Suchergebnisse

This study was envisaged to map the coconut growing areas in Kerala state of India, using multidate NDVI obtained from sentinel 2A MSI data, having spatial resolution as 10 m. 95% Cloud free satellite images were taken for classification and date of pass considered for the study were 16th February, 2017 and 18th December, 2017 for Kozhikode district of Kerala. In this study bio-window of coconut plantation was identified using NDVI images of two dates. It was observed that interclass variations were more prominent in February image. Forest, dense and moderately dense coconut plantations have significantly different NDVI values in February image whereas in December image all three features have similar values. Hence, February image was classified using three classification methods i.e. ISODATA, maximum likelihood and random forest classification to assess which method is better to distinguish coconut plantation from other classes. Random Forest classification technique was found to be more accurate in identifying coconut plantation. Area was also estimated for Kozhikode district and compared with the government statistics. Google Earth was taken as reference to identify coconut plantation as it has a unique star shaped canopy, which is clearly visible in high-resolution imagery.

This study was envisaged to map the coconut growing areas in Kerala state of India, using multidate NDVI obtained from sentinel 2A MSI data, having spatial resolution as 10 m. 95% Cloud free satellite images were taken for classification and date of pass considered for the study were 16th February, 2017 and 18th December, 2017 for Kozhikode district of Kerala. In this study bio-window of coconut plantation was identified using NDVI images of two dates. It was observed that interclass variations were more prominent in February image. Forest, dense and moderately dense coconut plantations have significantly different NDVI values in February image whereas in December image all three features have similar values. Hence, February image was classified using three classification methods i.e. ISODATA, maximum likelihood and random forest classification to assess which method is better to distinguish coconut plantation from other classes. Random Forest classification technique was found to be more accurate in identifying coconut plantation. Area was also estimated for Kozhikode district and compared with the government statistics. Google Earth was taken as reference to identify coconut plantation as it has a unique star shaped canopy, which is clearly visible in high-resolution imagery.

Abstract
Background
The COVID-19 pandemic posed considerable risks to populations affected by humanitarian crises in low- and middle-income countries (LMICs). However, there is limited understanding of how the pandemic may have affected non-COVID health outcomes among crisis-affected populations. Our aim was to examine the evidence on the impact of the COVID-19 pandemic on non-COVID-19 health outcomes for crisis-affected populations in LMICs.

Methods
A systematic review methodology was applied following PRISMA guidelines. Eligibility criteria were: crisis-affected populations in LMICS; COVID-19; and all health topics, except for sexual and reproductive health which was covered in a linked review. Five bibliographic databases and additional grey literature sources were searched. The search period was from 2019 to 31 July 2022. Eligible papers were extracted and analysed using a narrative synthesis approach based on the study objectives and relevant health access and systems frameworks. A quality appraisal was also conducted.

Findings
4320 articles were screened, and 15 eligible studies were identified and included in this review. Ten studies collected health outcomes data. Eight related to mental health, which generally showed worse mental health outcomes because of the pandemic, and pandemic-related stressors were identified. Two studies assessed physical health outcomes in children, while none addressed physical health outcomes among adults. Nine studies reported on access to healthcare, revealing worse access levels due to the pandemic and noting key barriers to care. Seven studies reported on the impact on health systems, with key challenges including reduced and distorted health care funding, reduced staff capacity, interrupted medicines and supplies, weak information and mixed-messaging, and weak leadership. All fifteen studies on the social determinants of health, particularly highlighting the effect of increasing poverty, the role of gender, and food insecurity on health outcomes. The quality of papers was limited overall.

Conclusion
This review found some limited evidence indicating negative mental health effects, increased barriers to accessing care, damage to health systems and magnified impacts on the social determinants of health for crisis-affected people during the COVID-19 pandemic. However, the small number and limited quality of the studies make the overall strength of evidence quite weak.

The Paris Agreement established a global goal on adaptation and invites parties to review the effectiveness of adaptation actions. However, the measurement of adaptation success remains elusive. Focusing on the capabilities of households and governments to pursue a range of adaptation futures provides a more robust foundation.