Search results for 'Remote Sensing'

https://agfuse.com/article/remote-sensing-in-california-rice-production

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Authors: Joanne C. White, Nicholas C. Coops, Michael A. Wulder, Mikko Vastaranta, Thomas Hilker and Piotr Tompalski

Publisher: Taylor & Francis Online

Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.

Figure: TLS intensity images of plot thinning experiments in Alberta, Canada, observed from full-waveform TLS LiDAR. Distortion is due to the projection of the hemispherical data onto a 2-dimensional plane. LIDAR data acquired from the CSIRO ECHIDNA© Validation Instrument (EVI). (Credits: Joanne C. White, Nicholas C. Coops, Michael A. Wulder, Mikko Vastaranta, Thomas Hilker and Piotr Tompalski)
https://adalidda.net/posts/DLzjNjNGLHf7E6398/remote-sensing-technologies-for-enhancing-forest-inventories

Call for applications: Africa4Future Airbus Acceleration Programme for African Startups

Ideal candidates are startups/solution providers/companies that:

Create value in Agriculture and Infrastructure sectors.

Have the ability to leverage remote sensing technologies.

Have been running operations for at least 1 year.

Have an existing product with traction (customers and/or revenue).

Have a strong management team.

Have experience leveraging partnerships to create impact.

In addition to the above, companies must be registered in Africa and at least one active member of the founding team must be a Citizen of an African country.

Application deadline: March 14, 2020

Illustration Photo: Laser leveling is a process of smoothing the land surface (± 2 cm) from its average elevation using laser-equipped tractor-pulled scrapers. This practice uses large horsepower tractors and soil movers that are equipped with global positioning systems (GPS) and/or laser-guided instrumentation so that the soil can be moved either by cutting or filling to create the desired slope/level. This technique is well known for achieving higher levels of accuracy in land leveling and offers great potential for water savings and higher crop yields. (credits: Faseeh Shams / IWMI / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

Check more https://adalidda.com/posts/MEsTN8r2rkxRZH39c/call-for-applications-africa4future-airbus-acceleration

Project-Based Learning Applied to Unmanned Aerial Systems and Remote Sensing

Authors: Francisco Javier Mesas-Carrascosa,Fernando Pérez Porras, Paula Triviño-Tarradas, Jose Emilio Meroño de Larriva and Alfonso García-Ferrer

Publisher: MDPI

The development of unmanned aerial vehicle (UAV) technology and the miniaturization of sensors have changed the way remote sensing (RS) is used, popularizing this geoscientific discipline in other fields, such as precision agriculture. This makes it necessary to implement the use of these technologies in teaching RS alongside the classical platforms (satellite and manned aircraft). This manuscript describes how The Higher Technical School of Agricultural Engineering at the University of Córdoba (Spain) has introduced UAV RS into the academic program by way of project-based learning (PBL). It also presents the basic characteristics of PBL, the design of the subject, the description of the teacher-guided and self-directed activities, as well as the degree of student satisfaction. The teaching and learning objectives of the subject are to learn how to determine the vigor, temperature, and water stress of a crop through the use of RGB, multispectral, and thermographic sensors onboard a UAV platform. From the onset, students are motivated, actively participate in the tasks related to the realization of UAV flights, and subsequent processing and analysis of the registered images. Students report that PBL is more engaging and allows them to develop a better understanding of RS.

Figure: Some teacher-guided activities: (a) learning about UAV platforms and (b) fieldwork to measure temperature and spectral signature for atmospheric corrections. (credits: Francisco Javier Mesas-Carrascosa,Fernando Pérez Porras, Paula Triviño-Tarradas, Jose Emilio Meroño de Larriva and Alfonso García-Ferrer)

Check more https://adalidda.com/posts/7JoJ5YDYGMyETjmqs/project-based-learning-applied-to-unmanned-aerial-systems

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Authors: Ovidiu, John Cherbini, Robert Johnson, Andy Lyons and Maggi Kelly

Journal: Drones 2018, 2(4), 39

Publisher: MDPI

Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

Illustration Photo: California Citrus (credits: Jasperdo / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

Check more https://adalidda.com/posts/TH38Wm36SyDkCFdtF/identification-of-citrus-trees-from-unmanned-aerial-vehicle

The Applications of Remote Sensing in Plant Health

Authors: D'onghia Anna Maria, Brown Paul, Riccioni Luca, Vaglio Laurin Gaia, Beck Pieter S.A., Santoro Franco

Publisher: Zenodo

Remote sensing is the science of gathering data on an object/area without making physical contact. Aircraft, satellite and drone-based cameras and sensors are used to measure reflected and/or emitted electromagnetic radiation. This information, often captured as images, can then be analysed to extract additional, valuable data which can be mirrored in a GIS environment for spatial mapping. In the last decade great progress has been achieved in the use of remote sensing for the detection and mapping of several pests and relative host species at territorial basis. However, much research on this subject is still ongoing, but few applications have been made in plant health programmes due to some gaps that need to be identified and addressed.

The project aimed to bring together experts from research organisations and companies to share knowledge on remote sensing applications in the plant health sector. The partners were interested in:

State of the art, research needs and gaps on remote sensing methodologies in plant health, including the use of GIS and IT tools.
Advancements of research for the qualitative and quantitative identification of host plant species by remote sensing over larger areas.
Advancements of research on remote sensing applications for the identification of specific pests over larger areas.

Illustration Photo: Multiple-Rotor Remote Sensing Aerial Copter (credits: University of Arkansas Division of Agriculture photo by Mary Hightower / Flickr Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0))

Check more https://adalidda.com/posts/jDdL4sDBFBfzyiWo3/the-applications-of-remote-sensing-in-plant-health

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Authors: Pasquale Tripodi, Daniele Massa, Accursio Venezia and Teodoro Cardi

Journal: Agronomy 2018, 8(4), 57; https://doi.org/10.3390/agronomy8040057

Publisher: MDPI

Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with “omics” disciplines will also be discussed.

Illustration Photo: Tomato seedlings (credits: Aris Papachristou / Flickr Creative Commons Attribution 2.0 Generic (CC BY 2.0))

Check more https://adalidda.com/posts/dbTgoE4dn9XR4wjHe/sensing-technologies-for-precision-phenotyping-in-vegetable

Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields

Authors: Ahmed Laamrani, Renato Pardo Lara, Aaron A. Berg, Dave Branson and Pamela Joosse

Journal: Sensors 2018, 18(3), 708; doi:10.3390/s18030708

Publisher: MDPI AG

Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping.

Illustration Photo: Maize field (CC0 Creative Commons from Pixabay.com)

Check more https://adalidda.com/posts/kZSiHgpG9hMYXt5KA/using-a-mobile-device-app-and-proximal-remote-sensing

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

Authors: Carlos Ballester, John Hornbuckle, James Brinkhoff, John Smith and Wendy Quayle

Journal Title: Remote Sensing

ISSN: 2072-4292 (Print)

Publisher: MDPI AG

The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm.

Illustration Photo: Cotton Field (credits: chapstickaddict / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

Check more https://adalidda.com/posts/LKqz3MZz5JMenkdnH/assessment-of-in-season-cotton-nitrogen-status-and-lint

"Remote sensing technology is playing an increasingly important role in the agriculture sector. From industry consolidation to investment to business agreements, a lot of big dollar decisions are being made. But how well do you truly understand the technology around remote sensing in agriculture? Here are four key concepts that anyone doing business in the sector should understand."
https://agfundernews.com/4-things-know-remote-sensing-agriculture.html