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Advances of Remote Sensing in Pasture Management

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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: closed (31 July 2020) | Viewed by 56255

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Special Issue Editors

Dr. Thomas Corpetti

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Guest Editor

CNRS – LETG Rennes, Place du Recheur Henri Le Moal, CEDEX, 35043 Rennes, France
Interests: remote sensing; urban environments; physical models; time series; data assimilation; classification; regression, fusion; pasture systems; agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Laurence Hubert-Moy

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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

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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 submissions that pass pre-check are 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.

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Published Papers (8 papers)

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Research

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25 pages, 2678 KiB

Open AccessArticle

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

by Gustavo Togeiro de Alckmin, Arko Lucieer, Gerbert Roerink, Richard Rawnsley, Idse Hoving and Lammert Kooistra

Remote Sens. 2020, 12(18), 2958; https://doi.org/10.3390/rs12182958 - 11 Sep 2020

Cited by 8 | Viewed by 4455

Abstract

Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model’s prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha

^{- 1}

). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha

^{- 1}

). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading. Full article

(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)

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20 pages, 15528 KiB

Open AccessArticle

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

by Aliny A. Dos Reis, João P. S. Werner, Bruna C. Silva, Gleyce K. D. A. Figueiredo, João F. G. Antunes, Júlio C. D. M. Esquerdo, Alexandre C. Coutinho, Rubens A. C. Lamparelli, Jansle V. Rocha and Paulo S. G. Magalhães

Remote Sens. 2020, 12(16), 2534; https://doi.org/10.3390/rs12162534 - 6 Aug 2020

Cited by 47 | Viewed by 8199

Abstract

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures. Full article

(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)

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18 pages, 2674 KiB

Open AccessArticle

Can Low-Cost Unmanned Aerial Systems Describe the Forage Quality Heterogeneity? Insight from a Timothy Pasture Case Study in Southern Belgium

by Adrien Michez, Lejeune Philippe, Knoden David, Cremer Sébastien, Decamps Christian and Jérôme Bindelle

Remote Sens. 2020, 12(10), 1650; https://doi.org/10.3390/rs12101650 - 21 May 2020

Cited by 24 | Viewed by 5099

Abstract

Applied to grazing management, unmanned aerial systems (UASs) allow for the monitoring of vegetation at the level of each individual on the pasture while covering a significant area (>10 ha per flight). Few studies have investigated the use of UASs to describe the forage quality in terms of nutritive value or chemical composition, while these parameters are essential in supporting the productive functions of animals and are known to change in space (i.e., sward species and structure) and time (i.e., sward phenology). Despite interest, these parameters are scarcely assessed by practitioners as they usually require important laboratory analyses. In this context, our study investigates the potential of off-the-shelf UAS systems in modeling essential parameters of pasture productivity in a precision livestock context: sward height, biomass, and forage quality. In order to develop a solution which is easily reproducible for the research community, we chose to avoid expensive solutions such as UAS LiDAR (light detection and ranging) or hyperspectral sensors, as well as comparing several UAS acquisition strategies (sensors and view angles). Despite their low cost, all tested strategies provide accurate height, biomass, and forage quality estimates of timothy pastures. Considering globally the three groups of parameters, the UAS strategy using the DJI Phantom 4 pro (Nadir view angle) provides the most satisfactory results. The UAS survey using the DJI Phantom 4 pro (Nadir view angle) provided R² values of 0.48, 0.72, and 0.7, respectively, for individual sward height measurements, mean sward height, and sward biomass. In terms of forage quality modeling, this UAS survey strategy provides R² values ranging from 0.33 (Acid Detergent Lignin) to 0.85 (fodder units for dairy and beef cattle and fermentable organic matter). Even if their performances are of lower order than state-of-art techniques such as LiDAR for sward height or hyperspectral sensors (for biomass and forage quality modeling), the important trade-off in terms of costs between UAS LiDAR (>100,000 €) or hyperspectral sensors (>50,000 €) promotes the use of such low-cost UAS solutions. This is particularly true for sward height modeling and biomass monitoring, where our low-cost solutions provide more accurate results than state-of-the-art field approaches, such as rising plate meters, with a broader extent and a finer spatial grain. Full article

(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)

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20 pages, 62103 KiB

Open AccessArticle

Ultrasonic Arrays for Remote Sensing of Pasture Biomass

by Mathew Legg and Stuart Bradley

Remote Sens. 2020, 12(1), 111; https://doi.org/10.3390/rs12010111 - 30 Dec 2019

Cited by 25 | Viewed by 6756

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 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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21 pages, 5203 KiB

Open AccessFeature PaperArticle

Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands

by Laurence Hubert-Moy, Jeanne Thibault, Elodie Fabre, Clémence Rozo, Damien Arvor, Thomas Corpetti and Sébastien Rapinel

Remote Sens. 2019, 11(24), 3041; https://doi.org/10.3390/rs11243041 - 17 Dec 2019

Cited by 9 | Viewed by 4465

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 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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20 pages, 7953 KiB

Open AccessArticle

Ultrasonic Proximal Sensing of Pasture Biomass

by Mathew Legg and Stuart Bradley

Remote Sens. 2019, 11(20), 2459; https://doi.org/10.3390/rs11202459 - 22 Oct 2019

Cited by 22 | Viewed by 4803

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 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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16 pages, 1567 KiB

Open AccessArticle

Can we Monitor Height of Native Grasslands in Uruguay with Earth Observation?

by Guadalupe Tiscornia, Walter Baethgen, Andrea Ruggia, Martín Do Carmo and Pietro Ceccato

Remote Sens. 2019, 11(15), 1801; https://doi.org/10.3390/rs11151801 - 1 Aug 2019

Cited by 12 | Viewed by 3697

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 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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Review

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32 pages, 7255 KiB

Open AccessReview

Remote Sensing of Grassland Production and Management—A Review

by Sophie Reinermann, Sarah Asam and Claudia Kuenzer

Remote Sens. 2020, 12(12), 1949; https://doi.org/10.3390/rs12121949 - 17 Jun 2020

Cited by 192 | Viewed by 16220

Abstract

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far. Full article

(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)

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