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J. Imaging
Special Issues
Remote and Proximal Sensing Applications in Agriculture
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Remote and Proximal Sensing Applications in Agriculture

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 December 2017) | Viewed by 58674

Special Issue Editor

Dr. Thomas Alexandridis

E-Mail Website
Guest Editor
Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: agronomic applications of earth observation; remote sensing; digital image processing; geoinformatics; spectroscopy; precision agriculture; UAV; crops; irrigation; soil
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing allows the mapping of the Earth's surface from satellite or airborne systems, while proximal sensing systems collect detailed information near the surface. This offers a great advantage over previous data sources, as repetitive wide area coverage can be performed at a low cost, and estimations are performed in a non-destructive way. From satellite remote sensing to low-flying unmanned aerial vehicles (UAVs) and in-situ spectrometers, a large amount of data is collected to help farmers and agricultural policy makers to take informed decisions. Remote and proximal sensing systems can provide a multitude of information for agricultural applications, with the main objective being to map, monitor and model agricultural resources and the environmental impacts of agriculture.

The aim of this Special Issue is to collect state-of-the-art research of leading scientists in the world of agricultural applications of remote and proximal sensing. Submissions on the following topics are invited (but not limited to), as long as they focus on remote and proximal sensing:

  • crop and grassland area estimates with pixel and object-based methods,
  • yield predictions of crops and grasslands,
  • remote and proximal data assimilation in crop growth models,
  • early detection of crop stressors (pests, disease, deficiencies),
  • crop damage assessment (frost, droughts, hail),
  • change detection of agricultural lands and grasslands,
  • UAVs for site-specific management (water, agrochemicals),
  • precision agriculture,
  • irrigated water use estimation,
  • soil moisture mapping,
  • spatio-temporal analysis of time series of agricultural parameters,
  • analysis of hyperspectral data cubes for crop, soil and agricultural water,
  • digital soil mapping,
  • soil and plant spectroscopy,
  • sensors for proximal sensing at field level,
  • soil erosion, desertification and land degradation,
  • environmental impacts of agriculture (e.g. impact on downstream water bodies related to water quantity and quality),
  • mapping aquacultures
  • quantitative remote sensing for mapping agricultural parameters (e.g. evapotranspiration, green biomass, leaf area index).

Papers must present innovative methods and approaches, or novel applications of existing tools.  

Dr. Thomas Alexandridis
Guest Editor

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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • remote sensing
  • proximal sensing
  • agricultural parameters
  • precision farming
  • spectrometer
  • UAV
  • satellite image
  • airborne

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Published Papers (7 papers)

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Research

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14 pages, 3244 KiB  
Article
Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches
by Theodota Zisi, Thomas K. Alexandridis, Spyridon Kaplanis, Ioannis Navrozidis, Afroditi-Alexandra Tamouridou, Anastasia Lagopodi, Dimitrios Moshou and Vasilios Polychronos
J. Imaging 2018, 4(11), 132; https://doi.org/10.3390/jimaging4110132 - 9 Nov 2018
Cited by 26 | Viewed by 6154
Abstract
Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary [...] Read more.
Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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7043 KiB  
Article
Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis
by Anders K. Mortensen, Henrik Karstoft, Karen Søegaard, René Gislum and Rasmus N. Jørgensen
J. Imaging 2017, 3(4), 59; https://doi.org/10.3390/jimaging3040059 - 6 Dec 2017
Cited by 11 | Viewed by 7342
Abstract
The clover-grass ratio is an important factor in composing feed ratios for livestock. Cameras in the field allow the user to estimate the clover-grass ratio using image analysis; however, current methods assume the total dry matter is known. This paper presents the preliminary [...] Read more.
The clover-grass ratio is an important factor in composing feed ratios for livestock. Cameras in the field allow the user to estimate the clover-grass ratio using image analysis; however, current methods assume the total dry matter is known. This paper presents the preliminary results of an image analysis method for non-destructively estimating the total dry matter of clover-grass. The presented method includes three steps: (1) classification of image illumination using a histogram of the difference in excess green and excess red; (2) segmentation of clover and grass using edge detection and morphology; and (3) estimation of total dry matter using grass coverage derived from the segmentation and climate parameters. The method was developed and evaluated on images captured in a clover-grass plot experiment during the spring growing season. The preliminary results are promising and show a high correlation between the image-based total dry matter estimate and the harvested dry matter ( R 2 = 0.93 ) with an RMSE of 210 kg ha − 1 . Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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3843 KiB  
Article
Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring
by Christos Karydas, Sandra Gewehr, Miltiadis Iatrou, George Iatrou and Spiros Mourelatos
J. Imaging 2017, 3(4), 57; https://doi.org/10.3390/jimaging3040057 - 4 Dec 2017
Cited by 20 | Viewed by 6109
Abstract
The objective of this study was to develop a methodology for mapping olive plantations on a sub-tree scale. For this purpose, multispectral imagery of an almost 60-ha plantation in Greece was acquired with an Unmanned Aerial Vehicle. Objects smaller than the tree crown [...] Read more.
The objective of this study was to develop a methodology for mapping olive plantations on a sub-tree scale. For this purpose, multispectral imagery of an almost 60-ha plantation in Greece was acquired with an Unmanned Aerial Vehicle. Objects smaller than the tree crown were produced with image segmentation. Three image features were indicated as optimum for discriminating olive trees from other objects in the plantation, in a rule-based classification algorithm. After limited manual corrections, the final output was validated by an overall accuracy of 93%. The overall processing chain can be considered as suitable for operational olive tree monitoring for potential stresses. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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5563 KiB  
Article
Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring
by Andrea Lessio, Vanina Fissore and Enrico Borgogno-Mondino
J. Imaging 2017, 3(4), 49; https://doi.org/10.3390/jimaging3040049 - 5 Nov 2017
Cited by 26 | Viewed by 5503
Abstract
The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give [...] Read more.
The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested “absolutely” (in respect of a selected reference frame) and “relatively” (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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4246 KiB  
Article
Using SEBAL to Investigate How Variations in Climate Impact on Crop Evapotranspiration
by Giorgos Papadavid, Damianos Neocleous, Giorgos Kountios, Marinos Markou, Anastasios Michailidis, Athanasios Ragkos and Diofantos Hadjimitsis
J. Imaging 2017, 3(3), 30; https://doi.org/10.3390/jimaging3030030 - 20 Jul 2017
Cited by 7 | Viewed by 6386
Abstract
Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural processes. Deficit or excessive irrigation could create either crop health-related problems or water over-consumption, respectively. The latter could lead to groundwater depletion and [...] Read more.
Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural processes. Deficit or excessive irrigation could create either crop health-related problems or water over-consumption, respectively. The latter could lead to groundwater depletion and deterioration of its quality through deep percolation of agrichemical residuals. In this context, and under the current conditions where Cyprus is facing effects of possible climate changes, the purpose of this study seeks to estimate the needed crop water requirements of the past (1995–2004) and the corresponding ones of the present (2005–2015) in order to test if there were any significant changes regarding the crop water requirements of the most water-intensive trees in Cyprus. The Mediterranean region has been identified as the region that will suffer the most from variations of climate. Thus the paper refers to effects of these variations on crop evapotranspiration (ETc) using remotely-sensed data from Landsat TM/ETM+/OLI employing a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL). Though the general feeling is that of changes on climate will consequently affect ETc, our results indicate that there is no significant effect of climate variation on crop evapotranspiration, despite the fact that some climatic factors have changed. Applying Student’s t-test, the mean values for the most water-intensive trees in Cyprus of the 1994–2004 decade have shown no statistical difference from the mean values of 2005–2015 for all the cases, concluding that the climate change taking place in the past decades in Cyprus have either not affected the crop evapotranspiration or the crops have managed to adapt to the new environmental conditions through time. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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Review

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19 pages, 7410 KiB  
Review
Contribution of Remote Sensing on Crop Models: A Review
by Dimitrios A. Kasampalis, Thomas K. Alexandridis, Chetan Deva, Andrew Challinor, Dimitrios Moshou and Georgios Zalidis
J. Imaging 2018, 4(4), 52; https://doi.org/10.3390/jimaging4040052 - 23 Mar 2018
Cited by 176 | Viewed by 17398
Abstract
Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation [...] Read more.
Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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237 KiB  
Review
Nitrogen (N) Mineral Nutrition and Imaging Sensors for Determining N Status and Requirements of Maize
by Abdelaziz Rhezali and Rachid Lahlali
J. Imaging 2017, 3(4), 51; https://doi.org/10.3390/jimaging3040051 - 14 Nov 2017
Cited by 19 | Viewed by 5927
Abstract
Nitrogen (N) is one of the most limiting factors for maize (Zea mays L.) production worldwide. Over-fertilization of N may decrease yields and increase NO3− contamination of water. However, low N fertilization will decrease yields. The objective is to optimize [...] Read more.
Nitrogen (N) is one of the most limiting factors for maize (Zea mays L.) production worldwide. Over-fertilization of N may decrease yields and increase NO3− contamination of water. However, low N fertilization will decrease yields. The objective is to optimize the use of N fertilizers, to excel in yields and preserve the environment. The knowledge of factors affecting the mobility of N in the soil is crucial to determine ways to manage N in the field. Researchers developed several methods to use N efficiently relying on agronomic practices, the use of sensors and the analysis of digital images. These imaging sensors determine N requirements in plants based on changes in Leaf chlorophyll and polyphenolics contents, the Normalized Difference Vegetation Index (NDVI), and the Dark Green Color index (DGCI). Each method revealed limitations and the scope of future research is to draw N recommendations from the Dark Green Color Index (DGCI) technology. Results showed that more effort is needed to develop tools to benefit from DGCI. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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