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Special Issues
Spatial Data Acquisition, Handling, and Analysis in Agro-Geoinformatics
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Spatial Data Acquisition, Handling, and Analysis in Agro-Geoinformatics

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 November 2014) | Viewed by 32520

Special Issue Editors

Prof. Dr. Georg Bareth

E-Mail Website
Guest Editor
GIS & RS Group, Institute of Geography, University of Cologne, D-50923 Cologne, Germany
Interests: low-weight UAVs; hyperspectral and multisepctral remote sensing; field spectrometry; terrestrial lasercanning; 3D analysis; plant biomass; plant nitrogen; change detection; matter fluxes; precision agriculture; spatial data management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fei Yuan

E-Mail Website
Guest Editor
Department of Geography, Minnesota State University, Mankato, MN 56001, USA
Interests: remote sensing; GIS; resource mapping; monitoring; management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues:

In the last two decades, spatial data acquisition, management, analysis, and visualization have become more important in precision agriculture. Relevant remote sensing data have been collected by satellite, airborne, and mobile remote and proximal sensing platforms. Such data supplement spatial data on management, soil, elevation, topography, and weather. Meanwhile, big data processing has been a major research focus.

Recently, farmers and service providers have had to handle large amounts of spatial data, which are used for spatial decision support in an agricultural context. In many cases, Farm Management Information Systems (FMIS) are used for data management. However, these systems often lack the capabilities of the latest developments, such as Web Map Services (WMS) or Sensor Observation Services (SOS). Moreover, automatic data mining functionalities for available remote sensing or geo-data are usually excluded. In the agricultural decision making process, geoinformation on agricultural-related objects and processes (i.e., agro-geoinformation), is becoming more and more important. Nowadays, farmers and service providers rely on such information technologies to increase yields and marginal incomes, and to address challenges, such as sustainable resource protection, traceability of products, and quality issues, which are set by policies and industries. Therefore, it is imperative to investigate how far the latest developments in agro-geoinformatics and remote and proximal sensing can be applied for agricultural monitoring, management, and decision making.

Consequently, this Special Issue focuses on Progress in Agro-Geoinformatics. The emphasis of this issue includes, but is not limited to, significant improvements in data capture, management, analysis, interoperability, standardization, and assimilation. Additionally, agriculturally related spatial models for crop growth, matter fluxes, and water processes, which support decision making, are also within the scope of this issue. Prospective authors are invited to contribute to this Special Issue of ISPRS International Journal of Geo-Information by submitting an original manuscript. Contributions may focus on, but are not limited to:

  • Mapping, monitoring, and managing agricultural ecosystems
  • Satellite-, airborne-, and UAV/UAS-based imaging
  • Spatial data handling and management for agriculture
  • Agro-ecosystem modeling
  • Crop growth and yield modeling
  • Data assimilation
  • Precision agriculture
  • Proximal sensing: terrestrial and mobile approaches
  • hyperspectral sensing
  • Thermal sensing
  • Fluorescence sensing
  • Microwave sensing
  • Laser scanning/LIDAR
  • Sensor development
  • Sensor networks
  • Geostatistics
  • Land cover and land use changes (LULCC)
  • monitoring water fluxes in agricultural ecosystems
  • Agricultural disaster monitoring
  • Matter fluxes of agricultural ecosystems
  • Agricultural Information Systems (AIS)
  • Farm Management Systems (FMS)
  • Spatial Decision Support Systems (SDSS)
  • Data standards

Prof. Dr. Georg Bareth
Prof. Dr. Fei Yuan
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. ISPRS International Journal of Geo-Information 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 1700 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

  • agro-geoinformatics
  • remote sensing
  • GIS
  • agro-ecosystem modeling
  • spatial decision support system (SDSS)
  • farm management information systems (FMIS)
  • spatial data management
  • data services
  • monitoring
  • yield
  • biomass
  • nitrogen
  • nutrients
  • crops
  • pasture
  • soil
  • land use
  • crop rotations
  • change detection
  • phenology
  • sensor networks
  • sensor web
  • UAV
  • RGB
  • multispectral
  • hyperspectral
  • laser scanning
  • thermal
  • fluorescence
  • vegetation indices
  • data standards
  • spatio-temporal patterns
  • GPS/DGPS/RTK
  • accuracy and precision
  • error propagation

Published Papers (4 papers)

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5095 KiB  
Article
Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps
by Micol Rossini, Cinzia Panigada, Chiara Cilia, Michele Meroni, Lorenzo Busetto, Sergio Cogliati, Stefano Amaducci and Roberto Colombo
ISPRS Int. J. Geo-Inf. 2015, 4(2), 626-646; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4020626 - 20 Apr 2015
Cited by 28 | Viewed by 6425
Abstract
This study evaluates the potential of airborne remote sensing images to detect water stress in maize. Visible and near infrared CASI (Itres Research Ltd., Calgary, AL, Canada) and thermal AHS-160 (Sensytech Inc., Beverly, MA, USA) data were acquired at three different times during [...] Read more.
This study evaluates the potential of airborne remote sensing images to detect water stress in maize. Visible and near infrared CASI (Itres Research Ltd., Calgary, AL, Canada) and thermal AHS-160 (Sensytech Inc., Beverly, MA, USA) data were acquired at three different times during the day on a maize field (Zea mays L.) grown with three different irrigation treatments. An intensive field campaign was also conducted concurrently with image acquisition to measure leaf ecophysiological parameters and the leaf area index. The analysis of the field data showed that maize plants were experiencing moderate to severe water stress in rainfed plots and a weaker stress condition in the plots with a water deficit imposed between stem elongation and flowering. Vegetation indices including the normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) computed from the CASI images, sun-induced chlorophyll fluorescence (F760) and canopy temperature (Tc) showed different performances in describing the water stress during the day. During the morning overpass, NDVI was the index with the highest discriminant power due to the sensitivity of NDVI to maize canopy structure, affected by the water irrigation treatment. As the day progressed, processes related to heat dissipation through plant transpiration became more and more important and at midday Tc showed the best performances. Furthermore, Tc retrieved from the midday image was the only index able to distinguish all the three classes of water status. Finally, during the afternoon, PRI and F760 showed the best performances. These results demonstrate the feasibility to detect water stress using thermal and optical airborne data, pointing out the importance of careful planning of the airborne surveys as a function of the specific aims of the study. Full article
(This article belongs to the Special Issue Spatial Data Acquisition, Handling, and Analysis in Agro-Geoinformatics)
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3054 KiB  
Article
A Sensor Web-Enabled Infrastructure for Precision Farming
by Jakob Geipel, Markus Jackenkroll, Martin Weis and Wilhelm Claupein
ISPRS Int. J. Geo-Inf. 2015, 4(1), 385-399; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4010385 - 18 Mar 2015
Cited by 16 | Viewed by 6973
Abstract
The use of sensor technologies is standard practice in the domain of precision farming. The variety of vendor-specific sensor systems, control units and processing software has led to increasing efforts in establishing interoperable sensor networks and standardized sensor data infrastructures. This study utilizes [...] Read more.
The use of sensor technologies is standard practice in the domain of precision farming. The variety of vendor-specific sensor systems, control units and processing software has led to increasing efforts in establishing interoperable sensor networks and standardized sensor data infrastructures. This study utilizes open source software and adapts the standards of the Open Geospatial Consortium to introduce a method for the realization of a sensor data infrastructure for precision farming applications. The infrastructure covers the control of sensor systems, the access to sensor data, the transmission of sensor data to web services and the standardized storage of sensor data in a sensor web-enabled server. It permits end users and computer systems to access the sensor data in a well-defined way and to build applications on top of the sensor web services. The infrastructure is scalable to large scenarios, where a multitude of sensor systems and sensor web services are involved. A real-world field trial was set-up to prove the applicability of the infrastructure. Full article
(This article belongs to the Special Issue Spatial Data Acquisition, Handling, and Analysis in Agro-Geoinformatics)
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13708 KiB  
Article
Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China
by Quanying Zhao, Victoria I.S. Lenz-Wiedemann, Fei Yuan, Rongfeng Jiang, Yuxin Miao, Fusuo Zhang and Georg Bareth
ISPRS Int. J. Geo-Inf. 2015, 4(1), 236-261; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4010236 - 03 Feb 2015
Cited by 17 | Viewed by 8630
Abstract
Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast [...] Read more.
Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error (RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture. Full article
(This article belongs to the Special Issue Spatial Data Acquisition, Handling, and Analysis in Agro-Geoinformatics)
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1169 KiB  
Article
Effects of Pansharpening on Vegetation Indices
by Brian Johnson
ISPRS Int. J. Geo-Inf. 2014, 3(2), 507-522; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi3020507 - 02 Apr 2014
Cited by 52 | Viewed by 9539
Abstract
This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods—Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), [...] Read more.
This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods—Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), Additive Wavelet Transform (AWT), and Smoothing Filter-based Intensity Modulation (SFIM)—and two VIs—Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR)—were tested. The NDVI and SR formulas were both found to cause some spatial information loss in the pansharpened multispectral (MS) bands, and this spatial information loss from VI transformations was not specific to Landsat 8 imagery (it will occur for any type of imagery). BT, SFIM, and other similar pansharpening methods that inject spatial information from the panchromatic (Pan) band by multiplication, lose all of the injected spatial information after the VI calculations. FIHS, AWT, and other similar pansharpening methods that inject spatial information by addition, lose some spatial information from the Pan band after VI calculations as well. Nevertheless, for all of the single- and multi-date VI images, the FIHS and AWT pansharpened images were more similar to the higher resolution reference data than the unsharpened VI images were, indicating that pansharpening was effective in downscaling the VI data. FIHS best enhanced the spectral and spatial information of the single-date and multi-date VI images, followed by AWT, and neither significantly over- or under-estimated VI values. Full article
(This article belongs to the Special Issue Spatial Data Acquisition, Handling, and Analysis in Agro-Geoinformatics)
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