Remote Ecological Monitoring with Smartphones and Tasker

Donovan, Therese M.; Balantic, Cathleen; Katz, Jonathan E.; Massar, Mark; Knutson, Randy L.; Duh, K.G.; Jones, Peter J.S.; Epstein, Keith; Lacasse-Roger, Julien; Dias, J R

doi:10.3996/jfwm-20-071

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…However, the advent and ubiquity of modern smartphones, plus the continuous update on sensors (e.g., high‐resolution cameras and LiDAR) has led to the rapid increase of software applications (hereafter “apps”) that can be used to estimate these indicators, and facilitate data collection and entry (Camp & Wheaton 2014; Andrachuk et al 2019). As such, there has been a recent focus on using smartphones as an alternative to traditional field methods, albeit with different degrees of efficacy (e.g., Aitkenhead et al 2014; Donovan et al 2021; Howard et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Review and assessment of smartphone apps for forest restoration monitoring

Schweizer,

Cole,

Werden

et al. 2024

Restoration Ecology

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With increased interest in forest restoration comes an urgent need to provide accurate, scalable, and cost‐effective monitoring tools. The ubiquity of smartphones has led to a surge in monitoring apps. We reviewed and assessed monitoring apps found through web searches and conversations with practitioners. We identified 42 apps that (1) automatically monitor indicators or (2) facilitate data entry. We selected the five most promising from the first category, based on their relevance, availability, stability, and user support. We compared them to traditional field techniques in a well‐studied restoration project in Costa Rica. We received further feedback from 15 collaborator organizations that evaluated these in their corresponding field restoration sites. Diameter measurements correlated well with traditional tape‐based measurements (R2 = 0.86–0.89). Canopy openness and ground cover showed weaker correlations to densiometer and quadrat cover measurements (R2 = 0.42–0.51). Apps did not improve labor efficiency but do preclude the purchase of specialized field equipment. The apps reviewed here need further development and validation to support monitoring adequately, especially in the tropics. Estimates of development and maintenance costs, as well as statistics on user uptake, are required for cost‐effective development. We recommend a coordinated effort to develop dedicated restoration monitoring apps that can speed up and standardize the collection of indicators and provide evidence on restoration outcomes alongside a centralized repository of this information.

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Section: Introductionmentioning

confidence: 99%

Review and assessment of smartphone apps for forest restoration monitoring

Schweizer,

Cole,

Werden

et al. 2024

Restoration Ecology

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“…Few studies have attempted to use smartphones for the purpose of monitoring pollinators. Donovan et al (2021) investigated the potential of using smartphones and motion capture for wildlife monitoring, including the possibility of monitoring pollinators. However, the authors found their setup to be inefficient for capturing clear images of pollinators.…”

Section: Introductionmentioning

confidence: 99%

Utilising affordable smartphones and open-source time-lapse photography for monitoring pollinators

Ștefan,

Workman,

Cobain

et al. 2024

Preprint

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Monitoring plant-pollinator interactions is crucial for understanding factors that influence these relationships across space and time. While traditional methods in pollination ecology are time-consuming and resource-intensive, the growing availability of photographic technology, coupled with advancements in artificial intelligence classification, offers the potential for non-destructive and automated techniques. However, it is important that the photographs are of high enough quality to enable insects to be identified at lower taxonomic levels, preferably genus or species levels. This study assessed the feasibility of using smartphones to automatically capture images of insects visiting flowers and evaluated whether the captured images offered sufficient resolution for precise insect identification. Smartphones were positioned above target flowers from various plant species to capture time-lapse images of any flower visitor in urban green areas around Leipzig and Halle, Germany. We present the proportions of insect identifications achieved at different taxonomic levels, such as order, family, genus, and species, and discuss whether limitations stem from the automated approach (e.g., inability to observe distinguishing features in images despite high image quality) or low image quality. Practical recommendations are provided to address these challenges. Our results indicate that for bee families, nearly three quarters of all cases could be identified to genus level. Flies were more difficult, due to the small size of many individuals and the more challenging features needed for identification (e.g., in the wing veins). Overall, we suggest that smartphones are an effective tool when optimised by researchers. As technology continues to advance, smartphones are becoming increasingly accessible, affordable, and user-friendly, rendering them an appealing option for pollinator monitoring.

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Remote Ecological Monitoring with Smartphones and Tasker

Cited by 2 publications

References 23 publications

Review and assessment of smartphone apps for forest restoration monitoring

Review and assessment of smartphone apps for forest restoration monitoring

Utilising affordable smartphones and open-source time-lapse photography for monitoring pollinators

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