scholarly journals Remote Sensing of Floodpath Lakes and Wetlands: A Challenging Frontier in the Monitoring of Changing Environments

2018 ◽  
Vol 10 (12) ◽  
pp. 1955 ◽  
Author(s):  
Yeqiao Wang ◽  
Hervé Yésou
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Anthropogenic Impacts ◽  
Management Practice ◽  
Innovation Management ◽  
Special Issue ◽  
Water Surface Area ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Primary Focus

Monitoring of changing lake and wetland environments has long been among the primary focus of scientific investigation, technology innovation, management practice, and decision-making analysis. Floodpath lakes and wetlands are the lakes and associated wetlands affected by seasonal variations of water level and water surface area. Floodpath lakes and wetlands are, in particular, sensitive to natural and anthropogenic impacts, such as climate change, human-induced intervention on hydrological regimes, and land use and land cover change. Rapid developments of remote sensing science and technologies, provide immense opportunities and capacities to improve our understanding of the changing lake and wetland environments. This special issue on Remote Sensing of Floodpath Lakes and Wetlands comprise featured articles reporting the latest innovative research and reflects the advancement in remote sensing applications on the theme topic. In this editorial paper, we review research developments using state-of-the-art remote sensing technologies for monitoring dynamics of floodpath lakes and wetlands; discuss challenges of remote sensing in inventory, monitoring, management, and governance of floodpath lakes and wetlands; and summarize the highlights of the articles published in this special issue.

scholarly journals Special Issue: Remote Sensing Applications for Investigations of Fire Regime Attributes

Fire Ecology ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Andrew T. Hudak ◽  
Andrea E. Thode ◽  
Jan W. van Wagtendonk
Keyword(s):  
Remote Sensing ◽  
Fire Regime ◽  
Special Issue ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals Application of Remote Sensing Methods to Monitor Coastal Zones

2020 ◽  
Vol 8 (6) ◽  
pp. 391 ◽  
Author(s):  
Luis Pedro Almeida ◽  
Rafael Almar
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Original Research ◽  
Coastal Zones ◽  
Special Issue ◽  
Research Papers ◽  
Sensing Applications ◽  
Wide Range ◽  
Remote Sensing Applications ◽  
Remote Sensing Methods

In this Special Issue “Application of Remote Sensing Methods to Monitor Coastal Zones” nine original research papers were published, with topics covering a wide range of ranging of remote sensing applications including coastal topography, bathymetry, land cover, and nearshore hydrodynamics [...]

scholarly journals Editorial on Special Issue “Remote Sensing Applications in Coastal Environment”

2021 ◽  
Vol 13 (23) ◽  
pp. 4734
Author(s):  
Paweł Terefenko ◽  
Jacek Lubczonek ◽  
Dominik Paprotny
Keyword(s):  
Remote Sensing ◽  
Coastal Environment ◽  
Special Issue ◽  
Coastal Regions ◽  
Sensing Applications ◽  
Rapid Changes ◽  
Remote Sensing Applications

Coastal regions are susceptible to rapid changes as they constitute the boundary between the land and the sea [...]

scholarly journals Editorial for the Special Issue “Microwave Indices from Active and Passive Sensors for Remote Sensing Applications”

2019 ◽  
Vol 11 (5) ◽  
pp. 561
Author(s):  
Simonetta Paloscia ◽  
Emanuele Santi
Keyword(s):  
Remote Sensing ◽  
Special Issue ◽  
Sensing Applications ◽  
Satellite Sensors ◽  
Passive Sensors ◽  
Remote Sensing Applications

Since the early 1980s, the capabilities of satellite sensors operating at microwaves for the remote sensing of Earth’s surface have been widely assessed in a number of studies (e [...]

scholarly journals Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2482
Author(s):  
Pedro Zamboni ◽  
José Marcato Junior ◽  
Jonathan de Andrade Silva ◽  
Gabriela Takahashi Miyoshi ◽  
Edson Takashi Matsubara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
State Of The Art ◽  
Free State ◽  
Two Stage ◽  
Learning Methods ◽  
Single Tree ◽  
Sensing Applications ◽  
Remote Sensing Applications

Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications.

Sign in / Sign up

Export Citation Format

Share Document