Linking Earth Observations for Assessing the Food Security Situation in Vietnam: A Landscape Approach

Poortinga, Ate; Nguyen, Quyen; Tenneson, Karis; Troy, Austin; Saah, David; Bhandari, Biplov; Ellenburg, W. L.; Aekakkararungroj, Aekkapol; Ha, Lan; Pham, Hai N.; Nguyen, Giang V.; Chishtie, Farrukh

doi:10.3389/fenvs.2019.00186

Cited by 18 publications

(15 citation statements)

References 63 publications

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“…The Thirty-Sixth AAAI Conference on Artificial Intelligence tivity assessments and planning (Nakalembe 2020). Land cover mapping (Poortinga et al 2019) and socioeconomic status prediction (Ayush et al 2020) have also been explored. However, these factors are arguably directly visible in satellite data, e.g., to predict socioeconomic status, Ayush et al (2020) search for objects directly in satellite data, such as trucks.…”

Section: Background and Related Workmentioning

confidence: 99%

Micronutrient Deficiency Prediction via Publicly Available Satellite Data

Bondi

Chen

Golden

et al. 2022

AAAI

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Micronutrient deficiency (MND), which is a form of malnutrition that can have serious health consequences, is difficult to diagnose in early stages without blood draws, which are expensive and time-consuming to collect and process. It is even more difficult at a public health scale seeking to identify regions at higher risk of MND. To provide data more widely and frequently, we propose an accurate, scalable, low-cost, and interpretable regional-level MND prediction system. Specifically, our work is the first to use satellite data, such as forest cover, weather, and presence of water, to predict deficiency of micronutrients such as iron, Vitamin B12, and Vitamin A, directly from their biomarkers. We use real-world, ground truth biomarker data collected from four different regions across Madagascar for training, and demonstrate that satellite data are viable for predicting regional-level MND, surprisingly exceeding the performance of baseline predictions based only on survey responses. Our method could be broadly applied to other countries where satellite data are available, and potentially create high societal impact if these predictions are used by policy makers, public health officials, or healthcare providers.

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Section: Background and Related Workmentioning

confidence: 99%

Micronutrient Deficiency Prediction via Publicly Available Satellite Data

Bondi

Chen

Golden

et al. 2022

AAAI

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“…SERVIR harnesses space technology and geospatial technologies to to help decision makers to integrate geospatial information into their decision-making process. High quality surface water maps will support efforts for the regional land cover monitoring system [82][83][84][85][86], food security [87], monitoring of ecosystems [88,89] and water management applications [90]. The application of the presented surface water maps integrated with near real-time information systems for water have the potential to support effective decision making processes for sustainable landscape management.…”

Section: Future Workmentioning

confidence: 99%

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

Markert

Mayer

et al. 2020

Remote Sensing

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Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

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“…Table 1. The left column shows the 'Specific to Generic' category (1)(2)(3)(4)(5)(6)(7)(8), these contain transitions from a specific class to any other. The right column, the 'Generic to Specific' category (9)(10)(11)(12)(13)(14)(15)(16), includes transitions from any class into a specific class.…”

Section: Data Samplingmentioning

confidence: 99%

“…Undoubtedly, the influence of anthropogenic land use and land cover change has allowed for a rapid increase in food production and enabled the provision of a large variety of important commodities. Land cover transitions have been important drivers of social and economic development however, they have also been a significant source of anthropogenic carbon emissions [1][2][3] and reduced resilience to natural disasters. For example, the conversion of forest to farmland has been reported to negatively affect the hydrology and natural drainage due to a reduction of infiltration and groundwater recharge, generating increased surface runoff [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region

Poortinga¹,

Aekakkararungroj

Kityuttachai³

et al. 2020

Remote Sensing

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Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research we have developed a robust machine learning (random forest) approach utilizing EO and Geographic Information System (GIS) data, which enables an innovative means for our simulations to be driven only by historical drivers of change and hotspot prediction based on probability to change. We used the Mekong region as a case study to generate a training and validation sample from historical land cover patterns of change and used this information to train a random forest machine learning model. Data samples were created from the SERVIR-Mekong land cover data series. Data sets were created for 2 categories both containing 8 classes. The 2 categories included—any generic class to change into a specific one and vice versa. Classes included the following: Aquaculture; Barren; Cropland; Flooded Forest; Mangroves; Forest; Plantations; Wetlands; and Urban. The training points were used to sample a series of satellite-derived surface reflectance products and other data layers such as information on slope, distance to road and census data, which represent the drivers of change. The classifier was trained in binary mode and showed a clear separation between change and no change. An independent validation dataset of historical change pixels show that all median change probabilities are greater than 80% and all lower quantiles, except one, are greater than 70%. The 2018 probability change maps show high probabilities for the Plantations and Forest classes in the ‘Generic to Specific’ and ’Specific to generic’ category, respectively. A time-series analysis of change probability shows that forests have become more likely to convert into other classes during the last two decades, across all countries. We successfully demonstrated that historical change patters combined with big data and machine learning technologies are powerful tools for predictive change analytics on a planetary scale.

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Linking Earth Observations for Assessing the Food Security Situation in Vietnam: A Landscape Approach

Cited by 18 publications

References 63 publications

Micronutrient Deficiency Prediction via Publicly Available Satellite Data

Micronutrient Deficiency Prediction via Publicly Available Satellite Data

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region

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