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 Walking the Line: Conducting Transect Walks in Burkina Faso

2021 ◽  
Vol 43 (1) ◽  
pp. 18-21
Author(s):  
Alfredo Rojas ◽  
Koffi Nomedji ◽  
Colin Thor West
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Burkina Faso ◽  
Satellite Imagery ◽  
Land Use Land Cover ◽  
Community Based ◽  
Ecological Processes ◽  
High Resolution Satellite Imagery ◽  
Over Time ◽  
Local Farmers

Abstract In this article we present results from transect walks and participatory mapping done in Burkina Faso. Since the Sahelian drought of the 1970s, researchers have continued to depict the Sahelian region of West Africa as an environment experiencing severe degradation; a narrative that persists over time. Recently, however, analyses of satellite imagery have identified remarkable patterns of greening across the Sahel. The causes of this greening are hotly debated. Through this project we aim to inform these debates with on-the-ground perceptions of local farmers and pastoralists. The transect walk method is a community-based process that collects information on the land-use/land-cover (LULC) features across villages. Transects help triangulate data by combining high-resolution satellite imagery, firsthand observations, and local experiences of ecological processes. We describe the methodology behind transects and discuss how they contextualize an otherwise removed process of environmental analysis. We also describe the challenges that arise throughout the fieldwork process.

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

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