Predicting Environmental Impacts Associated with Population Distribution and Land Use Using Remote Sensing and GIS: A Stormwater Runoff Example

Author(s):  
Kathleen M. Trauth ◽  
Aderbal C. Corrêa ◽  
Hao Wang ◽  
Janggam Adhityawarma
Data ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 63
Author(s):  
Dong Chen ◽  
Varada Shevade ◽  
Allison Baer ◽  
Jiaying He ◽  
Amanda Hoffman-Hall ◽  
...  

Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.


2001 ◽  
Vol 22 (11) ◽  
pp. 2095-2108 ◽  
Author(s):  
T. Sharma ◽  
P. V. Satya Kiran ◽  
T. P. Singh ◽  
A. V. Trivedi ◽  
R. R. Navalgund

Author(s):  
O. S. Olokeogun ◽  
K. Iyiola ◽  
O. F. Iyiola

Mapping of LULC and change detection using remote sensing and GIS techniques is a cost effective method of obtaining a clear understanding of the land cover alteration processes due to land use change and their consequences. This research focused on assessing landscape transformation in Shasha Forest Reserve, over an 18 year period. LANDSAT Satellite imageries (of 30 m resolution) covering the area at two epochs were characterized into five classes (Water Body, Forest Reserve, Built up Area, Vegetation, and Farmland) and classification performs with maximum likelihood algorithm, which resulted in the classes of each land use. <br><br> The result of the comparison of the two classified images showed that vegetation (degraded forest) has increased by 30.96 %, farmland cover increased by 22.82 % and built up area by 3.09 %. Forest reserve however, has decreased significantly by 46.12 % during the period. <br><br> This research highlights the increasing rate of modification of forest ecosystem by anthropogebic activities and the need to apprehend the situation to ensure sustainable forest management.


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