Suchergebnisse

In recent times, multi-spectral drone imagery has proved to be a useful tool for measuring tree crop canopy structure. In this context, establishing the most appropriate flight planning variable settings is an essential consideration due to their controls on the quality of the imagery and derived maps of tree and crop biophysical properties. During flight planning, variables including flight altitude, image overlap, flying direction, flying speed and solar elevation, require careful consideration in order to produce the most suitable drone imagery. Previous studies have assessed the influence of individual variables on image quality, but the interaction of multiple variables has yet to be examined. This study assesses the influence of several flight variables on measures of data quality in each processing step, i.e. photo alignment, point cloud densification, 3D model building, and ortho-mosaicking. The analysis produced a drone flight planning and image processing workflow that delivers accurate measurements of tree crops, including the tie point quality, densified point cloud density, and the measurement accuracy of height and plant projective cover derived from individual trees within a commercial avocado orchard. Results showed that flying along the hedgerow, at high solar elevation and with low image pitch angles improved the data quality. Optimal flying speed needs to be set to achieve the required forward overlap. The impacts of each image acquisition variable are discussed in detail and protocols for flight planning optimisation for three scenarios with different drone settings are suggested. Establishing protocols that deliver optimal image acquisitions for the collection of drone data over horticultural tree crops, will create greater confidence in the accuracy of subsequent algorithms and resultant maps of biophysical properties. ; This research was funded by Department of Agriculture and Water Resources, Australian Government and Horticulture Innovation Australia as part of its Rural R&D for Profit Program's subproject "Multi-Scale Monitoring Tools for Managing Australia Tree Crops - Industry Meets Innovation" [grant number RnD4Profit-14-01-008].

Multi-spectral imagery captured from unmanned aerial systems (UAS) is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS-based data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate bidirectional reflectance distribution function (BRDF) correction. Future UAS-based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties. ; The authors would like to acknowledge the support from local farmers, Chad Simpson and Chris Searle; fieldwork assistance from Dan Wu and Aaron Aeberli; and technical supports from online forums. This research was funded by Department of Agriculture and Water Resources, Australian Government as part of its Rural R&D for Profit Program's subproject "Multi-Scale Monitoring Tools for Managing Australia Tree Crops-Industry Meets Innovation".

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management. ; The authors would like to acknowledge the support from Earle Lawrence (farm holder) and Barry Sullivan (Australian Banana Growers Council), and fieldwork assistance from Yu-Hsuan Tu and Dan Wu. D.W.L. acknowledges the support of Food Agility CRC Ltd., funded under the Commonwealth Government CRC Program. The CRC Program supports industry-led collaborations between industry, researchers, and the community.

Terrestrial laser scanning (TLS) can be used to characterize a woodland site by measuring structural attributes of the vegetation community. In Australia, government funded programs monitor vegetation structure using manual field surveys to assess change and ecological condition. In this study, we examined whether structural attributes commonly assessed in woodland ecology surveys can be extracted from a single TLS scan. Attributes of the ground, shrub and overstory vegetation layers were evaluated at nine open woodland sites in central Western Queensland. We used 0.1 m voxels to aggregate returns. Our results show that, compared with field assessment by highly experienced ecologists, TLS can rapidly characterize structural attributes for tree canopy cover, maximum tree height, average tree height (R² > 0.9) and average diameter at breast height (R² = 0.77). However, we could not accurately determine shrub height, shrub canopy cover, shrub average height, ground cover (grass, litter and coarse woody debris) or the number of trees per hectare (R² 0.9) and average diameter at breast height (R² = 0.77). However, we could not accurately determine shrub height, shrub canopy cover, shrub average height, ground cover (grass, litter and coarse woody debris) or the number of trees per hectare (R²

Rapid sea level rise over the 21st century threatens coastal settlements and populations worldwide. Significant land-use policy reform will be needed to mitigate exposure to hazards in the coastal zone. Sea-level rise maps that indicate areas that are potentially prone to future inundation are a valuable tool for policymakers and decision makers. However, errors, assumptions, and uncertainties inherent in spatial data are not often explicitly recognised or communicated. In 2011, the state of Queensland, Australia, published a series of 'state of the art' sea-level rise maps as part of its coastal planning regime. This article uses the Queensland coastal planning regime as a case study to explore how errors, uncertainties and variability in physical, geographical and biological processes in the coastal zone pose challenges for policy makers. Analysis of the case study shows that the use of spatial data in sea-level rise policy formulation is complicated by the need to: (1) acknowledge and communicate uncertainties in existing and projected rates of rise; (2) engage in site-specific mapping based upon best available scientific information; (3) incorporate probabilities of extreme weather events; (4) resolve whether coastal engineering solutions should be included in mapping; (5) ensure that mapping includes areas required for future ecosystem migration; (6) manage discretion in planning and policy decision-making processes; (7) create flexible policies which can be updated in line with scientific developments; and (8) balance the need for consistency with the ability to apply developments in science and technology. Scientists working with spatial data and governments developing and implementing coastal planning policies can recognise, communicate, and seek to overcome uncertainty by addressing these factors.

104 ; 16 ; 18 ; 3 ; European Union's Horizon 2020