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%.
In recent decades, global oil palm production has shown an abrupt increase, with almost 90% produced in Southeast Asia alone. To understand trends in oil palm plantation expansion and for ...landscape-level planning, accurate maps are needed. Although different oil palm maps have been produced using remote sensing in the past, here we use Sentinel 1 imagery to generate an oil palm plantation map for Indonesia, Malaysia and Thailand for the year 2017. In addition to location, the age of the oil palm plantation is critical for calculating yields. Here we have used a Landsat time series approach to determine the year in which the oil palm plantations are first detected, at which point they are 2 to 3 years of age. From this, the approximate age of the oil palm plantation in 2017 can be derived.
Abstract
The development of remotely sensed products such as land cover requires large amounts of high-quality reference data, needed to train remote sensing classification algorithms and for ...validation. However, due to the lack of sharing and the high costs associated with data collection, particularly ground-based information, the amount of reference data available has not kept up with the vast increase in the availability of satellite imagery, e.g. from Landsat, Sentinel and Planet satellites. To fill this gap, the Geo-Wiki platform for the crowdsourcing of reference data was developed, involving visual interpretation of satellite and aerial imagery. Here we provide an overview of the crowdsourcing campaigns that have been run using Geo-Wiki over the last decade, including the amount of data collected, the research questions driving the campaigns and the outputs produced such as new data layers (e.g. a global map of forest management), new global estimates of areas or percentages of land cover/land use (e.g. the amount of extra land available for biofuels) and reference data sets, all openly shared. We demonstrate that the amount of data collected and the scientific advances in the field of land cover and land use would not have been possible without the participation of citizens. A relatively conservative estimate reveals that citizens have contributed more than 5.3 years of the data collection efforts of one person over short, intensive campaigns run over the last decade. We also provide key observations and lessons learned from these campaigns including the need for quality assurance mechanisms linked to incentives to participate, good communication, training and feedback, and appreciating the ingenuity of the participants.
Deforestation contributes to global greenhouse gas emissions and must be reduced if the 1.5°C limit to global warming is to be realized. Protected areas represent one intervention for decreasing ...forest loss and aiding conservation efforts, yet there is intense human pressure on at least one-third of protected areas globally. There have been numerous studies addressing the extent and identifying drivers of deforestation at the local, regional, and global level. Yet few have focused on drivers of deforestation in protected areas in high thematic detail. Here we use a new crowdsourced data set on drivers of tropical forest loss for the period 2008–2019, which has been collected using the Geo-Wiki crowdsourcing application for visual interpretation of very high-resolution imagery by volunteers. Extending on the published data on tree cover and forest loss from the Global Forest Change initiative, we investigate the dominant drivers of deforestation in tropical protected areas situated within 30° north and south of the equator. We find the deforestation rate in protected areas to be lower than the continental average for the Latin Americas (3.4% in protected areas compared to 5.4%) and Africa (3.3% compared to 3.9%), but it exceeds that of unprotected land in Asia (8.5% compared to 8.1%). Consistent with findings from foregoing studies, we also find that pastures and other subsistence agriculture are the dominant deforestation driver in the Latin Americas, while forest management, oil palm, shifting cultivation and other subsistence agriculture dominate in Asia, and shifting cultivation and other subsistence agriculture is the main driver in Africa. However, we find contrasting results in relation to the degree of protection, which indicate that the rate of deforestation in Latin America and Africa in strictly protected areas might even exceed that of areas with no strict protection. This crucial finding highlights the need for further studies based on a bottom up crowdsourced, data collection approach, to investigate drivers of deforestation both inside and outside protected areas.
During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries ...reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform ( www.geo-wiki.org ) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public.
A community palm model Clinton, Nicholas; Vollrath, Andreas; D'annunzio, Remi ...
arXiv.org,
05/2024
Paper, Journal Article
Odprti dostop
Palm oil production has been identified as one of the major drivers of deforestation for tropical countries. To meet supply chain objectives, commodity producers and other stakeholders need timely ...information of land cover dynamics in their supply shed. However, such data are difficult to obtain from suppliers who may lack digital geographic representations of their supply sheds and production locations. Here we present a "community model," a machine learning model trained on pooled data sourced from many different stakeholders, to develop a specific land cover probability map, in this case a semi-global oil palm map. An advantage of this method is the inclusion of varied inputs, the ability to easily update the model as new training data becomes available and run the model on any year that input imagery is available. Inclusion of diverse data sources into one probability map can help establish a shared understanding across stakeholders on the presence and absence of a land cover or commodity (in this case oil palm). The model predictors are annual composites built from publicly available satellite imagery provided by Sentinel-1, Sentinel-2, and ALOS DSM. We provide map outputs as the probability of palm in a given pixel, to reflect the uncertainty of the underlying state (palm or not palm). The initial version of this model provides global accuracy estimated to be approximately 90% (at 0.5 probability threshold) from spatially partitioned test data. This model, and resulting oil palm probability map products are useful for accurately identifying the geographic footprint of palm cultivation. Used in conjunction with timely deforestation information, this palm model is useful for understanding the risk of continued oil palm plantation expansion in sensitive forest areas.
The purpose of the research is to highlight the formation and development of the movement against nuclear energy in the USA in the 1970s and 1980s. The research methodology is based on the principle ...of historicism and problem-chronological and complex approaches. We used the following methods: historical and systemic – in the process of considering the movement against nuclear energy in the USA as an integrated part of the social activity of the American public in the period of the 1970s – 1980s; historical-chronological – to identify features and changes in the dynamics of the movement against atomic energy; problem-genetic – in the study of the impact on the movement of key incidents in atomic energy; comparative – to establish common and different forms, character and activity of anti-nuclear power protest organizations in the USA. The scientific novelty of the article. For the first time in Ukrainian historiography, an attempt was made to investigate the origins of the movement against nuclear energy in the USA, its dynamics and key events as one of the largest social phenomena of the country in the 1970s and 1980s. In the example of the Three Mile Island station district, the development of protest organizations and the strategy of rallying American society in the conditions of a radiation incident is traced; the impact of the Chornobyl disaster on the activation of the movement against nuclear energy in the USA was revealed. The article also shows the impact of anti-nuclear public activism on the social and political sphere of the USA. Conclusions. An organized movement against nuclear energy emerged in the mid-1970s as opposition to the development of the U.S. civilian nuclear program. The Three Mile Island incident in March 1979 undermined social confidence in the industry, fueling anti-nuclear activism. The Chornobyl disaster in Ukraine on April 26, 1986, mobilized and consolidated the movement against nuclear energy in the United States, finally halting the development of the industry for more than 30 years.
Abstract
Background
Being a scavenger of free radicals, C
60
fullerenes can influence on the physiological processes in skeletal muscles, however, the effect of such carbon nanoparticles on muscle ...contractility under acute muscle inflammation remains unclear. Thus, the aim of the study was to reveal the effect of the C
60
fullerene aqueous solution (C
60
FAS) on the muscle contractile properties under acute inflammatory pain.
Methods
To induce inflammation a 2.5% formalin solution was injected into the rat triceps surae (TS) muscle. High-frequency electrical stimulation has been used to induce tetanic muscle contraction. A linear motor under servo-control with embedded semi-conductor strain gauge resistors was used to measure the muscle tension.
Results
In response to formalin administration, the strength of TS muscle contractions in untreated animals was recorded at 23% of control values, whereas the muscle tension in the C
60
FAS-treated rats reached 48%. Thus, the treated muscle could generate 2-fold more muscle strength than the muscle in untreated rats.
Conclusions
The attenuation of muscle contraction force reduction caused by preliminary injection of C
60
FAS is presumably associated with a decrease in the concentration of free radicals in the inflamed muscle tissue, which leads to a decrease in the intensity of nociceptive information transmission from the inflamed muscle to the CNS and thereby promotes the improvement of the functional state of the skeletal muscle.
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