Extracting oil palm crown from WorldView-2 satellite image

Korom, Alexius; Phua, Mui How; Hirata, Yasumasa; Matsuura, T.

doi:10.1088/1755-1315/18/1/012188

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…Delineation of individual tree crowns in tropical forest ecosystems is extremely challenging [ 18 ]. Watershed segmentation has been successfully used for identifying oil palm tree crowns using WorldView-2 [ 19 ] and for tree crown delineation in North Borneo using IKONOS [ 20 ] aerial imagery. However, the basic premise of watershed segmentation limits its use to subtropical and tropical forests, where the determination of radiometric maximums and geometric centers is not straightforward [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

et al. 2015

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At present, there is very limited information on the ecology, distribution, and structure of Cambodia’s tree species to warrant suitable conservation measures. The aim of this study was to assess various methods of analysis of aerial imagery for characterization of the forest mensuration variables (i.e., tree height and crown width) of selected tree species found in the forested region around the temples of Angkor Thom, Cambodia. Object-based image analysis (OBIA) was used (using multiresolution segmentation) to delineate individual tree crowns from very-high-resolution (VHR) aerial imagery and light detection and ranging (LiDAR) data. Crown width and tree height values that were extracted using multiresolution segmentation showed a high level of congruence with field-measured values of the trees (Spearman’s rho 0.782 and 0.589, respectively). Individual tree crowns that were delineated from aerial imagery using multiresolution segmentation had a high level of segmentation accuracy (69.22%), whereas tree crowns delineated using watershed segmentation underestimated the field-measured tree crown widths. Both spectral angle mapper (SAM) and maximum likelihood (ML) classifications were applied to the aerial imagery for mapping of selected tree species. The latter was found to be more suitable for tree species classification. Individual tree species were identified with high accuracy. Inclusion of textural information further improved species identification, albeit marginally. Our findings suggest that VHR aerial imagery, in conjunction with OBIA-based segmentation methods (such as multiresolution segmentation) and supervised classification techniques are useful for tree species mapping and for studies of the forest mensuration variables.

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Section: Introductionmentioning

confidence: 99%

Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

et al. 2015

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“…For instance, Li et al [18] reported that the overall accuracy of detection of oil palm trees ranged between 84.2 and 95.3% using the local maxima filter, however, these improved with an artificial neural network (ANN) and convolutional neural network (CNN) approach to 94.0-98.2% and 96.1-98.8%, respectively. 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.…”

Section: Detection Accuracymentioning

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%

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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“…However, it is very suitable to extract information based on the spectral signature of the object [9]. In more recent years, the object-based classification has been widely applied because of its ability to identify additional information such as the size, shape, texture, and also adjacent object occurrences related to the other [10]. More recently, the object-based image analysis (OBIA) method has evolved for fast analyzing of the high-resolution images [11].…”

Section: Introductionmentioning

confidence: 99%

Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis

et al. 2018

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The current study proposes a new method for oil palm age estimation and counting from Worldview-3 satellite image and light detection and range (LiDAR) airborne imagery. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. The sensitivity analysis was conducted on four SVM kernel types with associated segmentation parameters to obtain the optimal crown coverage delineation. Extracting tree’s crown was integrated with height model and multiregression methods to accurately estimate the age of trees. The multiregression model with multikernel sizes was examined to achieve the most optimized model for age estimation. Applied models were trained and examined over five different oil palm plantations. The results of oil palm counting had an overall accuracy of 98.80%, while the overall accuracy of age estimation showed 84.91%, over all blocks. The relationship between tree’s height and age was significant which supports the polynomial regression function (PRF) model with a 3×3 kernel size for under 10–12-year-old oil palm trees, while exponential regression function (ERF) is more fitted for older trees (i.e., 22 years old). Overall, recent remote sensing dataset and machine learning techniques are useful in monitoring and detecting oil palm plantation to maximize productivity.

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Extracting oil palm crown from WorldView-2 satellite image

Cited by 7 publications

References 11 publications

Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

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

Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis

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