Special Issue "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).

Special Issue Editors

Dr. Emanuel Peres
E-Mail Website
Guest Editor
University of Trás-os-Montes e Alto Douro & INESC TEC, Quinta de Prados, 5000-801 Vila Real, Portugal
Interests: distributed sensor networks; precision agriculture; UAS; precision viticulture; machine learning; precision viticulture
Special Issues and Collections in MDPI journals
Prof. Dr. Joaquim João Moreira de Sousa
E-Mail Website
Guest Editor
Department of Engineering, School of Sciences and Technology of the University of Trás-os-Montes e Alto Douro, Vila Real, 5000-801, Portugal
Interests: UAS; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
Special Issues and Collections in MDPI journals
Dr. Alessandro Matese
E-Mail Website
Guest Editor
Institute of Bioeconomy - IBE
National Research Council - CNR
Via Caproni 8, 50145, Florence, Italy.
Tel. +39 055 30 33 711; mobile +39 320 9223934
Interests: UAV; precision agriculture; precision forestry
Special Issues and Collections in MDPI journals
Dr. Huaguo Huang
E-Mail Website
Guest Editor
Forestry College, Beijing Forestry University, No. 35 Qinghua East Road, Beijing, China
Interests: unified radiative transfer modelling of vegetation on reflectance, thermal emission, lidar and microwave; remote sensing application in forestry
Dr. Raul Morais
E-Mail Website
Guest Editor
University of Trás-os-Montes e Alto Douro & INESC TEC, University of Trás-os-Montes e Alto Douro, Quinta de Prados, Polo I ECT, 5000-801 Vila Real, Portugal
Interests: wireless sensors network; precision viticulture; UAV; precision agriculture; remote sensing
Dr. Robert Moorhead
E-Mail Website
Guest Editor
Mississippi State University, P.O. Box 9627, Mississippi State, MS 39762, USA
Interests: exploitation of UAS for environmental and agricultural research
Dr. Salvatore Filippo Di Gennaro
E-Mail Website
Guest Editor
IBIMET CNR–Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, via G. Caproni 8, 50145 Firenze, Italy
Interests: precision agriculture; spatial variability; remote sensing; wireless sensors network; agrometeorology; high throughput phenotyping; viticulture; UAV
Special Issues and Collections 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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV
  • Aerial and satellite
  • Precision agriculture
  • Canopy management
  • Crop Growth Models
  • Diseases evolution models
  • ML & Big Data in Remote Sensing

Published Papers (4 papers)

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Research

Open AccessCommunication
Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars
Remote Sens. 2019, 11(10), 1242; https://doi.org/10.3390/rs11101242 - 24 May 2019
Cited by 2
Abstract
The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance [...] Read more.
The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance patterns (green520–600, red630–690 and near-infrared760–900 bands) and an image segmentation process was evaluated on an open-field olive grove with 10 different scion/rootstock combinations (two scions by five rootstocks). Univariate (ANOVA) and multivariate (principal components analysis—PCA and linear discriminant analysis—LDA) statistical approaches were applied. The efficacy of VIs in scion recognition emerged clearly from all the approaches applied, whereas discrimination between rootstocks appeared unclear. The results of LDA ascertained the efficacy of VI application to discriminate between scions with an accuracy of 90.9%, whereas recognition of rootstocks failed in more than 68.2% of cases. Full article
(This article belongs to the Special Issue Remote Sensing for Agroforestry)
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Open AccessArticle
Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images
Remote Sens. 2019, 11(9), 1023; https://doi.org/10.3390/rs11091023 - 30 Apr 2019
Cited by 2
Abstract
Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) [...] Read more.
Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications. Full article
(This article belongs to the Special Issue Remote Sensing for Agroforestry)
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Open AccessArticle
Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification
Remote Sens. 2019, 11(7), 831; https://doi.org/10.3390/rs11070831 - 07 Apr 2019
Cited by 10
Abstract
Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and [...] Read more.
Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and palm oil plantations using fusion of Landsat-8, Sentinel 1 and 2. We applied cloud and shadow masking, bidirectional reflectance distribution function (BRDF), atmospheric and topographic corrections to the optical imagery and a speckle filter and harmonics for Synthetic Aperture Radar (SAR) data. In this workflow, we created yearly composites for all sensors and combined the data into a single composite. A series of covariates were calculated from optical bands and sampled using reference data of the land cover classes including surface water, forest, urban and built-up, cropland, rubber, palm oil and mangrove. This training dataset was used to create biophysical probability layers (primitives) for each class. These primitives were then used to create land cover and probability maps in a decision tree logic and Monte-Carlo simulations. Validation showed good overall accuracy (84%) for the years 2017 and 2018. Filtering for validation points with high error estimates improved the accuracy up to 91%. We demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection. Our overall analysis supports and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations. Full article
(This article belongs to the Special Issue Remote Sensing for Agroforestry)
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Open AccessArticle
Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data
Remote Sens. 2019, 11(4), 414; https://doi.org/10.3390/rs11040414 - 18 Feb 2019
Cited by 7
Abstract
Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic [...] Read more.
Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg·ha−1 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms. Full article
(This article belongs to the Special Issue Remote Sensing for Agroforestry)
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