scholarly journals Effects of land‐cover changes on the partitioning of surface energy and water fluxes in Amazonia using high‐resolution satellite imagery

Ecohydrology ◽  
2019 ◽  
Vol 12 (6) ◽  
Author(s):  
Gabriel Oliveira ◽  
Nathaniel A. Brunsell ◽  
Elisabete C. Moraes ◽  
Yosio E. Shimabukuro ◽  
Thiago V. Santos ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Energy ◽  
Land Cover ◽  
Satellite Imagery ◽  
Land Cover Changes ◽  
Water Fluxes ◽  
High Resolution Satellite Imagery

scholarly journals Studying Land Cover Changes in a Malaria-Endemic Cambodian District: Considerations and Constraints

2020 ◽  
Vol 12 (18) ◽  
pp. 2972 ◽  
Author(s):  
Anaïs Pepey ◽  
Marc Souris ◽  
Amélie Vantaux ◽  
Serge Morand ◽  
Dysoley Lek ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Malaria Incidence ◽  
Health Concern ◽  
Land Use Land Cover ◽  
Vector Ecology ◽  
Increased Risk ◽  
High Resolution Satellite Imagery

Malaria control is an evolving public health concern, especially in times of resistance to insecticides and to antimalarial drugs, as well as changing environmental conditions that are influencing its epidemiology. Most literature demonstrates an increased risk of malaria transmission in areas of active deforestation, but knowledge about the link between land cover evolution and malaria risk is still limited in some parts of the world. In this study, we discuss different methods used for analysing the interaction between deforestation and malaria, then highlight the constraints that can arise in areas where data is lacking. For instance, there is a gap in knowledge in Cambodia about components of transmission, notably missing detailed vector ecology or epidemiology data, in addition to incomplete prevalence data over time. Still, we illustrate the situation by investigating the evolution of land cover and the progression of deforestation within a malaria-endemic area of Cambodia. To do so, we investigated the area by processing high-resolution satellite imagery from 2018 (1.5 m in panchromatic mode and 6 m in multispectral mode) and produced a land use/land cover map, to complete and homogenise existing data from 1988 and from 1998 to 2008 (land use/land cover from high-resolution satellite imagery). From these classifications, we calculated different landscapes metrics to quantify evolution of deforestation, forest fragmentation and landscape diversity. Over the 30-year period, we observed that deforestation keeps expanding, as diversity and fragmentation indices globally increase. Based on these results and the available literature, we question the mechanisms that could be influencing the relationship between land cover and malaria incidence and suggest further analyses to help elucidate how deforestation can affect malaria dynamics.

scholarly journals Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Guie Li ◽  
Zhongliang Cai ◽  
Yun Qian ◽  
Fei Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Low Income ◽  
Urban Poverty ◽  
Image Features ◽  
Housing Affordability ◽  
Learning Approaches ◽  
Housing Inequality ◽  
High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

scholarly journals Lake-to-Lake Cloud Bands: Frequencies and Locations

2007 ◽  
Vol 135 (12) ◽  
pp. 4202-4213 ◽  
Author(s):  
Yarice Rodriguez ◽  
David A. R. Kristovich ◽  
Mark R. Hjelmfelt
Keyword(s):  
High Resolution ◽  
Great Lakes ◽  
Satellite Imagery ◽  
Lake Superior ◽  
Great Lakes Region ◽  
Lake Effect ◽  
Time Period ◽  
High Resolution Satellite Imagery

Abstract Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.

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