Monitoring rubber plantation distribution on Hainan Island using Landsat OLI imagery

Han, Pengpeng; Chen, Jinsong; Han, Yu; Lin, Yi; Zhang, Yannan; Jiang, Xiaoli

doi:10.1080/01431161.2017.1420933

Cited by 15 publications

(13 citation statements)

References 28 publications

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“…GEE hosted extensive publicly available RS datasets that can be effectively utilized for productivity, quality, profitability, and sustainability studies of agriculture production. Researchers have applied GEE to plantation mapping and monitoring [75], [76], phenology-based classification [77], [78], cropland mapping [79], [80], crop condition monitoring [81], [82], crop yield estimation [83], [84], irrigation mapping [85], [86], and other agricultural studies [87], [88]. For example, seasonal median composites of Sentinel-1 and Sentinel-2 were calculated in GEE to predict the Maize yield in Kenya and Tanzania [83].…”

Section: B Agriculturementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

601

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: B Agriculturementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

601

216

View full text Add to dashboard Cite

show abstract

“…From the perspective of the radiometric resolution, the data ranges from as low as 6 bits, from Landsat MSS [57], to as high as 12 bits, from Landsat 8 [58]. The radiometric resolution for the satellites used in the majority of studies are Landsat MSS (6 bits), IRS-LISS III (7 bits), Landsat TM (8 bits), and Landsat ETM+ (9 bits).…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…The analysis was employed to classify the study areas into several LULC types such as rubber, forest, oil palm, built-up area and others (e.g., [18]). Other studies in this subject area have only classified study areas into two classes: Rubber and non-rubber area [39,58,67].…”

Section: Plantation Mappingmentioning

confidence: 99%

“…The most common approach in mapping rubber plantations using remote sensing was conventional supervised classifiers. Among the supervised classifiers used were Maximum Likelihood algorithm [18,65,67]; Mahalanobis typicalities [36]; Mahalanobis with multi-layer perceptron neural network [68]; Mahalanobis and Maximum Likelihood [69] and Decision Tree [58,70]. Random Forest classification has been employed in a recent study to investigate the textural feature's effect on the classification of rubber plantations [71].…”

Section: Plantation Mappingmentioning

confidence: 99%

“…It can be seen that the application of the same algorithm can provide different results. For example, while adopting the Decision Tree algorithm, Dibs et al [29], Shidiq et al [70] and Han et al [58] reported an accuracy of 80.8%, 87.5% and 92.2%, respectively. The results are influenced by parameters such as: (1) The remote sensing dataset used; (2) the reference data set; and (3) the physical characteristics of study location [82,83].…”

Section: Plantation Mappingmentioning

confidence: 99%

See 2 more Smart Citations

Application of Optical Remote Sensing in Rubber Plantations: A Systematic Review

et al. 2021

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Rubber (Hevea brasiliensis) is a tropical tree crop cultivated for the industrial production of latex. The trees are tall, perennial and long-lived, and are typically grown in plantations. In most rubber-producing countries, smallholders account for more than 85% of plantation area. Traditional practices mean that it can be difficult to monitor rubber plantations for management purposes. To overcome issues associated with monitoring traditional practices, remote sensing approaches have been successfully applied in this field. However, information on this is lacking. Therefore, this study aims to document the current status, history, development and prospects for remote sensing applications in rubber plantations by using the PRISMA framework. The review focuses on the application of optical remote sensing data in rubber. In this paper, we discuss the current role of remote sensing on specific subject areas, namely mapping, change detection, stand age estimation, carbon and biomass assessment, leaf area index (LAI) prediction and disease detection. In addition, we elaborate on the benefits gained and challenges faced while adapting this technology. These include the availability and free access to satellite imagery as the greatest benefit and the presence of clouds as one of the toughest challenges. Finally, we highlighted four potential areas where future work can be done: (1) Advancements in remote sensing data, (2) algorithm enhancements, (3) emerging processing platforms, and (4) application to less studied subject areas. This paper gives insight into strengthening the potential of remote sensing for delivering efficient and long-term services for rubber plantations.

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Spatial-temporal changes of forests and agricultural lands in Malaysia from 1990 to 2017

Yan

Gao

et al. 2020

Environ Monit Assess

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Monitoring rubber plantation distribution on Hainan Island using Landsat OLI imagery

Cited by 15 publications

References 28 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Application of Optical Remote Sensing in Rubber Plantations: A Systematic Review

Spatial-temporal changes of forests and agricultural lands in Malaysia from 1990 to 2017

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