Potential of low altitude multispectral imaging for in-field apple tree nursery inventory mapping

Quirós, Juan José; Khot, Lav R.

doi:10.1016/j.ifacol.2016.10.077

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Normally, the explained variations by our model on predicting physical traits were greater than on chemical ones, of which the model interpretations on CVC and CIN were almost triple those of OC% and TN%. This difference is related to the capability of lens in capturing spectral information of the target attributes (Quiros & Khot, 2016). Visible lens tend to perform better in capturing spectral differences caused by changes in land cover rather than by internal differences of objects (Jones, 1985).…”

Section: Discussionmentioning

confidence: 99%

Remotely piloted aircraft systems remote sensing can effectively retrieve ecosystem traits of alpine grasslands on the Tibetan Plateau at a landscape scale

Tang

Zhang

Cong

et al. 2021

Remote Sens Ecol Conserv

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Ecosystem trait is a standardized description of biological features of a community, and it bridges individual plants and ecosystem. Conventionally most ecosystem trait data are collected from field survey and the generated data is hard to meet the requirements as set in the concept of ecosystem trait. To a great extent, remotely piloted aircraft systems (RPAS) remote sensing, which is capable of retrieving ecosystem traits across multiple scales, can overcome constraints in field plot survey. In this study, we selected alpine grassland ecosystem on the Tibetan Plateau (TP), which is under-studied due to scarcity of field monitoring data, as the research target. A new data framework was proposed by integrating field plot and RPAS remote sensing data to map spatial patterns of ecosystem traits for the alpine grasslands. Across four landscapes on the TP, ecosystem traits of vegetation coverage (CVC), species number (CSN), individual number (CIN), above ground biomass (AGB), organic carbon content (OC%) and total nitrogen content (TN%) were retrieved. We also calculated Shannon's Diversity Index and Shannon's Evenness Index for each plot. The results showed that RPAS-based high spatial resolution RGB image is capable of predicting both physical and chemical ecosystem traits for alpine grasslands on the TP. Remote sensing on physical traits are overall more efficient than on chemical traits, with the highest R 2 of 0.86 and 0.48 for physical trait and chemical one, respectively. The bands of Red and Green contributed more to the prediction model than band of Blue did, and the spectral mean value played a greater role than the spectral standard deviation. Based on the retrieved results, a set of spatial patterns on ecosystem traits can be revealed. This study represents an advance on ecosystem trait study and can significantly improve our understanding on ecosystem functions of the alpine ecosystem on the TP.

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Section: Discussionmentioning

confidence: 99%

Remotely piloted aircraft systems remote sensing can effectively retrieve ecosystem traits of alpine grasslands on the Tibetan Plateau at a landscape scale

Tang

Zhang

Cong

et al. 2021

Remote Sens Ecol Conserv

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“…Acquisition with photogrammetric techniques: To build a point cloud from aerial photographs, it is necessary to respect certain criteria (flight altitude, flight speed, overlaps and adequate weather conditions). For example, Fang et al (2019) or Quirós and Khot (2016) present an algorithm to automatically count the number of apple trees. For this purpose, a flight altitude of between 10 and 25 m is recommended in order to minimize errors on the result.…”

Section: 12mentioning

confidence: 99%

Use of Tree Height for Mangrove Species Classification

Roy

Marchand

Landes

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Mangrove forests play an important role in the balance of biodiversity. However, they are threatened by agriculture, aquaculture, urbanization and global warming. That’s why it is imperative to monitor this ecosystem and understand how it evolves in the face of these threats in order to better preserve it. The traditional methods are invasive and time consuming. Besides, it is often difficult to get into mangroves because of the particular structure of some species, so measurements cannot be taken in those areas. That’s why it is very interesting to use aerial data provided by unmanned aerial vehicles (UAVs) photos or airborne laser scanning systems (ALS). Moreover, some representative elements of mangroves are only a few tens of centimeters high. This is the case of pneumatophores. Traditional measurements would be much too long. In this case, it is interesting to use terrestrial laser scanning systems (TLS) to make measurements and to follow them. A research project began in 2021 to try to understand how urban mangroves develop in semi-arid regions, using remote sensing techniques (photogrammetry, airborne and terrestrial laser scanning). The purpose of this paper is first to present the project and the issues of monitoring mangrove forests. Then, it proposes a state of the art of the methodologies used to record mangrove. Finally, it presents the different acquisitions made as well as the first results of species classification based on photogrammetric point cloud processing. The assessment based on ground truth shows already promising results.

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“…Gnädinger el al.[18] adopted a decorrelation stretch contrast enhancement procedure to enhance color different which enabled detection of the center of maize plants for counting purposes. An apple tree counting method was developed by Quirós et al [19] which applied filters based on the size and location of the polygons to rasterized the image in order to isolate apple plants.All the aforementioned counting methods included a two-step process in common: (1) segment plants/fruits pixels from background to rasterize images; and (2) development of the counting scheme. The segmentation is application-dependent as different segmentation methods have been used to detect and distinguish leaves, fruits, trees, and weeds.…”

mentioning

confidence: 99%

“…[18] adopted a decorrelation stretch contrast enhancement procedure to enhance color different which enabled detection of the center of maize plants for counting purposes. An apple tree counting method was developed by Quirós et al [19] which applied filters based on the size and location of the polygons to rasterized the image in order to isolate apple plants.…”

mentioning

confidence: 99%

Applications of High-Resolution Imaging for Open Field Container Nursery Counting

et al. 2018

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Frequent inventory data of container nurseries is needed by growers to ensure proper management and marketing strategies. In this paper, inventory data are estimated from aerial images. Since there are thousands of nursery species, it is difficult to find a generic classification algorithm for all cases. In this paper, the development of classification methods was confined to three representative categories: green foliage, yellow foliage, and flowering plants. Vegetation index thresholding and the support vector machine (SVM) were used for classification. Classification accuracies greater than 97% were obtained for each case. Based on the classification results, an algorithm based on canopy area mapping was built for counting. The effects of flight altitude, container spacing, and ground cover type were evaluated. Results showed that container spacing and interaction of container spacing with ground cover type have a significant effect on counting accuracy. To mimic the practical shipping and moving process, incomplete blocks with different voids were created. Results showed that the more plants removed from the block, the higher the accuracy. The developed algorithm was tested on irregular-or regular-shaped plants and plants with and without flowers to test the stability of the algorithm, and accuracies greater than 94% were obtained.2 of 20 approaches. To the best knowledge of the authors, only one research trial has been reported using aerial imagery to count container-grown nursery plants. Leiva [4] used Feature Analyst (Textron Systems, Providence, RI), an object-based image analysis software, to count open-field container nursery plants based on images collected by UAV. However, this method requires subjective user input and parameter settings, which could be a source of potential error. Moreover, directly applying methods developed via third-party software provides little flexibility and embedded cost to allow researchers or growers to develop customized methods for nursery counting.Although there is limited research focusing on application of imaging for container-grown nursery counting, there has been a long history of its broad application on other counting tasks in agriculture, such as fruit and tree counting. Wang et al. [11] used saturation, hue, and local maximum of specular reflections in four directions to detect apple pixels and then split touching apples and merged the occluded apple based on the estimate diameter. Payne at al. [12] segmented images into mango fruits and background with color information in red-green-blue (RGB), YCbCr space and texture information. Then a lower and an upper limit were applied to block size and subsequently estimate mango number. An automated kiwifruit counting technique was proposed so that kiwifruit was firstly extracted by minimum distance classification in L*a*b color space, and then a regression relationship was utilized to estimate the true fruit count [13]. Nuske et al. [14] detected grape locations using radial symmetry transformation and removed false p...

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Potential of low altitude multispectral imaging for in-field apple tree nursery inventory mapping

Cited by 7 publications

References 12 publications

Remotely piloted aircraft systems remote sensing can effectively retrieve ecosystem traits of alpine grasslands on the Tibetan Plateau at a landscape scale

Remotely piloted aircraft systems remote sensing can effectively retrieve ecosystem traits of alpine grasslands on the Tibetan Plateau at a landscape scale

Use of Tree Height for Mangrove Species Classification

Applications of High-Resolution Imaging for Open Field Container Nursery Counting

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