Remote Sensing for Agroforestry
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".
Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 33633
Special Issue Editors
Interests: remote sensing; precision agriculture; in-field data processing; remote monitoring; UAV; UAS; precision forestry; sensors and data processing; human–computer interfaces; augmented reality; virtual reality; embedded systems
Special Issues, Collections and Topics in MDPI journals
Interests: UAV; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
Special Issues, Collections and Topics in MDPI journals
Interests: environmental governance; institutional and ecological economics; climate change; biodiversity protection; land management
Special Issues, Collections and Topics in MDPI journals
Interests: 3D modelling of remote sensing in all spectrum including visual, NIR, thermal lidar, microwave and remote sensing of forests
Special Issues, Collections and Topics in MDPI journals
Interests: sensor interfaces; microelectronics; wireless sensor networks; IoT; precision viticulture; energy harvesting; proximal sensing
Special Issues, Collections and Topics in MDPI journals
Interests: exploitation of UAS for environmental and agricultural research
Interests: precision agriculture; spatial variability; remote sensing; wireless sensors network; agrometeorology; high throughput phenotyping; viticulture; UAV
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Technological development, integration, and adoption in both agriculture and forest management practices is booming. The need to increase yield and quality, reducing simultaneously disease incidence and minimizing chemical inputs, which would also have a significant contribution for sustainable practices in agriculture and forests, requires careful and detailed management.
Being able to manage requires knowledge with the highest detail level possible about context, culture and environmental parameters that can influence both agriculture and forests’ high variabilities.
Remote sensing enables the acquisition of diverse data with variable levels of detail, both in farms and in forests. Indeed, due to its different hardware and software options and their complementarity, it allows to deal with crops, social, economic, geographic and environmental contexts heterogeneity. As such, the use of satellites, manned aircrafts and unmanned aerial vehicles, equipped with different types of sensors (e.g. RGB, NIR, LiDAR, multi and hyperspectral and thermal) has been gaining special attention in their different applications in agriculture and forests.
The need for systems able to deal with the massive amounts of data generated by remote sensing also begins to emerge. They must be capable of aggregating and extracting useful and intelligible information to stakeholders preferably in a (semi) automatic way, throughout the application of Machine Learning (ML) and Artificial Intelligence (IA) algorithms. Thus, data aggregation platforms capable of processing it, based on a set of standard algorithms or with adapted context-aware algorithms (depending where information is needed), can be fundamental for a box-to-box approach to Precision Agriculture and Precision Forestry management.
Dr. Emanuel Peres
Dr. Joaquim J. Sousa
Dr. Alessandro Matese
Dr. Huaguo Huang
Dr. Raul Morais
Dr. Robert Moorhead
Dr. Salvatore Filippo Di Gennaro
Guest Editors
Manuscript Submission Information
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Keywords
- UAV
- Aerial and satellite
- Precision agriculture
- Canopy management
- Crop Growth Models
- Diseases evolution models
- ML & Big Data in Remote Sensing
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