Phenological heterogeneities of invasive Spartina alterniflora salt marshes revealed by high-spatial-resolution satellite imagery

Xiao, Han; Wang, Yiming; Ke, Yinghai; Liu, Tianqi; Zhou, Demin

doi:10.1016/j.ecolind.2022.109492

Cited by 11 publications

(8 citation statements)

References 59 publications

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“…Figure 9D shows that the coverage of S.alterniflora gradually increased during the first five years of the invasion (average values from 55.04% with 1 year of invasion to 75.42% with 5 years of invasion), and then kept high (around 70%). It is worth mentioning that in other studies (Wang et al, 2021b;Han et al, 2022a), they are also reported that the first five years of invasion is the key period for the expansion of S.alterniflora.…”

Section: Fractional Cover Of Saltmarsh Vegetation Species Across Yrdmentioning

confidence: 84%

“…S.alterniflora was first found in Zone B in 2008, then expanded to Zone A and Zone C, and finally expanded to Zone D in 2017 (Wang et al, 2021b). In addition, it has been reported that the live stem density of S.alterniflora is related to the invasion ages (Han et al, 2022a). Therefore, we calculated the statistics of fractional cover of S.alterniflora with different invasion ages (Figure 9D).…”

Section: Fractional Cover Of Saltmarsh Vegetation Species Across Yrdmentioning

confidence: 99%

“…P. autstralis starts to grow in April, reaches the maximum greenness during July and August, and enters senescence in September. S.alterniflora starts to grow in late May and early June, reaches the maximum greenness during August and September, and then enters senescence in late October to early November (Han et al, 2022a). S. salsa presents red-purple color and the abundance reached the maximum during October.…”

Section: Necessities Of Cloud Removal In Fractional Cover Estimation ...mentioning

confidence: 99%

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Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Wang

Ke²,

Lu³

et al. 2022

Front. Mar. Sci.

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Saltmarshes in coastal wetlands provide important ecosystem services. Satellite remote sensing has been widely used for mapping and classification of saltmarsh vegetation, however, medium-spatial-resolution satellite datasets such as Landsat-series imagery may induce mixed pixel problems over saltmarsh landscapes which are spatially heterogeneous. Sub-pixel fractional cover estimation of saltmarsh vegetation at species level are required to better understand the distribution and canopy structure of saltmarsh vegetation. In this study, we presented an approach framework for estimating and mapping the fractional cover of major saltmarsh species in the Yellow River Delta, China based on time series Landsat 8 Operational Land Imager data. To solve the problem that the coastal area is frequently covered by clouds, we adopted the recently developed virtual image-based cloud removal (VICR) algorithm to reconstruct missing image values under the cloud/cloud shadows over the time series Landsat imagery. Then, we developed an ensemble learning model (ELM), which incorporates Random Forest Regression (RFR), K-Nearest Neighbor Regression (KNNR) and Gradient Boosted Regression Tree (GBRT) based on temporal-spectral features derived from the time-series cloudless images to estimate the fractional cover of major vegetation types, i.e., Phragmites australis, Suaeda salsa and the invasive species, Spartina alterniflora. High spatial resolution imagery acquired by the Unmanned Aerial Vehicle and Gaofen-6 satellites were used for reference sample collections. The results showed that our approach successfully estimated the fractional cover of each saltmarsh species (average of R-square:0.891, RMSE: 7.48%). Through four scenarios of experiments, we found that the ELM is advantageous over each individual model. When the images during key months were absent, cloud removal for the Landsat images considerably improved the estimation accuracies. In the study area, Spartina alterniflora covers the largest area (5753.97 ha), followed by Phragmites australis with spatial extent area of 4208.4 ha and Suaeda salsa of 1984.41 ha. The average fractional cover of S. alterniflora was 58.45%, that of P. australis was 51.64% and that of S.salsa was 51.64%.

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Section: Fractional Cover Of Saltmarsh Vegetation Species Across Yrdmentioning

confidence: 84%

Section: Fractional Cover Of Saltmarsh Vegetation Species Across Yrdmentioning

confidence: 99%

Section: Necessities Of Cloud Removal In Fractional Cover Estimation ...mentioning

confidence: 99%

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Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Wang

Ke²,

Lu³

et al. 2022

Front. Mar. Sci.

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“…The phenological changes induced by WPE may disrupt synchrony between species and trophic resources ( Kharouba et al., 2018 ) and modify the competitive dynamics among coexisting species ( Zhu et al., 2018 ), thereby having significant implications for biodiversity and ecosystem functioning ( Woods et al., 2022 ). Therefore, gaining a deeper understanding of the mechanisms behind WPE-induced phenological changes can inform restoration strategies for native wetland vegetation and enhancing wetland management ( Han et al., 2022 ; Helfter et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Woody encroachment induced earlier and extended growing season in boreal wetland ecosystems

Sun,

Wang,

Liu

et al. 2024

Front. Plant Sci.

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Woody plant encroachment (WPE), a widespread ecological phenomenon globally, has significant impacts on ecosystem structure and functions. However, little is known about how WPE affects phenology in wetland ecosystems of middle and high latitudes. Here, we investigated the regional-scale effects of WPE on the start (SOS), peak (POS), end (EOS), and length (GSL) of the growing season in boreal wetland ecosystems, and their underlying mechanisms, using remote sensing dataset during 2001–2016. Our results showed that WPE advanced the annual SOS and POS, while delaying EOS and extending GSL in boreal wetlands with these impacts increasing over time. When boreal wetland ecosystems were fully encroached by woody plants, the SOS and POS were advanced by 12.17 and 5.65 days, respectively, the EOS was postponed by 2.74 days, and the GSL was extended by 15.21 days. We also found that the impacts of WPE on wetland SOS were predominantly attributed to the increased degree of WPE (α), while climatic factors played a more significant role in controlling the POS and EOS responses to WPE. Climate change not only directly influenced phenological responses of wetlands to WPE but also exerted indirect effects by regulating soil moisture and α. Winter precipitation and spring temperature primarily determined the effects of WPE on SOS, while its impacts on POS were mainly controlled by winter precipitation, summer temperature, and precipitation, and the effects on EOS were mainly determined by winter precipitation, summer temperature, and autumn temperature. Our findings offer new insights into the understanding of the interaction between WPE and wetland ecosystems, emphasizing the significance of considering WPE effects to ensure accurate assessments of phenology changes.

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“…In this study, we employed the derivative method to extract two phenological parameters closely related to plant photosynthesis, namely the start of the growing season (SOS) and the end of the growing season (EOS). 52,53 Specifically, we computed the curvature change rate (CCR) by taking the second-order derivative of the reconstructed NDVI time series data and determined the phenological parameters by locating the extreme values of CCR. The maximum rate of change of the vegetation index was identified as SOS, and the maximum rate of change of the decline rate was considered as EOS.…”

Section: Phenological Parameters and Unique Phenological Periods Extr...mentioning

confidence: 99%

Pixel-based unique phenological feature composite method: tackle the challenges of Spartina alterniflora identification and salt marsh classification in mixed vegetations area

Dai

Luo

Chen

et al. 2023

J. Appl. Rem. Sens.

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Coastal salt marshes are valuable wetland ecosystems, and obtaining accurate information on their spatial and temporal distribution is crucial. However, due to the strong spectral similarity among various types of salt marshes, distinguishing them is difficult, especially in areas with mixed salt marsh growth. In this study, we developed a pixel-based unique phenological feature composite method (PUpf-CM). First, we identified a unique phenological period for each type of salt marsh by analyzing the normalized difference vegetation index time-series of Spartina alterniflora (S. alterniflora) and native salt marshes [Suaeda salsa (S. salsa), Phragmites australis (P. australis), and Imperata cylindrica (I. cylindrica)] in Yancheng National Nature Reserve, Jiangsu Province, China. Then, we generated a composite image for each unique phenological period, maximizing the spectral separability between the corresponding salt marsh and background species. Second, we used the composite images obtained at the unique phenological period of S. alterniflora as input to the random forest classifier, achieving high precision identification of S. alterniflora. Compared with the previously developed pixel-based PUpf-CM, the overall accuracy (OA) of PUpf-CM was 98.26% to 99.42%, and the Kappa was 0.940 to 0.975, indicating an improvement of 0.87% to 4.94% and 0.031 to 0.179, respectively. Finally, we tested the possibility of applying PUpf-CM to salt marsh classification by stacking four composite images obtained at different unique phenological periods. We found that the maximum spectral separability was achieved for the combination of four unique phenological periods, and the OA and Kappa of the PUpf-CM-based salt marsh classification reached 97.54% and 0.969, respectively.

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Phenological heterogeneities of invasive Spartina alterniflora salt marshes revealed by high-spatial-resolution satellite imagery

Cited by 11 publications

References 59 publications

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Woody encroachment induced earlier and extended growing season in boreal wetland ecosystems

Pixel-based unique phenological feature composite method: tackle the challenges of Spartina alterniflora identification and salt marsh classification in mixed vegetations area

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