Cost effective malaria risk control using remote sensing and environmental data

2012 ◽  
Author(s):  
Md. Z. Rahman ◽  
Leonid Roytman ◽  
Abdel Hamid Kadik
Keyword(s):  
Remote Sensing ◽  
Risk Control ◽  
Cost Effective ◽  
Malaria Risk ◽  
Environmental Data

scholarly journals Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming

Telecom ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 255-270
Author(s):  
Saeid Pourroostaei Ardakani ◽  
Ali Cheshmehzangi
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
Data Collection ◽  
Cost Effective ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Q Learning ◽  
Sensing Applications ◽  
Learning Technique ◽  
Target Locations

UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.

scholarly journals Operationalization of Remote Sensing Solutions for Sustainable Forest Management

2021 ◽  
Vol 13 (4) ◽  
pp. 572
Author(s):  
Gintautas Mozgeris ◽  
Ivan Balenović
Keyword(s):  
Remote Sensing ◽  
Forest Management ◽  
Natural Resources ◽  
Sustainable Management ◽  
Sustainable Forest Management ◽  
Cost Effective ◽  
Development Trends ◽  
Comprehensive Information ◽  
The Status

The pre-requisite for sustainable management of natural resources is the availability of timely, cost-effective, and comprehensive information on the status and development trends of the management object [...]

scholarly journals Remote Sensing for Land Administration

2020 ◽  
Vol 12 (15) ◽  
pp. 2497
Author(s):  
Rohan Bennett ◽  
Peter van Oosterom ◽  
Christiaan Lemmen ◽  
Mila Koeva
Keyword(s):  
Remote Sensing ◽  
Land Tenure ◽  
Laser Scanning ◽  
Poverty Reduction ◽  
Spatial Information ◽  
Cost Effective ◽  
Terrestrial Lidar ◽  
Land Administration ◽  
Tools And Techniques ◽  
Vertical Development

Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.

scholarly journals Towards malaria risk prediction in Afghanistan using remote sensing

2010 ◽  
Vol 9 (1) ◽  
pp. 125 ◽  
Author(s):  
Farida Adimi ◽  
Radina P Soebiyanto ◽  
Najibullah Safi ◽  
Richard Kiang
Keyword(s):  
Remote Sensing ◽  
Risk Prediction ◽  
Malaria Risk

scholarly journals Leveraging machine learning to produce a cost-effective national building height map of Ireland

2021 ◽  
Author(s):  
Eoghan Keany ◽  
Geoffrey Bessardon ◽  
Emily Gleeson
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Cost Effective ◽  
Land Cover Classification ◽  
National Scale ◽  
Building Height ◽  
Height Data

<p>To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.</p><p>Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.</p><p> </p><p>(1) G. Bessardon and E. Gleeson, “Using the best available physiography to improve weather forecasts for Ireland,” in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.</p><p>(2) E. Walsh, et al., “Using machine learning to produce a very high-resolution land-cover map for Ireland, ” Advances in Science and Research,  (accepted for publication).</p><p>(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki</p><p>(4) D. Frantz, et al., “National-scale mapping of building height using sentinel-1 and sentinel-2 time series,” Remote Sensing of Environment, vol. 252, 2021.</p><p>(5) M. Fitrzyk, et al., “Esa Copernicus sentinel-1 exploitation activities,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.</p>

scholarly journals Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha forest reserve, Nigeria

2014 ◽  
Vol XL-8 ◽  
pp. 613-616 ◽  
Author(s):  
O. S. Olokeogun ◽  
K. Iyiola ◽  
O. F. Iyiola
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Sustainable Forest Management ◽  
Cost Effective ◽  
Remote Sensing And Gis ◽  
Clear Understanding ◽  
Forest Reserve ◽  
Landsat Satellite

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.

Biogeographical patterns derived from remote sensing variables: the amphibians and reptiles of the Iberian Peninsula

2009 ◽  
Vol 30 (2) ◽  
pp. 185-206 ◽  
Author(s):  
◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Iberian Peninsula ◽  
Gap Analysis ◽  
Hierarchical Cluster ◽  
Environmental Data ◽  
Species Density ◽  
Biogeographic Patterns ◽  
North East ◽  
Density Maps ◽  
Reptile Species

AbstractThe biogeographic patterns in species density of herptiles were analysed in the Iberian Peninsula. Geoclimatic regions were identified using a PCA. Individual habitat suitability (HS) models for 23 amphibians and 35 reptiles at 10 × 10 km scale were calculated with ENFA, using 12 environmental factors established with Remote Sensing (RS) techniques. The species presence proportion in each geoclimatic region was calculated through a cross-tabulation between each potential occurrence model and the geoclimatic regions. Species chorotypes were determined through Hierarchical Cluster Analysis using Jaccard's index as association measure and by the analysis of marginality and tolerance factors from individual HS models. Predicted species density maps were calculated for each geoclimatic region. Probable under-sampled areas were estimated through differences between the predicted species density maps and observed (Gap analysis). The selected PCA components divided the Iberian Peninsula in two major geoclimatic regions largely corresponding to the Atlantic and Mediterranean climates. The Jaccard's index clustered herptiles in two main taxonomic groups, with distribution similar to the Atlantic and Mediterranean geoclimatic regions (7 amphibian + 13 reptile species in three Atlantic subgroups and 16 amphibian + 22 reptile species in four Mediterranean subgroups). Marginality and tolerance factor scores identified species groups of herptile specialists and generalists. The highest observed and predicted species density areas were broadly located in identical regions. Predicted gaps are located in north-western, north-east and central Iberia. RS is a useful tool for biogeographical studies, as it provides consistent environmental data from large areas with high accuracy.

High-resolution root zone soil moisture-based indices for drought monitoring in the Ebro basin

2021 ◽  
Author(s):  
Vivien-Georgiana Stefan ◽  
Maria-José Escorihuela ◽  
Pere Quintana-Seguí
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Root Zone ◽  
Cost Effective ◽  
Strong Relationship ◽  
Drought Indices ◽  
Ebro Basin ◽  
Small Areas ◽  
Cost Effective Approach

&lt;h3&gt;Agriculture is an important factor on water resources, given the constant population growth and the strong relationship between water availability and food production. In this context, root zone soil moisture (RZSM) measurements are used by modern irrigators in order to detect the onset of crop water stress and to trigger irrigations. Unfortunately, in situ RZSM measurements are costly; combined with the fact they are available only over small areas and that they might not be representative at the field scale, remote sensing is a cost-effective approach for mapping and monitoring extended areas. A recursive formulation of an exponential filter was used in order to derive 1 km resolution RZSM estimates from SMAP (Soil Moisture Active Passive) surface soil moisture (SSM) over the Ebro basin. The SMAP SSM was disaggregated to a 1 km resolution by using the DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) algorithm. The pseudodiffusivity parameter of the exponential filter was calibrated per land cover type, by using ISBA-DIF (Interaction Soil Biosphere Atmosphere) surface and root zone soil moisture data as an intermediary step. The daily 1 km RZSM estimates were then used to derive 1 km drought indices such as soil moisture anomalies and soil moisture deficit indices (SMDI), on a weekly time-scale, covering the entire 2020 year. Results show that both drought indices are able to capture rainfall and drying events, with the weekly anomaly being more responsive to sudden events such as heavy rainfalls, while the SMDI is slower to react do the inherent inertia it has. Moreover, a quantitative comparison with drought indices derived from a model-based RZSM estimates has also been performed, with results showing a strong correspondence between the different indices. For comparison purposes, the weekly soil moisture anomalies and SMDI derived using 1 km SMAP-derived SSM were also estimated. The analysis shows that the anomalies and SMDI based on the RZSM are more representative of the hydric stress level of the plants, given that the RZSM is better suited than the SSM to describe the moisture conditions at the deeper layers, which are the ones used by plants during growth and development.&lt;/h3&gt;&lt;h3&gt;The study provides an insight into obtaining robust, high-resolution remote-sensing derived drought indices based on remote-sensing derived RZSM estimates. The 1 km resolution proves an improvement from other currently available drought indices, such as the European Drought Observatory&amp;#8217;s 5 km resolution drought index, which is not able to capture as well the spatial variability present within heterogeneous areas. Moreover, the SSM-derived drought indices are currently used in a drought observatory project, covering a region in the Tarragona province of Catalonia, Spain. The project aims at offering irrigation recommendations to water agencies, and the introduction of RZSM-derived drought indices will further improve such advice.&lt;/h3&gt;

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