Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Wang, Jie; Li, Congcong; Hu, Luanyun; Zhao, Yuanyuan; Huang, Huabing; Gong, Peng

doi:10.3390/rs70100865

Cited by 19 publications

(13 citation statements)

References 36 publications

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“…However, this may require a series of trials before reaching a satisfactory result. Another possible solution is to perform dynamic water mapping with multi-temporal scenes [25,36,37], because the chances of being covered or shadowed by clouds in multiple images would be significantly lower for certain pixels where the number of cloudy days is small. By fusing multi-temporal images, the cloud issue should be easily overcome for those pixels.…”

Section: Discussionmentioning

confidence: 99%

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Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Gong

Geng

et al. 2015

Remote Sensing

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Abstract:The finer resolution observation and monitoring of the global land cover (FROM-GLC) product makes it the first 30 m resolution global land cover product from which one can extract a global water mask. However, two major types of misclassification exist with this product due to spectral similarity and spectral mixing. Mountain and cloud shadows are often incorrectly classified as water since they both have very low reflectance, while more water pixels at the boundaries of water bodies tend to be misclassified as land. In this paper, we aim to improve the accuracy of the 30 m FROM-GLC water mask by addressing those two types of errors. For the first, we adopt an object-based method by computing the topographical feature, spectral feature, and geometrical relation with cloud for every water object in the FROM-GLC water mask, and set specific rules to determine whether a water object is misclassified. For the second, we perform a local spectral unmixing using a two-endmember linear mixing model for each pixel falling in the water-land boundary zone that is 8-neighborhood connected to water-land boundary pixels. Those pixels with big enough water fractions are determined as water. The procedure is automatic. Experimental results show that the total area of inland water has been decreased by 15.83% in the new global water mask compared with the FROM-GLC water mask. Specifically, OPEN ACCESSRemote Sens. 2015, 7 13508 more than 30% of the FROM-GLC water objects have been relabeled as shadows, and nearly 8% of land pixels in the water-land boundary zone have been relabeled as water, whereas, on the contrary, fewer than 2% of water pixels in the same zone have been relabeled as land. As a result, both the user's accuracy and Kappa coefficient of the new water mask (UA = 88.39%, Kappa = 0.87) have been substantially increased compared with those of the FROM-GLC product (UA = 81.97%, Kappa = 0.81).

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Section: Discussionmentioning

confidence: 99%

“…Many studies show that water bodies are highly dynamic, and a large number of mixed pixels exist, particularly near the water boundaries [25][26][27]. The FROM-GLC water mask is derived from a hard classifier, which assigns each pixel to one of the defined land-cover types.…”

Section: Solving the Spectral Mixing Problem Using Local Spectral Unmmentioning

confidence: 99%

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Gong

Geng

et al. 2015

Remote Sensing

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“…Parcels are basic units used in this classification scheme with the assumption that they are homogeneous in terms of urban functions [39]. The parcels were then separated into built-up areas and non-built-up areas based on classified impervious surface areas [37] and defined our classification system based on these two regions (Figure 2-2). The function of each parcel was inferred using the normalized feature distance (or similarity) to the pre-collected training sample units.…”

Section: Methodsmentioning

confidence: 99%

“…First the proportion of impervious area for each parcel was computed based on the classified impervious surface map in the land cover map product [37]. In this study, a threshold of 0.3 were used as suggested by [45,46], to differentiate the built-up and non-built-up areas.…”

Section: Classification Systemmentioning

confidence: 99%

Mapping Urban Land Use by Using Landsat Images and Open Social Data

et al. 2016

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High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government.

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“…RF is an ensemble classifier consists of many tree-structured classifiers [48]. RF can handle many training data and variables and keep accuracy when a proportion of data is missing [49][50][51]. In this study, the number of trees was set to 100, and the number of variables per split was set to the square root of the number of variables.…”

Section: Land Cover Classification and Accuracy Assessmentmentioning

confidence: 99%

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

Huang

Yang

et al. 2017

Remote Sensing

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Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by urban green spaces in 28 megacities worldwide between 2005 and 2015 by using availability and accessibility as proxy indicators. We first mapped land covers of 28 megacities using 10,823 scenes of Landsat images and a random forest classifier running on Google Earth Engine. We then calculated the availability and accessibility of urban green spaces using the land cover maps and gridded population data. The results showed that the mean availability of urban green spaces in these megacities increased from 27.63% in 2005 to 31.74% in 2015. The mean accessibility of urban green spaces increased from 65.76% in 2005 to 72.86% in 2015. The increased availability and accessibility of urban green spaces in megacities have brought more health benefits to their residents.

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Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Cited by 19 publications

References 36 publications

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Mapping Urban Land Use by Using Landsat Images and Open Social Data

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

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