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.

scholarly journals The Contribution of Trees Outside of Forests to Landscape Carbon and Climate Change Mitigation in West Africa

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1652
Author(s):  
David L. Skole ◽  
Jay H. Samek ◽  
Moussa Dieng ◽  
Cheikh Mbow
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Climate Change Mitigation ◽  
Focal Point ◽  
Carbon Stocks ◽  
Sub Saharan Africa ◽  
Tree Cover ◽  
Individual Tree ◽  
Mitigation And Adaptation ◽  
Change Mitigation

While closed canopy forests have been an important focal point for land cover change monitoring and climate change mitigation, less consideration has been given to methods for large scale measurements of trees outside of forests. Trees outside of forests are an important but often overlooked natural resource throughout sub-Saharan Africa, providing benefits for livelihoods as well as climate change mitigation and adaptation. In this study, the development of an individual tree cover map using very high-resolution remote sensing and a comparison with a new automated machine learning mapping product revealed an important contribution of trees outside of forests to landscape tree cover and carbon stocks in a region where trees outside of forests are important components of livelihood systems. Here, we test and demonstrate the use of allometric scaling from remote sensing crown area to provide estimates of landscape-scale carbon stocks. Prominent biomass and carbon maps from global-scale remote sensing greatly underestimate the “invisible” carbon in these sparse tree-based systems. The measurement of tree cover and carbon in these landscapes has important application in climate change mitigation and adaptation policies.

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Sustainable crossbreeding systems of beef cattle in the era of climate change

2015 ◽  
Vol 44 (5) ◽  
pp. 8-11
Author(s):  
MC Mokolobate ◽  
A Theunissen ◽  
MM Scholtz ◽  
FWC Neser
Keyword(s):  
Climate Change ◽  
Beef Cattle ◽  
Carbon Footprint ◽  
Production Systems ◽  
Metabolizable Energy ◽  
Daily Weight Gain ◽  
Ghg Mitigation ◽  
Mitigation And Adaptation ◽  
Wide Range ◽  
Mitigation And Adaptation Strategies

Beef cattle are unique, because they not only suffer from climate change, but they also contribute to climate change through the emission of greenhouse gases (GHG). Mitigation and adaptation strategies are therefore needed. An effective way to reduce the carbon footprint from beef cattle would be to reduce the numbers and increase the production per animal, thereby improving their productivity. Sustainable crossbreeding systems can be an effective way to reduce GHG, as it has been shown to increase production. There are a wide range of different cattle breeds in South Africa which can be optimally utilized for effective and sustainable crossbreeding. This paper reports on the effects of crossbreeding on the kilogram calf weaned per Large Stock Unit (kgC/LSU) for 29 genotypes. These genotypes were formed by crossing Afrikaner (A) cows with Brahman (B), Charolais (C), Hereford (H) and Simmentaler (S) bulls and by back-crossing the F1 cows to the sire lines. A LSU is the equivalent of an ox of 450 kg with a daily weight gain of 500 g on grass pastures with a mean digestible energy (DE) content of 55% and a requirement of 75 MJ metabolizable energy (ME). Crossbreeding with A as dam line increased the kgC/LSU on average by 8 kg (+6%) - with the CA cross producing the most kgC/LSU (+8%) above that of the AA. The BA dam in crosses with C, H and S, increased kgC/LSU on average by 26 kg (+18%) above that of the AA dam, with the H x BA cross, producing the most kgC/LSU (+21%). The BA, CA, HA and SA F1 dam lines, back-crossed to the sire line breeds, increased kgC/LSU on average by 30 kg (21%), 21 kg (15%), 19kg (13%) and 26 kg (18%) above the that of the AA, respectively. The big differences between breeds in kgC/LSU provide the opportunity to facilitate effective crossbreeding that can be useful in the era of climate change. From this study it is clear that cow productivity can be increased by up to 21% through properly designed, sustainable crossbreeding systems, thereby reducing the carbon footprint of beef production.Keywords: Carbon footprint, cow productivity, kilogram calf, production systems

Broad-band Viscoelastic Rotational Vibration Control for Remote Sensing Applications

2005 ◽  
Vol 11 (11) ◽  
pp. 1339-1356 ◽  
Author(s):  
Adam L. Webster ◽  
William H. Semke
Keyword(s):  
Remote Sensing ◽  
Vibration Isolation ◽  
Imaging System ◽  
Broad Band ◽  
Space Station ◽  
Single Element ◽  
Sensing Applications ◽  
Wide Range ◽  
Rotational Vibration ◽  
Remote Sensing Applications

The ability to eliminate, or effectively control, vibration in remote sensing applications is critical. Any perturbations of an imaging system are greatly magnified over the hundreds of kilometers from the orbiting space platform to the Earth's surface. Space platforms, such as the International Space Station, are not as predictable or stable as many other spacecraft. Therefore, an effective vibration isolation and/or absorber system is needed that operates over a wide range of excitation frequencies. A passive system is also preferred to reduce the resources required, as well as to provide a reliable and self-contained system. To accomplish these goals, a vibration amplitude limiting system has been developed that uses both vibration isolation and absorber components. Viscoelastic structural elements that act as both a spring and a damper in a single element are implemented in the design. This configuration also demonstrates a favorable frequencydependent response and produces a system with improved dynamic behavior compared to conventional spring and damper designs. This rotation limiting vibration system has been designed and analyzed for use in digital remote sensing imaging. The transmissibility and the ground jitter associated with the system are determined. A summary of these results will be presented along with a comparison to a more conventional vibration isolation/absorber system.

scholarly journals THE ROLE OF FOREST ECOSYSTEMS IN THE PROCESS OF MITIGATION AND ADAPTATION TO THE EFFECTS OF CLIMATE CHANGE

2019 ◽  
Vol 40 (1) ◽  
pp. 25
Author(s):  
Ratko Ristić ◽  
Ivan Malušević ◽  
Boris Radić ◽  
Slobodan Milanović ◽  
Vukašin Milčanović ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Ecosystems ◽  
Life Support ◽  
Annual Precipitation ◽  
Southeast Europe ◽  
Mountainous Regions ◽  
Mitigation And Adaptation ◽  
Wide Range ◽  
Huge Impact ◽  
Rain Events

Forest ecosystems provide a wide range of environmental services with an important role in the Earth’s life-support system. Climate change in Southeastern Europe (SEE) and forecasts for the period until 2070 have a huge impact on the present and future planning in forestry and watershed management, due to the observed trends: the increment of mean annual air temperature from 2,5–5,0 °C until the end of the XXI century; redistribution of annual precipitation, with much more precipitation in the spring-summer period, during short, intensive rain events; a decrease of annual precipitation and soil moisture of 10–20 %, with extreme consequences: dieback and disappearance of forests in huge areas of hilly-mountainous regions. Degradation and loss of forests leads to spread and intensification of soil erosion, with frequent torrential floods, mudflows, landslides, and avalanches. Stable forest ecosystems are pillars of sustainable development, repopulation and could provide means and resources to battle and overcome poverty in moun-tainous regions of southeast Europe.

scholarly journals Remote Sensing by using Unsupervised Algorithm

2021 ◽  
Author(s):  
Afshan Saleem
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Hyperspectral Image ◽  
Relevant Information ◽  
Spectral Signature ◽  
Sensing Applications ◽  
Hyper Spectral ◽  
Wide Range ◽  
Remote Sensing Applications

Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.

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 Advances in Modelling and Prediction on the Impact of Human Activities and Extreme Events on Environments

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1768
Author(s):  
Matteo Rubinato ◽  
Min Luo ◽  
Xing Zheng ◽  
Jaan H. Pu ◽  
Songdong Shao
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Human Activities ◽  
Health And Safety ◽  
Coastal Environments ◽  
Complex Flow ◽  
Mitigation And Adaptation ◽  
Wide Range ◽  
The Impact ◽  
Mountainous River

Fast urbanization and industrialization have progressively caused severe impacts on mountainous, river, and coastal environments, and have increased the risks for people living in these areas. Human activities have changed ecosystems hence it is important to determine ways to predict these consequences to enable the preservation and restoration of these key areas. Furthermore, extreme events attributed to climate change are becoming more frequent, aggravating the entire scenario and introducing ulterior uncertainties on the accurate and efficient management of these areas to protect the environment as well as the health and safety of people. In actual fact, climate change is altering rain patterns and causing extreme heat, as well as inducing other weather mutations. All these lead to more frequent natural disasters such as flood events, erosions, and the contamination and spreading of pollutants. Therefore, efforts need to be devoted to investigate the underlying causes, and to identify feasible mitigation and adaptation strategies to reduce negative impacts on both the environment and citizens. To contribute towards this aim, the selected papers in this Special Issue covered a wide range of issues that are mainly relevant to: (i) the numerical and experimental characterization of complex flow conditions under specific circumstances induced by the natural hazards; (ii) the effect of climate change on the hydrological processes in mountainous, river, and coastal environments, (iii) the protection of ecosystems and the restoration of areas damaged by the effects of climate change and human activities.

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 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.

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