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Dr. Wanwan Liang
Center for Geospatial Analytics, NC State University

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Research Keywords & Expertise

0 Ecological Modeling
0 Machine Learning
0 Remote Sensing Applications
0 Spatial Analysis
0 Image Processing and Analysis

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Journal article
Published: 15 October 2020 in Sustainability
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ACS Style

Wanwan Liang; Liem Tran; Jerome Grant; Vivek Srivastava. Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread. Sustainability 2020, 1 .

AMA Style

Wanwan Liang, Liem Tran, Jerome Grant, Vivek Srivastava. Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread. Sustainability. 2020; ():1.

Chicago/Turabian Style

Wanwan Liang; Liem Tran; Jerome Grant; Vivek Srivastava. 2020. "Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread." Sustainability , no. : 1.

Journal article
Published: 29 July 2020 in Insects
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Invasive species experience biotic and abiotic conditions that may (or may not) resemble their native environment. We explored the methodology of determining climatic niches and compared the native and post-invasion niches of four invasive forest pests to determine if these species experienced shifts or changes in their new climatic niches. We used environmental principle components analysis (PCA-env) method to quantify climatic niche shifts, expansions, and temporal changes. Furthermore, we assessed the effect of variable selection in the delineation and comparison of niche space. We found that variable selection influenced the delineation and overlap of each niche, whereas the subset of climatic variables selected from the first two PCA-env axes explained more variance in environmental conditions than the complete set of climatic variables for all four species. Most focal species showed climatic niche shifts in their invasive range and had not yet fully occupied the available niche within the invaded range. Our species varied the proportion of niche overlap between the native and invasive ranges. By comparing native and invasive niches, we can help predict a species’ potential range expansion and invasion potential. Our results can guide monitoring and help inform management of these and other invasive species.

ACS Style

Vivek Srivastava; Wanwan Liang; Melody A. Keena; Amanda D. Roe; Richard C. Hamelin; Verena C. Griess. Assessing Niche Shifts and Conservatism by Comparing the Native and Post-Invasion Niches of Major Forest Invasive Species. Insects 2020, 11, 479 .

AMA Style

Vivek Srivastava, Wanwan Liang, Melody A. Keena, Amanda D. Roe, Richard C. Hamelin, Verena C. Griess. Assessing Niche Shifts and Conservatism by Comparing the Native and Post-Invasion Niches of Major Forest Invasive Species. Insects. 2020; 11 (8):479.

Chicago/Turabian Style

Vivek Srivastava; Wanwan Liang; Melody A. Keena; Amanda D. Roe; Richard C. Hamelin; Verena C. Griess. 2020. "Assessing Niche Shifts and Conservatism by Comparing the Native and Post-Invasion Niches of Major Forest Invasive Species." Insects 11, no. 8: 479.

Journal article
Published: 12 February 2020 in Remote Sensing
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Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

ACS Style

Wanwan Liang; Mongi Abidi; Luis Carrasco; Jack McNelis; Liem Tran; Yingkui Li; Jerome Grant. Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu. Remote Sensing 2020, 12, 609 .

AMA Style

Wanwan Liang, Mongi Abidi, Luis Carrasco, Jack McNelis, Liem Tran, Yingkui Li, Jerome Grant. Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu. Remote Sensing. 2020; 12 (4):609.

Chicago/Turabian Style

Wanwan Liang; Mongi Abidi; Luis Carrasco; Jack McNelis; Liem Tran; Yingkui Li; Jerome Grant. 2020. "Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu." Remote Sensing 12, no. 4: 609.

Preprint
Published: 31 December 2019
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Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

ACS Style

Wanwan Liang; Mongi Abidi; Luis Carrasco; Jack McNelis; Liem Tran; Jerome Grant; Yingkui Li. Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data over Large Spatial Area: A Case Study with Kudzu. 2019, 1 .

AMA Style

Wanwan Liang, Mongi Abidi, Luis Carrasco, Jack McNelis, Liem Tran, Jerome Grant, Yingkui Li. Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data over Large Spatial Area: A Case Study with Kudzu. . 2019; ():1.

Chicago/Turabian Style

Wanwan Liang; Mongi Abidi; Luis Carrasco; Jack McNelis; Liem Tran; Jerome Grant; Yingkui Li. 2019. "Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data over Large Spatial Area: A Case Study with Kudzu." , no. : 1.

Journal article
Published: 06 March 2019 in Environmental Entomology
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By the end of 2017, kudzu bug was reported in 652 counties in the United States since it was first observed in Georgia in 2009. Modeling its invasion dynamics is valuable to guide management through early detection and prevention of further invasion. Herein, we initially estimated the spread rate of kudzu bug with county-level invasion records and then determined important spatial factors affecting its spread during years 2010-2016. As kudzu bug infests a large heterogeneous area and shows asymmetric spread, we first utilized spatially constrained clustering (SCC), an unsupervised machine learning method, to divide the infested area into eight spatially contiguous and environmentally homogenous neighborhoods. We then used distance regression and boundary displacement methods to estimate the spread rates in all neighborhoods. Finally, we applied multiple regression to determine spatial factors influencing the spread of kudzu bug. The average spread rate reached 76 km/yr by boundary displacement method; however, the rate varied largely among eight neighborhoods (45-144 km/yr). In the southern region of the infested area, host plant density and wind speed were positively associated with the spread rate, whereas mean annual temperature, precipitation in the fall, and elevation had inverse relationships. In the northern region, January minimum temperature, wind speed, and human population density showed positive relationships. This study increases the knowledge on the spread dynamics of kudzu bug. Our research highlights the utility of SCC to determine natural clustering in a large heterogeneous region for better modeling of local spread patterns and determining important factors affecting the invasions.

ACS Style

Wanwan Liang; Liem Tran; Gregory Wiggins; Jerome F Grant; Scott D Stewart; Robert Washington-Allen. Determining Spread Rate of Kudzu Bug (Hemiptera: Plataspidae) and Its Associations With Environmental Factors in a Heterogeneous Landscape. Environmental Entomology 2019, 48, 309 -317.

AMA Style

Wanwan Liang, Liem Tran, Gregory Wiggins, Jerome F Grant, Scott D Stewart, Robert Washington-Allen. Determining Spread Rate of Kudzu Bug (Hemiptera: Plataspidae) and Its Associations With Environmental Factors in a Heterogeneous Landscape. Environmental Entomology. 2019; 48 (2):309-317.

Chicago/Turabian Style

Wanwan Liang; Liem Tran; Gregory Wiggins; Jerome F Grant; Scott D Stewart; Robert Washington-Allen. 2019. "Determining Spread Rate of Kudzu Bug (Hemiptera: Plataspidae) and Its Associations With Environmental Factors in a Heterogeneous Landscape." Environmental Entomology 48, no. 2: 309-317.

Journal article
Published: 26 September 2018 in Ecological Modelling
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Transferability of species distribution models (SDMs) is key to predicting invasion patterns and can be challenged if niche shift occurs in the invaded range. When using native occurrences to estimate potential invasions with presence-only modeling methods, it is important to constrain the pseudo-absence (PA) sampling to the species’ native range. However, some studies including highly cited ones, do not follow this approach to selecting PA samples. In this research, we addressed two questions using an invasive species in the United States (U.S.), kudzu bug (Megacopta cribraria): 1) is model transferability challenged by a non-adaptive niche shift? and 2) is model performance affected by use of PA samples from outside the native range of the species? Kudzu bug is native to Asia, with recently observed non-adaptive niche shift in the U.S. To answer the first question, we quantified the environmental space anisotropy and non-adaptive niche change, and then evaluated the performances of seven SDMs. To answer the second question, we further compared the interpolation and transferability of seven SDMs trained with PAs from the native range and from both native and invaded ranges. We confirmed that the environmental space anisotropy (P = 0.01) and non-adaptive niche change (P = 0.01) are both statistically significant. Of the seven SDMs used, four models had transferability indices higher than 0.9. Boosted regression tree and random forests both had good interpolation and transferability (AUC>0.80 and kappa>0.60), whereas three other models showed good interpolation and fair transferability (AUC>0.70 and kappa>0.40). Inclusion of pseudo-absences from the invaded range significantly increased the interpolation (P < 0.001) but decreased the transferability (P < 0.01) of almost all models. Our findings suggest that SDMs can show good transferability with non-adaptive niche shift, thus native occurrence information should be used in similar situation. We confirmed that it is crucial to constrain the PAs to the same spatial range as presences to accurately model potential invasions.

ACS Style

Wanwan Liang; Monica Papeş; Liem Tran; Jerome Grant; Robert Washington-Allen; Scott Stewart; Gregory Wiggins. The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift. Ecological Modelling 2018, 388, 1 -9.

AMA Style

Wanwan Liang, Monica Papeş, Liem Tran, Jerome Grant, Robert Washington-Allen, Scott Stewart, Gregory Wiggins. The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift. Ecological Modelling. 2018; 388 ():1-9.

Chicago/Turabian Style

Wanwan Liang; Monica Papeş; Liem Tran; Jerome Grant; Robert Washington-Allen; Scott Stewart; Gregory Wiggins. 2018. "The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift." Ecological Modelling 388, no. : 1-9.

Original paper
Published: 03 May 2018 in Biological Invasions
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Biological invasions have long placed challenges on ecosystems, agricultural production, and human health. Modeling potential invasion of an introduced organism becomes a critical tool for early management of damaging species, such as kudzu bug, Megacopta cribraria (F.) (Hemiptera:Heteroptera:Plataspidae). Since it was first found in the United States in 2009, kudzu bug has spread rapidly, economically impacted agricultural production, and became a household pest. To better predict the potential invasion of kudzu bug in North and South America, we used the species distribution models Genetic Algorithm for Rule-set Production (GARP) and Maximum Entropy (Maxent). We used the D metric to test for niche equivalency and similarity between native and invaded populations of kudzu bug. We found that kudzu bugs currently occupied unequal environmental space between the two ranges. Therefore, distribution models using GARP and Maxent were constructed using occurrences in both native and invaded ranges. Area under the curve (AUC), true skill statistics (TSS), and omission rate (OR) were used to evaluate and compare the models. Results indicated both models had good performance, but Maxent (AUC = 0.971, TSS = 0.946, OR = 0.019) performed better than GARP (AUC = 0.922, TSS = 0.860, OR = 0.037). This research confirmed the effectiveness of using occurrence data in both ranges to predict potential invasions. Kudzu bugs prefer warm (annual mean temperature around 15 °C) and humid (annual mean precipitation around 1300 mm) regions. Distribution models generated by both methods indicated similar regions with high invasion risk. Management programs that include quarantine and prevention measures are suggested for these regions to avoid outbreaks of kudzu bug.

ACS Style

Wanwan Liang; Liem Tran; Robert Washington-Allen; Gregory Wiggins; Scott Stewart; James Vogt; Jerome Grant. Predicting the potential invasion of kudzu bug, Megacopta cribraria (Heteroptera: Plataspidae), in North and South America and determining its climatic preference. Biological Invasions 2018, 20, 2899 -2913.

AMA Style

Wanwan Liang, Liem Tran, Robert Washington-Allen, Gregory Wiggins, Scott Stewart, James Vogt, Jerome Grant. Predicting the potential invasion of kudzu bug, Megacopta cribraria (Heteroptera: Plataspidae), in North and South America and determining its climatic preference. Biological Invasions. 2018; 20 (10):2899-2913.

Chicago/Turabian Style

Wanwan Liang; Liem Tran; Robert Washington-Allen; Gregory Wiggins; Scott Stewart; James Vogt; Jerome Grant. 2018. "Predicting the potential invasion of kudzu bug, Megacopta cribraria (Heteroptera: Plataspidae), in North and South America and determining its climatic preference." Biological Invasions 20, no. 10: 2899-2913.