scholarly journals Can disaster events reporting be used to drive remote sensing applications? A Latin America weather index insurance case study

2019 ◽  
Vol 26 (4) ◽  
pp. 632-641 ◽  
Author(s):  
Manuel Brahm ◽  
Daniel Vila ◽  
Sofia Martinez Saenz ◽  
Daniel Osgood
Keyword(s):  
Remote Sensing ◽  
Latin America ◽  
Index Insurance ◽  
Weather Index ◽  
Sensing Applications ◽  
Weather Index Insurance ◽  
Insurance Case ◽  
Remote Sensing Applications

scholarly journals The Use of Remotely Sensed Rainfall for Managing Drought Risk: A Case Study of Weather Index Insurance in Zambia

2016 ◽  
Vol 8 (4) ◽  
pp. 342 ◽  
Author(s):  
Emily Black ◽  
Elena Tarnavsky ◽  
Ross Maidment ◽  
Helen Greatrex ◽  
Agrotosh Mookerjee ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Index Insurance ◽  
Drought Risk ◽  
Weather Index ◽  
Weather Index Insurance

scholarly journals Unpacking Barriers to Socially Inclusive Weather Index Insurance: Towards a Framework for Inclusion

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2235
Author(s):  
Aheeyar ◽  
Silva ◽  
Senaratna-Sellamuttu ◽  
Arulingam
Keyword(s):  
Primary Data ◽  
Economic Losses ◽  
Index Insurance ◽  
Problem Analysis ◽  
Structural Barriers ◽  
Weather Index ◽  
Weather Index Insurance ◽  
Marginalized Women ◽  
And Gender

Floods account for a majority of disasters, especially in South Asia, where they affect 27 million people annually, causing economic losses of over US$1 billion. Climate change threatens to exacerbate these risks. Risk transfer mechanisms, such as weather index insurance (WII) may help buffer farmers against these hazards. However, WII programs struggle to attract the clients most in need of protection, including marginalized women and men. This risks re-enforcing existing inequalities and missing opportunities to promote pro-poor and gender-sensitive development. Key questions, therefore, include what factors constrain access to WIIs amongst heterogeneous communities, and how these can be addressed. This paper contributes to that end through primary data from two WII case studies (one in India, the other in Bangladesh) that identify contextual socio-economic and structural barriers to accessing WII, and strategies to overcome these. More significantly, this paper synthesizes the case study findings and those from a review of the literature on other WII initiatives into a framework to promote a systematic approach to address these challenges: an important step forward in moving from problem analysis to remedial action. The framework highlights actions across WII product design, implementation and post-implementation, to minimize risks of social exclusion in future WII schemes.

scholarly journals Farmer Perception, Recollection, and Remote Sensing in Weather Index Insurance: An Ethiopia Case Study

2018 ◽  
Vol 10 (12) ◽  
pp. 1887 ◽  
Author(s):  
Daniel Osgood ◽  
Bristol Powell ◽  
Rahel Diro ◽  
Carlos Farah ◽  
Markus Enenkel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Climate Risk ◽  
Weather Data ◽  
Index Insurance ◽  
Data Sets ◽  
Community Based ◽  
Weather Index ◽  
Management Interventions ◽  
Reported Data

A challenge in addressing climate risk in developing countries is that many regions have extremely limited formal data sets, so for these regions, people must rely on technologies like remote sensing for solutions. However, this means the necessary formal weather data to design and validate remote sensing solutions do not exist. Therefore, many projects use farmers’ reported perceptions and recollections of climate risk events, such as drought. However, if these are used to design risk management interventions such as insurance, there may be biases and limitations which could potentially lead to a problematic product. To better understand the value and validity of farmer perceptions, this paper explores two related questions: (1) Is there evidence that farmers reporting data have any information about actual drought events, and (2) is there evidence that it is valuable to address recollection and perception issues when using farmer-reported data? We investigated these questions by analyzing index insurance, in which remote sensing products trigger payments to farmers during loss years. Our case study is perhaps the largest participatory farmer remote sensing insurance project in Ethiopia. We tested the cross-consistency of farmer-reported seasonal vulnerabilities against the years reported as droughts by independent satellite data sources. We found evidence that farmer-reported events are independently reflected in multiple remote sensing datasets, suggesting that there is legitimate information in farmer reporting. Repeated community-based meetings over time and aggregating independent village reports over space lead to improved predictions, suggesting that it may be important to utilize methods to address potential biases.

scholarly journals Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1246
Author(s):  
Kyle B. Larson ◽  
Aaron R. Tuor
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Biological Invasions ◽  
Spatial Information ◽  
Wildlife Habitat ◽  
Ground Sample ◽  
Sensing Applications ◽  
Combining Data ◽  
Remote Sensing Applications

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

Pricing weather index insurance based on artificial controlled experiment: a case study of cold temperature for early rice in Jiangxi, China

2017 ◽  
Vol 91 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Qing Sun ◽  
Zaiqiang Yang ◽  
Xianghong Che ◽  
Wei Han ◽  
Fangmin Zhang ◽  
...  
Keyword(s):  
Controlled Experiment ◽  
Cold Temperature ◽  
Index Insurance ◽  
Weather Index ◽  
Weather Index Insurance ◽  
Early Rice
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