Introduction to Challenges and Future Directions in Remote Sensing and GIScience

2020 ◽  
pp. 3-7
Author(s):  
Raihan Ahmed ◽  
Pavan Kumar ◽  
Meenu Rani
Keyword(s):  
Remote Sensing ◽  
Future Directions

scholarly journals Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

2020 ◽  
Vol 12 (20) ◽  
pp. 3338
Author(s):  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Abdallah Shanableh ◽  
Abubakir Kais ◽  
Osman Hamed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Oil Spill ◽  
State Of The Art ◽  
Remotely Sensed ◽  
Classification Systems ◽  
Remotely Sensed Data ◽  
Future Directions ◽  
Oil Spill Detection ◽  
Different Sources

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

scholarly journals Review of Remote Sensing Methods to Map Coffee Production Systems

2020 ◽  
Vol 12 (12) ◽  
pp. 2041 ◽  
Author(s):  
David A. Hunt ◽  
Karyn Tabor ◽  
Jennifer H. Hewson ◽  
Margot A. Wood ◽  
Louis Reymondin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Production Systems ◽  
Radar Data ◽  
Future Directions ◽  
Synthetic Aperture Radar Data ◽  
Technological Advances ◽  
Quality Classification ◽  
Satellite Sensors ◽  
Coffee Sector ◽  
Remote Sensing Methods

The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize on recent technological advances to improve remote sensing methods and generate more accurate, reliable, and scalable approaches to coffee mapping. In this study, we provide a systematic review of satellite-based approaches to mapping coffee extent, which produced 43 articles in the peer-reviewed and gray literature. We outline key considerations for employing effective approaches, focused on the need to balance data affordability and quality, classification complexity and accuracy, and generalizability and site-specificity. We discuss research opportunities for improved approaches by leveraging the recent expansion of diverse satellite sensors and constellations, optical/Synthetic Aperture Radar data fusion approaches, and advances in cloud computing and deep learning algorithms. We highlight the need for differentiating between production systems and the need for research in important coffee-growing geographies. By reviewing the range of techniques successfully used to map coffee extent, we provide technical recommendations and future directions to enable accurate and scalable coffee maps.

PREHISPANIC MAYA BURNT LIME INDUSTRIES: PREVIOUS STUDIES AND FUTURE DIRECTIONS

2018 ◽  
Vol 30 (2) ◽  
pp. 199-219 ◽  
Author(s):  
Kenneth E. Seligson ◽  
Soledad Ortiz Ruiz ◽  
Luis Barba Pingarrón
Keyword(s):  
Remote Sensing ◽  
Significant Role ◽  
Natural Environment ◽  
Future Directions ◽  
Burnt Lime ◽  
The Past ◽  
Factors Influencing ◽  
Lime Consumption ◽  
Maya Area ◽  
Lime Production

AbstractBurnt lime has played a significant role in daily Maya life since at least as far back as 1100 b.c., and yet its ephemeral nature has limited archaeological studies of its production. The application of new surveying and remote sensing technologies in recent decades is now allowing for a more in-depth investigation of the burnt lime industries that existed in different subregions of the Maya area. This article provides an overview of the current understanding of pre-Hispanic Maya burnt lime production. It then presents an analysis of the factors influencing the development and identification of distinct subregional lime production industries, including: lime consumption requirements and inter-site spacing; natural environment; local social and economic trajectories; and the objectives and survey universes of archaeological investigations. In reporting the tremendous advances made over the past few decades, this paper encourages archaeologists to include a focus on identifying lime production features in their research agendas.

Remote sensing of shallow waters – A 50 year retrospective and future directions

2020 ◽  
Vol 240 ◽  
pp. 111619 ◽  
Author(s):  
Tiit Kutser ◽  
John Hedley ◽  
Claudia Giardino ◽  
Chris Roelfsema ◽  
Vittorio E. Brando
Keyword(s):  
Remote Sensing ◽  
Shallow Waters ◽  
Future Directions

scholarly journals Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Ruiliang Pu
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Method Development ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Airborne Lidar ◽  
Accurate Information ◽  
Natural Forests ◽  
Future Directions ◽  
The Status

Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.

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