Showing papers by "Linda See published in 2022"

Global forest management data for 2015 at a 100 m resolution

Abstract: Abstract Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Global forest management data for 2015 at a 100 m resolution

Abstract: Abstract Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Estimating global economic well-being with unlit settlements

Abstract: 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%.

A crowdsourced global data set for validating built-up surface layers

Abstract: Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas.

A crowdsourced global data set for validating built-up surface layers

Abstract: Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas.

Drivers of tropical forest loss between 2008 and 2019

Abstract: 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 Continental Assessment of the Drivers of Tropical Deforestation With a Focus on Protected Areas

Abstract: 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.

Drivers of tropical forest loss between 2008 and 2019

Abstract: 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.

Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience

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.

Europa Biodiversity Observation Network: integrating data streams to support policy

Abstract: Observations are key to understand the drivers of biodiversity loss, and the impacts on ecosystem services and ultimately on people. Many EU policies and initiatives demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor biodiversity are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. EuropaBON aims to bridge this gap by designing an EU-wide framework for monitoring biodiversity and ecosystem services. EuropaBON harnesses the power of modelling essential variables to integrate different reporting streams, data sources, and monitoring schemes. These essential variables provide consistent knowledge about multiple dimensions of biodiversity change across space and time. They can then be analyzed and synthesized to support decision-making at different spatial scales, from the sub-national to the European scale, through the production of indicators and scenarios. To develop essential biodiversity and ecosystem variables workflows that are policy relevant, EuropaBON is built around stakeholder engagement and knowledge exchange (WP2). EuropaBON will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the feasibility of setting up a center to coordinate monitoring activities across Europe (WP2). Together with stakeholders, EuropaBON will assess current monitoring efforts to identify gaps, data and workflow bottlenecks, and analyse cost-effectiveness of different schemes (WP3). This will be used to co-design improved monitoring schemes using novel technologies to become more representative temporally, spatially and taxonomically, delivering multiple benefits to users and society (WP4). Finally, EuropaBON will demonstrate in a set of showcases how workflows tailored to the Birds Directive, Habitats Directive, Water Framework Directive, Climate and Restoration Policy, and the Bioeconomy Strategy, can be implemented (WP5).

How many people need to classify the same image? A method for optimizing volunteer contributions in binary geographical classifications

Abstract: 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.

The grand challenges facing environmental citizen science

Abstract: Citizen science is becoming increasingly popular as a way of engaging citizens in environmental monitoring. There has been a wide discussion around what citizen science is and is not. For example, should it include only active or deliberate contributions by participants, or can it also include passive or involuntary citizen participation, e.g., where crowdsourced data on social media are analyzed for a specific scientific purpose? Should it only be voluntary, or can it also include payments? While the debate is important (Haklay et al., 2021), here, we use the term citizen science in the broadest sense possible, to mean the engagement and participation of non-professional scientists in the production of scientific knowledge. Although citizen science has traditionally been used in an environmental monitoring context, an important and very valuable characteristic is that it has the potential to reach beyond individual scientific disciplines and to attract wider public participation (Bonney et al., 2009). This is particularly true in the digital age where technology has been a key enabler of citizen participation in a much wider range of research, including technologically demanding fields such as particle physics or synthetic biology, as well as whole disciplines such as medicine, the social sciences and the humanities (Pykett et al., 2020; Tauginienė et al., 2020; Haklay et al., 2021). Hence, for this special collection of environmental citizen science that was launched in March 2022, we want to include any research using citizen science methodologies that is relevant to environmental science, in a broad and inclusive manner. The use of citizen science for environmental research is evolving rapidly, partly due to the increasingly visible impacts of climate change, which is galvanizing public participation globally. In this paper we discuss six grand challenges that face this growing area of research, namely: 1) Perennial issues that the scientific establishment raises about data quality in citizen science, as applied to environmental research; 2) the use of citizen science by governments, local authorities andNational Statistical Offices (NSOs) as a source of non-traditional data for monitoring and decision making; 3) new forms of engagement, motivation and retention of citizens; 4) open data and the sharing of citizen science data; 5) the integration of data from new digital technologies and how citizen science can help bridge the digital divide and inequalities in participation; and 6) how environmental citizen science relates to research on the global environmental challenges that humanity is currently facing such as the climate emergency, the continued destruction of the rainforests, dramatic losses in biodiversity, and increasing inequality, to name just a few. On this last point, in a recent youth survey by OPEN ACCESS

A Modelling System for Dead Wood Assessment in the Forests of Northern Eurasia

Abstract: Dead wood, including coarse woody debris, CWD, and fine woody debris, FWD, plays a substantial role in forest ecosystem functioning. However, the amount and dynamics of dead wood in the forests of Northern Eurasia are poorly understood. The aim of this study was to develop a spatially distributed modelling system (limited to the territories of the former Soviet Union) to assess the amount and structure of dead wood by its components (including snags, logs, stumps, and the dry branches of living trees) based on the most comprehensive database of field measurements to date. The system is intended to be used to assess the dead wood volume and the amount of dead wood in carbon units as part of the carbon budget calculation of forests at different scales. It is presented using multi-dimensional regression equations of dead wood expansion factors (DWEF)—the ratio of the dead wood component volume to the growing stock volume of the stands. The system can be also used for the accounting of dead wood stock and its dynamics in national greenhouse gas inventories and UNFCCC reporting. The system’s accuracy is satisfactory for the average level of disturbance regimes but it may require corrections for regions with accelerated disturbance regimes.

A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia

Abstract: Abstract Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters.

Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach

Abstract: Citizen science has become an increasingly popular approach to scientific data collection, where classification tasks involving visual interpretation of images is one prominent area of application, e.g., to support the production of land cover and land-use maps. Achieving a minimum accuracy in these classification tasks at a minimum cost is the subject of this study. A Bayesian approach provides an intuitive and reasonably straightforward solution to achieve this objective. However, its application requires additional information, such as the relative frequency of the classes and the accuracy of each user. While the former is often available, the latter requires additional data collection. In this paper, we present a two-stage approach to gathering this additional information. We demonstrate its application using a hypothetical two-class example and then apply it to an actual crowdsourced dataset with five classes, which was taken from a previous Geo-Wiki crowdsourcing campaign on identifying the size of agricultural fields from very high-resolution satellite imagery. We also attach the R code for the implementation of the newly presented approach.