scholarly journals Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 540 ◽  
Author(s):  
Cheryl Doughty ◽  
Kyle Cavanaugh
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Spatial Scales ◽  
Biomass Estimation ◽  
Spatial And Temporal Variability ◽  
Estimation Model ◽  
Spatial And Temporal Scales

Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales.

scholarly journals Estimating Aboveground Biomass and Its Spatial Distribution in Coastal Wetlands Utilizing Planet Multispectral Imagery

2019 ◽  
Vol 11 (17) ◽  
pp. 2020 ◽  
Author(s):  
Gwen J. Miller ◽  
James T. Morris ◽  
Cuizhen Wang
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Salt Marshes ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Plant Biomass ◽  
Temporal Trends ◽  
Landscape Scale ◽  
North Inlet ◽  
Vertical Range

Coastal salt marshes are biologically productive ecosystems that generate and sequester significant quantities of organic matter. Plant biomass varies spatially within a salt marsh and it is tedious and often logistically impractical to quantify biomass from field measurements across an entire landscape. Satellite data are useful for estimating aboveground biomass, however, high-resolution data are needed to resolve the spatial details within a salt marsh. This study used 3-m resolution multispectral data provided by Planet to estimate aboveground biomass within two salt marshes, North Inlet-Winyah Bay (North Inlet) National Estuary Research Reserve, and Plum Island Ecosystems (PIE) Long-Term Ecological Research site. The Akaike information criterion analysis was performed to test the fidelity of several alternative models. A combination of the modified soil vegetation index 2 (MSAVI2) and the visible difference vegetation index (VDVI) gave the best fit to the square root-normalized biomass data collected in the field at North Inlet (Willmott’s index of agreement d = 0.74, RMSE = 223.38 g/m2, AICw = 0.3848). An acceptable model was not found among all models tested for PIE data, possibly because the sample size at PIE was too small, samples were collected over a limited vertical range, in a different season, and from areas with variable canopy architecture. For North Inlet, a model-derived landscape scale biomass map showed differences in biomass density among sites, years, and showed a robust relationship between elevation and biomass. The growth curve established in this study is particularly useful as an input for biogeomorphic models of marsh development. This study showed that, used in an appropriate model with calibration, Planet data are suitable for computing and mapping aboveground biomass at high resolution on a landscape scale, which is needed to better understand spatial and temporal trends in salt marsh primary production.

scholarly journals Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 914
Author(s):  
Adeel Ahmad ◽  
Hammad Gilani ◽  
Sajid Rashid Ahmad
Keyword(s):  
High Resolution ◽  
Literature Review ◽  
Satellite Imagery ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Independent Variables ◽  
Non Linear ◽  
Multiple Variables ◽  
Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

Strategies for Restoring River Ecosystems: Sources of Variability and Uncertainty in Natural and Managed Systems

2003 ◽  
Keyword(s):  
Spatial Variation ◽  
Spatial Scales ◽  
Spatial And Temporal Variability ◽  
Key Factors ◽  
Policy Makers ◽  
River Ecosystems ◽  
Spatial And Temporal Scales ◽  
Lower Productivity ◽  
Biological Attributes ◽  
Stream Biota

<em>Abstract</em>.—Productivity and biodiversity of stream and river ecosystems vary at multiple spatial and temporal scales. Spatial variation in productivity of salmonid fishes varies over two orders of magnitude worldwide and shows lesser, but still considerable, variation at the regional and watershed level. Spatial variation in production and diversity is related to variation in physical, chemical, and biological attributes of watersheds and channels. Channel constraint, gradient, and size are key factors in determining productivity and diversity. Constrained reaches generally support different species and lower productivity than lower-gradient, unconstrained channels. Variation in the condition of stream reaches is greatly influenced by disturbances. Severe disturbances fundamentally change the functional and structural properties of stream ecosystems and alter the way in which the surrounding watershed interacts with the stream. Periodic occurrence of disturbances and the process of recovery play a key role in maintaining spatial and temporal variability in stream conditions and thereby contribute to the productivity and diversity of stream biota. Land use by humans alters the frequency and characteristics of disturbances. As a result, human-altered disturbance patterns often homogenize channel conditions across a watershed rather than introducing diversity. Watershed restoration plans need to recognize the role variability and disturbance play in maintaining the productivity and diversity of stream biota. Incorporating this understanding into watershed management and restoration will require scientists, managers, and policy makers to view watersheds at much longer temporal and larger spatial scales than is currently done.

scholarly journals Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1101 ◽  
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Erxue Chen ◽  
Xinliang Wei
Keyword(s):  
Aboveground Biomass ◽  
Forest Type ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Estimation Model ◽  
Forest Aboveground Biomass

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

scholarly journals Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity

2021 ◽  
Vol 13 (11) ◽  
pp. 2105
Author(s):  
Yan Shi ◽  
Jay Gao ◽  
Xilai Li ◽  
Jiexia Li ◽  
Daniel Marc G. dela Torre ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Index ◽  
Grazing Intensity ◽  
Tibet Plateau ◽  
Estimation Accuracy ◽  
Bare Ground ◽  
Estimation Model ◽  
Plateau Pika ◽  
Ground Modification ◽  
Aerial Vehicle

Accurate approaches to aboveground biomass (AGB) estimation are required to support appraisal of the effectiveness of land use measures, which seek to protect grazing-adapted grasslands atop the Qinghai-Tibet Plateau (QTP). This methodological study assesses the effectiveness of one commonly used visible band vegetation index, Red Green Blue Vegetation Index (RGBVI), obtained from unmanned aerial vehicle (UAV), in estimating AGB timely and accurately at the local scale, seeking to improve the estimation accuracy by taking into account in situ collected information on disturbed grassland. Particular emphasis is placed upon the mapping and quantification of areas disturbed by grazing (simulated via mowing) and plateau pika (Ochotona curzoniae) that have led to the emergence of bare ground. The initial model involving only RGBVI performed poorly in AGB estimation by underestimating high AGB by around 10% and overestimating low AGB by about 10%. The estimation model was modified by the mowing intensity ratio and bare ground metrics. The former almost doubled the estimation accuracy from R2 = 0.44 to 0.81. However, this modification caused the bare ground AGB to be overestimated by about 38 and 19 g m−2 for 2018 and 2019, respectively. Although further modification of the model by bare ground metrics improved the accuracy slightly to 0.88, it markedly reduced the overestimation of low AGB values. It is recommended that grazing intensity be incorporated into the micro-scale estimation of AGB, together with the bare ground modification metrics, especially for severely disturbed meadows with a sizable portion of bare ground.

scholarly journals Understanding the variation in wood densities of trees and its implications for carbon assessments

2019 ◽  
Author(s):  
Karthik Teegalapalli ◽  
Chandan Kumar Pandey ◽  
Anand M Osuri ◽  
Jayashree Ratnam ◽  
Mahesh Sankaran
Keyword(s):  
Intraspecific Variation ◽  
Wood Density ◽  
Aboveground Biomass ◽  
Spatial Scales ◽  
Regional Scale ◽  
Interspecific Variation ◽  
Carbon Stocks ◽  
Biomass Estimation ◽  
Continental Scale ◽  
Genus Level

AbstractWood density is a key functional trait used to estimate aboveground biomass (AGB) and carbon stocks. A common practice in forest AGB and carbon estimation is to substitute genus averages (across species with known wood densities) in cases where wood densities of particular species are unknown. However, the extent to which genus-level averages are reflective of species wood densities across tree genera is uncertain, and understanding this is critical for estimating the accuracy of carbon stock estimates. Using primary field data from India and secondary data from a published global dataset, we quantified the extent to which wood density varied among individuals within species (intraspecific variation) at the regional scale and among species within genera (interspecific variation) at regional to global scales. We used a published global database with wood density data for 7743 species belonging to 1741 genera. Linear models were used to compare the species values with the genera averages and the individual values with the species averages, respectively. To estimate the error associated with using genus-level averages for carbon stocks estimation, we compared genus values averaged at the global, old world and continental scales with species values from actually measured data. We also ran a simulation using vegetation data from a published database to calculate the estimation errors in a 1 hectare plot level when genera-averaged wood densities are used. Intraspecific variation was significantly lower than interspecific variation. Continental level genera averages led to estimates closer to the species values for the 10 genera for which most data on species was available. This was also evident from a comparison of genera averages at these three spatial scales with species values from our data. Species within certain ‘hypervarying’ genera showed relatively high levels of variation, irrespective of the spatial scale of the dataset used. The error in estimation of AGB when genera-averaged values were used for species wood densities was 0.35, 0.71 and 2.43% when 0, 10 and 25% of the girth of the trees in the simulated plot were from hypervariable genera. Our findings indicate that species values provide the most accurate estimates for individuals. Genus average wood density values at the continental scale provided more reliable estimates than those at larger spatial scales. The aboveground biomass estimation error when species wood densities were approximated to the genera-average values was 1.4 to 3.7 tonnes per ha when 10% and 25%, respectively, of the girth of trees was from species from hypervariable genera. Our findings indicate that regional or continental scale genera averages provide more reliable estimates than global data and we propose a method to identify hypervariable genera, for which species values rather than genera averages can provide better estimates of carbon stocks.

scholarly journals Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245784
Author(s):  
Jérôme Théau ◽  
Étienne Lauzier-Hudon ◽  
Lydiane Aubé ◽  
Nicolas Devillers
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Bare Soil ◽  
The Other ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Estimation Model

Grasslands are among the most widespread ecosystems on Earth and among the most degraded. Their characterization and monitoring are generally based on field measurements, which are incomplete spatially and temporally. The recent advent of unmanned aerial vehicles (UAV) provides data at unprecedented spatial and temporal resolutions. This study aims to test and compare three approaches based on multispectral imagery acquired by UAV to estimate forage biomass or vegetation cover in grasslands. The study site is composed of 30 pasture plots (25 × 50 m), 5 bare soil plots (25 x 50), and 6 control plots (5 × 5 m) on a 14-ha field maintained at various biomass levels by grazing rotations and clipping over a complete growing season. A total of 14 flights were performed. A first approach based on structure from motion was used to generate a volumetric-based biomass estimation model (R2 of 0.93 and 0.94 for fresh biomass [FM] and dry biomass [DM], respectively). This approach is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2 (0.1 kg DM/m2). The Green Normalized Difference Vegetation Index (GNDVI) was selected to develop two additional approaches. One is based on a regression biomass prediction model (R2 of 0.80 and 0.66 for FM and DM, respectively) and leads to an accurate estimation at levels of FM lower than 3 kg/m2 (0.6 kg DM/m2). The other approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. These three approaches are relatively simple to use and applicable in an operational context. They are also complementary and can be adapted to specific applications in grassland characterization.

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