Spatial pattern changes of Spartina alterniflora with different invasion ages in the Yancheng coastal wetland

Liu, Xiang; Liu, Huiyu; Zhen-shan, Lin; Li, Lihe; Lv, Shicheng

doi:10.1016/j.chnaes.2017.09.002

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Compared with BSVM, Maxent appeared to have a more balanced performance over the summer months. Although a large number of studies has reported that Maxent has a great advantage over other OCC methods based on remote sensing data, model complexity was completely ignored in most cases [13,29,36,45,46,51,55,58,59,62]. Only a few studies have taken into account the problem of model complexity in remote sensing mapping [81,82].…”

Section: Performance Of Occ Methods For S Alterniflora Detection Andmentioning

confidence: 99%

Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis

Liu

Gong

et al. 2017

Remote Sensing

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Spartina alterniflora has become the main invasive plant along the Chinese coast and now threatens the local ecological environment. Accurately monitoring the distribution of S. alterniflora is urgent and essential for developing cost-effective control strategies. In this study, we applied the One-Class Classification (OCC) methods of Maximum entropy (Maxent) and Biased Support Vector Machine (BSVM) based on Landsat time-series imagery to detect the species on the middle coast of Jiangsu in east China. We conducted four experimental setups (i.e., single-scene analysis, time-series analysis, Normalized Difference Vegetation Index (NDVI) time-series analysis and a compressed time-series analysis), using OCC methods to recognize the species. Then, we tested the performance of a compressed time-series model for S. alterniflora detection and evaluated the expansibility of this approach when it was applied to a larger region. Our principal findings are as follows: (1) Maxent and BSVM performed equally well, and Maxent appeared to have a more balanced performance over the summer months; (2) the Maxent model with the Default Parameter Set (Maxent-DPS) showed a slightly higher accuracy and more overfitting than Maxent with the Akaike Information Criterion corrected for small samples sizes (AICc)-selected parameter set model, but a t-test found no significant difference between these two settings; (3) April and December were deemed to be important periods for the detection of S. alterniflora; (4) a compressed time-series analysis model-including only three variables (December NDVI, March green and the third Principal Component in January, PC3)-yielded higher accuracy than single-scene analyses, which indicated that time-series analysis can better detect S. alterniflora than single-scene analyses; and (5) the Maxent model using the reconstructed optimal variables and 70 training samples over a larger region produced encouraging results with an overall accuracy of 90.88% and a Kappa of 0.78. The one-class classification method combined with a phenology-based detection strategy is therefore promising for the application of the long-term detection of S. alterniflora over extended areas.

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Section: Performance Of Occ Methods For S Alterniflora Detection Andmentioning

confidence: 99%

Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis

Liu

Gong

et al. 2017

Remote Sensing

Self Cite

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“…Yancheng coastal wetland is located in east of Jiangsu Province, China (32°34′-34°28′N, 119°27′-121°16′E), having jurisdiction over Xiangshui, Binhai, Sheyang, Dafeng and Dongtai five cities, with the coastline length 582 km and an area of 4.553 × 103 km 2 [55]. It has a monsoon climate, receiving rich rainfall and solar radiation energy.…”

Section: Study Areamentioning

confidence: 99%

“…Yancheng coastal wetland is located in east of Jiangsu Province, China [55]. It has a monsoon climate, receiving rich rainfall and solar radiation energy.…”

Section: Datasetsmentioning

confidence: 99%

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

et al. 2019

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Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

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“…Ai et al [2] accurately extracted the distribution of S. alterniflora in the Dongtan Wetland of Chongming, China, by using the Landsat OLI image data in late August. Li et al [3] used the object-oriented method to monitor S. alterniflora in invading Guangxi. Wang et al [4] utilized SVM classifiers to respectively long time series monitoring of S. alterniflora in Yueqing Bay and the modern Yellow River Delta.…”

Section: Introductionmentioning

confidence: 99%

“…Many scholars have conducted research on the invasion of S. alterniflora and its impact on the evolution of landscape patterns [2][3][4][5]. Most of researches have larger spatial and shorter temporal scales, and there are fewer studies on WNHS.…”

Section: Introductionmentioning

confidence: 99%

Analyses on spatio-temporal changes of spartina alterniflor invasion in the yellow sea wetland world natural heritage (south section) based on RS and GIS

Liu,

Wang,

Zhang

et al. 2024

Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023)

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Spatial pattern changes of Spartina alterniflora with different invasion ages in the Yancheng coastal wetland

Cited by 8 publications

References 12 publications

Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis

Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

Analyses on spatio-temporal changes of spartina alterniflor invasion in the yellow sea wetland world natural heritage (south section) based on RS and GIS

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