Journal Paper Digests 2018 #22
- Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image
- Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley, California
- Soil sampling with drones and augmented reality in precision agriculture
- Informing transport infrastructure investments using TraNSIT: A case study for Australian agriculture and forestry
- START: A data preparation tool for crop simulation models using web-based soil databases
- An open-source spatial analysis system for embedded systems
Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image
Authors: Shuai, GY; Zhang, JS; Basso, B; Pan, YZ; Zhu, XF; Zhu, S; Liu, HL
Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 74 1-15; FEB 2019
Abstract: Due to its ability to penetrate the cloud, Synthetic Aperture Radar (SAR) has been a great resource for crop mapping. Previous research has verified the applicability of SAR imagery in object-oriented crop classification, however, speckle noise limits the generation of optimal segmentation. This paper proposed an innovative SARbased maize mapping method supported by optical image, Gaofen-1 PMS, based segmentation, named as parcel based SAR classification assisted by optical imagery-based segmentation (os-PSC). Polarimetric decomposition was applied to extract polarimetric parameters from multi-temporal RADARSAT-2 data. One Gaofen-1 image was then used for parcel extraction, which was the basic unit for SAR image analysis. The final step was a multistep classification for final maize mapping including: the potential maize mask extraction, pure/mixed maize parcel division and an integrated maize map production. Results showed that the overall accuracy of the os-PSC method was 89.1%, higher than those of pixel-level classification and SAR-based segmentation methods. The comparison between optical- and SAR-based segmentation demonstrated that optical-based segmentation would be better at representing maize field boundaries than the SAR-based segmentation. Moreover, the parcel- and pixel-level integrated classification will be suitable for many agricultural systems with small landownership where inter-cropping is common. Through integrating advantages of the SAR and optical data, os-PSC shows promising potentials for crop mapping.
Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley, California
Authors: Li, HP; Zhang, C; Zhang, SQ; Atkinson, PM
Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 74 45-56; FEB 2019
Abstract: Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude-Pottier and Freeman-Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (> 40%), while surface scattering (> 40%) dominated for alfalfa and winter wheat, although double-bounce scattering (> 30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (> 40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (> 40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research.
Soil sampling with drones and augmented reality in precision agriculture
Authors: Huuskonen, J; Oksanen, T
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 154 25-35; NOV 2018
Abstract: Soil sampling is an important tool to gather information for making proper decisions regarding the fertilization of fields. Depending on the national regulations, the minimum frequency may be once per five years and spatially every ten hectares. For precision farming purposes, this is not sufficient. In precision farming, the challenge is to collect the samples from such regions that are internally consistent while limiting the number of samples required. For this purpose, management zones are used to divide the field into smaller regions. This article presents a novel approach to automatically determine the locations for soil samples based on a soil map created from drone imaging after ploughing, and a wearable augmented reality technology to guide the user to the generated sample points. Finally, the article presents the results of a demonstration carried out in southern Finland.
Informing transport infrastructure investments using TraNSIT: A case study for Australian agriculture and forestry
Authors: Higgins, A; McFallan, S; Marinoni, O; McKeown, A; Bruce, C; Chilcott, C; Pinkard, L
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 154 187-203; NOV 2018
Abstract: Transport infrastructure is essential to moving over 85 million tonnes of agricultural products and 30 million cubic metres of timber from farms and production areas to domestic and international markets each year in Australia. Agriculture supply chains in Australia are characterised by long distances with transport costs accounting for up to 40 percent of the market price. Targeted infrastructure investment and/or regulatory changes can substantially reduce transport-related logistics costs. To provide a comprehensive view of transport logistics costs and benefits due to infrastructure investments and regulatory changes in agriculture and forestry supply chains, CSIRO developed a computer-based tool - the Transport Network Strategic Investment Tool (TraNSIT). TraNSIT optimises transport routes for upto hundreds of thousands of enterprises and millions of vehicle trips between farms and their markets, providing information on routing to maximise cost efficiencies. Through an Australian Government initiative, TraNSIT was applied to over 30 commodities representing 98% of Australian agricultural and plantation forestry volume transported by road and rail. TraNSIT is now a comprehensive logistics tool that has been applied to the largest agricultural supply chain dataset ever assembled in Australia. This paper provides an overview of TraNSIT, its adaptation to agriculture and forestry logistics as part of the project, and its application to several case studies in Australia including new road links, rail versus road and flooding.
START: A data preparation tool for crop simulation models using web-based soil databases
Authors: Kim, KS; Yoo, BH; Shelia, V; Porter, CH; Hoogenboom, G
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 154 256-264; NOV 2018
Abstract: Soil profile data that characterize the physical and chemical properties of a soil are among the required set of inputs for ecological, crop and other dynamic simulation models. A web-based soil information system often provides the site-specific soil data of which formats are not readily compatible to crop models. The Soil daTA Retrieval Tool (START) was developed to automate a series of procedures for preparation of soil input data that includes the retrieval of soil profile data from the information system, reorganization of data, estimation of soil parameters, and creation of input files foi simulation models. In a case study, the START was implemented to support the SoilGrids database operated by the International Soil Reference and Information Center. It took about 0.33% of time for the START to create soil input files compared with manual preparation. These results suggest that the START could provide an efficient approach for preparation of soil input files especially for sites where little soil information is available.
An open-source spatial analysis system for embedded systems
Authors: Coelho, ALD; de Queiroz, DM; Valente, DSM; Pinto, FDD
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 154 289-295; NOV 2018
Abstract: Soil and plant monitoring systems are important tools for applying precision agriculture techniques. To acquire soil-plant system data, the user establishes a sampling strategy, goes to the field, collects data and finally goes to the office for data analysis. Sometimes, when the analysis is performed, the user realizes that the sampling strategy was not adequate and needs to return to the field in order to collect more data. To avoid problems with the sampling strategy, the solution is to have a system that performs the data analysis immediately after its collection, while the user is still in the field. To do that, we can use single board computers; these types of platforms have ports to communicate to sensors and good processing capabilities. Therefore, the objective of this work was to develop an embedded system to perform spatial variability data analysis in the field, right after data acquisition. The software was developed using Python 3.6; the PyQt Integrated Development Environment (Riverbank Computer Limited, Dorchester, United Kingdom) was used to design a graphical user interface. The BeagleBone Black board, running Debian version 8.6, was used to implement the software. The analysis was divided into three steps: in the first one, an outlier and inlier analysis was performed to remove unwanted data; in the second one, the semivariogram was generated, and the variable and standard deviation map was produced by performing ordinary kriging; and in the last one, a cluster analysis was performed to create management classes using a fuzzy k-means algorithm. The graphical user interface showed the variable map and the variable classes map. To test the developed software, soybean yield data that was collected in a 31.6-ha field were used. The developed software was shown to be efficient at performing the spatial variability of soybean yields. The comparison of the generated maps shows the importance of filtering the data before performing the analysis. The developed software is available at the GitHub website.