Mapping multispectral Digital Images using a Cloud Computing software: applications from UAV images

Saura, José Ramón; Reyes-Menéndez, Ana; Palos-Sánchez, Pedro R.

doi:10.1016/j.heliyon.2019.e01277

Cited by 22 publications

(7 citation statements)

References 26 publications

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“…However, since SDS can move large areas, the SDS receptor areas can become potential risk areas where vegetation loss and soil erosion can occur continuously, affected by SDS [3,34]. Since remote sensing technology can monitor continuous land surface changes and accumulate information that can be connected with other environmental industry, it is advantageous to use this technology to be able to systematically adapt and manage the SDS, which has characteristics of the event [71].…”

Section: Discussionmentioning

confidence: 99%

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

et al. 2020

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Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective.

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Section: Discussionmentioning

confidence: 99%

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

et al. 2020

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“…Smart farming uses local soil data such as structure, composition, moisture, salinity, cation exchange capacity (CEC), and pH to optimize production (Corwin et al, 2003;Grisso et al, 2005;Ould Ahmed et al, 2010). One approach is remote sensing with satellites or aerial vehicles such as drones with hyper-/multispectral imaging (Mulla, 2013;Saura et al, 2019). For the continuous monitoring of environmental parameters, the installation of local sensors is common.…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #3 of "MeteoMex: open infrastructure for networked environmental monitoring and agriculture 4.0 (v0.1)"

Miller¹

2021

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Air, water, and soil are essential for terrestrial life, but pollution, overexploitation, and climate change jeopardize the availability of these primary resources. Thus, assuring human health and food production requires efficient strategies and technologies for environmental protection. Knowing key parameters such as soil moisture, air, and water quality is essential for smart farming and urban development. The MeteoMex project aims to build simple hardware kits and their integration into current Internet-of-Things (IoT) platforms. This paper shows the use of low-end Wemos D1 mini boards to connect environmental sensors to the open-source platform ThingsBoard. Two printed circuit boards (PCB) were designed for mounting components. Analog, digital and I 2 C sensors are supported. The Wemos ESP8266 microchip provides WiFi capability and can be programmed with the Arduino IDE. Application examples for the MeteoMex aeria and terra kits demonstrate their functionality for air quality, soil, and climate monitoring. Further, a prototype for monitoring wastewater treatment is shown, which exemplifies the capabilities of the Wemos board for signal processing. The data are stored in a PostgreSQL database, which enables data mining. The MeteoMex IoT system is highly scalable and of low cost, which makes it suitable for deployment in agriculture 4.0, industries, and public areas. Circuit drawings, PCB layouts, and code examples are free to download from https://github.com/robert-winkler/MeteoMex .

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“…Unmanned aerial systems (UAS) represent a noncontact and nondestructive measurement method that can obtain the spectral, structural, and texture features of the target at different spatiotemporal scales (Jiang et al, 2019). These systems have the ability to obtain high spatial and temporal resolution data and have great application potential (Niu et al, 2019;Ramon Saura et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Applications of UAS in Crop Biomass Monitoring: A Review

et al. 2021

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Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

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Mapping multispectral Digital Images using a Cloud Computing software: applications from UAV images

Cited by 22 publications

References 26 publications

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

Peer Review #3 of "MeteoMex: open infrastructure for networked environmental monitoring and agriculture 4.0 (v0.1)"

Applications of UAS in Crop Biomass Monitoring: A Review

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