Multi-Sensor Data Fusion for Modeling African Palm in the Ecuadorian Amazon

Santos, Carolina Carvalho de Oliveira; Messina, Joseph P.

doi:10.14358/pers.74.6.711

Cited by 27 publications

(18 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can be a combination of multi-spectral/panchromatic data, multi-spectral/ hyperspectral data, or even multi-temporal data. Due to the unique features of microwave remote sensing with additional information on surface roughness, it often serves as complementary data to optical imagery to improve the classification result (Santos and Messina 2008;Morel, Fisher, and Malhi 2012;Fadaei et al 2013;Sim et al 2013). In one particular research aimed to map oil palms in a heterogeneous environment, the authors combined data from Landsat and Phased Array-Type L-band Synthetic Aperture Radar (PALSAR) and managed to achieve overall higher accuracy for oil palm (94%) compared to the standalone data-set (Landsat: 84%; PALSAR: 89%) (Cheng et al 2016).…”

Section: Land Cover Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A review of remote sensing applications for oil palm studies

Chong

Kanniah

Pohl

et al. 2017

Geo-spatial Information Science

144

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

Section: Land Cover Classificationmentioning

confidence: 99%

“…First of all, multi-sensor data fusion is very effective in oil palm classification (Santos and Messina 2008;Razi, Ismail, and Shafri 2013;Sim et al 2013). The improved classification results aid in distinguishing oil palms for further applications like automatic tree counting, change detection, and age estimation.…”

Section: Multi-sensor Approachmentioning

confidence: 99%

A review of remote sensing applications for oil palm studies

Chong

Kanniah

Pohl

et al. 2017

Geo-spatial Information Science

144

View full text Add to dashboard Cite

show abstract

“…Shafri et al [12] implemented the spectral angle mapper (SAM) classifier using high spatial resolution, airborne imaging spectrometer data in 2004 to extract oil palm trees in Selangor, Malaysia. However, whether using certain spectral class thresholds or implementing spectral angle mapper classifier, image-based methods are challenging because of the rapidly developed canopy that makes oil palm plantations spectrally similar to other land cover types [13,14]. Phenology-based methods usually rely on the temporal signals of optical sensors to identify oil palm plantations.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, many researchers turn to SAR data for monitoring in tropical areas [17,18]. SAR data have successfully shown potential and suitability for oil palm mapping [13,14,19].…”

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

“…However, the establishment of a correct relationships between backscatter and reality parameters of oil palm trees needs to consider multimodal data, ranging from multitemporal acquisitions, through multimodal SAR images, up to verification data from optical imagery or fieldwork [43].…”

Section: Oil Palm Plantation Monitoring With Sarmentioning

confidence: 99%

Mapping palm oil expansion using SAR to study the impact on the CO₂cycle

Pohl

2014

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Multi-Sensor Data Fusion for Modeling African Palm in the Ecuadorian Amazon

Cited by 27 publications

References 34 publications

A review of remote sensing applications for oil palm studies

A review of remote sensing applications for oil palm studies

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

Mapping palm oil expansion using SAR to study the impact on the CO₂cycle

Contact Info

Product

Resources

About

Multi-Sensor Data Fusion for Modeling African Palm in the Ecuadorian Amazon

Cited by 27 publications

References 34 publications

A review of remote sensing applications for oil palm studies

A review of remote sensing applications for oil palm studies

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

Mapping palm oil expansion using SAR to study the impact on the CO2cycle

Contact Info

Product

Resources

About

Mapping palm oil expansion using SAR to study the impact on the CO₂cycle