Special Issue "Advances of Remote Sensing in Pasture Management"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Dr. Thomas Corpetti
E-Mail Website
Guest Editor
CNRS – LETG Rennes, Place du Recheur Henri Le Moal, 35043 Rennes Cedex, France
Interests: remote sensing; urban environments; physical models; time series; data assimilation; classification; regression, fusion; pasture systems; agriculture
Special Issues and Collections in MDPI journals
Dr. Laurence Hubert-Moy
E-Mail Website
Guest Editor
LETG-Rennes, University of Rennes 2, F-35043 Rennes, France
Interests: remote sensing of agricultural landscapes; land use and cover changes; remote sensing of grasslands and wetlands; habitat mapping
Prof. Pauline Dusseux
E-Mail Website
Guest Editor
Université Grenoble Alpes, PACTE, Cité des Territoires 14 av. M. Reynoard 38100 GRENOBLE, France
Interests: remote sensing; grasslands; agriculture

Special Issue Information

As a result of agriculture intensification, land use and land cover may have important negative impacts on environmental systems (increasing water and air pollution, soil degradation or biodiversity loss, socio-economic systems for stock, winter fodder, etc.). In this context, grasslands devoted to pasture may make significant contributions (increase in nitrate leaching, decrease in carbon storage in soils) and sustainable agriculture requires the smart control of pasture management modes.

Considering the increase in cropland at the expense of grasslands observed in many regions of the Earth during the last half century, the identification of pasture systems is a key issue. To this end, remote sensing data enable us to observe crops and grasslands at various spatio-temporal scales. Taking into account the large variety of existing pasture systems, it is however not clear today which modality (optical/radar/combinations), which spatial scales, which indexes (spectral, biophysical, raw data, unsupervised indexes, etc.), which temporal resolutions and which methodologies (the usual methodologies or more recent approaches based on neural networks) are required to detect the large variety of pasture systems.

This is the topic of this Special Issue, which will gather recent work on remote sensing techniques related to pasture management. We invite you to submit the most recent advancements on the following, and related, topics:

  • Methodological innovations devoted to pasture:
    • Classification
    • Time series
    • Data assimilation
    • Regression
    • Machine learning
    • Large-scale estimation
    • Data fusion
    • Multispectral/hyperspectral remote sensing
    • LiDAR/RADAR data
    • UAV images
    • etc.
  • Pasture studies using remote sensing
    • Grass production
    • Spatial/temporal features for pasture identification
    • Impact of pasture management on the environment
    • Grassland management
    • Grassland biomass monitoring
    • Ecosystem services
    • Temporal variability (within season and between years)
    • Precision agriculture
    • etc.

Prof. Thomas Corpetti
Prof. Laurence Hubert-Moy
Prof. Pauline Dusseux
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.

Published Papers (4 papers)

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Research

Open AccessArticle
Ultrasonic Arrays for Remote Sensing of Pasture Biomass
Remote Sens. 2020, 12(1), 111; https://doi.org/10.3390/rs12010111 - 30 Dec 2019
Abstract
The profitability of agricultural industries that utilise pasture can be strongly affected by the ability to accurately measure pasture biomass. Pasture height measurement is one technique that has been used to estimate pasture biomass. However, pasture height measurement errors can occur if the [...] Read more.
The profitability of agricultural industries that utilise pasture can be strongly affected by the ability to accurately measure pasture biomass. Pasture height measurement is one technique that has been used to estimate pasture biomass. However, pasture height measurement errors can occur if the sensor is mounted to a farm vehicle that experiences tilting or bouncing. This work describes the development of novel low ultrasonic frequency arrays for pasture biomass estimation. Rather than just measuring the distance to the top of the pasture, as previous ultrasonic studies have done, this hardware is designed to also allow ultrasonic measurements to be made vertically through the pasture to the ground. The hardware was mounted to a farm bike driving over pasture at speeds of up to 20 km/h. The analysed results show the ability of the hardware to measure the ground location through the grass. This allowed pasture height measurement to be independent of tilting and bouncing of the farm vehicle, leading to 20 to 25% improvement in the R 2 value obtained for biomass estimation compared with the traditional technique. This corresponded to a reduction in root mean squared error of predicted biomass from about 350 to 270 kg/ha, where the average biomass of the pasture was 1915 kg/ha. Full article
(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)
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Open AccessFeature PaperArticle
Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands
Remote Sens. 2019, 11(24), 3041; https://doi.org/10.3390/rs11243041 - 17 Dec 2019
Abstract
Semi-natural grasslands are perennial ecosystems and an important part of agricultural landscapes that are threatened by urbanization and agricultural intensification. However, implementing national grassland conservation policies remains challenging because their inventory, based on short-term observation, rarely discriminate semi-natural permanent from temporary grasslands. This [...] Read more.
Semi-natural grasslands are perennial ecosystems and an important part of agricultural landscapes that are threatened by urbanization and agricultural intensification. However, implementing national grassland conservation policies remains challenging because their inventory, based on short-term observation, rarely discriminate semi-natural permanent from temporary grasslands. This study aims to map grassland frequency at a national scale over a long period using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m satellite time-series. A three-step method was applied to the entire area of metropolitan France (543,940 km²). First, land-use and land-cover maps—including grasslands—were produced for each year from 2006–2017 using the random forest classification of MOD13Q1 and MYD13Q1 products, which were calibrated and validated using field observations. Second, grassland frequency from 2006–2017 was calculated by combining the 12 annual maps. Third, sub-pixel analysis was performed using a reference layer with 20 m spatial resolution to quantify percentages of land-use and land-cover classes within MODIS pixels classified as grassland. Results indicate that grasslands were accurately modeled from 2006–2017 (F1-score 0.89–0.93). Nonetheless, modeling accuracy varied among biogeographical regions, with F1-score values that were very high for Continental (0.94 ± 0.01) and Atlantic (0.90 ± 0.02) regions, high for Alpine regions (0.86 ± 0.04) but moderate for Mediterranean regions (0.62 ± 0.10). The grassland frequency map for 2006–2017 at 250 m spatial resolution provides an unprecedented view of stable grassland patterns in agricultural areas compared to existing national and European GIS layers. Sub-pixel analysis showed that areas modeled as grasslands corresponded to grassland-dominant areas (60%–94%). This unique long-term and national monitoring of grasslands generates new opportunities for semi-natural grassland inventorying and agro-ecological management. Full article
(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)
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Open AccessArticle
Ultrasonic Proximal Sensing of Pasture Biomass
Remote Sens. 2019, 11(20), 2459; https://doi.org/10.3390/rs11202459 - 22 Oct 2019
Cited by 1
Abstract
The optimization of pasture food value, known as ‘biomass’, is crucial in the management of the farming of grazing animals and in improving food production for the future. Optical sensing methods, particularly from satellite platforms, provide relatively inexpensive and frequently updated wide-area coverage [...] Read more.
The optimization of pasture food value, known as ‘biomass’, is crucial in the management of the farming of grazing animals and in improving food production for the future. Optical sensing methods, particularly from satellite platforms, provide relatively inexpensive and frequently updated wide-area coverage for monitoring biomass and other forage properties. However, there are also benefits from direct or proximal sensing methods for higher accuracy, more immediate results, and for continuous updates when cloud cover precludes satellite measurements. Direct measurement, by cutting and weighing the pasture, is destructive, and may not give results representative of a larger area of pasture. Proximal sensing methods may also suffer from sampling small areas, and can be generally inaccurate. A new proximal methodology is described here, in which low-frequency ultrasound is used as a sonar to obtain a measure of the vertical variation of the pasture density between the top of the pasture and the ground and to relate this to biomass. The instrument is designed to operate from a farm vehicle moving at up to 20 km h−1, thus allowing a farmer to obtain wide coverage in the normal course of farm operations. This is the only method providing detailed biomass profile information from throughout the entire pasture canopy. An essential feature is the identification of features from the ultrasonic reflectance, which can be related sensibly to biomass, thereby generating a physically-based regression model. The result is significantly improved estimation of pasture biomass, in comparison with other proximal methods. Comparing remotely sensed biomass to the biomass measured via cutting and weighing gives coefficients of determination, R2, in the range of 0.7 to 0.8 for a range of pastures and when operating the farm vehicle at speeds of up to 20 km h−1. Full article
(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)
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Open AccessArticle
Can we Monitor Height of Native Grasslands in Uruguay with Earth Observation?
Remote Sens. 2019, 11(15), 1801; https://doi.org/10.3390/rs11151801 - 01 Aug 2019
Abstract
In countries where livestock production based on native grasslands is an important economic activity, information on structural characteristics of forage is essential to support national policies and decisions at the farm level. Remote sensing is a good option for quantifying large areas in [...] Read more.
In countries where livestock production based on native grasslands is an important economic activity, information on structural characteristics of forage is essential to support national policies and decisions at the farm level. Remote sensing is a good option for quantifying large areas in a relative short time, with low cost and with the possibility of analyzing annual evolution. This work aims at contributing to improve grazing management, by evaluating the ability of remote sensing information to estimate forage height, as an estimator of available biomass. Field data (forage height) of 20 commercial paddocks under grazing conditions (322 samples), and their relation to MODIS data (FPAR, LAI, MIR, NIR, Red, NDVI and EVI) were analyzed. Correlations between remote sensing information and field measurements were low, probably due to the extremely large variability found within each paddock for field observations (CV: Around 75%) and much lower when considering satellite information (MODIS: CV: 4%–6% and Landsat:CV: 12%). Despite this, the red band showed some potential (with significant correlation coefficient values in 41% of the paddocks) and justifies further exploration. Additional work is needed to find a remote sensing method that can be used to monitor grasslands height. Full article
(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)
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