Leveraging Multi-Source Data and Digital Technology to Support the Monitoring of Localized Water Changes in the Mekong Region

Polpanich, Orn-uma; Bhatpuria, Dhyey; Santos, Tania Fernanda Santos; Krittasudthacheewa, Chayanis

doi:10.3390/su14031739

Cited by 6 publications

(5 citation statements)

References 73 publications

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“…In the land domain (biosphere and hydrosphere subdomains), eight ECVs and seven products were reported. The most used s-ECV was land cover, and the most used satellite sensor was Landsat with all the archives (i.e., MSS = Multispectral Scanner, TM = Thematic Mapper, ETM+ = Enhanced Thematic Mapper Plus, OLI = Operational Land Imager, and NLCD = USGS National Land Cover Database) [29,33,35,36,[41][42][43][44][45][46][47][48][49][50][51][52][53]. These approaches contributed to computing the indicators for SDGs 6, 11, and 15 by the following indicators: 6.6.1 (Extent of water-related ecosystems) [41,43] [42], 15.5 (Red List Index) [45], 15.8.1 (Legislation and adequately resourcing the prevention or control of invasive alien species) [49], and 16.7 (Effective, accountable and transparent institutions) [47].…”

Section: Land: Biospherementioning

confidence: 99%

“…Meanwhile, Sentinel-2 was used to report indicators 15.1.1 and 15.1.2 [52], 11.1.1 and 11.3.1 [58], as well as new definitions for indicators 11.1.1, 11.2.1, 11.3.1, and 11.6.2 [59]. Polpanich et al [36] used Sentinel-2 in relation to SDGs 4,6,11,13, and 14 in their work for monitoring localized water changes.…”

Section: Land: Biospherementioning

confidence: 99%

“…Moreover, land surface temperature from Landsat contributed to SDG 11 [35] and SDGs 4, 6, 11, 13, and 14 [36] and that from National Earth System Science Data Center (NESSDC-China) to SDG 15 [30].…”

Section: Land: Biospherementioning

confidence: 99%

See 2 more Smart Citations

Satellite Earth Observation for Essential Climate Variables Supporting Sustainable Development Goals: A Review on Applications

Ballari,

Vilches-Blázquez,

Orellana-Samaniego

et al. 2023

Remote Sensing

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Essential climate variables (ECVs) have been recognized as crucial information for achieving Sustainable Development Goals (SDGs). There is an agreement on 54 ECVs to understand climate evolution, and multiple rely on satellite Earth observation (abbreviated as s-ECVs). Despite the efforts to encourage s-ECV use for SDGs, there is still a need to further integrate them into the indicator calculations. Therefore, we conducted a systematic literature review to identify s-ECVs used in SDG monitoring. Results showed the use of 14 s-ECVs, the most frequent being land cover, ozone, precursors for aerosols and ozone, precipitation, land surface temperature, soil moisture, soil carbon, lakes, and leaf area index. They were related to 16 SDGs (mainly SDGs 3, 6, 11, 14, and 15), 33 targets, and 23 indicators. However, only 10 indicators (belonging to SDGs 6, 11, and 15) were calculated using s-ECVs. This review raises research opportunities by identifying s-ECVs yet to be used in the indicator calculations. Therefore, indicators supporting SDGs must be updated to use this valuable source of information which, in turn, allows a worldwide indicator comparison. Additionally, this review is relevant for scientists and policymakers for future actions and policies to better integrate s-ECVs into the Agenda 2030.

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Section: Land: Biospherementioning

confidence: 99%

Section: Land: Biospherementioning

confidence: 99%

See 1 more Smart Citation

Satellite Earth Observation for Essential Climate Variables Supporting Sustainable Development Goals: A Review on Applications

Ballari,

Vilches-Blázquez,

Orellana-Samaniego

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

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“…The lack of observation stations reduces the reliability of the monitoring results, making it difficult to accurately identify droughts [16]. Additionally, current weather and climate conditions have negatively affected the accuracy and reliability of traditional prediction indicators used by small-scale farmers in the region [21]. These indicators, which are based on traditional environmental cues, may not be as effective in predicting drought events under the current conditions of climate uncertainty and variability [22].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duong,

Phong,

et al. 2024

Preprint

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Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, par-ticularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought pre-diction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to con-ventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

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“…Thus, major achievements in sensing technologies, wireless communication and artificial intelligence have been made by research efforts in agriculture globally [1,2]. In the context of climate change, real-time monitoring of drought is of primary interest which yielded a forked remote sensing approach, deploying satellite imagery to spot drought occurrence [3][4][5][6][7], or utilizing the recent developments in affordable sensor solutions, generating data in a more (unmanned aerial vehicles-UAV) or less (pole, machine or tower mounted) remote manner [8][9][10][11][12]. In conventional agricultural production, the only objective data collected on plant side (grain yield) are collected when the plant is already dead, so the real-time monitoring practices represent a paradigmatic shift for most farmers around the world.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit

et al. 2022

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Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitoring of agricultural production. The 3 × 2 multispectral arrays with responses at 610 and 680 nm (red), 730 and 760 nm (red-edge) and 810 and 860 nm (infrared) spectra were used to assess the occurrence of leaf rolling (LR) in 545 experimental maize plots measured four times for calibration dataset (n = 2180) and 145 plots measured once for external validation. Multispectral reads were used to calculate 15 simple normalized vegetation indices. Four ML algorithms were assessed: single and multilayer perceptron (SLP and MLP), convolutional neural network (CNN) and support vector machines (SVM) in three validation procedures, which were stratified cross-validation, random subset validation and validation with external dataset. Leaf rolling occurrence caused visible changes in spectral responses and calculated vegetation indexes. All algorithms showed good performance metrics in stratified cross-validation (accuracy >80%). SLP was the least efficient in predictions with external datasets, while MLP, CNN and SVM showed comparable performance. Combining ML with multispectral sensing shows promise in transition towards agriculture based on data-driven decisions especially considering the novel Internet of Things (IoT) avenues.

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Leveraging Multi-Source Data and Digital Technology to Support the Monitoring of Localized Water Changes in the Mekong Region

Cited by 6 publications

References 73 publications

Satellite Earth Observation for Essential Climate Variables Supporting Sustainable Development Goals: A Review on Applications

Satellite Earth Observation for Essential Climate Variables Supporting Sustainable Development Goals: A Review on Applications

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit

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