Mapping of Individual Oil Palm Trees Using Airborne Hyperspectral Sensing: An Overview

Jusoff, Kamaruzaman; Pathan, Mubeena

doi:10.5539/apr.v1n1p15

Cited by 23 publications

(12 citation statements)

References 25 publications

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“…Field-based tree counting is labour intensive, and the use of remote-sensing using high-resolution optical imagery from satellites has been suggested as a cost efficient alternative [48]. A semi-automatic technique to count oil palm trees using high-resolution airborne imaging spectrometer data has also been developed [16], and LiDAR has been used to detect and classify oil palm diseases [49].…”

Section: Discussionmentioning

confidence: 99%

Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches

et al. 2017

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Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies, such as airborne LiDAR, are vital for quantifying the effects of land use change for greenhouse gas emissions, and many papers have developed methods for mapping forests. However, nobody has yet mapped oil palm ACD from LiDAR. The development of carbon prediction models would open doors to remote monitoring of plantations as part of efforts to make the industry more environmentally sustainable. This paper compares the performance of tree-centric and area-based approaches to mapping ACD in oil palm plantations. We find that an area-based approach gave more accurate estimates of carbon density than tree-centric methods and that the most accurate estimation model includes LiDAR measurements of top-of-canopy height and canopy cover. We show that tree crown segmentation is sensitive to crown density, resulting in less accurate tree density and ACD predictions, but argue that tree-centric approach can nevertheless be useful for monitoring purposes, providing a method to detect, extract and count oil palm trees automatically from images.

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Section: Discussionmentioning

confidence: 99%

Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches

et al. 2017

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“…Jusoff and Pathan (2009) used the hyperspectral image to map palm trees. The study implemented the linear spectral mixture analysis to discover contributions of different materials on hyperspectral pixel vectors, then used the mixed-to-pure converter that assigns those vectors to signatures and the minimum distance based formula to detect palm trees.…”

Section: Introductionmentioning

confidence: 99%

Palm Tree Detection Using Circular Autocorrelation of Polar Shape Matrix

Manandhar¹,

Hoegner²,

Stilla³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape matrix representation of an image, as the shape feature, and the linear support vector machine to standardize and reduce dimensions of the feature. Finally, the study uses local maximum detection algorithm on the spatial distribution of standardized feature to detect palm trees. The method was applied to 8 images chosen from different tough scenarios and it performed on average with an accuracy of 84% and 76.1%, despite being subjected to different challenging conditions in the chosen test images.

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“…Rebelo et al (2007) investigated RS and GIS for inventory of wetlands by mapping and change detection in Seri Lanka. Jussof and Pathan (2009) utilized airborne hyperspectral sensing for mapping individual oil palm trees with high accuracy RS technique which can guide researchers to make plan for oil palms forest as one of the economical sources in Malaysia (Rebelo et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Predict Location(s) of <i>Apis dorsata</i> Nesting Sites Using Remote Sensing and Geographic Information System in Melaleuca Foresti

Saberioon¹

2010

American Journal of Applied Sciences

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Problem statement: The Asiatic Giant honeybees (Apis dorsata) are seasonally abounded throughout the upland and lowland rainforests in South East Asia. The colonies of A. dorsata are found to nest in aggregates on tall bee trees (tree emergent) in the open, as well as, nesting singly in concealed locations when nesting low, especially in the submerged forest of Melaleuca cajuputi as in the vast hectare of Melaleuca forest along the coastal areas of Terengganu. The trunk and branches of the Melaleuca trees are almost upright and the bark are papery and loose that it is unsuitable to serve as nesting support for A. dorsata. Therefore, other trees with sloping branches in the vicinity of the Melaleuca forest are chosen as nesting supports for the A. dorsata colonies. Melaleuca cajuputi and Acacia auriculiformis trees are major sources of nectar and pollen for A. dorsata. Approach: A pilot study were conducted to determine the vegetation distribution area of Melaleuca cajuputi and Acacia auriculiformis, colony distribution and density Apis dorsata colonies using the remote-sensing and field survey by GPS hand held in the submerged Melaleuca forest around the village of Gong Beris, Marang district, Terengganu (Upper left 5°219.223N, 103°104.092E and lower down 4°5342.31N, 103°2237.90E). Result: Based on the map and field survey, it is clear that almost all Apis dorsata nesting sites were located in the Acacia auriculiformis areas Melaleuca cajuputi is mostly using by Apis dorsata as source of nectar and pollen in Marang district, Malaysia Conclusion: The outcome on the distribution area and feral colonies nesting sites will be used to propose the locating of possible A. dorsata nesting sites and understand better nesting behavior of Apis dorsata to improvement searching ability for honey hunters in Marang district

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Mapping of Individual Oil Palm Trees Using Airborne Hyperspectral Sensing: An Overview

Cited by 23 publications

References 25 publications

Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches

Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches

Palm Tree Detection Using Circular Autocorrelation of Polar Shape Matrix

Predict Location(s) of <i>Apis dorsata</i> Nesting Sites Using Remote Sensing and Geographic Information System in Melaleuca Foresti

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