Promising Developments and Future Challenges for Remote Sensing of Wetlands

2015 ◽  
pp. 552-563 ◽  
Keyword(s):  
Remote Sensing ◽  
Future Challenges

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

2019 ◽  
Vol 64 (20) ◽  
pp. 1540-1556 ◽  
Author(s):  
Kun Shi ◽  
Yunlin Zhang ◽  
Boqiang Qin ◽  
Botian Zhou
Keyword(s):  
Remote Sensing ◽  
Present Knowledge ◽  
Cyanobacterial Blooms ◽  
Inland Waters ◽  
Future Challenges

Remote Sensing of Diseases

2020 ◽  
Vol 58 (1) ◽  
pp. 225-252
Author(s):  
Erich-Christian Oerke
Keyword(s):  
Remote Sensing ◽  
Disease Control ◽  
Recent Literature ◽  
Plant Diseases ◽  
Disease Assessment ◽  
Sensor Technology ◽  
Electronic Noses ◽  
Disease Patterns ◽  
Volatile Organic ◽  
Future Challenges

Detection, identification, and quantification of plant diseases by sensor techniques are expected to enable a more precise disease control, as sensors are sensitive, objective, and highly available for disease assessment. Recent progress in sensor technology and data processing is very promising; nevertheless, technical constraints and issues inherent to variability in host–pathogen interactions currently limit the use of sensors in various fields of application. The information from spectral [e.g., RGB (red, green, blue)], multispectral, and hyperspectral sensors that measure reflectance, fluorescence, and emission of radiation or from electronic noses that detect volatile organic compounds released from plants or pathogens, as well as the potential of sensors to characterize the health status of crops, is evaluated based on the recent literature. Phytopathological aspects of remote sensing of plant diseases across different scales and for various purposes are discussed, including spatial disease patterns, epidemic spread of pathogens, crop characteristics, and links to disease control. Future challenges in sensor use are identified.

scholarly journals Hyperspectral Remote Sensing Data Analysis and Future Challenges

2013 ◽  
Vol 1 (2) ◽  
pp. 6-36 ◽  
Author(s):  
Jose M. Bioucas-Dias ◽  
Antonio Plaza ◽  
Gustavo Camps-Valls ◽  
Paul Scheunders ◽  
Nasser Nasrabadi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data ◽  
Future Challenges

scholarly journals Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1809
Author(s):  
Muhammad Huzaifah Mohd Roslim ◽  
Abdul Shukor Juraimi ◽  
Nik Norasma Che’Ya ◽  
Nursyazyla Sulaiman ◽  
Muhammad Noor Hazwan Abd Manaf ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Weed Management ◽  
Management Practices ◽  
Crop Yields ◽  
Supply And Demand ◽  
Learning Approaches ◽  
Future Challenges ◽  
Aerial Vehicle ◽  
Remote Sensing Systems ◽  
Remote Sensing Techniques

Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.

scholarly journals EUROSPEC: at the interface between remote-sensing and ecosystem CO<sub>2</sub> flux measurements in Europe

2015 ◽  
Vol 12 (20) ◽  
pp. 6103-6124 ◽  
Author(s):  
A. Porcar-Castell ◽  
A. Mac Arthur ◽  
M. Rossini ◽  
L. Eklundh ◽  
J. Pacheco-Labrador ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Dynamics ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Spectral Measurements ◽  
Cost Action ◽  
Challenges And Opportunities ◽  
Future Challenges

Abstract. Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicate the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as a bridge between EC measurements and remote-sensing data. In situ spectral measurements have already been conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of these measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global estimates of GPP over terrestrial ecosystems.

scholarly journals Building capacity in remote sensing for conservation: present and future challenges

2016 ◽  
Vol 3 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Ilaria Palumbo ◽  
Robert A. Rose ◽  
Rachel M. K. Headley ◽  
Janet Nackoney ◽  
Anthony Vodacek ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Building Capacity ◽  
Future Challenges

scholarly journals Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

2021 ◽  
Vol 13 (24) ◽  
pp. 4972
Author(s):  
Nasem Badreldin ◽  
Beatriz Prieto ◽  
Ryan Fisher
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Analytical Framework ◽  
Sensing Data ◽  
Data Dimensionality Reduction ◽  
Native Grassland ◽  
Dimensionality Reduction Technique ◽  
Future Challenges

Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan’s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user’s accuracy and 88.40% producer’s accuracy, tame grassland had 81.4% user’s accuracy and 93.8% producer’s accuracy, whereas mixed grassland class had very low user’s accuracy (45.8%) and producer’s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.

scholarly journals DATA QUALITY IN REMOTE SENSING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 447-453 ◽  
Author(s):  
C. Batini ◽  
T. Blaschke ◽  
S. Lang ◽  
F. Albrecht ◽  
H. M. Abdulmutalib ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Earth Observation ◽  
Data Sets ◽  
New Era ◽  
Group Iii ◽  
Future Challenges ◽  
Earth Observation Satellites

The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb “DQ” identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO) which was established and endorsed by the Committee on Earth Observation Satellites (CEOS) but aims to broaden the view by including experts from computer science and particularly database science. The main activities and outcomes include: providing a taxonomy of DQ dimensions in the RS domain, achieving a global approach to DQ for heterogeneous-format RS data sets, investigate DQ dimensions in use, conceive a methodology for managing cost effective solutions on DQ in RS initiatives, and to address future challenges on RS DQ dimensions arising in the new era of the big Earth data.

Sign in / Sign up

Export Citation Format

Share Document