Application of mobile devices for community based forest monitoring

The bacterial flagellar motor is a reversible rotary nano-machine, about 45 nm in diameter, embedded in the bacterial cell envelope. It is powered by the flux of H+ or Na+ ions across the cytoplasmic membrane driven by an electrochemical gradient, the proton-motive force or the sodium-motive force. Each motor rotates a helical filament at several hundreds of revolutions per second (hertz). In many species, the motor switches direction stochastically, with the switching rates controlled by a network of sensory and signalling proteins. The bacterial flagellar motor was confirmed as a rotary motor in the early 1970s, the first direct observation of the function of a single molecular motor. However, because of the large size and complexity of the motor, much remains to be discovered, in particular, the structural details of the torque-generating mechanism. This review outlines what has been learned about the structure and function of the motor using a combination of genetics, single-molecule and biophysical techniques, with a focus on recent results and single-molecule techniques

Mobile devices for community-based REDD+ monitoring: A case study for Central Vietnam

Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic process of assessing forest stands properties with respect to reference data, adopting the current REDD+ requirements for implementing monitoring at national levels is a challenging task. Recently, the advancement in Information and Communications Technologies (ICT) and mobile devices has enabled local communities to monitor their forest in a basic resource setting such as no or slow internet connection link, limited power supply, etc. Despite the potential, the use of mobile device system for community based monitoring (CBM) is still exceptional and faces implementation challenges. This paper presents an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. This paper also assesses the accuracy and reliability of CBM data and proposes a way to fit them into national REDD+ Monitoring, Reporting and Verification (MRV) scheme. The system performance is evaluated at Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were tracked. The results show that the local community is able to provide data with accuracy comparable to expert measurements (index of agreement greater than 0.88), but against lower costs. Furthermore, the results confirm that communities are more effective to monitor small scale forest degradation due to subsistence fuel wood collection and selective logging, than high resolution remote sensing SPOT imagery

Combining satellite data and community-based observations for forest monitoring

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring

Building a community-based open harmonised reference data repository for global crop mapping

Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles

WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping

The challenge of global food security in the face of population growth, conflict and climate change requires a comprehensive understanding of cropped areas, irrigation practices and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereals maps, seasonal irrigation maps, seasonal active cropland maps, and confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. The presented products are fully validated, e.g., global user's and producer's accuracies for the annual temporary crop product are 88.5 % and 92.1 %, respectively. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and push the boundaries for global agricultural mapping from space. The global products are available at https://doi.org/10.5281/zenodo.7875104 (Van Tricht et al., 2023)

Interactive community-based tropical forest monitoring using emerging technologies

Forests cover approximately 30% of the Earth’s land surface and have played an indispensable role in the human development and preserving natural resources. At the moment, more than 300 million people are directly dependent on these forests and their resources. Forests also provide habitats for a wide variety of species and offer several ecological necessities to natural and anthropological systems. In spite of this importance, unprecedented destruction of tropical forest cover has been witnessed over the past four decades. Annually, approximately 2.1x105 hectares of forests are lost, with serious negative consequences on the regulation of the world’s climate cycle, biodiversity and other environmental variables. To mitigate these consequences, the United Nations Framework Convention on Climate Change (UNFCCC) has requested the developing countries to adapt new policy in reducing emissions from deforestation and forest degradation (REDD+). Under this policy, countries have been mandated to engage local communities and indigenous groups as critical stakeholders in the design and implementation of a national forest monitoring system (NFMS) that supports measuring, reporting and verification (MRV) of actions and achievements of REDD+ activities. Current schemes for tropical monitoring are based on remote sensing and field measurements which typically originate from national forest inventories. Remotely sensed imagery has been considered as the principal data source used to calculate forest area change across large areas, assess rates of deforestation and establish baselines for national forest area change databases. Advancements in medium and high resolution satellites, open data policies, time-series analysis methods and big data processing environments are considered valuable for deforestation monitoring at local to global scales. However, cloud cover, seasonality and the restricted spatial and temporal resolution of remote sensing observations limits their applicability in the tropics. Enhancing the interpretation of remote sensing analysis require substantial ground verification and validation. Accomplishing these tasks through national forest inventory data is expensive, time-consuming and difficult to implement across large spatial scales. Next to remote sensing, community-based monitoring (CBM) has also demonstrated potential in the collection and interpretation of forest monitoring data. However effective implementation of community-based forest monitoring systems is currently lacking due to two reasons: 1) the role of communities in NFMS is unclear and 2) tools that can support local communities to explore opportunities and facilitate forest monitoring are still scarce. This thesis addresses these two issues by proposing technical solutions (computer and geo-information science) and assessing the capacities and needs of communities in developing countries with a REDD+ implementation and forest monitoring context. The main goal of this thesis, therefore, is to develop an approach that combines emerging technologies and community-based observations for tropical forest monitoring. To accomplish the main goal, four specific research questions were formulated: 1) What are the potentials to link community-based efforts to national forest monitoring systems? 2) How can information and communication technologies (ICTs) support the automation of community data collection process for monitoring forest carbon stocks and change activities using modern handheld devices? 3) What is the accuracy and compatibility of community collected data compared to other data (e.g., optical remote sensing and expert field measurements) for quantifying forest carbon stocks and changes? and 4) What is a suitable design for an interactive remote sensing and community-based near real-time forest change monitoring system and how can such system be operationalized? In Chapter 2, scientific literature and 28 readiness preparation proposals from the World Bank Forest Carbon Partnership Facility are reviewed to better define the role and technical conditions for CBM. Based on this review, a conceptual framework was developed under which CBM can contribute as a dedicated and independent stream of measuring and monitoring data to national level forest monitoring efforts. The following chapters are built upon this framework. Chapter 3 describes a process of designing and implementing an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. The usability of the system is evaluated in the Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were measured by different means such as local, regional and national experts and high resolution satellite imagery. The results indicate that the local communities were able to provide forest carbon measurements with accuracy comparable to that of expert measurements at lower costs. Furthermore, the results show that communities are more effective in detecting small scale forest degradation caused by subsistence fuelwood collection and selective logging than image analysis using SPOT imagery. To support the findings of chapter 3, the data acquisition form (mostly activity data related to forest change) for mobile device was further improved in chapter 4. The system was tested by thirty local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. High resolution satellite imagery and professional measurements were combined to assess the accuracy and complementary use of local datasets in terms of spatial, temporal and thematic accuracy. Results indicate that the local communities were capable of describing processes of change associated with deforestation, forest degradation and reforestation, in terms of their spatial location, extent, timing and causes within ten administrative units. Furthermore, the results demonstrate that communities offer complementary information to remotely sensed data, particularly to signal forest degradation and mapping deforestation over small areas. Based on this complementarity, a framework is proposed for integrating local expert monitoring data with satellite-based monitoring data into a NFMS in support of REDD+ MRV and near real-time forest change monitoring. Having identified the framework for integrated monitoring systems in chapter 4, chapter 5 describes an interactive web-based forest monitoring system using four levels of geographic information services: 1) the acquisition of continuous data streams from satellite and community-based monitoring using mobile devices, 2) near real-time forest disturbance detection based on satellite time-series, 3) presentation of forest disturbance data through a web-based application and social media and 4) interaction of the satellite-based disturbance alerts with the end-user communities to enhance the collection of ground data. The system was developed using open source technologies and has been implemented together with local experts in UNESCO Kafa Biosphere Reserve, Ethiopia. The results show that the system was able to provide easy access to information on forest change and considerably improve the collection and storage of ground observation by local experts. Social media lead to higher levels of user interaction and noticeably improved communication among stakeholders. Finally, an evaluation of the system confirmed its usability in Ethiopia. Chapter 6 presents the final conclusions and provides recommendations for further research. The overall conclusion is that the emerging technologies, such as smartphones, Web-GIS and social media, incorporated with user friendly interface improve the interactive participation of local communities in forest monitoring and decrease errors in data collection. The results show that CBM can provide data on forest carbon stocks, forest area changes as well as data that help to understand local drivers of emissions. The thesis also shows, in theory and in practice, how local data can be used to link with medium and high resolution remote sensing satellite images for an operational near real-time forest monitoring system at a local scale. The methods presented in this thesis are applicable to a broader geographic scope. Hence, this thesis emphasizes that policies and incentives should be implemented to empower communities and to create institutional frameworks for community-based forest monitoring in the tropics

NIVA Geotagged Photos - Danish AI Challenge (detection of livestock grazing)

As part of the NIVA project, a platform for AI powered auto review and pre-validation of geo-tagged agriculture images for IACS (Integrated Administration and Control System) will be developed. This platform will be hosted at the Walton Institute on behalf of NIVA. The platform is split into two main aspects, a general facility for image uploading and tagging, and a facility for reviewing by a human or auto-reviewing by an AI model. For the targeted initial three auto-review (AI) tasks, suitable auto-review challenges have been proposed by Paying Agencies. Including the Danish challenge for which the model training and validation is described in this document. In Denmark it is already possible for farmers to use images from a geo-tagged app as prove the activity demand on livestock grazing on grasslands. However, this is resulting in many images being sent in and needing manual inspection for approval. A possible pre-selection by an AI model is helpful. The initial challenge for the AI model is straight-forward, a classification task of approval or non-approval of the grazing activity. Such a model might be relevant for other countries in Europe, especially those with a high percentage of grasslands. All source code is available in NIVA gitlab - –https://gitlab.com/niva-ai-platform. A full wiki is also available - –https://gitlab.com/niva-ai-platform/infrastructure/-/wikis/Architecture