A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Duong, Nguyen Anh; Hazarika, Manzul Kumar

doi:10.1016/j.rse.2012.08.022

Cited by 95 publications

(101 citation statements)

References 55 publications

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“…The Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) from Japan Aerospace Exploration Agency (JAXA) provided multi-year cloud-free images (2006)(2007)(2008)(2009)(2010) for the global land surface, and several studies have used multiple polarization images (HH, HV, VH, VV) to map forests at local scale in tropical regions [14,15,18,19]. Based on the backscatter coefficient values (HH, HV) of PALSAR orthorectified mosaic data, forest cover maps at 50-m spatial resolution were developed at regional and global scales [18,20,21]. However, these PALSAR-based forest cover maps do not differentiate between evergreen forests and deciduous forests (e.g., rubber plantations).…”

Section: Introductionmentioning

confidence: 99%

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

et al. 2015

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Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by integration of PALSAR 50-m mosaic images and multi-temporal Landsat TM/ETM+ images. The L-band PALSAR 50-m mosaic images were used to map forests (including both natural forests and rubber trees) and non-forests. For those PALSAR-based forest pixels, we analyzed the multi-temporal Landsat TM/ETM+ images from 2000 to 2009. We first studied phenological signatures of deciduous rubber plantations (defoliation and foliation) and natural forests through analysis of surface reflectance, Normal Difference OPEN ACCESSRemote Sens. 2015, 7 1049Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) and generated a map of rubber plantations in 2009. We then analyzed phenological signatures of rubber plantations with different stand ages and generated a map, in 2009, of rubber plantation stand ages (≤5, 6-10, >10 years-old) based on multi-temporal Landsat images. The resultant maps clearly illustrated how rubber plantations have expanded into the mountains in the study area over the years. The results in this study demonstrate the potential of integrating microwave (e.g., PALSAR) and optical remote sensing in the characterization of rubber plantations and their expansion over time.

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Section: Introductionmentioning

confidence: 99%

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

et al. 2015

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“…Several studies have used PALSAR data for forest monitoring and classification [17,[20][21][22][23][24][25][26][27][28][29]. PALSAR data are often combined with optical images such as Landsat and MODIS.…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Tenku

et al. 2015

Remote Sensing

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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

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“…Many studies stemmed from the data collected, generally focused on specific land cover types. A similar approach is taken by Dong et al (2012), that analyses pictures uploaded by citizens using a different web application. The research conducted by Dorn et al (2014) …”

Section: Land Cover/land Usementioning

confidence: 99%

Citizen observations contributing to flood modelling: opportunities and challenges

Assumpção¹,

Popescu²,

Jonoski³

et al. 2017

Preprint

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Abstract. Citizen contributions to science have been successfully implemented in many fields -and water resources is one of them. Through citizens, it is possible to collect data and obtain a more integrated decision-making process. Specifically, data scarcity has always been an issue in flood modelling, which has been addressed in the last decades by remote sensing and is already being discussed in a citizen science scenario. In this context, this article aims to review the literature on the 10 topic and analyse the opportunities and challenges that lie ahead. The literature on monitoring, mapping and modelling, was evaluated according to the flood-related variable citizens contributed to. Pros and cons of the collection/analysis methods were summarised. Then, pertinent publications were mapped into the flood modelling cycle, considering how citizen data properties (spatial and temporal coverage, uncertainty and volume) are related to its integration into modelling. It was clear that the number of studies in the area is rising. There are positive experiences reported in collection and analysis methods, 15 for instance with velocity and land cover, and also when modelling is concerned, for example by using social media mining.However, matching the data properties necessary for each part of the modelling cycle with citizen generated data is still challenging. Nevertheless, the concept that citizen contributions can be used for simulation and forecasting is proved and further work lies in continuing developing and improving not only methods for collection and analysis but certainly for integration into models as well. Finally, in view of recent automated sensors and satellite technologies, it is through studies 20 as the ones analysed in this article that the value of citizen contributions is demonstrated.

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A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA

Cited by 95 publications

References 55 publications

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

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

Citizen observations contributing to flood modelling: opportunities and challenges

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