SATVeg: A web-based tool for visualization of MODIS vegetation indices in South America

Esquerdo, J. C. D. M.; Antunes, J. F. G.; Coutinho, A. C.; Speranza, Eduardo Antonio; Kondo, Andréia Akemi; Santos, João Luis dos

doi:10.1016/j.compag.2020.105516

Cited by 22 publications

(5 citation statements)

References 23 publications

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Section: Methodological Trends and Current Challenges In Dryland Mappingmentioning

confidence: 99%

“…A trendy free web-based tool that also supports data validation is the Temporal Vegetation Analysis System (SATVeg) [175], developed for instantaneously accessing temporal profiles of MODIS vegetation indices in South America. Systems of this kind help support numerous LULC monitoring activities, allowing quick queries and efficient updating [175]. Another noteworthy initiative is a field-based ecosystem monitoring protocol for Argentinian and Chilean Patagonia, called the Environmental Monitoring of Arid and Semiarid Regions (MARAS) network [47].…”

Section: Methodological Trends and Current Challenges In Dryland Mappingmentioning

confidence: 99%

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Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges

et al. 2022

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The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.

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Section: Methodological Trends and Current Challenges In Dryland Mappingmentioning

confidence: 99%

Section: Methodological Trends and Current Challenges In Dryland Mappingmentioning

confidence: 99%

Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges

et al. 2022

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“…It appears to be a dominant instrument from the time when computer technology was developed, based on the measurement and arrangement of records. Since information was being passed from one point to the next, communication structures were also maturing at the same time [1] [16], [17], [24]. The lack of a sound high-tech framework on which Pakistan's current problems of complexity and scarcity can be based is one of the main causes of the country's current problems of complexity and scarcity.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Agriculture on Computers and Handheld Devices

2021

IJATCSE

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Technology plays an important role in development, even though it seems that technology has modified certain aspects of it in areas where development is challenging. Telecom has been successfully implemented all over the world. In a developing country like Pakistan, technology can be found in even the most remote villages. Nowadays, a mobile tower can be seen anywhere, and many people use mobile phones. With the help of Information and Communication Technology, this beautiful use of technology has helped growth of almost every profession. Saving crops from diseases has become a major concern for policymakers in the twenty-first century. Countries are also working to increase yields in order to satisfy rising domestic demand and generate exportable surpluses. Farmers fertilize a variety of crops for food, fiber, and hay for their livestock. The available resources are limited and cannot be increased. Different diseases, such as black stem rust of wheat, orange or leaf rust of wheat, fungicides, and others, cause damage to wheat, cotton, maize, and millet, resulting in a marginal to 100% loss in production. All major food crops, such as wheat, cotton, maize, and millet, are continuously attacked by unique diseases, making it difficult to enforce policies or procedures that would improve per-hectare production. There are several apps available for agricultural guidance, but ours is interesting in that it is written in Sindhi, while other apps are written in English. Sindh has a large population, and the female literacy rate was 43% last year, compared to 47% the year before. In contrast to 2012-19, male education in Sindh has decreased from 72% to 67%. So, if a person does not understand the language, how can he be properly guided? That is why we have developed an app in Sindhi

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“…These applications enrich data handling and analysis by modeling geographical and spatial phenomena as instances of spatial data types (e.g., points, lines, and regions) typically provided by spatial database systems and Geographical Information Systems (GIS) [Güting 1994]. Examples of applications handling spatial objects include agricultural applications for monitoring land use and land cover changes [Esquerdo et al 2020], location-based services for delivering information based on the location of users [Jensen et al 2004;Huang et al 2018], and spatial data science applications for revealing useful information from spatial data [Anselin 2020;Carniel and Schneider 2021].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Spatial Approximations in Spatial Database Systems

Bertella

Lopes

Oliveira

et al. 2022

jidm

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Many applications rely on spatial information retrieval, which involves costly computational geometric algorithms to process spatial queries. Spatial approximations simplify the geometric shape of complex spatial objects, allowing faster spatial queries at the expense of result accuracy. In this sense, spatial approximations have been employed to efficiently reduce the number of objects under consideration, followed by a refinement step to restore accuracy. For instance, spatial index structures employ spatial approximations to organize spatial objects in hierarchical structures (e.g., the R-tree). It leads to the interest in studying how spatial approximations can be efficiently employed to improve spatial query processing. This article presents a systematic review on this topic. We gather relevant studies by performing a search string on several digital libraries. We further expand the studies under consideration by employing a single iteration of the snowballing approach, where we track the reference list of selected papers. As a result, we provide an overview and comparison of existing approaches that propose, evaluate, or make use of spatial approximations to optimize the performance of spatial queries. The spatial approximations mentioned by the approaches are also summarized. Further, we characterize the approaches and discuss some future trends.

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SATVeg: A web-based tool for visualization of MODIS vegetation indices in South America

Cited by 22 publications

References 23 publications

Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges

Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges

Smart Agriculture on Computers and Handheld Devices

A Systematic Review of Spatial Approximations in Spatial Database Systems

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