An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data

Abstract

High-precision canopy chlorophyll content (CCC) inversion for marsh vegetation is of great significance for marsh protection and restoration. However, it is difficult to collect the CCC measured data for marsh vegetation that matches the pixel scale of remote sensing image. This study proposes a new method based on unmanned aerial vehicle (UAV) multispectral images to obtain multi-scale marsh vegetation CCC sample data. A random forest (RF) regression algorithm was used to evaluate the application performance of GF-1 WFV, Landsat-8 OLI, and Sentinel-2 MSI satellite remote sensing data in marsh vegetation CCC inversion. In addition, parameter optimization of the RF regression model was used to construct an optimization algorithm suitable for marsh vegetation, and the importance of input variables was quantitatively evaluated. The results showed that the UAV multispectral images assisted in the acquisition of marsh vegetation CCC sample data, as the method expanded the number of CCC samples while quantifying the CCC sample data collection accuracy (R 2 ≥0.86, RMSE≤6.98 SPAD), which improved the CCC inversion accuracy compared with traditional sampling methods. Extracting pure vegetation pixels through binary classification reduces the uncertainty of the UAV-scale CCC inversion results. Parameter optimization of the RF regression model further improves the CCC inversion accuracy at GF-1 WFV, Landsat-8 OLI, and Sentinel-2 MSI scales. Among the three satellite remote sensing data, Sentinel-2 MSI achieved the highest CCC inversion accuracy for marsh vegetation (R 2 = 0.79, RMSE = 10.96 SPAD) due to the inclusion of red-edge bands that are more sensitive to vegetation properties. Red-edge Chlorophyll Index (Clred-edge) and Green Chlorophyll Index (Clgreen) have the highest influence on the CCC inversion accuracy among input variables. Index Terms-Canopy chlorophyll content, unmanned aerial vehicle (UAV), multi-scale remote sensing data, random forest (RF) regression, scale matching.

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

“…Recently, these indices have been used as input data for prediction and classification purposes alike, the spectrum of tree canopies can be considered a distinctive feature of the specific vegetation, thus making VIs useful for both vegetation identification in aerial photographs and for tree classification (Abdollahnejad and Panagiotidis, 2020;Imangholiloo et al, 2020;Yang and Kan, 2020;Guo et al, 2021;Arevalo-Ramirez et al, 2022;Cabrera-Ariza et al, 2022;Shovon et al, 2022). Photosynthetic pigments have a distinctive reflectance in some bands, thus the prediction of chlorophyll content and other pigments is suitable with the appropriate VI (Watt et al, 2020;Kopackova-Strnadováet al, 2021;Lou et al, 2021;Lu et al, 2021;Raddi et al, 2021;Raj et al, 2021;Zhuo et al, 2022). Another application using VIs is the prediction of biomass in different and (Morgan et al, 2021;Torre-Tojal et al, 2022;Yan et al, 2022).…”

“…Random Forests were also used for regression purposes. In the work presented by Lou et al (2021), the feasibility of predicting canopy chlorophyll content in marsh vegetation was evaluated using multispectral images from UAVs, and from satellite platforms including Landsat-8 and Sentinel-2. The predicted canopy from the random forest was validated with the real value through a linear regression achieving a correlation value of 0, 92.…”

“…Forest structure parameters and tree phenotypic features are predicted using machine learning techniques with input data gathered from LiDAR, RGB, and Multi-spectral cameras (Shin et al, 2018;McClelland et al, 2019;Puliti et al, 2019;Abdollahnejad and Panagiotidis, 2020;Fan et al, 2020;Imangholiloo et al, 2020;Ahmad et al, 2021;Cai et al, 2021;Neuville et al, 2021;Sangjan and Sankaran, 2021;Yu et al, 2021). Predictions of leaf moisture, chlorophyll, and nitrogen content, have been achieved using machine learning methods (Watt et al, 2020;Lou et al, 2021;Raddi et al, 2021;Raj et al, 2021;Narmilan et al, 2022;Zhuo et al, 2022). The most common predictor is linear regression, but other common ones are support vector machine regression, random forest regression, and gradient boost machines (McClelland et al, 2019;Blanco-Sacristań et al, 2021;Fraser and Congalton, 2021b;Yu et al, 2021;Torre-Tojal et al, 2022).…”

Regional Monitoring of Leaf ChlorophyII Content of Summer Maize by Integrating Multi-Source Remote Sensing Data

“…LCC remote sensing monitoring has included collecting multi-spectral data from UAV, referring to satellites such as the Chinese GaoFen series, the American Landsat series, and the European Sentinel series that have been employed in previous investigations around the globe [30][31][32][33]. Methods of inversion have included the regression of vegetation index (VI) or a combination of VIs, machine learning regression, lookup-table (LUT)-based inversion, hybrid regression, etc.…”

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

“…A new method for obtaining multiscale sample data of chlorophyll content of swamp vegetation canopy based on UAV multispectral images was proposed. The application performance of GF-1 WFV, Landsat-8 OLI and Sentinel-2 MSI satellite remote sensing images in quantitative inversion of chlorophyll content in swamp vegetation canopy was evaluated by random forest regression algorithm [26]. Sun et al believe that accurate inversion of Chl is very important for effective monitoring of forest productivity and environmental stress.…”