Dr. Xiaodong Na Profile

Dr. Xiaodong Na

at Harbin Normal Univ

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SPIE Journal Paper | 17 October 2022

Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

Zile Huang, Xiaodong Na

JARS, Vol. 16, Issue 04, 048501, (October 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.048501

KEYWORDS: Image classification, Associative arrays, Radar, Feature extraction, Vegetation, Sodium, Remote sensing, Landsat, Earth observing sensors, Clouds

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Wetlands have very important and irreplaceable ecological service functions in protecting biodiversity and maintaining ecological balance. Due to the unique hydrological characteristics of wetlands, large-scale wetland mapping methods are usually time-consuming, labor intensive, and expensive. With the rise of big data and cloud computing platforms, there are opportunities for long time series and large-scale spatial data processing that improve large-scale wetland mapping. In this study, the Sentinel-1, Sentinel-2, and digital elevation model derived from the Google Earth Engine were used to extract the wetland extent in the Zhalong Nature Reserve based on the Jeffries–Matusita (JM) distance for feature optimization and random forest. Research shows the following: (1) The JM distance for each feature showed that the optical features had the highest separation in all five features and (2) the classification after feature optimization performed best. User accuracy (UA) and producer accuracy (PA) for wetland reached 89.64% and 83.00%, respectively, and overall accuracy reached 90.76%. After feature optimization, PA and UA of wetland were each increased 4.26% and 1.07%, respectively, and the number of features was reduced from 46 to 28. The method used in this study is highly accurate in wetland mapping and can effectively avoid the problem of data redundancy.

Proceedings Article | 24 October 2013 Paper

Wetland mapping and flood extent monitoring using optical and radar remotely sensed data and ancillary topographical data in the Zhalong National Natural Reserve, China

Xiaodong Na, Shuying Zang, Yuhong Zhang, Lei Liu

Proceedings Volume 8893, 88931M (2013) https://doi.org/10.1117/12.2029117

KEYWORDS: Floods, Radar, Vegetation, Landsat, Backscatter, Associative arrays, Polarization, Ecosystems, Synthetic aperture radar, Earth observing sensors

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Information regarding the spatial extent and inundation state in the internationally important Wetlands as designated by Ramsar Convention is important to a series of research questions including wetland ecosystem functioning and services, water management and habitat suitability assessment. This study develops an expedient digital mapping technique using optical remotely sensed imagery of the Landsat Thematic Mapper (TM), ENVISAT ASAR active radar C-band imagery, and topographical indices derived from topographic maps. All data inputs were resampled to a common 30 m resolution grid. An ensemble classifiers based on trees (random forest) procedure was employed to produce a final map of per-grid cell wetland probability map. This study also provides a general approach to delineate the extent of flooding builds upon documented relationships between fields measured inundation state and SAR data response on each vegetation types. The current study indicated that multi-source data (i.e. optical, radar and topography) are useful in the characterization of freshwater marshes and their inundation state. This analysis constitutes a necessary step towards improved herbaceous wetland monitoring and provides ecologists and managers with vital information that is related to ecology and hydrology in a wetland area.

SPIE Journal Paper | 29 May 2013

Wetland mapping in the Zhalong National Natural Reserve, China, using optical and radar imagery and topographical data

Lei Liu, Xiaodong Na, Shuying Zang, Miao Li

JARS, Vol. 7, Issue 01, 073554, (May 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.073554

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Knowledge of the spatial extent of wetlands is important to a series of research questions and applications such as wetland ecosystem functioning, water management, and habitat suitability assessment. This study develops a practical digital mapping technique using an optical image of a Landsat thematic mapper (TM), Envisat advanced synthetic aperture radar (SAR) image, and topographical indices derived from topographic maps. An ensemble classifier based on classification tree procedure [random forests (RFs)] is applied to three different com- binations of predictors: (1) TM imagery alone (TM-only model); (2) TM imagery plus ancillary topographical data [TM + digital terrain model (DTM)]; and (3) TM imagery, ancillary topographical data and radar imagery (TM + DTM + SAR model). Accuracy assessment results indicate that the radar and topographical variables reduce classification error of marsh. The kappa coefficients for the land cover classification increases significantly as radar imagery and ancillary topographical data are added. The per-grid cell probabilities of each land-cover types are estimated based on the RFs model making use of all available predictors. A final land-cover map is generated by defining pixels as the land-cover type with the highest probabilities. Compared with a single classification and regression tree and a conventional maximum likelihood classifier, RFs produce the highest overall accuracy (72%) with a kappa coefficient of 0.6474, and marsh wetland accuracies ranging from 81.2% to 83.33%. The current study indicates that multisource data (i.e., optical, radar, and topography) are useful in the characterization of freshwater marshes and their adjacent land-cover types. The approach developed in the current study is automated, relatively easy to implement, and could be applicable in other settings over large extents.

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