A Forest Vitality and Change Monitoring Tool Based on RapidEye Imagery

Marx, Alexander; Tetteh, Gideon Okpoti

doi:10.1109/lgrs.2017.2675164

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…For the purpose of enhancing feasibility, IoT devices use the solar-powered system and communicate with the monitoring centre based on the LoRa network. In [91], the authors proposed a solution to control forest changes and vitality by using high-resolution RapidEye satellite imagery. This solution has been deployed commercially in several states in Germany and has detected leaf diseases in a pine forest affected by pests.…”

Section: Monitoringmentioning

confidence: 99%

IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges

et al. 2022

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The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

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Section: Monitoringmentioning

confidence: 99%

IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges

et al. 2022

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“…Reasonable planning and maximization of the observed profits are therefore key problems that need to be solved. At present, the cooperative operation of multiple EOSs (multi-EOSs) has been applied to several instances, in-cluding the German commercial satellite constellation Rapid-Eye [2], the GPM mission deployed by NASA and JAXA [3], the QB50 mission launched by the European Union in 2011 [4] and Chinese BeiDou satellites [5].…”

Section: Introductionmentioning

confidence: 99%

“…The roles of solar energy and ground station in multi-EOSs mission scheduling are fully considered because satellite resources are recoverable via charging using solar energy and transmitting data to ground stations. (2) In the first stage, inspired by the rule of survival of the fittest, an evolutionary ant colony optimization (EACO) method based on cooperation and competition mechanism is proposed to solve the mission pre-planning of multi-EOSs. To further improve the convergence and increase the searchability of the algorithm, we propose a dynamic adjustment approach for information heuristic factor, expected heuristic factor and pheromone evaluation factor.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Autonomous Scheduling Approach for Multiple Earth Observation Satellites With Intensive Missions

Guo

Wang

et al. 2021

IEEE Access

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Autonomous mission scheduling of multiple earth observation satellites (multi-EOSs) is considered as a complicated combinatorial optimization problem, which requires simultaneous consideration of imaging needs, resource constraints (electricity and memory) and possible emergencies. However, EOS resources are extremely scarce relative to intensive mission observation demands and most of the existing algorithms seldom consider emergencies. To address these challenges, this paper proposes a complete multi-EOSs scheduling scheme composed of two coupling stages, including mission pre-planning and mission replanning. We aim to obtain the optimal scheduling scheme for each EOS at the same time by maximizing the observation profits and balancing the resource consumption of each EOS. In this study, the roles of solar energy and ground stations in multi-EOSs mission scheduling are also considered. In the first stage, based on the cooperation and competition mechanism as well as the dynamic adjustment approach, an evolutionary ant colony optimization (EACO) method is developed to obtain the optimal solution for multi-EOSs preplanning. In the second stage, using the results produced by EACO, we propose an interactive replanning approach to replan the missions that cannot be performed by faulty EOS in the event of unexpected accidents. Finally, several target scenarios are designed and numerical experiments are performed to show that the proposed algorithm presents better performance for large-scale multi-EOSs missions than other state-of-the-art algorithms.INDEX TERMS Multiple earth observation satellites, autonomous mission scheduling, evolutionary ant colony optimization, dynamic adjustment approach, interactive replanning approach

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