Drought hot spot analysis using local indicators of spatial autocorrelation: An experience from Bangladesh

Islam, S M Samiul; Islam, K. M. Ashraful; Mullick, Md. Reaz Akter

doi:10.1016/j.envc.2021.100410

Cited by 21 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The global spatial autocorrelation studies the correlation within the entire spatial system. However, in the study area, local land patches may be randomly distributed even if there is a global correlation ( 57 ). The limitation of Moran's I is that if there is a positive correlation between carbon emissions in some regions and a negative correlation in the other region, the two may offset each other and reduce Moran's I.…”

Section: Methodsmentioning

confidence: 99%

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

Zhang

Liao²,

Zhao³

2022

Front. Public Health

View full text Add to dashboard Cite

An accurate carbon emissions map is of great significance for urban planning to reduce carbon emissions, mitigate the heat island effect, and avoid the impact of high temperatures on human health. However, little research has focused on carbon emissions maps at the land patch level, which makes poor integration with small and medium-sized urban planning based on land patches. In this study, a vectorization method for spatial allocation of carbon emissions at the land patch level was proposed. The vector maps and spatial autocorrelation of carbon emissions in Zhangdian City, China were explored using multi-source data. In addition, the differences between different streets were analyzed, and the carbon emissions ratio of the land patch was compared. The results show that the vector carbon emissions map can help identify the key carbon reduction land patches and the impact factors of carbon emissions. The vector maps of Zhangdian City show that in 2021, the total carbon emissions and carbon absorptions were 4.76 × 109kg and 4.28 × 106kg respectively. Among them, industrial land accounted for 70.16% of carbon emissions, mainly concentrated in three industrial towns. Forest land carbon absorption accounted for 98.56%, mainly concentrated in the peripheral streets away from urban areas. The Moran's I of land patch level carbon emissions was 0.138, showing a significant positive spatial correlation. The proportion of land patches is an important factor in determining carbon emissions, and the adjustment of industrial structure is the most critical factor in reducing carbon emissions. The results achieved can better help governments develop different carbon reduction strategies, mitigate the heat island effect, and support low-carbon and health-oriented urban planning.

show abstract

Section: Methodsmentioning

confidence: 99%

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

Zhang

Liao²,

Zhao³

2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…The annual average temperature is 25 • C. The average precipitation from November to March is below 20 mm, April and October are below 100 mm, and the remaining 5 months are over 200 mm (Wikipedia, 2022). Due to the low precipitation rate, the district is considered one of the top drought-prone areas in Bangladesh (Afrin et al, 2019;Islam et al, 2022;Rahman et al, 2022). As a result, food insecurity and poverty rates are high (BBS, WFP, 2020).…”

Section: Study Area and Sampling Proceduresmentioning

confidence: 99%

Understanding the determinants and impact of the adoption of technologies for sustainable farming systems in water-scarce areas of Bangladesh

Sunny

Karimanzira²,

Peng³

et al. 2022

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

IntroductionBangladesh's quest to achieve sustainable development goals has highlighted the need to enhance resilience against the challenges that interconnected food, energy, and water (FEW) nexus systems faces to support human well-being. The government has decided to promote the adoption of competent and cutting-edge technologies that can significantly contribute to balancing energy and water resource utilization in achieving a more sustainable and climate-smart food production system. Hence, scaled-up adoption of solar-powered irrigation systems and recommended fertilizer dose (SIRFD) applications were proposed. This study, to provide practical policy implications, attempts to identify the determinants and impact of SIRFD adoption in water-scarce areas of Bangladesh.MethodsDeterminants of adoption were analyzed using multinomial logistic regression, and the adoption impact was analyzed using treatment effect models.ResultsThe results revealed that land typology, soil fertility perception, soil water retention, knowledge, environmental awareness, secondary income, close acquaintance adoption, and cash availability significantly influenced adoption decisions. The treatment effect model result indicated that farmers who adopted both technologies could reduce production costs by 1.36% and obtain an 8.92% higher ROI than non-adopters.ConclusionThe study findings suggest that policy interventions on scaling up SIRFD adoption require focusing on knowledge development village-based demonstration activities, group farming models backed by micro-finance, and avoiding launching conflicting schemes.

show abstract

“…In this way, the area develops its own urban climate as a result of regional climate changes and local environment of the area creates a superficial urban heat island (Masson et al, 2020). In urban areas, mainly due to the reflection from concrete structures, there is an increase in LST and the consequent formation of heat pockets (Islam et al, 2022). Vegetation absorbs and reflects solar radiation controlling the exchange of latent heat, which in turn affects the LST (Duveiller et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Diksha

Kumari

2023

Journal of Landscape Ecology

View full text Add to dashboard Cite

The land use and land cover (LULC) characteristics of Ghaziabad have experienced dynamic changes because of the city's ongoing industrialization and urbanisation processes. These shifts can be directly attributed to human actions. These shifts can be directly attributed to human actions. Thermal variation in the study area necessitates LULC analysis. Landsat and Sentinel satellite data for 2011 and 2021 were used to map LULC, estimate land surface temperature (LST) and analysis spatial autocorrelation among the variables using ArcGIS software and the Google Earth Engine (GEE) cloud platform. A sharp descent is observed in the cropland while built-up area has increased during the study period. With the increase in the built-up surface in the area, the ambient temperatures have also increased from 18.70 °C in 2011 to 21.81 °C in 2021 leading to urban heat island effect. At all spatial scales, spatial autocorrelation is a characteristic property of most ecological parameters. The spatial clustering of LST in an ecosystem can play a crucial role in determining the dynamics of LULC.The Moran’s, I show that there is a considerable level of spatial autocorrelation in the values of LST and highly clustered pattern for both the years. Monitoring and understanding the surface thermal environment is crucial to discerning the causes of climate change.

show abstract

Drought hot spot analysis using local indicators of spatial autocorrelation: An experience from Bangladesh

Cited by 21 publications

References 35 publications

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

Understanding the determinants and impact of the adoption of technologies for sustainable farming systems in water-scarce areas of Bangladesh

Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Contact Info

Product

Resources

About