scholarly journals A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1933 ◽  
Author(s):  
Tien Dat Pham ◽  
Junshi Xia ◽  
Nam Thang Ha ◽  
Dieu Tien Bui ◽  
Nga Nhu Le ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Salt Marshes ◽  
Vital Role ◽  
Blue Carbon ◽  
Aerial Photographs ◽  
Machine Learning Techniques ◽  
Coastal Vegetation ◽  
Multispectral Data ◽  
Sensing Applications ◽  
Optical Imagery

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.

scholarly journals Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

2019 ◽  
Vol 11 (3) ◽  
pp. 230 ◽  
Author(s):  
Tien Pham ◽  
Naoto Yokoya ◽  
Dieu Bui ◽  
Kunihiko Yoshino ◽  
Daniel Friess
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Mangrove Ecosystem ◽  
Carbon Stocks ◽  
Machine Learning Techniques ◽  
Species Structure ◽  
Mangrove Species ◽  
Mitigation And Adaptation ◽  
Sensing Applications ◽  
Wide Range

The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

Use of remote sensing for forest vegetation management: A problem analysis

1997 ◽  
Vol 73 (4) ◽  
pp. 459-477 ◽  
Author(s):  
Douglas G. Pitt ◽  
Robert G. Wagner ◽  
Ronald J. Hall ◽  
Douglas J. King ◽  
Donald G. Leckie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Management ◽  
Forest Vegetation ◽  
Aerial Photographs ◽  
Problem Analysis ◽  
Area Index ◽  
Sensing Applications ◽  
Computer Based ◽  
Digital Frame ◽  
Forest Vegetation Management

Forest managers require accurate and timely data that describe vegetation conditions on cutover areas to assess vegetation development and prescribe actions necessary to achieve forest regeneration objectives. Needs for such data are increasing with current emphasis on ecosystem management, escalating silvicultural treatment costs, evolving computer-based decision support tools, and demands for greater accountability. Deficiencies associated with field survey methods of data acquisition (e.g. high costs, subjectivity, and low spatial and temporal coverage) frequently limit decision-making effectiveness. The potential for remotely sensed data to supplement field-collected forest vegetation management data was evaluated in a problem analysis consisting of a comprehensive literature review and consultation with remote sensing and vegetation management experts at a national workshop. Among curently available sensors, aerial photographs appear to offer the most suitable combination of characteristics, including high spatial resolution, stereo coverage, a range of image scales, a variety of film, lens, and camera options, capability for geometric correction, versatility, and moderate cost. A flexible strategy that employs a sequence of 1:10,000-, 1:5,000-, and 1:500-scale aerial photographs is proposed to: 1) accurately map cutover areas, 2) facilitate location-specific prescriptions for silvicultural treatments, sampling, buffer zones, wildlife areas, etc., and 3) monitor and document conditions and activities at specific points during the regeneration period. Surveys that require very detailed information on smaller plants (<0.5-m tall) and/or individual or rare plant species are not likely to be supported by current remote sensing technologies. Recommended areas for research include : 1) digital frame cameras, or other cost-effective digital imagers, as replacements for conventional cameras, 2) computer-based classification and interpretation algorithms for digital image data, 3) relationships between image measures and physical measures, such as leaf-area index and biomass, 4) imaging standards, 5) airborne video, laser altimeters, and radar as complementary sensors, and 6) remote sensing applications in partial cutting systems. Key words: forest vegetation management, regeneration, remote sensing, aerial photography

scholarly journals Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming

Telecom ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 255-270
Author(s):  
Saeid Pourroostaei Ardakani ◽  
Ali Cheshmehzangi
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
Data Collection ◽  
Cost Effective ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Q Learning ◽  
Sensing Applications ◽  
Learning Technique ◽  
Target Locations

UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.

NOISE MEASUREMENT OF III-V COMPOUND DETECTORS FOR 2 μm LIDAR/DIAL REMOTE SENSING APPLICATIONS

2002 ◽  
Vol 12 (02) ◽  
pp. 531-540 ◽  
Author(s):  
M. NURUL ABEDIN ◽  
TAMER F. REFAAT ◽  
UPENDRA N. SINGH
Keyword(s):  
Remote Sensing ◽  
Infrared Detectors ◽  
Avalanche Photodiodes ◽  
Vital Role ◽  
Low Noise ◽  
Noise Equivalent Power ◽  
Pin Diodes ◽  
Figures Of Merit ◽  
Sensing Applications ◽  
Remote Sensing Applications

Noise of a photodetector plays a vital role in determining the minimum detectable signal for lidar and DIAL receivers. A low noise trans-impedance amplifier circuit has been employed to examine the noise of III-V compound infrared detectors. These infrared detectors include InGaAs PIN diodes and newly developed InGaAsSb avalanche photodiodes (APDs) with separate absorption and multiplication (SAM) structure. The noise of these detectors are compared with well-established Si APDs. These measured noises are utilized to compute the figures-of-merit, such as noise-equivalent-power (NEP) and detectivity (D*) of these devices and are presented in this paper.

scholarly journals RAPID IDENTIFICATION OF ABIOTIC STRESS (FROST) IN <i>IN-FILED</i> MAIZE CROP USING UAV REMOTE SENSING

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 467-471 ◽  
Author(s):  
J. Goswami ◽  
V. Sharma ◽  
B. U. Chaudhury ◽  
P. L. N. Raju
Keyword(s):  
Remote Sensing ◽  
Abiotic Stress ◽  
Computational Cost ◽  
Cost Effective ◽  
Rapid Identification ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Maize Crop ◽  
Multispectral Data

<p><strong>Abstract.</strong> Stress in the crop not only decreases the production but can also have devastating consequences for farmers whose life depends upon the healthy crops. In recent time (January 2018) a such abiotic stress event (hoar frost) was experienced at ICAR research complex experimental filed, Ri-Bhoi district of Meghalaya on standing Maize crop. Therefore, remote sensing (Multispectral UAV- Unmanned Aerial Vehicle) technology were used to detect the effect of frost on <i>in-filed</i> Maize crop. Two set of multispectral data (before frost and after frost) with four advanced machine learning techniques viz. Random Forest (RF), Random Committee (RC), Support Vector Machine (SVM) and Artificial Neural Network were employed for detection of stress free crop and stressed crop due to frost. Results revealed that all the four methods of classification could able to identify / detect stress-free vs. stressed crops at satisfactory level. However, among the classifiers RF achieved relatively higher overall accuracy (OA&amp;thinsp;=&amp;thinsp;86.47%) with Kappa Indexanalysis (KIA&amp;thinsp;=&amp;thinsp;0.80) and found very cost effective in context of computational cost (time complexity&amp;thinsp;=&amp;thinsp;0.08 Seconds) to train the model. In addition, we have also recorded the area of each classes and found that after frost stress-free area (36.01% of all over filed) is decreased by 11% in comparison of before frost (25.036% of all over filed). Based on the results we can suggest that the RF ensemble classification method can be used for further other crop classification in order to estimate the yield, detect the condition, monitoring the health etc.</p>

scholarly journals GIS and Remote Sensing Applications in Wetland Mapping and Monitoring

2017 ◽  
Author(s):  
Qiusheng Wu
Keyword(s):  
Remote Sensing ◽  
Global Climate ◽  
Aerial Photographs ◽  
World Population ◽  
Wetland Mapping ◽  
Gis And Remote Sensing ◽  
Wetland Ecosystems ◽  
Sensing Applications ◽  
Flow Pathways ◽  
Wetland Resources

Wetlands are recognized as one of the world&rsquo;s most valuable natural resources. With the increasing world population, human demands on wetland resources for agricultural expansion and urban development continue to increase. In addition, global climate change has pronounced impacts on wetland ecosystems through alterations in hydrological regimes. To better manage and conserve wetland resources, we need to know the distribution and extent of wetlands and monitor their dynamic changes. Wetland maps and inventories can provide crucial information for wetland conservation, restoration, and management. Geographic Information System (GIS) and remote sensing technologies have proven to be useful for mapping and monitoring wetland resources. Recent advances in geospatial technologies have greatly increased the availability of remotely sensed imagery with better and finer spatial, temporal, and spectral resolution. This chapter presents an introduction to the uses of GIS and remote sensing technologies for wetland mapping and monitoring. A case study is presented to demonstrate the use of high-resolution light detection and ranging (LiDAR) data and aerial photographs for mapping prairie potholes and surface hydrologic flow pathways. &nbsp;

A FUNDAMENTAL STUDY ON CARBON STORAGE BY ZOSTERA MARINA IN ISE BAY, JAPAN

2017 ◽  
Author(s):  
Hideki Kokubu ◽  
Hideki Kokubu
Keyword(s):  
Carbon Storage ◽  
Salt Marshes ◽  
Zostera Marina ◽  
Standing Stock ◽  
Coastal Ecosystems ◽  
Blue Carbon ◽  
Mangrove Forests ◽  
Fundamental Study ◽  
Strong Argument ◽  
Ise Bay

Blue Carbon, which is carbon captured by marine organisms, has recently come into focus as an important factor for climate change initiatives. This carbon is stored in vegetated coastal ecosystems, specifically mangrove forests, seagrass beds and salt marshes. The recognition of the C sequestration value of vegetated coastal ecosystems provides a strong argument for their protection and restoration. Therefore, it is necessary to improve scientific understanding of the mechanisms that stock control C in these ecosystems. However, the contribution of Blue Carbon sequestration to atmospheric CO2 in shallow waters is as yet unclear, since investigations and analysis technology are ongoing. In this study, Blue Carbon sinks by Zostera marina were evaluated in artificial (Gotenba) and natural (Matsunase) Zostera beds in Ise Bay, Japan. 12-hour continuous in situ photosynthesis and oxygen consumption measurements were performed in both areas by using chambers in light and dark conditions. The production and dead amount of Zostera marina shoots were estimated by standing stock measurements every month. It is estimated that the amount of carbon storage as Blue Carbon was 237g-C/m2/year and 197g-C/m2/year in the artificial and natural Zostera marina beds, respectively. These results indicated that Zostera marina plays a role towards sinking Blue Carbon.

Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

1994 ◽  
Vol 29 (1-2) ◽  
pp. 135-144 ◽  
Author(s):  
C. Deguchi ◽  
S. Sugio
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Aerial Photographs ◽  
Estimation Errors ◽  
Remote Sensing Imagery ◽  
Impervious Area ◽  
Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

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