Recently, an active debate has emerged around greenhouse gas emissions due to indirect land use change (iLUC) of expanding agricultural areas dedicated to biofuel production. In this paper we provide ...a detailed analysis of the iLUC effect, and further address the issues of deforestation, irrigation water use, and crop price increases due to expanding biofuel acreage. We use GLOBIOM – an economic partial equilibrium model of the global forest, agriculture, and biomass sectors with a bottom-up representation of agricultural and forestry management practices. The results indicate that second generation biofuel production fed by wood from sustainably managed existing forests would lead to a negative iLUC factor, meaning that overall emissions are 27% lower compared to the “No biofuel” scenario by 2030. The iLUC factor of first generation biofuels global expansion is generally positive, requiring some 25 years to be paid back by the GHG savings from the substitution of biofuels for conventional fuels. Second generation biofuels perform better also with respect to the other investigated criteria; on the condition that they are not sourced from dedicated plantations directly competing for agricultural land. If so, then efficient first generation systems are preferable. Since no clear technology champion for all situations exists, we would recommend targeting policy instruments directly at the positive and negative effects of biofuel production rather than at the production itself.
Vegetation impacts on ecosystem functioning are mediated by mycorrhizas, plant-fungal associations formed by most plant species. Ecosystems dominated by distinct mycorrhizal types differ strongly in ...their biogeochemistry. Quantitative analyses of mycorrhizal impacts on ecosystem functioning are hindered by the scarcity of information on mycorrhizal distributions. Here we present global, high-resolution maps of vegetation biomass distribution by dominant mycorrhizal associations. Arbuscular, ectomycorrhizal, and ericoid mycorrhizal vegetation store, respectively, 241 ± 15, 100 ± 17, and 7 ± 1.8 GT carbon in aboveground biomass, whereas non-mycorrhizal vegetation stores 29 ± 5.5 GT carbon. Soil carbon stocks in both topsoil and subsoil are positively related to the community-level biomass fraction of ectomycorrhizal plants, though the strength of this relationship varies across biomes. We show that human-induced transformations of Earth's ecosystems have reduced ectomycorrhizal vegetation, with potential ramifications to terrestrial carbon stocks. Our work provides a benchmark for spatially explicit and globally quantitative assessments of mycorrhizal impacts on ecosystem functioning and biogeochemical cycling.
A global-scale data set of mining areas Maus, Victor; Giljum, Stefan; Gutschlhofer, Jakob ...
Scientific data,
09/2020, Letnik:
7, Številka:
1
Journal Article
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The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral ...extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10 km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale data set consisting of 21,060 polygons that add up to 57,277 km
. The polygons cover all mining above-ground features that could be identified from the satellite images, including open cuts, tailings dams, waste rock dumps, water ponds, and processing infrastructure. The data set is available for download from https://doi.org/10.1594/PANGAEA.910894 and visualization at www.fineprint.global/viewer .
In the light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is ...crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human‐induced changes in terrestrial ecosystems that do not result in land‐cover conversions. Here, we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process understanding and data availability. Our review shows that ca. one‐tenth of the ice‐free land surface is under intense human management, half under medium and one‐fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (i) management activities for which data sets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (ii) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global data sets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N fertilization); and (iii) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest). Although we identify multiple impediments to progress, we conclude that the current status of process understanding and data availability is sufficient to advance with incorporating management in, for example, Earth system or dynamic vegetation models in order to provide a systematic assessment of their role in the Earth system. This review contributes to a strategic prioritization of research efforts across multiple disciplines, including land system research, ecological research and Earth system modelling.
Involving members of the public in image classification tasks that can be tricky to automate is increasingly recognized as a way to complete large amounts of these tasks and promote citizen ...involvement in science. While this labor is usually provided for free, it is still limited, making it important for researchers to use volunteer contributions as efficiently as possible. Using volunteer labor efficiently becomes complicated when individual tasks are assigned to multiple volunteers to increase confidence that the correct classification has been reached. In this paper, we develop a system to decide when enough information has been accumulated to confidently declare an image to be classified and remove it from circulation. We use a Bayesian approach to estimate the posterior distribution of the mean rating in a binary image classification task. Tasks are removed from circulation when user-defined certainty thresholds are reached. We demonstrate this process using a set of over 4.5 million unique classifications by 2783 volunteers of over 190,000 images assessed for the presence/absence of cropland. If the system outlined here had been implemented in the original data collection campaign, it would have eliminated the need for 59.4% of volunteer ratings. Had this effort been applied to new tasks, it would have allowed an estimated 2.46 times as many images to have been classified with the same amount of labor, demonstrating the power of this method to make more efficient use of limited volunteer contributions. To simplify implementation of this method by other investigators, we provide cutoff value combinations for one set of confidence levels.
There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is ...increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but there are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and non-experts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non-experts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future.
Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but ...measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from
in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100
m and 1
km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Québec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on
in situ data. The retrieved GSV showed no saturation up to 300
m
3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1
km pixel size. Larger errors were obtained at 100
m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10
×
10
pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data.
Ongoing deforestation is a pressing, global environmental issue with direct impacts on climate change, carbon emissions, and biodiversity. There is an intuitive link between economic development and ...overexploitation of natural resources including forests, but this relationship has proven difficult to establish empirically due to both inadequate data and convoluting geo-climactic factors. In this analysis, we use satellite data on forest cover along national borders in order to study the determinants of deforestation differences across countries. Controlling for trans-border geo-climactic differences, we find that income per capita is the most robust determinant of differences in cross-border forest cover. We show that the marginal effect of per capita income growth on forest cover is strongest at the earliest stages of economic development, and weakens in more advanced economies, presenting some of the strongest evidence to date for the existence of at least half of an environmental Kuznets curve for deforestation.
This study deals with approaches for a social-ecological friendly European bioeconomy based on biomass from industrial crops cultivated on marginal agricultural land. The selected crops to be ...investigated are: Biomass sorghum, camelina, cardoon, castor, crambe, Ethiopian mustard, giant reed, hemp, lupin, miscanthus, pennycress, poplar, reed canary grass, safflower, Siberian elm, switchgrass, tall wheatgrass, wild sugarcane, and willow. The research question focused on the overall crop growth suitability under low-input management. The study assessed: (i) How the growth suitability of industrial crops can be defined under the given natural constraints of European marginal agricultural lands; and (ii) which agricultural practices are required for marginal agricultural land low-input systems (MALLIS). For the growth-suitability analysis, available thresholds and growth requirements of the selected industrial crops were defined. The marginal agricultural land was categorized according to the agro-ecological zone (AEZ) concept in combination with the marginality constraints, so-called ‘marginal agro-ecological zones’ (M-AEZ). It was found that both large marginal agricultural areas and numerous agricultural practices are available for industrial crop cultivation on European marginal agricultural lands. These results help to further describe the suitability of industrial crops for the development of social-ecologically friendly MALLIS in Europe.
It is well established that nighttime radiance, measured from satellites, correlates with economic prosperity across the globe. In developing countries, areas with low levels of detected radiance ...generally indicate limited development - with unlit areas typically being disregarded. Here we combine satellite nighttime lights and the world settlement footprint for the year 2015 to show that 19% of the total settlement footprint of the planet had no detectable artificial radiance associated with it. The majority of unlit settlement footprints are found in Africa (39%), rising to 65% if we consider only rural settlement areas, along with numerous countries in the Middle East and Asia. Significant areas of unlit settlements are also located in some developed countries. For 49 countries spread across Africa, Asia and the Americas we are able to predict and map the wealth class obtained from ~2,400,000 geo-located households based upon the percent of unlit settlements, with an overall accuracy of 87%.
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