Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China

Ran, Jing; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Deng, Lei

doi:10.1016/j.isprsjprs.2017.11.002

Cited by 49 publications

(44 citation statements)

References 33 publications

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“…Total biomass is often separated into belowground and aboveground biomass (AGB). Measurements of AGB are logistically easier to collect and are valuable in both agricultural [3][4][5] and non-agricultural settings [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In a non-agricultural context, estimation of AGB gives important insight into ecosystem structure and function. Forests, grasslands, wetlands, mangroves, dryland ecosystems and other vegetated areas provide important services for humans, such as carbon sequestration, oxygen production and biofuel, 2 of 46 as well as habitat for plant and animal species [9,[28][29][30]. Many ecosystems are also at increasing risk from climate change and land-use conversion and it is valuable to be able to quantify AGB at appropriate spatial and temporal scales and monitor it over time to assess the impacts of these changes on the global carbon cycle and to understand the resulting effects on ecosystem resilience and health [6,7,31,32] AGB is most accurately measured by collecting and weighing samples of vegetation [3,19,33] but this method is time-consuming, labor-intensive and destructive [16,34,35].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Poley

McDermid

2020

Remote Sensing

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Interest in the use of unmanned aerial systems (UAS) to estimate the aboveground biomass (AGB) of vegetation in agricultural and non-agricultural settings is growing rapidly but there is no standardized methodology for planning, collecting and analyzing UAS data for this purpose. We synthesized 46 studies from the peer-reviewed literature to provide the first-ever review on the subject. Our analysis showed that spectral and structural data from UAS imagery can accurately estimate vegetation biomass in a variety of settings, especially when both data types are combined. Vegetation-height metrics are useful for trees, while metrics of variation in structure or volume are better for non-woody vegetation. Multispectral indices using NIR and red-edge wavelengths normally have strong relationships with AGB but RGB-based indices often outperform them in models. Including measures of image texture can improve model accuracy for vegetation with heterogeneous canopies. Vegetation growth structure and phenological stage strongly influence model accuracy and the selection of useful metrics and should be considered carefully. Additional factors related to the study environment, data collection and analytical approach also impact biomass estimation and need to be considered throughout the workflow. Our review shows that UASs provide a capable tool for fine-scale, spatially explicit estimations of vegetation AGB and are an ideal complement to existing ground- and satellite-based approaches. We recommend future studies aimed at emerging UAS technologies and at evaluating the effect of vegetation type and growth stages on AGB estimation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Poley

McDermid

2020

Remote Sensing

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“…When the quality of the generated point cloud data is insufficient for distinguishing the ground and vegetation point clouds, the algorithm cannot accurately invert the vegetation height information. As a result, few studies have been conducted over grassland [29,54] and wetland [55], since the height of herbage is much smaller than that of forest and shrub. In this study, we focused on the development of a novel method for estimating the quadrat-scale aboveground biomass of low-statute vegetation.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

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“…Crop surface models (CSMs) derived from 3D point clouds contain crop canopy vertical distribution information, which can be used for crop monitoring, e.g., plant height measurement [16], biomass estimation [17][18][19][20], and yield prediction [21]. In addition to CSMs, RGB images, and multi-and hyper-spectral images acquired for UAV were combined with CSMs to estimate biomass [22][23][24][25][26][27].Although CSMs derived from 3D point clouds were used in the research mentioned above, they focused mostly only on the plant heights derived from gridded CSMs. Triangulated irregular network (TIN) [28] is a kind of digital elevation model (DEM) derived from point clouds.…”

mentioning

confidence: 99%

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Jiang

Fang

et al. 2019

Remote Sensing

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Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.

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Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China

Cited by 49 publications

References 33 publications

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

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