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Journal articles on the topic 'Remote areas'

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Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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1

ARDENER, Edwin. "“Remote areas”." HAU: Journal of Ethnographic Theory 2, no. 1 (March 2012): 519–33. http://dx.doi.org/10.14318/hau2.1.023.

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2

Hardern, R. "Medicine in remote areas." Emergency Medicine Journal 22, no. 7 (June 24, 2005): 532. http://dx.doi.org/10.1136/emj.2004.016899.

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3

G, Ashraf Salah I. El. "Challenges Facing Junior Doctors in Remote Areas." Gastroenterology & Hepatology International Journal 5, no. 1 (January 4, 2020): 1. http://dx.doi.org/10.23880/ghij-16000168.

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4

Fasching, Gunter, Gerhard Schippinger, and Rudolf Pretscher. "Paragliding accidents in remote areas." Wilderness & Environmental Medicine 8, no. 3 (August 1997): 129–33. http://dx.doi.org/10.1580/1080-6032(1997)008[0129:paira]2.3.co;2.

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5

Maktav, D., F. S. Erbek, and C. Jürgens. "Remote sensing of urban areas." International Journal of Remote Sensing 26, no. 4 (February 2005): 655–59. http://dx.doi.org/10.1080/01431160512331316469.

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6

Smith, Pat, and Eileen Brebner. "Tele-ultrasound for remote areas." Journal of Telemedicine and Telecare 8, no. 2 (August 10, 2002): 80–81. http://dx.doi.org/10.1258/135763302320302145.

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7

McCabe, S. "General practice in remote areas." BMJ 324, no. 7343 (April 20, 2002): 121Sa—121. http://dx.doi.org/10.1136/bmj.324.7343.s121a.

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8

McCabe, Ruth. "Medicine in remote areas (MIRA)." BMJ 333, no. 7562 (August 5, 2006): s56. http://dx.doi.org/10.1136/bmj.333.7562.s56.

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9

Smith, Pat, and Eileen Brebner. "Tele-Ultrasound for Remote Areas." Journal of Telemedicine and Telecare 8, no. 2_suppl (January 2002): 80–81. http://dx.doi.org/10.1177/1357633x020080s237.

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10

Padeken, D., D. Sotiriou, K. Boddy, and R. Gerzer. "Health care in remote areas." Journal of Medical Systems 19, no. 1 (February 1995): 69–76. http://dx.doi.org/10.1007/bf02257192.

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11

S., Kanimozhi, Radhika M V., Shreemathi N., and Supriya G. "ECG MONITORING AND ANALYSIS SYSTEM FOR RURAL/REMOTE AREAS." International Journal of Current Engineering and Scientific Research 6, no. 6 (June 2019): 45–49. http://dx.doi.org/10.21276/ijcesr.2019.6.6.9.

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12

Denney, Dennis. "Waste-Management Facilities in Remote Areas." Journal of Petroleum Technology 56, no. 07 (July 1, 2004): 69–70. http://dx.doi.org/10.2118/0704-0069-jpt.

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13

Mankova, Petia. "Making Sense of the Remote Areas." Sibirica 17, no. 2 (June 1, 2018): 60–84. http://dx.doi.org/10.3167/sib.2018.170205.

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Narratives of globalization, conceived of as large-scale political, economic, and cultural processes flowing from metropolitan centers, often emphasize the loss of tradition and cultural originality in the remote and wild peripheries. All three television programs filmed in the past 10 years in Krasnoshchel’e, a remote Arctic village in Northwest Russia where I did anthropological fieldwork, are marked by such sentimental pessimism. Here, I juxtapose them with several local stories, which do not resonate with the melancholic and nostalgic notes of the media. The stories show how new inventions are welcomed and incorporated with laughter and astonishment into everyday life. The sentimental dissonance between mediascape and local imagination brings valuable insights about how globalization is accommodated on different scales and in different geographic settings.
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14

Liu, Guodong, Zhi Li, Yaosuo Xue, and Kevin Tomsovic. "Microgrid Assisted Design for Remote Areas." Energies 15, no. 10 (May 19, 2022): 3725. http://dx.doi.org/10.3390/en15103725.

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In this work, we present a three-stage multiobjective mixed-integer linear programming (MILP) for the optimal expansion planning and operation of isolated multienergy microgrids in remote areas. By selecting the optimal distributed generators (DGs) and energy storage systems (ESSs) mix selection, siting, sizing, and scheduling in the remote microgrid, the proposed model is targeted to minimize the annualized total cost of microgrids while enhancing the performance of the system, i.e., minimizing the voltage deviations and line power loss. To represent the electricity and heat flow between generation resources and various electrical, heating, and cooling loads in the isolated microgrid, linearized power flow, and heat flow constraints are employed in the proposed optimization model. The available capacity of DGs and ESSs are modeled as discrete constants instead of continuous variables for practical purpose. Numerical simulation results on a remote microgrid consisting of DGs, ESSs, and various loads validate the proposed method.
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15

Oyaro, Kwamboka. "Taking health services to remote areas." Africa Renewal 30, no. 3 (March 13, 2017): 22–23. http://dx.doi.org/10.18356/474aefe7-en.

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16

Dwidayatiand, N., and Zaenuri. "Ethnomathematics exploration in the remote areas." Journal of Physics: Conference Series 1918, no. 4 (June 1, 2021): 042117. http://dx.doi.org/10.1088/1742-6596/1918/4/042117.

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17

Lacava, Teodosio, and Emanuele Ciancia. "Remote Sensing Applications in Coastal Areas." Sensors 20, no. 9 (May 8, 2020): 2673. http://dx.doi.org/10.3390/s20092673.

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18

Mirtschin, P. J. "Snake‐bite treatment in remote areas." Medical Journal of Australia 150, no. 12 (June 1989): 725. http://dx.doi.org/10.5694/j.1326-5377.1989.tb136781.x.

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19

Hardin, Perry, and Andrew Hardin. "Hyperspectral Remote Sensing of Urban Areas." Geography Compass 7, no. 1 (January 2013): 7–21. http://dx.doi.org/10.1111/gec3.12017.

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20

Raftery, Chris. "Autonomy vs Collaboration in Remote Areas?" Journal for Nurse Practitioners 11, no. 7 (July 2015): 737. http://dx.doi.org/10.1016/j.nurpra.2015.05.007.

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21

Kuenzer, Claudia, and Kim Knauer. "Remote sensing of rice crop areas." International Journal of Remote Sensing 34, no. 6 (November 16, 2012): 2101–39. http://dx.doi.org/10.1080/01431161.2012.738946.

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22

Bhati, Nirbhay. "Healthcare Monitoring System for Remote Areas." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 443–47. http://dx.doi.org/10.22214/ijraset.2023.48500.

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Abstract: Doctors now place a high importance on ongoing patient health monitoring since it gives them the chance to save a patient's life. So, the primary objective is to develop a patient monitoring system that can monitor a patient's various physiological data when they are in a remote location and provide the doctor with this information in real time. The information is made public online so that any doctor in the globe can access it. The necessity for a patient to visit the doctor is lessened via remote patient monitoring. The Raspberry Pi employed here is not only a sensor node but also a CPU, and IOT plays a significant part in this complete system by delivering several apps and services. This data can be sensed, gathered, and published online by an intelligent gadget. The paper suggests a general health monitoring system utilising a neural network-based HDPS (Heart Disease Prediction System). The HDPS system forecasts a patient's risk of developing heart disease. The technique uses medical parameters like sex, blood pressure, age, height, and weight for prediction. as an improvement over the work done in this area up until now.
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23

Rootenberg, J. D. "Computers for medicine in remote areas." JAMA: The Journal of the American Medical Association 265, no. 17 (May 1, 1991): 2275. http://dx.doi.org/10.1001/jama.265.17.2275.

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24

Pappala, Appalanaidu. "Productivity of Horticulture in Remote Tribal Areas of Andhra Pradesh." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 13–16. http://dx.doi.org/10.31142/ijtsrd20253.

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25

A.A., Mustaffa, A. W. Rasib, M. I. Rosli, M. A. M. Razi, M. S. Adnan, and L. W. Tan. "Identification of flood-prone areas by integrated remote sensing model." Lowland Technology International 17, no. 2 (2015): 105–10. http://dx.doi.org/10.14247/lti.17.2_105.

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26

Barua, P., NK Hazarika, N. Barua, E. Rasul, and N. Laskar. "Microscopy for cryptosporidiosis screening in remote areas." Indian Journal of Medical Microbiology 26, no. 2 (2008): 203. http://dx.doi.org/10.4103/0255-0857.40550.

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27

Edvardsen, Trine Lise, Geir F. Lorem, and Grete Mehus. "Palliative care in rural and remote areas." Nordisk sygeplejeforskning 10, no. 03 (October 2, 2020): 150–63. http://dx.doi.org/10.18261/issn.1892-2686-2020-03-02.

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28

Munjal, Naveen Kuma, and Shiv Ratan Singh. "REMOTE HEALTH MONITORING SYSTEM FOR RURAL AREAS." International Journal of Technical Research & Science 5, no. 6 (June 15, 2020): 1–7. http://dx.doi.org/10.30780/ijtrs.v05.i06.001.

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29

Barua, P., NK Hazarika, N. Barua, E. Rasul, and N. Laskar. "Microscopy for Cryptosporidiosis Screening in Remote Areas." Indian Journal of Medical Microbiology 26, no. 2 (April 2008): 203–4. http://dx.doi.org/10.1016/s0255-0857(21)01952-6.

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30

Gracey, Michael, Helen Sullivan, and Gareth Goodier. "Food costs and nutrition in remote areas." Medical Journal of Australia 154, no. 4 (February 1991): 294. http://dx.doi.org/10.5694/j.1326-5377.1991.tb121111.x.

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31

Obayashi, Shigeru, Tetsuya Suhara, Yuji Nagai, Takashi Okauchi, Jun Maeda, and Atsushi Iriki. "Monkey brain areas underlying remote-controlled operation." European Journal of Neuroscience 19, no. 5 (March 2004): 1397–407. http://dx.doi.org/10.1111/j.1460-9568.2004.03200.x.

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32

de Paul, Obade Vincent. "Remote Sensing: New Applications for Urban Areas." Proceedings of the IEEE 95, no. 12 (December 2007): 2267–68. http://dx.doi.org/10.1109/jproc.2007.908065.

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33

Ellis, Chris. "Training general practitioners for very remote areas." Medical Teacher 30, no. 8 (January 2008): 809–11. http://dx.doi.org/10.1080/01421590802203470.

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34

Leck, C., E. K. Bigg, and E. D. Nilsson. "Sources of particles over remote marine areas." Journal of Aerosol Science 30 (September 1999): S833—S834. http://dx.doi.org/10.1016/s0021-8502(99)80427-3.

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35

Aikman, Sheila, and Pat Pridmore. "Multigrade schooling in ‘remote’ areas of Vietnam." International Journal of Educational Development 21, no. 6 (November 2001): 521–36. http://dx.doi.org/10.1016/s0738-0593(01)00012-8.

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36

Bardenhagen, Chris J., Courtney A. Pinard, Rich Pirog, and Amy Lazarus Yaroch. "Characterizing Rural Food Access in Remote Areas." Journal of Community Health 42, no. 5 (April 17, 2017): 1008–19. http://dx.doi.org/10.1007/s10900-017-0348-1.

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37

Singh, AtulKumar. "Ophthalmic problems in remote areas of India." Indian Journal of Ophthalmology 66, no. 7 (2018): 975. http://dx.doi.org/10.4103/ijo.ijo_658_18.

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38

Pérez de la Ossa, Natalia, Sònia Abilleira, Laura Dorado, Xabier Urra, Marc Ribó, Pere Cardona, Eva Giralt, et al. "Access to Endovascular Treatment in Remote Areas." Stroke 47, no. 5 (May 2016): 1381–84. http://dx.doi.org/10.1161/strokeaha.116.013069.

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39

Charman, J. H. "Preliminary landslide hazard assessment in remote areas." Geological Society, London, Engineering Geology Special Publications 18, no. 1 (2001): 91–95. http://dx.doi.org/10.1144/gsl.eng.2001.018.01.14.

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40

Bortoluzzi, Daniele, Fabio Casciati, Lorenzo Elia, and Lucia Faravelli. "Remote monitoring of urban and infrastructural areas." Earthquakes and Structures 7, no. 4 (October 30, 2014): 449–62. http://dx.doi.org/10.12989/eas.2014.7.4.449.

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41

Charters, W. W. S., and Y. L. Oo. "Solar vaccine storage units for remote areas." International Journal of Refrigeration 10, no. 5 (September 1987): 301–4. http://dx.doi.org/10.1016/0140-7007(87)90075-2.

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42

Low, John. "Pharmacy Practice in Rural and Remote Areas." Australian Journal of Hospital Pharmacy 28, no. 5 (October 1998): 301–2. http://dx.doi.org/10.1002/jppr1998285301.

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43

Nugroho, Piter Joko, and Theo Jhoni Hartanto. "Quality Profile of Remote Areas Elementary Schools." AL-ISHLAH: Jurnal Pendidikan 15, no. 1 (February 8, 2023): 922–33. http://dx.doi.org/10.35445/alishlah.v15i1.2456.

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Not all schools understand the policies and procedures for carrying out the school functions based on SNP, among others, caused by due to the lack of understanding/inability of schools to conduct internal self-evaluations, which will provide the description of the real school quality gaps against the SNP, as well as a basis for reference in preparing the RKS and RKAS. This study aims to describe the quality profile of remote elementary schools (SD) in Kapuas Districts, Central Kalimantan Province, Indonesia. The descriptive study with a qualitative approach was conducted in 6 (six) remote areas in SD. The data sources in this study consisted of 6 (six) principals and the Kapuas Districts Education Office personnel. Through interview techniques, observation, and filling out the e-EDS instrument developed by the researchers referring to the SNP for primary and secondary education, quality data of the remote areas SD was collected; then analyzed using interactive data analysis. The results showed that all of the remote areas SD get a “red report”. This study provides additional evidence that disparity problem of primary education quality in remote areas has not been handled properly by education policy makers in Kapuas Districts and related stakeholders. We recommend to the related parties to “borrowing” policies from other countries that have succeeded to overcoming the problems of remote areas education as a means to carry out lessons learned and redesign policies in the future.
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44

Zhang, Tao, Baolin Li, Jinfeng Wang, Maogui Hu, and Lili Xu. "Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model." Advances in Meteorology 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7643753.

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This study presented a method to estimate areal mean rainfall (AMR) using a Biased Sentinel Hospital Based Area Disease Estimation (B-SHADE) model, together with biased rain gauge observations and Tropical Rainfall Measuring Mission (TRMM) data, for remote areas with a sparse and uneven distribution of rain gauges. Based on the B-SHADE model, the best linear unbiased estimation of AMR could be obtained. A case study was conducted for the Three-River Headwaters region in the Tibetan Plateau of China, and its performance was compared with traditional methods. The results indicated that B-SHADE obtained the least estimation biases, with a mean error and root mean square error of −0.63 and 3.48 mm, respectively. For the traditional methods including arithmetic average, Thiessen polygon, and ordinary kriging, the mean errors were 7.11, −1.43, and 2.89 mm, which were up to 1027.1%, 127.0%, and 358.3%, respectively, greater than for the B-SHADE model. The root mean square errors were 10.31, 4.02, and 6.27 mm, which were up to 196.1%, 15.5%, and 80.0%, respectively, higher than for the B-SHADE model. The proposed technique can be used to extend the AMR record to the presatellite observation period, when only the gauge data are available.
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45

Tuminah, Tuminah, Yustina Selong, and Kunto Nur Cahyoko. "The Impact Of Online Learning For Teachers In Remote Areas." JETL (Journal of Education, Teaching and Learning) 7, no. 1 (March 28, 2022): 60. http://dx.doi.org/10.26737/jetl.v7i1.2733.

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Online learning in remote areas is certainly not as easy as online learning in big cities in Indonesia. Online learning in remote areas, especially in the Landak district, faces many obstacles due to limited facilities and infrastructure. To overcome these problems, teachers in remote areas in Landak are trying to find solutions so that online learning can still take place well, even though it is limited. This study aims to describe the difficulties experienced by teachers when teaching and the solutions offered to overcome the problems of online learning in these remote areas. The results of this study indicate that there are still some difficulties encountered related to online learning experienced by teachers in remote areas. Difficulties in the internet network and the provision of laptops or smartphones still color the implementation of online learning experienced by teachers in remote areas. 84% of teachers experienced network difficulties in the online learning process during the pandemic. One thing is very proud that teachers in remote areas are willing to do anything to overcome these difficulties. So even though there are many difficulties faced by online learning in remote areas, it can still run well. One of the efforts made is blended learning, taking turns online and offline learning.
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46

Allaw, Kamel, Jocelyne Adjizian Gerard, Makram Zouheir Chehayeb, and Nada Badaro Saliba. "Population estimation using geographic information system and remote sensing for unorganized areas." Geoplanning: Journal of Geomatics and Planning 7, no. 2 (January 1, 2021): 75–86. http://dx.doi.org/10.14710/geoplanning.7.2.75-86.

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Population estimation using remotely sensed data has been largely discussed in the literature relative to human geography. However, the previously established models can be applied on organized areas (mainly urban areas) but they are not suitable for unorganized areas which already suffer from a lack of population data. So, the aim of this study is the establish a statistical model for population estimation based on remote sensing data and suitable for unorganized areas. To do so, the morphological characteristics have been studied and a bivariate analysis was carried out to determine factors having a strong relationship with population data as a first step. Second, factors with strongest correlations have been chosen to establish the required model. As a result, an equation has been generated which relates the population data to building volume, density of roads, number of nodes, actual urban areas, and urban trend.
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47

Zhao, Ling Li, Shuai Liu, and Li Ma. "Data Acquisition Considering of Fixed-Wing UAVs in Mountainous Areas." Applied Mechanics and Materials 543-547 (March 2014): 2151–54. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2151.

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Over the past decade, there has been a great demand of Unmanned Aerial Vehicles (UAVs) in numerous industrial and military operations around the world. This paper is focused on low fixed-wing UAV remote sensing system, put remote sensing technology and UAV technology closely to fixed-wing unmanned aircraft as a platform, which is equipped with high-resolution digital remote sensing sensors, it has easy transition since the airport does not depend on landing site, it is a new low-speed high-resolution remote sensing data acquisition system. It has capability of a survey of real-time quick monitoring, and has been an effective complement to conventional means for satellite remote sensing and aerial photography.
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48

Winter, Matthew, and Manish I. Patel. "Difficulties of delivering urological care to remote areas." ANZ Journal of Surgery 91, no. 4 (April 2021): 483–84. http://dx.doi.org/10.1111/ans.16688.

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49

Nagaraja, A., R. N. Sahoo, K. Usha, and V. K. Gupta. "Estimation of mango growing areas using remote sensing." Indian Journal of Horticulture 74, no. 2 (2017): 184. http://dx.doi.org/10.5958/0974-0112.2017.00040.8.

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50

Wang, Yeqiao, Zhong Lu, Yongwei Sheng, and Yuyu Zhou. "Remote Sensing Applications in Monitoring of Protected Areas." Remote Sensing 12, no. 9 (April 26, 2020): 1370. http://dx.doi.org/10.3390/rs12091370.

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Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue.
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