A simple method for detection and counting of oil palm trees using high-resolution multispectral satellite imagery

Santoso, Heri; Tani, Hiroshi; Wang, Xiufeng

doi:10.1080/01431161.2016.1226527

Cited by 40 publications

(26 citation statements)

References 28 publications

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“…It employs similar steps, which include the implementation of a Sobel edge detector, texture analysis co-occurrence, dilation, eroding, high-pass, and opening filters. The researchers were able to achieve an equally good overall accuracy of 90%-95% (Santoso, Tani, and Wang 2016).…”

Section: Change Detectionmentioning

confidence: 84%

A review of remote sensing applications for oil palm studies

Chong

Kanniah

Pohl

et al. 2017

Geo-spatial Information Science

138

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a faculty of Geoinformation and real estate, Universiti teknologi malaysia, Johor Bahru, malaysia; b centre for environmental sustainability and Water security (ipasa), research institute for sustainable environment (rise), Universiti teknologi malaysia, Johor Bahru, malaysia; c institute of computer science, University of osnabrueck, osnabrueck, Germany ABSTRACT Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean, sunflower, and rapeseed. The growth of the oil palm industry causes degradation to the environment, especially when the expansion of plantations goes uncontrolled. Remote sensing is a useful tool to monitor the development of oil palm plantations. In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability, this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring. In addition, the existing knowledge gaps are identified and recommendations for further research are given.

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Section: Change Detectionmentioning

confidence: 84%

A review of remote sensing applications for oil palm studies

Chong

Kanniah

Pohl

et al. 2017

Geo-spatial Information Science

138

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“…Korom et al [17] found a lower detection accuracy of oil palm trees of 77% post-watershed segmentation and masking non-oil palm trees using WorldView-2 satellite image. In addition, Santoso et al [13] used different pan-sharpening methods on QuickBird image to detect oil palm trees. They found that the accuracy of detection was 100% with the modified intensity-hue-saturation (IHS) pan-sharpening, 99.5% with normalized color (Brovey) pan-sharpening, 99.8% subtractive resolution merge pan-sharpening and 99.3% principal components (PC) spectral sharpening.…”

Section: Detection Accuracymentioning

confidence: 99%

“…To study the relationship between infestation and tree density, we need to count the number of trees in each area. Tree counting used to be done manually, but that required significant efforts and costs [9][10][11], and was susceptible to errors, especially in non-systemic fields where rows overlap and trees are of different sizes and ages [12,13]. Researchers have innovated many automatic and semi-automatic approaches for detecting single trees from high spatial resolution images of different types of sensors [14].…”

Section: Introductionmentioning

confidence: 99%

“…However, only a few studies have counted date palm trees P. dactylifera [15]. Indeed, several studies have counted oil palm Elaeis guineensis Jacquin trees [13,[16][17][18][19][20] and coconut palm trees Cocos nucifera Linnaeus [21,22]. In addition, other studies counted and classified different fruittrees, such as different citrus tree varieties [23,24], olive trees Olea europaea Linnaeus [25], walnut Juglans sp.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Relationship of Date Palm Tree Density to Dubas Bug Ommatissus lybicus Infestation in Omani Orchards

et al. 2018

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Date palm trees, Phoenix dactylifera, are the primary crop in Oman. Most date palm cultivation is under the traditional agricultural system. The plants are usually under dense planting, which makes them prone to pest infestation. The main pest attacking date palm crops in Oman is the Dubas bug Ommatissus lybicus. This study integrated modern technology, remote sensing and geographic information systems to determine the number of date palm trees in traditional agriculture locations to find the relationship between date palm tree density and O. lybicus infestation. A local maxima method for tree identification was used to determine the number of date palm trees from high spatial resolution satellite imagery captured by WorldView-3 satellite. Window scale sizes of 3, 5 and 7 m were tested and the results showed that the best window size for date palm trees number detection was 7 m, with an overall estimation accuracy 88.2%. Global regression ordinary least square (OLS) and local geographic weighted regression (GWR) were used to test the relationship between infestation intensity and tree density. The GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R 2 = 0.59 and medium positive significant relationship in the autumn season with R 2 = 0.30. In contrast, the OLS model results showed a weak positive significant relationship in the spring season with R 2 = 0.02, p < 0.05 and insignificant relationship in the autumn season with R 2 = 0.01, p > 0.05. The results indicated that there was a geographic effect on the infestation of O. lybicus, which had a greater impact on infestation severity, and that the impact of tree density was higher in the spring season than in autumn season.

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“…These types of classifier are able to model complex problems in which they perform better than the other approaches (LeCun, 2015). This deep learning approach was used on satellite imagery and has already demonstrated its ability to detect and localize oil palms under homogeneous and aligned planting conditions (Li et al, 2016 andSantoso, 2016). It remains to test deep learning approach on more heterogeneous areas, such as in oases and urban areas, where RPW is more difficult to detect and monitor.…”

Section: Introductionmentioning

confidence: 99%

Automatic localization of phoenix by satellite image analysis

Cousin¹,

Ferry²

2019

AJPP

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The Red palm weevil (RPW) Rhynchophorus ferrugineus is becoming one of the deadliest pests of the palms in the world. In order to effectively implement a RPW control programme to achieve rapid regression of this pest, it is necessary to have GPS coordinates of each palm present on the control perimeter. This location makes it possible to establish maps and databases which are essential for organizing, at the local and national level, the implementation and permanent monitoring of control measures. It is difficult, time-consuming and expensive to locate palms by visually exploring the entire perimeter from the ground. In the zone of regular plantations, this work can be processed but it becomes extremely heavy in the traditional oasis like in urban environment where the distribution of the palms is very irregular. With advances in satellite imagery, it is possible to acquire high quality images at very short intervals of time from a standard format for a large part of the earth. Combined with the progress of machine learning, particularly deep learning, this amount of data is able to feed a robust model. It would allow to automate the detection of palms at large scale and monitor their evolution at very short intervals, which in the fight against RPW is valuable information. This first work wants to test the interest in this solution. We build and train a convolution neural network in order to find two species of palms Phoenix canariensis and Phoenix dactylifera (C&D) in a very heterogeneous area of 100 km². Our model evaluation shows that 1/5 of the objects found are false positive and more than 2/3 of C&D are perfectly localized. These first results could be improved greatly by implementing a more robust algorithm using more data and using larger colour spectrum (as near infra-red). The question of the infested palms detection using satellite imagery and machine learning stays open.

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A simple method for detection and counting of oil palm trees using high-resolution multispectral satellite imagery

Cited by 40 publications

References 28 publications

A review of remote sensing applications for oil palm studies

A review of remote sensing applications for oil palm studies

Relationship of Date Palm Tree Density to Dubas Bug Ommatissus lybicus Infestation in Omani Orchards

Automatic localization of phoenix by satellite image analysis

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