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Dr. Hong Wang
Hohai University, Nanjing, China

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0 LiDAR
0 Remote Sensing
0 urban land use
0 forest health
0 geographic big data

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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.

Articles
Published: 27 March 2018 in International Journal of Remote Sensing
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The Robinia pseudoacacia forest in the Yellow River Delta (YRD), China, was planted in the 1970s and has continuously suffered dieback and mortality since the 1990s. Timely and accurate information on forest growth and forest condition and its dynamic change as well is essential for assessing and developing effective management strategies. In this study, multitemporal Landsat imagery was used to analyze and monitor changes of the R. pseudoacacia forest in the YRD from 1995 to 2013. To do so, Landsat image band reflectance, three fraction images calculated by using a multiple endmember spectral mixture analysis (MESMA) method, and four vegetation indices (VIs) were used to discriminate three health levels of R. pseudoacacia forest in years 1995, 2007, and 2013 with a random forest (RF) classifier. The four VIs include a difference infrared index (DII) developed in this study, normalized difference vegetation index, soil-adjusted vegetation index, and normalized difference infrared index (NDII), all of which were computed from Landsat Thematic Mapper and Operational Land Imager multispectral (MS) bands. The dynamic changes of the forest health levels during the periods of 1995–2007 and 2007–2013 were analysed. The analysis results demonstrate that three fraction images created by MESMA method and four VIs were powerful in separating the three forest health levels. In addition to the Landsat MS bands, the additional three fraction images increased the classification accuracy by 14−20%; if coupled with the four VIs, the overall accuracy was further increased by 5−6%. According to the importance values calculated by RF classifier for all input features, the DII vegetation index was the second effective feature, outperforming NDII. From 1995 to 2013, a total of 2615 ha of forest in the study area suffered from mortality or loss.

ACS Style

Hong Wang; Yi Zhong; Ruiliang Pu; Yu Zhao; Yin Song; Guilin Li. Dynamic analysis of Robinia pseudoacacia forest health levels from 1995 to 2013 in the Yellow River Delta, China using multitemporal Landsat imagery. International Journal of Remote Sensing 2018, 39, 4232 -4253.

AMA Style

Hong Wang, Yi Zhong, Ruiliang Pu, Yu Zhao, Yin Song, Guilin Li. Dynamic analysis of Robinia pseudoacacia forest health levels from 1995 to 2013 in the Yellow River Delta, China using multitemporal Landsat imagery. International Journal of Remote Sensing. 2018; 39 (12):4232-4253.

Chicago/Turabian Style

Hong Wang; Yi Zhong; Ruiliang Pu; Yu Zhao; Yin Song; Guilin Li. 2018. "Dynamic analysis of Robinia pseudoacacia forest health levels from 1995 to 2013 in the Yellow River Delta, China using multitemporal Landsat imagery." International Journal of Remote Sensing 39, no. 12: 4232-4253.

Journal article
Published: 16 July 2015 in Remote Sensing
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The textural and spatial information extracted from very high resolution (VHR) remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. Specifically, eight GLCM texture features (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) were first calculated from IKONOS NIR band (Band 4) to determine an optimal window size (13 × 13) and an optimal direction (45°). Then, the optimal window size and direction were applied to the three other IKONOS MS bands (blue, green, and red) for calculating the eight GLCM textures. Next, an optimal distance value (5) and an optimal neighborhood rule (Queen’s case) were determined for calculating the four Gi features from the four IKONOS MS bands. Finally, different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 93.3% created with the all 32 GLCM features calculated from the four IKONOS MS bands with a window size of 13 × 13 and direction of 45°; (4) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen’s neighborhood rule; and (5) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The most important feature ranked by RF classifier was GLCM texture mean calculated from Band 4, followed by Gi feature calculated from Band 4. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) the IKONOS NIR band was more powerful than visible bands in quantifying varying degrees of forest crown dieback.

ACS Style

Hong Wang; Yu Zhao; Ruiliang Pu; Zhenzhen Zhang. Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier. Remote Sensing 2015, 7, 9020 -9044.

AMA Style

Hong Wang, Yu Zhao, Ruiliang Pu, Zhenzhen Zhang. Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier. Remote Sensing. 2015; 7 (7):9020-9044.

Chicago/Turabian Style

Hong Wang; Yu Zhao; Ruiliang Pu; Zhenzhen Zhang. 2015. "Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier." Remote Sensing 7, no. 7: 9020-9044.

Articles
Published: 16 February 2015 in International Journal of Remote Sensing
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The largest artificial Robinia pseudoacacia forests in the Yellow River delta of China have been infected by dieback diseases. Over the past several decades, this has caused a large amount of mortality of Robinia pseudoacacia forests in this area. Timely and accurate information on the health levels of the forests is crucial to improving local ecological and economic conditions. Remote sensing has been demonstrated to be a useful tool to map forest diseases over a large area. In this study, IKONOS and Landsat 8 Operational Land Imager (OLI) sensor data were collected for comparing their capability of accurately mapping health levels of the artificial forests. There were three health levels (i.e. healthy, medium dieback, and severe dieback) based on explicit tree crown symptoms. After the IKONOS and OLI images were preprocessed, both spatial and spectral features were extracted from the IKONOS and OLI imagery, and a maximum likelihood classification method was used to identify and map health levels of Robinia pseudoacacia forests. The experimental results indicate that the IKONOS sensor has greater potential for identifying and mapping forest health levels. Furthermore, texture features, especially texture variance, derived from the IKONOS panchromatic band, contributed greatly to the accuracy of classification results, achieving an overall accuracy (OA) of 96% for the IKONOS sensor and an OA of 88% for the OLI 2, which used both OLI spectral and IKONOS spatial features, compared with an OA of 74% for the OLI sensor alone. Our results indicate that the texture features extracted from high resolution imagery can improve the classification accuracy of health levels of planted forests with a regular spatial pattern. Our experimental results also demonstrate that classification of an image with a spatial resolution similar to, or finer than, tree crown diameter outperforms that of relatively coarse resolution imagery for differentiating living tree crowns and understorey dense green grass.

ACS Style

Hong Wang; Ruiliang Pu; Qi Zhu; Liliang Ren; Zhenzhen Zhang. Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta, China, using IKONOS and Landsat 8 OLI imagery. International Journal of Remote Sensing 2015, 36, 1114 -1135.

AMA Style

Hong Wang, Ruiliang Pu, Qi Zhu, Liliang Ren, Zhenzhen Zhang. Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta, China, using IKONOS and Landsat 8 OLI imagery. International Journal of Remote Sensing. 2015; 36 (4):1114-1135.

Chicago/Turabian Style

Hong Wang; Ruiliang Pu; Qi Zhu; Liliang Ren; Zhenzhen Zhang. 2015. "Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta, China, using IKONOS and Landsat 8 OLI imagery." International Journal of Remote Sensing 36, no. 4: 1114-1135.

Journal article
Published: 14 February 2014 in Environmental Monitoring and Assessment
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This study aims to assess the relative importance of natural and anthropogenic variables on the change of the red-crowned crane habitat in the Yellow River Nature Reserve, East China using multitempopral remote sensing and geographic information system. Satellite images were used to detect the change in potential crane habitat, from which suitable crane habitat was determined by excluding fragmented habitat. In this study, a principal component analysis (PCA) with seven variables (channel flow, rainfall, temperature, sediment discharge, number of oil wells, total length of roads, and area of settlements) and linear regression analyses of potential and suitable habitat against the retained principal components were applied to explore the influences of natural and anthropogenic factors on the change of the red-crowned crane habitat. The experimental results indicate that suitable habitat decreased by 5,935 ha despite an increase of 1,409 ha in potential habitat from 1992 to 2008. The area of crane habitat changed caused by natural drivers such as progressive succession, retrogressive succession, and physical fragmentation is almost the same as that caused by anthropogenic forces such as land use change and behavioral fragmentation. The PCA and regression analyses revealed that natural factors (e.g., channel flow, rainfall, temperature, and sediment discharge) play an important role in the crane potential habitat change and human disturbances (e.g., oil wells, roads, and settlements) jointly explain 51.8 % of the variations in suitable habitat area, higher than 48.2 % contributed by natural factors. Thus, it is vital to reduce anthropogenic influences within the reserve in order to reverse the decline in the suitable crane habitat.

ACS Style

Hong Wang; Jay Gao; Ruiliang Pu; Liliang Ren; Yan Kong; He Li; Ling Li. Natural and anthropogenic influences on a red-crowned crane habitat in the Yellow River Delta Natural Reserve, 1992–2008. Environmental Monitoring and Assessment 2014, 186, 4013 -4028.

AMA Style

Hong Wang, Jay Gao, Ruiliang Pu, Liliang Ren, Yan Kong, He Li, Ling Li. Natural and anthropogenic influences on a red-crowned crane habitat in the Yellow River Delta Natural Reserve, 1992–2008. Environmental Monitoring and Assessment. 2014; 186 (7):4013-4028.

Chicago/Turabian Style

Hong Wang; Jay Gao; Ruiliang Pu; Liliang Ren; Yan Kong; He Li; Ling Li. 2014. "Natural and anthropogenic influences on a red-crowned crane habitat in the Yellow River Delta Natural Reserve, 1992–2008." Environmental Monitoring and Assessment 186, no. 7: 4013-4028.

Journal article
Published: 10 June 2012 in Regional Environmental Change
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As wildlife habitat is in constant evolution, periodic monitoring is essential to assess its quality. In this study, the change to the red-crowned crane habitat in the Yellow River Delta Nature Reserve was detected from multi-temporal remote sensing data from 1992 to 2008 in a geographic information system. Habitat fragmentation was derived from both physical constraints and human disturbance. The changing habitat quality was assessed against five landscape indices. The results obtained from Landsat TM images indicate that potential habitat shrank 37.9 % during 1992–2001, but recovered 99.4 % by 2008. Suitable habitat shrank by 4,329 ha to a level below that of 1992 despite an increase of 4,747 ha in potential habitat due to an increase of 9,075 ha in fragmented areas. Both landscape indices and the red-crowned crane population reveal that suitable habitat was the most fragmented in 2001, but the least fragmented in 1992. Therefore, it is inadequate to just restore wetland through artificial diversion of channel flow to the Reserve to preserve the crane habitat. Commensurate efforts should also be directed at improving habitat quality by minimizing human activities and spatially juxtaposing water and reed marshes harmoniously inside the Reserve.

ACS Style

Hong Wang; Jay Gao; Li-Liang Ren; Yan Kong; He Li; Ling Li. Assessment of the red-crowned crane habitat in the Yellow River Delta Nature Reserve, East China. Regional Environmental Change 2012, 13, 115 -123.

AMA Style

Hong Wang, Jay Gao, Li-Liang Ren, Yan Kong, He Li, Ling Li. Assessment of the red-crowned crane habitat in the Yellow River Delta Nature Reserve, East China. Regional Environmental Change. 2012; 13 (1):115-123.

Chicago/Turabian Style

Hong Wang; Jay Gao; Li-Liang Ren; Yan Kong; He Li; Ling Li. 2012. "Assessment of the red-crowned crane habitat in the Yellow River Delta Nature Reserve, East China." Regional Environmental Change 13, no. 1: 115-123.