization theory, one of the C3 grass species allocated relatively high biomass to the roots when water availability was high while for the C4 grass species the shoot:root ratios and the root mass fraction did not change under resource limitation conditions compared to ample resource availability. tRUE exhibited small changes in the C4 species with variations in resource availability while it did decrease substantially for the C3 species when at least one of the resources was limited. These results highlight belowground biomass importance in calculating RUE of grasses. [ABSTRACT FROM AUTHOR]

The radiation use efficiency (RUE) model is one of the most used tools to generate large spatial and temporal scale net primary productivity (NPP) estimations by remote sensing. It involves two key issues to make accurate estimations of NPP: the estimation of the fraction of photosynthetically active radiation (PAR) intercepted by vegetation (fPAR) and the estimation of the plant RUE. The objectives of this work were to quantify the above-ground RUE under optimal water and nutrient conditions in two C3 and one C4 grass species and to analyse the effect of restrictions in these factors upon RUE by comparing both metabolic pathways. Grasses were cultivated from seeds and four treatments combining contrasting availabilities of water and nutrients were applied. RUE values were calculated from measurements of the incoming PAR, fPAR and productivity. In each of the species, plants with sufficient water and nutrients showed the highest RUE (2.61-3.52 g MJ−1), whereas those with deficiencies in both resources presented the lowest RUE (1.15-2.39 g MJ−1). Cynodon dactylon (C4) was the species with higher value of RUE and no significant differences were detected between treatments. However, no significant differences were detected between C. dactylon and D. glomerata under no stress treatment (N1W1) and between C. dactylon and L. perenne under water stress treatment (N1W0). RUE values of Dactylis glomerata (C3) diminished if only one of the two stress factors was presented, while Lolium perenne (C3) only when both stress factors were present. The decreases under stress treatments were between 35% and 60% compared with the no stress treatment. When regional NPP is estimated it is therefore important to take into account the decrease in the RUE, especially in areas under severe stress. [ABSTRACT FROM AUTHOR]

Aims Quantification of the effects and interactions of natural and anthropogenic factors, including climate, canopy structure, land use and management conditions, on vegetation burning. The study of these relationships is fundamental to predict regional fire patterns and develop sound management and regulation policies for biomass burning at national and global levels. Location Southern South America, including Argentina, Brazil, Paraguay, Uruguay, Bolivia and Chile. Methods Based on National Oceanic and Atmosphere Administration–Advance Very High Resolution Radiometer (NOAA–AVHRR) satellite images, we identified fires in southern South America with a daily frequency for two periods (1999/2000 and 2000/01) using a contextual fire detection algorithm and integrating the density of these fires at a monthly scale into a 0.5 × 0.5° grid. We combined vegetation and climate global databases and land use information from national census data to explore the relationship of these factors with fires across the region. Results The whole study region had a mean fire density of 0.10 and 0.05 fires km−2 year−1 in 1999/2000 and 2000/01, respectively, with extreme values as high as 1.37 in fires km−2 year−1 in Para State, Brazil. Water deficit estimates, derived from a climatic water balance, showed the better correlation with fire density ( r = 0.28; P < 0.001; n = 4467), interacting strongly with land use. In areas with low agricultural use fire density increased with water deficit, whereas in highly agricultural areas this relationship was not observed. Agriculture significantly reduced fire density in prairies and savannas but increased its frequency in rain forests. Main conclusions These results suggest that agriculture prevents biomass burning in semiarid areas but enhances it in humid environments, where biomass accumulates at faster rates. [ABSTRACT FROM AUTHOR]

A digital land cover map of South America has been produced using remotely sensed satellite data acquired between 1995 and the year 2000. The mapping scale is defined by the 1 km spatial resolution of the map grid-cell. In order to realize the product, different sources of satellite data were used, each source providing either a particular parameter of land cover characteristic required by the legend, or mapping a particular land cover class. The map legend is designed both to fit requirements for regional climate modelling and for studies on land cover change. The legend is also compatible with a wider, global, land cover mapping exercise, which seeks to characterize the world's land surface for the year 2000. As a first step, the humid forest domain has been validated using a sample of high-resolution satellite images. The map demonstrates both the major incursions of agriculture into the remaining forest domains and the extensive areas of agriculture, which now dominate South America's grasslands. [ABSTRACT FROM AUTHOR]