Remote sensing and geographic information systems

Baumgartner, Michael F.; Apfl, G.

doi:10.1080/02626669609491527

Cited by 26 publications

(3 citation statements)

References 14 publications

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“…The diversity of data sources reveals the wide variety of instruments and missions available. The dominance of the multispectral-based approach could be due to (a) more extensive data available coming from different satellite missions since 1970′s first Landsat mission [ 142 ], (b) a large number of free-of-cost optical sensors with an improvement in their resolution, especially in recent years available, and (c) the easy and straight-forward interpretation of data [ 143 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.

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Section: Discussionmentioning

confidence: 99%

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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“…Generally, the reservoir inflow is deducted as a byproduct of the reservoir balance. The recent approach uses remote sensed data facilitate modeling activities of the flow using a hydrological model [13]. Hydrologic model can be classified into three main categories which are lumped, semi-distributed and distributed models.…”

Section: Hydrologic Modelingmentioning

confidence: 99%

A Real-Time Decision Support System for River Basin Management

Vyas

John

2016

MATEC Web of Conferences

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Abstract. The applications of computer technology to analysis of the rainfall-runoff process and the hydrological dynamics of natural rivers have greatly expanded in the past few years. A large number of general purpose programs and a few programs designed for specific application have been developed and applied to hydrologic engineering problems. This paper briefly describes the study of climate change effects on the hydrological dynamics of the Satluj and Beas river system using computer models like HEC-RTS (Hydrologic Engineering Center-Real Time Simulation). It also presents the use of real time data collection and processing on a GIS platform so as to derive a real time decision support system.

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“…Many examples are given in the literature covering regional to local scales studies, depending on sensor resolution and swath. Earth observation systems provide adequate spatial and spectral resolutions, even if revisit frequency is lower [1]. On the other hand, prevision also needs information over large areas to improve its predictions and requires much auxiliary information, that can issue from field measurement for the parameterization, calibration and validation of empirical models.…”

Section: Prevention Prevision Mitigationmentioning

confidence: 99%

Earth observation and case-based systems for flood risk management

Henry

Fellah

Clandillon

et al.

IEEE International Geoscience and Remote Sensing Symposium

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The case-based approach is presented through two of SERTIT's major involvements in flood risk management. The first is operational crisis management action, contracted by ESA on the River Saône March 2001 flood. Archived and scheduled remote sensing data are used, leading to the delivery of rapid flood mapping products to the Civil Protection authorities. The second consists of research and development actions on well documented historical events. The case definition is based on the analysis of imagery products and environmental data, to both describe flood hazard and vulnerability. This approach stresses the synergy between landscape, its evolution and flood impact. Both optical and radar data are used in these two applications, with specified pre-processing levels.Index terms -Case-based approach, flood management, optical and radar remote sensing, decision making, rapid mapping systems.

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Remote sensing and geographic information systems

Cited by 26 publications

References 14 publications

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

A Real-Time Decision Support System for River Basin Management

Earth observation and case-based systems for flood risk management

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