Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

Santoso, Heri; Gunawan, Totok; Jatmiko, Retnadi Heru; Darmosarkoro, W.; Minasny, Budiman

doi:10.1007/s11119-010-9172-7

Cited by 98 publications

(68 citation statements)

References 25 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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]. The detection of diseases, such as the Ganoderma basal stem rot, using airborne spectral imagery [50]) and space-borne multispectral sensors [51] has also been demonstrated.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…relationship of remotely sensed data with aGB of oil palm. diagnosis of the diseases or pest infestation based on the symptoms shown at specific spots (Shafri and Hamdan 2009;Santoso et al 2011;Liaghat et al 2014). Based on the hypothesis that Ganoderma-infected oil palm shows observable symptoms at an early stage, various studies were conducted to discriminate the Ganodermainfected oil palms.…”

Section: Estimation Of Agb and Carbon Productionmentioning

confidence: 99%

“…In such an effort, Quickbird high-resolution images were used to map and identify Ganodermainfected oil palms (Santoso et al 2011). The spectral reflectance, NDVI and other vegetation indices were tested to discriminate healthy and infected palms.…”

Section: Estimation Of Agb and Carbon Productionmentioning

confidence: 99%

A review of remote sensing applications for oil palm studies

Chong

Kanniah

Pohl

et al. 2017

Geo-spatial Information Science

138

View full text Add to dashboard Cite

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.

show abstract

“…Satellite remote sensing provides continuous observations of land surface and has been used to map oil palm plantations in many studies [7][8][9][10]. Optical data based methods which are commonly used can be divided into two broad categories: image-based approaches and phenology-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Dong

Tenku

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Oil palm plantations have expanded rapidly. Estimating either positive effects on the economy, or negative effects on the environment, requires accurate maps. In this paper, three classification algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) were explored to map oil palm plantations in Cameroon, using PALSAR 50 m Orthorectified Mosaic images and differently sized training samples. SVM had the ideal performance with overall accuracy ranging from 86% to 92% and a Kappa coefficient from 0.76 to 0.85, depending upon the training sample size (ranging from 20 to 500 pixels per class). The advantage of SVM was more obvious when the training sample size was smaller. K-Means required the user's intervention, and thus, the accuracy depended on the level of his/her expertise and experience. For large-scale mapping of oil palm plantations, the Decision Tree algorithm outperformed both SVM and K-Means in terms of speed and performance. In addition, the decision threshold values of Decision Tree for a large training sample size agrees with the results from previous studies, which implies the possible universality of the decision threshold. If it can be verified, the Decision Tree algorithm will be an easy and robust methodology for mapping oil palm plantations. OPEN ACCESSRemote Sens. 2015, 7 1207

show abstract

Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

Abstract: The application of remote sensing technology and precision agriculture

Cited by 98 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

A review of remote sensing applications for oil palm studies

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Contact Info

Product

Resources

About