Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China

2020 ◽  
Vol 71 (4) ◽  
pp. 482 ◽  
Author(s):  
Zhiguo Dou ◽  
Youzhi Li ◽  
Lijuan Cui ◽  
Xu Pan ◽  
Qiongfang Ma ◽  
...  
Keyword(s):  
Least Squares ◽  
Human Activity ◽  
Human Activities ◽  
Coastal Wetlands ◽  
Partial Least Squares Regression ◽  
Coastal Wetland ◽  
Hyperspectral Data ◽  
Suaeda Salsa ◽  
Least Squares Regression ◽  
Different Types

Human activities alter the growth of coastal wetland vegetation. In the present study, we used a spectrometer and hyperspectral data to determine and compare the biomass of Suaeda salsa in a coastal wetland under protective and destructive activities. Using typical discriminants, the hyperspectral data of Suaeda salsa were distinguished under the influence of two kinds of human activity, and the accuracy of the inversion model of biomass was established following improved differentiation of the data under the influence of human activities. The original spectral reflectance and vegetation index were selected, and the biomass-inversion model was established by linear regression and partial least-squares regression. The model established by partial least-squares regression had a good precision (R2>0.85, RMSE%<5.6%). Hyperspectral technology can accurately show plant biomass and the indirect effects of interference by human activities of different intensity on coastal wetlands. The accuracy of the models can be improved by distinguishing the vegetation patterns under the influence of different types of human activity, and then constructing the biomass models. This study provides technical support for the use of quantitative remote sensing-based methods to monitor the fragile ecology of coastal wetlands under the influence of human activities.

scholarly journals Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3855 ◽  
Author(s):  
Lin Bai ◽  
Cuizhen Wang ◽  
Shuying Zang ◽  
Changshan Wu ◽  
Jinming Luo ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Soil Ph ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Validation Dataset ◽  
Least Squares Regression ◽  
Songnen Plain ◽  
Mean Square Errors

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.

scholarly journals Analysis of the spatiotemporal correlation between vegetation pattern and human activity intensity in Yancheng coastal wetland, China

2019 ◽  
Vol 2 (1) ◽  
pp. 87-100 ◽  
Author(s):  
Zheng Zang ◽  
Xinqing Zou ◽  
Qiaochu Song ◽  
Yulong Yao
Keyword(s):  
Human Activity ◽  
Human Activities ◽  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Nearest Neighbor ◽  
Vegetation Type ◽  
Vegetation Pattern ◽  
Zonal Distribution ◽  
Activity Intensity ◽  
Suaeda Glauca

Remote sensing images were used to reproduce the changes in wetland vegetation since 1987, and the potential impact of policy changes and human activities on vegetation restoration and biodiversity conservation in coastal wetlands was explored based on the landscape pattern index and the human disturbance index (HDI). The results showed that the vegetation displayed a zonal distribution pattern in which, perpendicular to the coastline early in the study period, the vegetation type changed from coastal wetland to bare mud flat with Spartina alterniflora, Suaeda glauca, and Phragmites australis as well as to constructed wetlands dominated by rice. Under the influence of human activities, the number of patches (NP) and mean nearest-neighbor distance (MNN) between patches gradually increased during the study period, while the mean patch size gradually decreased. The patch density increased from 179 (1987) to 296 patches per ha (2013). Additionally, human activity in the study area intensified. The HDI increased from 0.353 (1987) to 0.471 (1987) and showed positive correlations (R2 > 80%, p < 0.01) with NP and MNN. Human activity, such as changes in land use, resulted in more fragmented vegetation patterns, and the nonzonal (intrazonal) distribution of the vegetation became more obvious in coastal wetlands.

Brightness-normalized Partial Least Squares Regression for hyperspectral data

2010 ◽  
Vol 111 (12-13) ◽  
pp. 1947-1957 ◽  
Author(s):  
Hannes Feilhauer ◽  
Gregory P. Asner ◽  
Roberta E. Martin ◽  
Sebastian Schmidtlein
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Hyperspectral Data ◽  
Least Squares Regression
Sign in / Sign up

Export Citation Format

Share Document