Remote Sensing in Development

Paul, C. K.; Mascarenhas, Adolfo

doi:10.1126/science.214.4517.139

Cited by 22 publications

(10 citation statements)

References 1 publication

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“…Cost savings of up to 20% have been reported as a result of fine‐tuning supply and demand (Christensen, ). This can make the difference between the survival of a small enterprise and its failure. Remote sensing tools have been used from as early as the late 1970s to acquire statistics on crops in developing countries and to locate petrol and mineral deposits (Paul and Mascarenhas, ). Nowadays robotic sensors monitor water quality and supply in river and estuary ecosystems through the movement of chemical constituents and large volumes of underwater acoustic data that track the behaviour of animals (IBM News, ), as with the 315‐mile Hudson River in New York state (IBM News, ).…”

Section: Application Of Big Data For Developmentmentioning

confidence: 99%

Big Data for Development: A Review of Promises and Challenges

Hilbert¹

2015

Development Policy Review

494

255

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The article uses a conceptual framework to review empirical evidence and some 180 articles related to the opportunities and threats of Big Data Analytics for international development. The advent of Big Data delivers a cost‐effective prospect for improved decision‐making in critical development areas such as healthcare, economic productivity and security. At the same time, the well‐known caveats of the Big Data debate, such as privacy concerns and human resource scarcity, are aggravated in developing countries by long‐standing structural shortages in the areas of infrastructure, economic resources and institutions. The result is a new kind of digital divide: a divide in the use of data‐based knowledge to inform intelligent decision‐making. The article systematically reviews several available policy options in terms of fostering opportunities and minimising risks.

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Section: Application Of Big Data For Developmentmentioning

confidence: 99%

Big Data for Development: A Review of Promises and Challenges

Hilbert¹

2015

Development Policy Review

494

255

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“…Remote sensing data and techniques are used as tools for monitoring changes in environmental protection projects reducing in most cases the prices of surveillance. An example is the LULC approach for monitoring Reduced Emissions from Deforestation and Forest Degradation (REDD+) [10,11] and for ecosystem services (ES) modeling and valuation [12,13,14]. For the latter purpose, the LULC mapping has been used to enhance the results found for Costanza et al [15] that provided global ES values.…”

Section: Introductionmentioning

confidence: 99%

Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Tavares

Beltrão

Guimarães

et al. 2019

Sensors

137

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In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.

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“…Aspects related to the temporal dynamics of UES have also been mentioned in the literature, such as the vegetation structural attributes [23] and the change in provision of UES [15,23,24]. Existing research [25][26][27] has already emphasized the role played by RS data/techniques in enhancing ecological studies given the number of open access data and software available for this purpose [27]. Its potential is still greater when considering data sources with daily images [28], especially for tropical regions with high cloud coverage [29].…”

Section: Introductionmentioning

confidence: 99%

Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review

et al. 2019

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Urban ecosystem services (UES) is an essential approach to the development of sustainable cities and must be incorporated into urban planning to be able to improve humans’ life quality. This paper aimed to identify remote sensing (RS) data/techniques used in the literature in five years (2013–2017) for UES investigation and to analyze the similarity between them. For this purpose, we used the Scopus database of scientific journals, and a set of appropriate filters were applied. A total of 44 studies were selected, being 93.18% of them located in the Northern Hemisphere, mostly in Europe. The most common dataset used was the secondary data, followed by the Landsat family products. Land use and land cover (LULC) was the most common approach utilized, succeeded by radiometric indexes and band related. All four main classes (provision, regulation, supporting, and cultural) of ecosystem services (ES) were identified in the reviewed papers, wherein regulating services were the most popular modality mentioned. Seven different groups were established as having 100% of similarity between methods and ES results. Therefore, RS is identified in the literature as an important technique to reach this goal. However, we highlight the lack of studies in the southern hemisphere.

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Remote Sensing in Development

Cited by 22 publications

References 1 publication

Big Data for Development: A Review of Promises and Challenges

Big Data for Development: A Review of Promises and Challenges

Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review

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