scholarly journals RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System

2021 ◽  
Vol 13 (17) ◽  
pp. 3406
Author(s):  
Grayson R. Morgan ◽  
Cuizhen Wang ◽  
James T. Morris
Keyword(s):  
Remote Sensing ◽  
Tidal Marsh ◽  
Explanatory Power ◽  
Canopy Height ◽  
Tidal Marshes ◽  
Biomass Estimation ◽  
Unmanned Aerial Systems ◽  
High Marsh ◽  
Environmental Biology ◽  
Marsh Vegetation

Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., Spartina alterniflora) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; R2 = 0.39) and excess green index (ExG; R2 = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for S. alterniflora biomass estimation using sUAS as an on-demand, personal remote sensing tool.

scholarly journals Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

2021 ◽  
Vol 13 (8) ◽  
pp. 1592
Author(s):  
Nikolai Knapp ◽  
Andreas Huth ◽  
Rico Fischer
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Canopy Height ◽  
Biomass Estimation ◽  
Inventory Data ◽  
Border Effects ◽  
Tree Crowns ◽  
Forest Inventory Data ◽  
Estimation Uncertainty ◽  
Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

Plant zonation in a brackish tidal marsh: descriptive verification of resource-based competition and community structure

1990 ◽  
Vol 68 (8) ◽  
pp. 1689-1697 ◽  
Author(s):  
Michael J. Pidwirny
Keyword(s):  
Soil Nitrogen ◽  
Tidal Marsh ◽  
Resource Competition ◽  
Plant Biomass ◽  
Soil Surface ◽  
Tidal Marshes ◽  
High Marsh ◽  
Plant Zonation ◽  
Total Species ◽  
Total Soil Nitrogen

This study examined the hypothesis that the zonal patterns of dominant species in brackish tidal marshes may be explained by resource competition for soil nitrogen and light. This hypothesis was tested by analyzing abiotic and biotic field data collected from a brackish tidal marsh located at Brunswick Point, British Columbia. Biotic data revealed that this tidal marsh is dominated by two species that occupy distinct separate zones correlated to marsh elevation. In particular, sites whose elevation was from −0.80 to 0.20 m (geodetic datum) were dominated by Scirpus americanus, while sites with an elevation 0.20 m were dominated by Carex lyngbyei. Analysis of the relationships between measured variables indicated that total species biomass, species height, and total soil nitrogen were all positively correlated to sample site elevation. Further, the availability of light at the soil surface was found to be negatively correlated to plant biomass and site elevation. These results may suggest that S. americanus is dominant in the low marsh because it is a better competitor for soil nitrogen. Carex lyngbyei may be competitively dominant in the high marsh because its greater biomass and height make it a superior competitor for light. Key words: competition, light, nitrogen, tidal marshes, zonation.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1273
Author(s):  
James Todd ◽  
Richard Johnson
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Block Design ◽  
Color Space ◽  
Multiple Linear Regression Model ◽  
Cane Yield ◽  
Unmanned Aerial Systems ◽  
Family Selection ◽  
Ratoon Crop ◽  
Color Indices

Remote sensing techniques and the use of Unmanned Aerial Systems (UAS) have simplified the estimation of yield and plant health in many crops. Family selection in sugarcane breeding programs relies on weighed plots at harvest, which is a labor-intensive process. In this study, we utilized UAS-based remote sensing imagery of plant-cane and first ratoon crops to estimate family yields for a second ratoon crop. Multiple families from the commercial breeding program were planted in a randomized complete block design by family. Standard red, green, and blue imagery was acquired with a commercially available UAS equipped with a Red–Green–Blue (RGB) camera. Color indices using the CIELab color space model were estimated from the imagery for each plot. The cane was mechanically harvested with a sugarcane combine harvester and plot weights were obtained (kg) with a field wagon equipped with load cells. Stepwise regression, correlations, and variance inflation factors were used to identify the best multiple linear regression model to estimate the second ratoon cane yield (kg). A multiple regression model, which included family, and five different color indices produced a significant R2 of 0.88. This indicates that it is possible to make family selection predictions of cane weight without collecting plot weights. The adoption of this technology has the potential to decrease labor requirements and increase breeding efficiency.

scholarly journals Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020

2021 ◽  
Vol 13 (7) ◽  
pp. 1279
Author(s):  
Tong Li ◽  
Lizhen Cui ◽  
Zhihong Xu ◽  
Ronghai Hu ◽  
Pawan K. Joshi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Web Of Science ◽  
Temporal Trends ◽  
Research Trends ◽  
Annual Number ◽  
Unmanned Aerial Systems ◽  
Comprehensive Overview ◽  
Research Areas ◽  
Research Studies

Grassland remote sensing (GRS) is an important research topic that applies remote sensing technology to grassland ecosystems, reflects the number of grassland resources and grassland health promptly, and provides inversion information used in sustainable development management. A scientometrics analysis based on Science Citation Index-Expanded (SCI-E) was performed to understand the research trends and areas of focus in GRS research studies. A total of 2692 papers related to GRS research studies and 82,208 references published from 1980 to 2020 were selected as the research objects. A comprehensive overview of the field based on the annual documents, research areas, institutions, influential journals, core authors, and temporal trends in keywords were presented in this study. The results showed that the annual number of documents increased exponentially, and more than 100 papers were published each year since 2010. Remote sensing, environmental sciences, and ecology were the most popular Web of Science research areas. The journal Remote Sensing was one of the most popular for researchers to publish documents and shows high development and publishing potential in GRS research studies. The institution with the greatest research documents and most citations was the Chinese Academy of Sciences. Guo X.L., Hill M.J., and Zhang L. were the most productive authors across the 40-year study period in terms of the number of articles published. Seven clusters of research areas were identified that generated contributions to this topic by keyword co-occurrence analysis. We also detected 17 main future directions of GRS research studies by document co-citation analysis. Emerging or underutilized methodologies and technologies, such as unmanned aerial systems (UASs), cloud computing, and deep learning, will continue to further enhance GRS research in the process of achieving sustainable development goals. These results can help related researchers better understand the past and future of GRS research studies.

scholarly journals Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

2020 ◽  
Vol 12 (12) ◽  
pp. 2013
Author(s):  
Konstantinos Topouzelis ◽  
Dimitris Papageorgiou ◽  
Alexandros Karagaitanakis ◽  
Apostolos Papakonstantinou ◽  
Manuel Arias Ballesteros
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Field Experiments ◽  
Spectral Unmixing ◽  
Sea Surface ◽  
Spectral Signature ◽  
Unmanned Aerial Systems ◽  
Real Environment ◽  
Promising Tool ◽  
Sentinel 2

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

scholarly journals Error simulations of uncorrected NDVI and DCVI during remote sensing measurements from UAS

2014 ◽  
Vol 18 (2) ◽  
pp. 35-45 ◽  
Author(s):  
Michał T. Chiliński ◽  
Marek Ostrowski
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Vegetation Index ◽  
Vegetation Mapping ◽  
Radiative Transfer Model ◽  
Unmanned Aerial Systems ◽  
Transfer Model ◽  
Digital Cameras ◽  
Aerial Systems

Abstract Remote sensing from unmanned aerial systems (UAS) has been gaining popularity in the last few years. In the field of vegetation mapping, digital cameras converted to calculate vegetation index (DCVI) are one of the most popular sensors. This paper presents simulations using a radiative transfer model (libRadtran) of DCVI and NDVI results in an environment of possible UAS flight scenarios. The analysis of the results is focused on the comparison of atmosphere influence on both indices. The results revealed uncertainties in uncorrected DCVI measurements up to 25% at the altitude of 5 km, 5% at 1 km and around 1% at 0.15 km, which suggests that DCVI can be widely used on small UAS operating below 0.2 km.

scholarly journals Biogeomorphic modeling to assess resilience of tidal marsh restoration to sea level rise and sediment supply

2021 ◽  
Author(s):  
Olivier Gourgue ◽  
Jim van Belzen ◽  
Christian Schwarz ◽  
Wouter Vandenbruwaene ◽  
Joris Vanlede ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Tidal Marsh ◽  
Model Performance ◽  
Suspended Sediments ◽  
Sediment Supply ◽  
Tidal Marshes ◽  
Marsh Restoration ◽  
Tidal Marsh Restoration ◽  
Increasing Demand

Abstract. There is an increasing demand for creation and restoration of tidal marshes around the world, as they provide highly valued ecosystem services. Yet, tidal marshes are strongly vulnerable to factors such as sea level rise and declining sediment supply. How fast the restored ecosystem develops, how resilient it is to sea level rise, and how this can be steered by restoration design, are key questions that are typically challenging to assess. In this paper, we apply a biogeomorphic model to a planned tidal marsh restoration by dike breaching. Our modeling approach integrates tidal hydrodynamics, sediment transport and vegetation dynamics, accounting for relevant fine-scale flow-vegetation interactions (less than 1 m2) and their impact on vegetation and landform development at the landscape scale (several km2) and on the long term (several decades). Our model performance is positively evaluated against observations of vegetation and geomorphic development in adjacent tidal marshes. Model scenarios demonstrate that the restored tidal marsh can keep pace with realistic rates of sea level rise and that its resilience is more sensitive to the availability of suspended sediments than to the rate of sea level rise. We further demonstrate that restoration design options can steer marsh resilience, as it affects the rates and spatial patterns of biogeomorphic development. By varying the width of two dike breaches, which serve as tidal inlets to the restored marsh, we show that a larger difference in the width of the two inlets leads to more diversity in restored habitats. This study showcases that biogeomorphic modeling can support management choices in restoration design to optimize tidal marsh development towards sustainable restoration goals.

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