The Quality of Remote Sensing Optical Images from Acquisition to Users

Selva, Massimo

doi:10.3390/rs13071295

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…The observed improvement in the explanatory power of the model after introducing the citizen scientist ID implies that CSs specific characteristics (here bundled into one fixed effect) play an important role in influencing the likelihood of landslides being detected. Thus, while CSbased inventories might depend on socio-cultural, infrastructural, motivational factors of the CSs and nature of the hazard, satellite imagery largely depends on nature of the target object in the feature space (size of the object) and the prevailing atmospheric conditions (Agapiou et al 2011, Selva 2021. In general, it should be noted that the ability of CSs to detect landslides that have occurred largely depend on whether they get informed about their occurrence.…”

Section: Sensitivity and Precision In Cs-and Rs-based Hazard Detectionmentioning

confidence: 99%

Can citizen scientists provide a reliable geo-hydrological hazard inventory? An analysis of biases, sensitivity and precision for the Rwenzori Mountains, Uganda

Sekajugo

Kagoro

Mutyebere

et al. 2022

Environ. Res. Lett.

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Spatio-temporal inventory of natural hazards is a challenging task especially in rural or remote areas in the Global South where data collection at regional scale is difficult. Citizen science, i.e., involvement of no-experts in collecting information and co-creation of knowledge with experts to solve societal and environmental problems, has been suggested as a viable approach to tackle this bottleneck, although the reliability of the resulting data is often questioned. Here we analyse an inventory of geo-hydrological hazards (landslides and floods) reported by a network of citizen scientists in the Rwenzori Mountains, Uganda, established since 2017. We assess the precision, sensitivity and potential biases affecting this citizen science-based hazard inventory. We compare the citizen science-based records with two independent inventories, one collected through systematic fieldwork and another by PlanetScope satellite imagery mapping for the period between May 2019 and May 2020. The precision of the geo-observer data is higher (99% and 100% for landslides and floods, respectively) than that of satellite-based data (44% and 84%, respectively) indicative of fewer false positives in the former inventory. Also, citizen scientists have a higher sensitivity in reporting landslides (51%) compared to satellite imagery (39%) in addition to being able to report the events a few days after the occurrence. In contrast, the sensitivity of satellite-based flood detection is higher than that of citizen scientists. The probability of landslide events being reported by citizen scientists depends both on citizen scientists and hazard specific features (impact, landslide-citizen scientist home distance, landslide-road access distance and altitude). Although satellite imagery mapping could result in a spatially less biased inventory, small landslides are often missed while shallow ones can easily be confused with freshly cleared vegetation. Also, in a dominantly cloudy environment, it can take several days to weeks before a cloud-free satellite image can be obtained. In summary, the typically rapid response time of citizen scientists can result in faster information with high reliability at the risk of missing out almost half of the occurrences. Citizen scientists also provide more data on impact and type of land use, something difficult to achieve using satellite imagery. Working with farmers at village level as citizen scientists can facilitate covering a wider geographical area while reducing the area monitored by each citizen scientist at the same time.

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Section: Sensitivity and Precision In Cs-and Rs-based Hazard Detectionmentioning

confidence: 99%

Can citizen scientists provide a reliable geo-hydrological hazard inventory? An analysis of biases, sensitivity and precision for the Rwenzori Mountains, Uganda

Sekajugo

Kagoro

Mutyebere

et al. 2022

Environ. Res. Lett.

View full text Add to dashboard Cite

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The Quality of Remote Sensing Optical Images from Acquisition to Users

Abstract: The need to observe and characterize the environment leads to a constant increase of the spatial, spectral, and radiometric resolution of new optical sensors [...]

Cited by 1 publication

References 12 publications

Can citizen scientists provide a reliable geo-hydrological hazard inventory? An analysis of biases, sensitivity and precision for the Rwenzori Mountains, Uganda

Can citizen scientists provide a reliable geo-hydrological hazard inventory? An analysis of biases, sensitivity and precision for the Rwenzori Mountains, Uganda

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