Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

Gong, Ping; Wang, Jie; Yu, Le; Zhao, Yongchao; Zhao, Yuanyuan; Lu, Liang; Niu, Zhenguo; Huang, Xiaomeng; Fu, Haohuan; Liu, Shuang; Li, Congcong; Li, Xueyan; Fu, Wei; Liu, Caixia; Xu, Yue; Wang, Xiaoyi; Cheng, Qu; Hu, Luanyun; Yao, Wenbo; Zhang, Han; Zhu, Peng; Zhao, Ziying; Zhang, Haiying; Zheng, Yunpu; Ji, Liu; Zhang, Yawen; Han, Chunyan; An, Yan; Guo, Jinyun; Liang, Yu; Wang, Lei; Liu, Xiaojun; Shi, Tingting; Mao, Zhu; Chen, Yanlei; Yang, Guangwen; Tang, Ping; Xu, Bing; Giri, Chandra; Clinton, Nicholas; Zhu, Zhiliang; Chen, Jin; Chen, Jun

doi:10.1080/01431161.2012.748992

Cited by 1,372 publications

(907 citation statements)

References 92 publications

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“…The survey is composed of over 13,000 sampling blocks of 10× 10 km within Landsat images. Other large RSS used wall-to-wall sets of Landsat images with comparable results to the ones obtained by the FRA RSS (Gong et al 2013;Hansen et al 2013) or also MODIS images (Hansen et al 2010). The use of different forest definitions produces varying estimates of the forest area.…”

Section: The Forest Resources Assessmentmentioning

confidence: 59%

The role of European National Forest Inventories for international forestry reporting

Vidal¹,

Alberdi²,

Redmond³

et al. 2016

Annals of Forest Science

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Section: The Forest Resources Assessmentmentioning

confidence: 59%

The role of European National Forest Inventories for international forestry reporting

Vidal¹,

Alberdi²,

Redmond³

et al. 2016

Annals of Forest Science

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“…It appears that separating 4 years old rubber stands remains problematic and therefore warrants a future investigation. While this research indicated that RF was favorable in terms of individual class accuracy, it should be noted that disagreement to select a better classifier between RF and SVM exists in the literature [Gong et al, 2013;Adam et al, 2014;Lawrence and Moran, 2015]. This may be sourced from various causes, including site- Figure 5.…”

Section: The Role Of Texturementioning

confidence: 99%

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

Trisasongko

2017

European Journal of Remote Sensing

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This paper presents an evaluation on strategies for rubber plantation mapping employing SAR data coupled with Random Forest (RF) and Support Vector Machine (SVM). Linear backscatter coefficients achieved saturation point at about 10 years, making this form of polarimetric data being robust only for young to mature stands. This research found that the performance of both algorithms was comparable. The addition of texture features gave substantial impact to the overall accuracy. As indicated by the analysis of variable importance, only some texture features have contributed to higher overall accuracy. Classification using a subset of texture features pointed out that accuracy could be improved using dual polarimetric data, while trivial enhancement was seen in combined HH, HV and VV backscatter intensities. The research showed that classification accuracy could be further augmented by setting proper classification parameters. Nonetheless, it is argued that the level of improvement would greatly depend on selecting a proper dataset fed into classifier, rather than tuning classifier parameters. ARTICLE HISTORY

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“…For artificial surfaces, it was also compared to other global land cover products for eight test areas around the world and accuracies ranged from 79% to 97%, outperforming both CLC and the FROM-GLC product of Gong et al [4]. There have been a few other studies that have independently compared GL30 to authoritative products in Italy [30], Germany [31] and water bodies in Scandinavian countries [32] and more recently, land cover in Iran [33], with high agreements, i.e., greater than 78%.…”

Section: Globeland30mentioning

confidence: 99%

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

Fonte

Minghini

Patriarca

et al. 2017

IJGI

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Abstract:With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible.

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Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

Cited by 1,372 publications

References 92 publications

The role of European National Forest Inventories for international forestry reporting

The role of European National Forest Inventories for international forestry reporting

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

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