scholarly journals Combining Data from Multiple Sources to Evaluate Spatial Variations in the Economic Costs of PM2.5-Related Health Conditions in the Beijing–Tianjin–Hebei Region

2019 ◽  
Vol 16 (20) ◽  
pp. 3994 ◽  
Author(s):  
Zhang ◽  
Hu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Population Density ◽  
Health Outcomes ◽  
Economic Costs ◽  
High Resolution Data ◽  
Geographical Variations ◽  
Combining Data ◽  
Bth Region ◽  
Resolution Data

Fine particulate matter, known as PM2.5, is closely related to a range of adverse health outcomes and ultimately imposes a high economic cost on the society. While we know that the costs associated with PM2.5-related health outcomes are not uniform geographically, a few researchers have considered the geographical variations in these costs because of a lack of high-resolution data for PM2.5 and population density. Satellite remote sensing provides highly precise, high-resolution data about how PM2.5 and population density vary spatially, which can be used to support detailed health-related assessments. In this study, we used high-resolution PM2.5 concentration and population density based on remote sensing data to assess the effects of PM2.5 on human health and the related economic costs in the Beijing–Tianjin–Hebei (BTH) region in 2016 using exposure-response functions and the relationship between health and economic costs. The results showed that the PM2.5-related economic costs were unevenly distributed and as with the population density, the costs were mainly concentrated in urban areas. In 2016, the economic costs of PM2.5-related health endpoints amounted to 4.47% of the total gross domestic product in the BTH region. Of the health endpoints, the cost incurred by premature deaths accounted for more than 80% of the total economic costs associated with PM2.5. The results of this study provide new and detailed information that could be used to support the implementation of national and regional policies to reduce air pollution.

scholarly journals Ten simple rules for working with high resolution remote sensing data

2021 ◽  
Author(s):  
Maxwell Benjamin Joseph ◽  
Anna Spiers ◽  
Michael J. Koontz ◽  
Nayani Ilangakoon ◽  
Kylen Solvik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Environmental Science ◽  
Ad Hoc ◽  
Remote Sensing Data ◽  
Trial And Error ◽  
High Resolution Data ◽  
Sensing Data ◽  
Simple Rules ◽  
Resolution Data

Researchers in Earth and environmental science can extract incredible value from high resolution remote sensing data, but these data can be hard to use. Pain free use requires skills from remote sensing and the data sciences that are seldom taught together. In practice, many researchers teach themselves how to use high resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. Here we outline ten “rules” with examples from Earth and environmental science to help applied researchers work more effectively with high resolution data.

scholarly journals EXTRACTION AND ANALYSIS OF TRADITIONAL CHINESE MEDICINE CROPS BASED ON MULTITEMPORAL HIGH RESOLUTION DATA-TAKING QIAOCHENG DISTRICT OF BOZHOU AS AN EXAMPLE

2018 ◽  
Vol XLII-3 ◽  
pp. 2153-2160
Author(s):  
H. Yu ◽  
J. He ◽  
H. Zhou ◽  
F. Guan ◽  
L. Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chinese Medicine ◽  
High Resolution ◽  
Traditional Chinese Medicine ◽  
High Resolution Data ◽  
Important Method ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Crop Planting ◽  
Resolution Data

Remote sensing technology has become an important method to rapidly acquireing of planting layout and composition of regional crops.It is very important to accurately master the planting area of Chinese medicine crops in the Characteristic planting area because it relations to accurately master the cultivation of Chinese medicine crops, formulate related policies and adjustment of crop planting structure.The author puts forward a method of using remote sencing technology for momitoring Chinese medicine which has good applicability and generalization. This paper took Qiaocheng District of Bozhou as an example to Verify the feasibility of the method, providing a reference for solving the problem of interpretation and extraction of Chinese medicinal materials in the region.

scholarly journals Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring

2019 ◽  
Author(s):  
Xiaowei Jia ◽  
Mengdie Wang ◽  
Ankush Khandelwal ◽  
Anuj Karpatne ◽  
Vipin Kumar
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Temporal Patterns ◽  
Small Scale ◽  
High Resolution Data ◽  
Sensing Data ◽  
Coarse Resolution ◽  
Sequential Models ◽  
Resolution Data

Effective and timely monitoring of croplands is critical for managing food supply. While remote sensing data from earth-observing satellites can be used to monitor croplands over large regions, this task is challenging for small-scale croplands as they cannot be captured precisely using coarse-resolution data. On the other hand, the remote sensing data in higher resolution are collected less frequently and contain missing or disturbed data. Hence, traditional sequential models cannot be directly applied on high-resolution data to extract temporal patterns, which are essential to identify crops. In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution. During the learning process, we leverage the temporal patterns learned from coarse-resolution data to generate missing high-resolution data. Additionally, the proposed model can track classification confidence in real time and potentially lead to an early detection. The evaluation in an intensively cultivated region demonstrates the effectiveness of the proposed method in cropland detection.

scholarly journals Long-term analysis of irrational water use processes based on cartographic materials and remote sensing data

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Andrey Medvedev ◽  
Natalia Alekseenko ◽  
Natalia Telnova ◽  
Alexander Koshkarev
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Level ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Topographic Maps ◽  
High Resolution Data ◽  
Sensing Data ◽  
The Past ◽  
Resolution Data

<p><strong>Abstract.</strong> Assessment and monitoring of environmental features based on large-scale and ultra-high resolution data, including remote sensing data, which have advantages in the repeatability of information and the speed of processing of incoming data, often face issues of completeness and duration of time series in retrospective analysis. Cartographic materials and remote sensing data allow monitoring for rapidly changing natural and anthropogenic features in the study areas, but very often face a problem when an event or phenomenon occurred many years ago and it is necessary to make a complete chronology.</p><p>Ultra-high-resolution data, remote sensing data and the results of the subsequent geoinformation analysis are widely used to solve problems in a number of socio-economic areas of territorial development, in particular:</p><ul><li>in environmental studies &amp;ndash; identification of local sources of water pollution, the consequences of their impact onecosystems, synthetic assessment of the ecological state of the territories and their comfort;</li><li>in the management of various resources, including water &amp;ndash; determination of biological productivity of water bodies, identification of water bioresources, detection of anthropogenically provoked and natural changes in water mass,implementation for glaciological studies, etc.</li></ul><p>Within the framework of the current study, a multi-time analysis of the water area and the coastal strip of Lake Sevan (the Republic of Armenia) at an altitude of about 1900 m above sea level, was carried out. The lake has repeatedly beensubjected to changes in the water level of the reservoir in the past. The 1930s and in the period between 1949 to 1962 were noted by the most intense drop in water level (more than 10 meters). In the 1990s, there was a slight increase inthe level, and then until 2001, the level of the lake continued to decrease.</p><p>The main factors affecting aquatic ecosystems and the overall ecological status of the lake are:</p><ol><li>Repeated changes in the water level of the reservoir in the past and its expected fluctuations in the future.</li><li>The uncontrolled discharge of harmful substances caused great damage to the lake, which affected the water qualityand biodiversity of this unique natural site.</li><li>Untimely cleaning of flooded forests, which increases the risk of eutrophication of the lake.</li><li>The poorly organized system of waste disposal and unauthorized landfills of municipal solid waste, as well as animalwaste.</li><li>Unauthorized construction of recreational facilities and capital structures in the coastal and water protection zonewhich may be flooded.</li></ol><p> The information support of the study is based on the materials of satellite imagery from the worldview2, SPOT 5/6,Resurs-P, Canopus-B, materials from the international space station (ISS), materials of archival aerial photography anddata obtained from the UAVs, in combination with other map data sources in the range of scales 1&amp;thinsp;:&amp;thinsp;5&amp;thinsp;000 &amp;ndash; 1&amp;thinsp;:&amp;thinsp;100&amp;thinsp;000,including digital topographic maps, land use maps, statistical and literary data. In fact, cartographic materials andremote sensing data provide a time history of 75 years, from large-scale topographic maps of 1942&amp;ndash;1943 to highlydetailed images of 2017&amp;ndash;2018.</p><p>According to the results of the study, it was possible to establish the position of the coastline for different time periods.The period between 1949 and 1962, when there was the most critical drop in the water level, was especially interestingand had not been studied before. Archival aerial photographs for 1943 and 1963 allowed to reconstruct the position ofthe coastline for almost every year of irrational water use.</p>

scholarly journals Performance evaluation of super resolution algorithms in generating high resolution images using MSE and PSNR

2021 ◽  
Vol 10 (02) ◽  
pp. 25284-25291
Author(s):  
Palani Murugan ◽  
Vivek Kumar Gautam ◽  
V. Ramanathan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Optical Systems ◽  
High Resolution Data ◽  
Sensing Data ◽  
Resolution Data

In recent days, requirement of high spatial resolution remote sensing data in various fields has increased tremendously.  High resolution satellite remote sensing data is obtained with long focal length optical systems and low altitude. As fabrication of high-resolution optical system and accommodating on the satellite is a challenging task, various alternate methods are being explored to get high resolution imageries. Alternately the high-resolution data can be obtained from super resolution techniques. The super resolution technique uses single or multiple low-resolution mis-registered data sets to generate high resolution data set.  Various algorithms are employed in super resolution technique to derive high spatial resolution. In this paper we have compared two methods namely overlapping and interleaving methods and their capability in generating high resolution data are presented.

Evaluating high-resolution remote sensing data for reconstructing the recent evolution of supra glacial debris

2018 ◽  
Vol 42 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Roberto S Azzoni ◽  
Davide Fugazza ◽  
Andrea Zerboni ◽  
Antonella Senese ◽  
Carlo D’Agata ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
European Alps ◽  
Pixel Size ◽  
High Resolution Data ◽  
Medium Resolution ◽  
Debris Cover ◽  
Resolution Data ◽  
Sentinel 2

Over the last decades, the expansion of supraglacial debris on worldwide mountain glaciers has been reported. Nevertheless, works dealing with the detection and mapping of supraglacial debris and detailed analyses aimed at identifying the temporal and spatial trends affecting glacier debris cover are still limited. In this study, we used different remote sensing sources to detect and map the supraglacial debris cover, to analyze its evolution, and to assess the potential of different remote-sensed image data. We performed our analyses on the glaciers of Ortles-Cevedale Group (Stelvio Park, Italy), one of the most representative glacierized sectors of the European Alps. High-resolution airborne orthophotos (pixel size 0.5 m × 0.5 m) acquired during the summer season in the years 2003, 2007, and 2012 permitted to map in detail, with an error lower than ±5%, the supraglacial debris cover through a maximum likelihood classification. Our findings suggest that over the period 2003–2012, supraglacial debris cover increased from 16.7% to 30.1% of the total glacier area. On Forni Glacier we extended these quantification thanks to the availability of UAV (Unmanned Aerial Vehicle) orthophotos from 2014 and 2015 (pixel size 0.15 m × 0.15 m): this detailed analysis permitted to confirm debris is increasing on the glacier melting surface (+20.4%) and confirms the requirement of high-resolution data in debris mapping on Alpine glaciers. Finally, we also checked the suitability of medium-resolution Landsat ETM+ data and Sentinel 2 data to map debris in a typical Alpine glaciation scenario where small ice bodies (<0.5 km2) are the majority. The results we obtained suggest that medium-resolution data are not suitable for a detailed description and evaluation of supraglacial debris cover in the Alpine scenario, nevertheless Sentinel 2 proved to be appropriate for a preliminary mapping of the main debris features.

scholarly journals Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

2021 ◽  
Vol 13 (9) ◽  
pp. 1754
Author(s):  
Jonathan Reith ◽  
Gohar Ghazaryan ◽  
Francis Muthoni ◽  
Olena Dubovyk
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
High Resolution ◽  
Land Degradation ◽  
Management Practices ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
High Resolution Data ◽  
Medium Resolution ◽  
Resolution Data

Monitoring land degradation (LD) to improve the measurement of the sustainable development goal (SDG) 15.3.1 indicator (“proportion of land that is degraded over a total land area”) is key to ensure a more sustainable future. Current frameworks rely on default medium-resolution remote sensing datasets available to assess LD and cannot identify subtle changes at the sub-national scale. This study is the first to adapt local datasets in interplay with high-resolution imagery to monitor the extent of LD in the semiarid Kiteto and Kongwa (KK) districts of Tanzania from 2000–2019. It incorporates freely available datasets such as Landsat time series and customized land cover and uses open-source software and cloud-computing. Further, we compared our results of the LD assessment based on the adopted high-resolution data and methodology (AM) with the default medium-resolution data and methodology (DM) suggested by the United Nations Convention to Combat Desertification. According to AM, 16% of the area in KK districts was degraded during 2000–2015, whereas DM revealed total LD on 70% of the area. Furthermore, based on the AM, overall, 27% of the land was degraded from 2000–2019. To achieve LD neutrality until 2030, spatial planning should focus on hotspot areas and implement sustainable land management practices based on these fine resolution results.

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