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Xuelian Meng
Department of Geography and Anthropology, College of Humanities and Social Sciences, Louisiana State University, Baton Rouge, LA 70803, USA

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Journal article
Published: 01 October 2019 in Water
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Coastal Louisiana hosts 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Louisiana has lost approximately 4900 km2 of wetland area since the early 1930s. Despite the economic relevance of both wetland biomass and net primary productivity (NPP) as ecosystem services, there is a lack of vegetation simulation models to forecast the trends of those functional attributes at the landscape level as hydrological restoration projects are implemented. Here, we review the availability of peer-reviewed biomass and NPP wetland data (below and aboveground) published during the period 1976–2015 for use in the development, calibration and validation of high spatial resolution (

ACS Style

Victor H. Rivera-Monroy; Courtney Elliton; Siddhartha Narra; Ehab Meselhe; Xiaochen Zhao; Eric White; Charles E. Sasser; Jenneke M. Visser; Xuelian Meng; Hongqing Wang; Zuo Xue; Fernando Jaramillo; Rivera- Monroy; Zhao; Meng; Wang; Xue. Wetland Biomass and Productivity in Coastal Louisiana: Base Line Data (1976–2015) and Knowledge Gaps for the Development of Spatially Explicit Models for Ecosystem Restoration and Rehabilitation Initiatives. Water 2019, 11, 2054 .

AMA Style

Victor H. Rivera-Monroy, Courtney Elliton, Siddhartha Narra, Ehab Meselhe, Xiaochen Zhao, Eric White, Charles E. Sasser, Jenneke M. Visser, Xuelian Meng, Hongqing Wang, Zuo Xue, Fernando Jaramillo, Rivera- Monroy, Zhao, Meng, Wang, Xue. Wetland Biomass and Productivity in Coastal Louisiana: Base Line Data (1976–2015) and Knowledge Gaps for the Development of Spatially Explicit Models for Ecosystem Restoration and Rehabilitation Initiatives. Water. 2019; 11 (10):2054.

Chicago/Turabian Style

Victor H. Rivera-Monroy; Courtney Elliton; Siddhartha Narra; Ehab Meselhe; Xiaochen Zhao; Eric White; Charles E. Sasser; Jenneke M. Visser; Xuelian Meng; Hongqing Wang; Zuo Xue; Fernando Jaramillo; Rivera- Monroy; Zhao; Meng; Wang; Xue. 2019. "Wetland Biomass and Productivity in Coastal Louisiana: Base Line Data (1976–2015) and Knowledge Gaps for the Development of Spatially Explicit Models for Ecosystem Restoration and Rehabilitation Initiatives." Water 11, no. 10: 2054.

Journal article
Published: 23 February 2018 in ISPRS International Journal of Geo-Information
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This paper proposes an open-boundary locally weighted dynamic time warping (OLWDTW) method using MODIS Normalized Difference Vegetation Index (NDVI) time-series data for cropland recognition. The method solves the problem of flexible planting times for crops in Southeast Asia, which has sufficient thermal and water conditions. For NDVI time series starting at the beginning of the year and terminating at the end of the year, the method can separate the non-growing season cycle and growing season cycle for crops. The non-growing season cycle may provide some useful information for crop recognition, such as soil conditions. However, the shape of the growing season’s NDVI time series for crops is the key to separating cropland from other land cover types because the shape contains all of the crop growth information. The principle of the OLWDTW method is to enhance the effects of the growing season cycle on the NDVI time series by adding a local weight to the growing season when comparing the similarity of time series based on the open-boundary dynamic time warping (DTW) method. Experiments with two satellite datasets located near the Khorat Plateau in the Lower Mekong Basin validate that OLWDTW effectively improves the precision of cropland recognition compared to a non-weighted open-boundary DTW method in terms of overall accuracy. The method’s classification accuracy on cropland exceeds the non-weighted open-boundary DTW by 5–7%. In future studies, an open-boundary self-adaption locally weighted DTW and a more effective combination rule for different crop types should be explored for the method’s best performance and highest extraction accuracy for cropland.

ACS Style

Xudong Guan; Gaohuan Liu; Chong Huang; Xuelian Meng; Qingsheng Liu; Chunsheng Wu; Xarapat Ablat; Zhuoran Chen; Qiang Wang. An Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping. ISPRS International Journal of Geo-Information 2018, 7, 75 .

AMA Style

Xudong Guan, Gaohuan Liu, Chong Huang, Xuelian Meng, Qingsheng Liu, Chunsheng Wu, Xarapat Ablat, Zhuoran Chen, Qiang Wang. An Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping. ISPRS International Journal of Geo-Information. 2018; 7 (2):75.

Chicago/Turabian Style

Xudong Guan; Gaohuan Liu; Chong Huang; Xuelian Meng; Qingsheng Liu; Chunsheng Wu; Xarapat Ablat; Zhuoran Chen; Qiang Wang. 2018. "An Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping." ISPRS International Journal of Geo-Information 7, no. 2: 75.

Journal article
Published: 19 November 2017 in Remote Sensing
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Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.

ACS Style

Xuelian Meng; Nan Shang; Xukai Zhang; Chunyan Li; Kaiguang Zhao; Xiaomin Qiu; Eddie Weeks. Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction. Remote Sensing 2017, 9, 1187 .

AMA Style

Xuelian Meng, Nan Shang, Xukai Zhang, Chunyan Li, Kaiguang Zhao, Xiaomin Qiu, Eddie Weeks. Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction. Remote Sensing. 2017; 9 (11):1187.

Chicago/Turabian Style

Xuelian Meng; Nan Shang; Xukai Zhang; Chunyan Li; Kaiguang Zhao; Xiaomin Qiu; Eddie Weeks. 2017. "Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction." Remote Sensing 9, no. 11: 1187.

Journal article
Published: 09 May 2017 in ISPRS International Journal of Geo-Information
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This research applied terrestrial LiDAR for laboratory beach evolution experiments to quantify the impact of resolution on topographic mapping and change analyses. The multi-site registration and multi-temporal scanning processes produced high accuracy (−0.002 ± 0.003 m) topographic models in a wave tank environment. Morphological analyses based on surface change and profiles showed that models of all resolutions were capable of capturing major sediment changes in relatively smooth areas. However, higher resolution models were necessary in areas with rough surfaces and sudden elevation changes, while coarser resolution models smoothed the roughness and underestimated feature height (e.g., peaks and troughs). Decreasing resolutions from 1 to 10 cm resulted in a 2% underestimation of erosional volumes with a linear regression of y = −0.0964x + 0.4185 (R2 = 0.9651) and 3.5% overestimation of depositional volumes with a linear regression of y = 0.0664x + 0.3308 (R2 = 0.3645). However, its impact on erosion and deposition volume assessment based on object-oriented beach evolution analysis is less significant, except when fragment objects dominate the sediment changes. For Coastal Morphology Analyst (CMA), the impact of resolution is more observable through 2D object mapping in terms of object size, number, and spatial distribution. Finally, wave modeling experiments proved that resolutions caused significant changes on the behavior of the maximum wave height, the shape of the wave fronts and magnitudes of the currents.

ACS Style

Xuelian Meng; Xukai Zhang; Rodolfo Silva; Chunyan Li; Lei Wang. Impact of High-Resolution Topographic Mapping on Beach Morphological Analyses Based on Terrestrial LiDAR and Object-Oriented Beach Evolution. ISPRS International Journal of Geo-Information 2017, 6, 147 .

AMA Style

Xuelian Meng, Xukai Zhang, Rodolfo Silva, Chunyan Li, Lei Wang. Impact of High-Resolution Topographic Mapping on Beach Morphological Analyses Based on Terrestrial LiDAR and Object-Oriented Beach Evolution. ISPRS International Journal of Geo-Information. 2017; 6 (5):147.

Chicago/Turabian Style

Xuelian Meng; Xukai Zhang; Rodolfo Silva; Chunyan Li; Lei Wang. 2017. "Impact of High-Resolution Topographic Mapping on Beach Morphological Analyses Based on Terrestrial LiDAR and Object-Oriented Beach Evolution." ISPRS International Journal of Geo-Information 6, no. 5: 147.

Journal article
Published: 08 January 2016 in Remote Sensing
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Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data has been widely used in the fields of crop and rice classification. The cloudy and rainy weather characteristics of the monsoon season greatly reduce the likelihood of obtaining high-quality optical remote sensing images. In addition, the diverse crop-planting system in Vietnam also hinders the comparison of NDVI among different crop stages. To address these problems, we apply a Dynamic Time Warping (DTW) distance-based similarity measure approach and use the entire yearly NDVI time series to reduce the inaccuracy of classification using a single image. We first de-noise the NDVI time series using S-G filtering based on the TIMESAT software. Then, a standard NDVI time-series base for rice growth is established based on field survey data and Google Earth sample data. NDVI time-series data for each pixel are constructed and the DTW distance with the standard rice growth NDVI time series is calculated. Then, we apply thresholds to extract rice growth areas. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice-cropping map reveal a high mapping accuracy at the national scale between the statistical data, with the corresponding R2 being as high as 0.809; however, the mapped rice accuracy decreased at the provincial scale due to the reduced number of rice planting areas per province. An analysis of the results indicates that the 500-m resolution MODIS data are limited in terms of mapping scattered rice parcels. The results demonstrate that the DTW-based similarity measure of the NDVI time series can be effectively used to map large-area rice cropping systems with diverse cultivation processes.

ACS Style

Xudong Guan; Chong Huang; Gaohuan Liu; Xuelian Meng; Qingsheng Liu. Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance. Remote Sensing 2016, 8, 19 .

AMA Style

Xudong Guan, Chong Huang, Gaohuan Liu, Xuelian Meng, Qingsheng Liu. Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance. Remote Sensing. 2016; 8 (1):19.

Chicago/Turabian Style

Xudong Guan; Chong Huang; Gaohuan Liu; Xuelian Meng; Qingsheng Liu. 2016. "Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance." Remote Sensing 8, no. 1: 19.