This paper monitored the outbreak of green tide in the Yellow Sea, China, in 2014 based on GOCI remote sensing image and NDVI extraction method, combined with GIS (Geographical Information System) and visual interpretation technologies. The results show: the green tide is firstly found in the open waters near Yancheng, Jiangsu Province in mid May, and drifted from the southwest to the northeast direction. When reached the neighboring waters between Jiangsu and Shandong in early June, the green tide entered an outbreak stage and reached the maximum coverage area of 2206.54 km2 in 18, June. In early July, the green tide began into a recession stage until all died in early August while its frontline preserved in Yantai – Weihai – Qingdao. Our work shows GOCI image with high temporal resolution is available for the study of migration path and drift speed of green tide.
In recent years, satellite remote sensing have been widely used in dynamic monitoring of Green Tide. However, the images captured by unmanned aerial vehicles (UAV) are rarely used in floating green tide monitoring. In this paper, a quad-rotor unmanned aerial vehicle was used to mapping the coverage of green tide on the seabeach in Haiyang with three algorithms based on RGB image.The conclusions are as follows: there is discrepancy in both maximum value band among RGB and the difference in the green band for a true color aerial photograph taken from a UAV; the best index for floating green tide mapping on seabeach is GLI. It is possible to have a comprehensive, objective and scientific understanding of the floating green tide mapping with aid of UAV based on RGB image in the seabeach.
Coastal wetland is a net carbon sink with a high carbon density. However, coastal reclamation
directly changes the structure of coastal wetland ecosystem and consequent carbon sink function.
The aim of this work was to estimate the reclamation-induced carbon loss in coastal wetlands
using time series GF-1 WVF data. For this purpose, GF-1 WVF imageries of 2013 (before
reclamation) and 2017 (after reclamation) in the Yangtze Estuary were collected and analyzed
combined with field monitoring. Results showed that the converted coastal wetland area occupied
up to 61.60% between 2013 and 2017. Carbon estimation indicated that the coastal wetland before
reclamation had greater potential contribution to the global warming mitigation than the wetland
reclamation to other land cover types. Finally the vulnerability of carbon stores and uncertain
analysis with remote sensing technology in coastal wetlands environment were discussed. We
emphasized that long-term monitoring of coastal wetlands and its carbon dynamic are urgently
needed, because so many uncertain factors exist in short-term monitoring.
Spartina alterniflora is an aggressive invasive plant species that replaces native species, changes the structure and function of the ecosystem across coastal wetlands in China, and is thus a major conservation concern. Mapping the spread of its invasion is a necessary first step for the implementation of effective ecological management strategies. The performance of a phenology-based approach for S. alterniflora mapping is explored in the coastal wetland of the Yangtze Estuary using a time series of GaoFen satellite no. 1 wide field of view camera (GF-1 WFV) imagery. First, a time series of the normalized difference vegetation index (NDVI) was constructed to evaluate the phenology of S. alterniflora. Two phenological stages (the senescence stage from November to mid-December and the green-up stage from late April to May) were determined as important for S. alterniflora detection in the study area based on NDVI temporal profiles, spectral reflectance curves of S. alterniflora and its coexistent species, and field surveys. Three phenology feature sets representing three major phenology-based detection strategies were then compared to map S. alterniflora: (1) the single-date imagery acquired within the optimal phenological window, (2) the multitemporal imagery, including four images from the two important phenological windows, and (3) the monthly NDVI time series imagery. Support vector machines and maximum likelihood classifiers were applied on each phenology feature set at different training sample sizes. For all phenology feature sets, the overall results were produced consistently with high mapping accuracies under sufficient training samples sizes, although significantly improved classification accuracies (10%) were obtained when the monthly NDVI time series imagery was employed. The optimal single-date imagery had the lowest accuracies of all detection strategies. The multitemporal analysis demonstrated little reduction in the overall accuracy compared with the use of monthly NDVI time series imagery. These results show the importance of considering the phenological stage for image selection for mapping S. alterniflora using GF-1 WFV imagery. Furthermore, in light of the better tradeoff between the number of images and classification accuracy when using multitemporal GF-1 WFV imagery, we suggest using multitemporal imagery acquired at appropriate phenological windows for S. alterniflora mapping at regional scales.
Accurate mapping of invasive species in a cost-effective way is the first step toward understanding and predicting the impact of their invasions. However, it is challenging in coastal wetlands due to confounding effects of biodiversity and tidal effects on spectral reflectance. The aim of this work is to describe a method to improve the accuracy of mapping an invasive plant (Spartina alterniflora), which is based on integration of pan-sharpening and classifier ensemble techniques. A framework was designed to achieve this goal. Five candidate image fusion algorithms, including principal component analysis fusion algorithm, modified intensity-hue-saturation fusion algorithm, wavelet-transform fusion algorithm, Ehlers fusion algorithm, and Gram–Schmidt fusion algorithm, were applied to pan-sharpening Landsat 8 operational land imager (OLI) imagery. We assessed the five fusion algorithms with respect to spectral and spatial fidelity using visual inspection and quantitative quality indicators. The optimal fused image was selected for subsequent analysis. Then, three classifiers, namely, maximum likelihood, artificial neural network, and support vector machine, were employed to preclassify the fused and raw OLI 30-m band images. Final object-based S. alterniflora maps were generated through classifier ensemble analysis of outcomes from the three classifiers. The results showed that the introduced method obtained high classification accuracy, with an overall accuracy of 90.96% and balanced misclassification errors between S. alterniflora and its coexistent species. We recommend future research to adopt the proposed method for monitoring long-term or multiseasonal changes in land coverage of invasive wetland plants.
This paper uses PROSAIL model to simulate vegetation canopy reflectance under different chlorophyll contents and Leaf area index (LAI). The changes of NDVIs with different LAIs and chlorophyll contents are analyzed. A simulated spectral dataset was built firstly by using PROSIAL vegetation radiative transfer model with various vegetation chlorophyll concentrations and leaf area index. The responses of NDVIs to LAIs are quantitatively analyzed further based on the dataset. The results show that chlorophyll contents affect canopy reflectance mainly in visible band. Canopy reflectance decreases with an increasing chlorophyll content. Under the same LAI value, NDVI values increase with an increase chlorophyll contents. Under constant content of chlorophyll, NDVIs increases with an increasing LAI. When the value of LAI is less than5, the canopy reflectance is significantly affected by soil background. When value of LAI is higher than5, the earth surface is almost completely covered with vegetation. The increase in LAI has little effect on canopy reflectance and NDVIs consequently. NDVIs increases with the adding of chlorophyll content, when chlorophyll is higher than 40, the rangeability of NDVIs is becoming stable.
Spartina alterniflora is one of the most serious invasive species in the coastal saltmarshes of China. An accurate quantitative estimation of its canopy leaf chlorophyll content is of great importance for monitoring plant physiological state and vegetation productivity. Hyperspectral reflectance data representing a range of canopy chlorophyll content were simulated by using the PROSAIL radiative transfer model at a 1nm sampling interval, which was based on prior knowledge of S.alterniflora. A set of indices was tested for estimating canopy chlorophyll content. Subsequently, validation were performed for testing the performance of indices, based on the PROSAIL model using in situ data measured by a Spectroradiometer with spectral range of 350-2500nm in a late autumn in a sub-tropical estuarine marsh. PROSAIL simulations showed that the most readily available indices were not good to be directly used in canopy chlorophyll estimation of S.alterniflora. The modified Chlorophyll Absorption in Reflectance Index MCARI[705,750] was linear related to the canopy chlorophyll content (R2=0.94) , but did not achieve a satisfactory estimation results with a high RMSE (RMSE=0.95 g.m-2). We optimized the index MCARI[705,750] by introducing a scale conversion coefficient to the formula to solve data units inconsistent, which is between the practical application unit and the unit used in the process of establishing the index, and balance scale transformation through radiative transfer models and examing corresponding canopy reflectance index values. We proposed index Optimized modified Chlorophyll Absorption in Reflectance Index OMCARI[705, 750]. The results showed that the index OMCARI[705, 750] had higher precision of prediction of chlorophyll for S.alterniflora (R2=0.94,RMSE=0.41 g.m-2 ).
Spartina alterniflora, an invasive plant, has been a threat to the local ecological security since it was introduced to Fujian coastal beach over 30 years ago. How to monitor its dynamic changes effectively is of great significance. Currently, hyperspectral remote sensing technology has become an important way to monitor invasive species dynamic changes. This paper investigates whether S. alterniflora could be discriminated from the other three native species using field spectrometer ranging from 350 nm to 2500 nm. In order to reduce and select the optimal bands for the potential discrimination of S. alterniflora, a hierarchical method is implemented to spectrally discriminate S. alterniflora from the other three native species. In the first level of the analysis using ANOVA, we found that there were statistically significance differences in spectral reflectance between S. alterniflora and the other three native species at different bands. The algorithm of classification and regression trees (CART) were used to further investigate in the second level of analysis to identify the most sensitive bands for spectral discrimination. We found that the greatest discrimination power for S. alterniflora is located in the red-edge, especially in the near infrared, and mid infrared regions. Subsequently, we used Jeffries-Matusita (JM) distance to assess spectral separability of bands selected by CART. Overall, results of this study offer the possibility of extending field measurements at canopy level to airborne and hyperspectral data for discriminating S. alterniflora in Min river wetland.
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