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Jing Sun
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

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Journal article
Published: 24 July 2020 in Remote Sensing
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High-spatial-resolution (HSR) urban land use maps are very important for urban planning, traffic management, and environmental monitoring. The rapid urbanization in China has led to dramatic urban land use changes, however, so far, there are no such HSR urban land use maps based on unified classification frameworks. To fill this gap, the mapping of 2018 essential urban land use categories in China (EULUC-China) was jointly accomplished by a group of universities and research institutes. However, the relatively lower classification accuracy may not sufficiently meet the application demands for specific cities. Addressing these challenges, this study took Nanjing city as the case study to further improve the mapping practice of essential urban land use categories, by refining the generation of urban parcels, resolving the problem of unbalanced distribution of point of interest (POI) data, integrating the spatial dependency of POI data, and evaluating the size of training samples on the classification accuracy. The results revealed that (1) the POI features played the most important roles in classification performance, especially in identifying administrative, medical, sport, and cultural land use categories, (2) compared with the EULUC-China, the overall accuracy for Level I and Level II in EULUC-Nanjing has increased by 11.1% and 5%, to 86.1% and 80% respectively, and (3) the classification accuracy of Level I and Level II would be stable when the number of training samples was up to 350. The methods and findings in this study are expected to better inform the regional to continental mappings of urban land uses.

ACS Style

Jing Sun; Hong Wang; Zhenglin Song; Jinbo Lu; Pengyu Meng; Shuhong Qin. Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data. Remote Sensing 2020, 12, 2386 .

AMA Style

Jing Sun, Hong Wang, Zhenglin Song, Jinbo Lu, Pengyu Meng, Shuhong Qin. Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data. Remote Sensing. 2020; 12 (15):2386.

Chicago/Turabian Style

Jing Sun; Hong Wang; Zhenglin Song; Jinbo Lu; Pengyu Meng; Shuhong Qin. 2020. "Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data." Remote Sensing 12, no. 15: 2386.

Journal article
Published: 24 November 2019 in International Journal of Applied Earth Observation and Geoinformation
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Forest plantations are an important source of terrestrial carbon sequestration. The forest of Robinia pseudoacacia in the Yellow River Delta (YRD) is the largest artificial ecological protection forest in China. However, more than half of the forest has appeared different degrees of dieback and even death since the 1990s. Timely and accurate estimation of the forest aboveground biomass (AGB) is a basis for studying the carbon cycle of forests. Light Detecting and Ranging (LiDAR) has been proved to be one of the most powerful methods for forest biomass estimation. However, because of an irregular and overlapping shape of the broadleaved forest canopy in a growing season, it is difficult to segment individual trees and estimate the tree biomass from airborne LiDAR data. In this study, a new method was proposed to solve this problem of individual tree detection in the Robinia pseudoacacia forest based on a combination of the Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) with the Backpack-LiDAR. The proposed method mainly consists of following steps: (i) at a plot level, trees in the UAV-LiDAR data were detected by seed points obtained by an individual tree segmentation (ITS) method from the Backpack-LiDAR data; (ii) height and diameter at breast height (DBH) of an individual tree would be extracted from UAV and Backpack LiDAR data, respectively; (iii) the individual tree AGB would be calculated through an allometric equation and the forest AGB at the plot level was accumulated; and (iv) the plot-level forest AGB was taken as a dependent variable, and various metrics extracted from UAV-LiDAR point cloud data as independent variables to estimate forest AGB distribution in the study area by using both multiple linear regression (MLR) and random forest (RF) models. The results demonstrate that: (1) the seed points extracted from Backpack-LiDAR could significantly improve the overall accuracy of individual tree detection (F = 0.99), and thus increase the forest AGB estimation accuracy; (2) compared with MLR model, the RF model led to a higher estimation accuracy (p < 0.05); and (3) LiDAR intensity information selected by both MLR and RF models and laser penetration rate (LP) played an important role in estimating healthy forest AGB.

ACS Style

Jinbo Lu; Hong Wang; Shuhong Qin; Lin Cao; Ruiliang Pu; Guilin Li; Jing Sun. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds. International Journal of Applied Earth Observation and Geoinformation 2019, 86, 102014 .

AMA Style

Jinbo Lu, Hong Wang, Shuhong Qin, Lin Cao, Ruiliang Pu, Guilin Li, Jing Sun. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds. International Journal of Applied Earth Observation and Geoinformation. 2019; 86 ():102014.

Chicago/Turabian Style

Jinbo Lu; Hong Wang; Shuhong Qin; Lin Cao; Ruiliang Pu; Guilin Li; Jing Sun. 2019. "Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds." International Journal of Applied Earth Observation and Geoinformation 86, no. : 102014.