Cover Estimates Research Articles

Monitoring canopy quality and improving equitable outcomes of urban tree planting using LiDAR and machine learning

Urban tree canopies are fundamental to mitigating the impacts of climate change within cities as well as providing a range of other important ecosystem, health, and amenity benefits. However, urban tree planting initiatives do not typically utilize data about both the horizontal and vertical dimensions of the tree canopy, despite height being a critical determinant of the quality and value of urban canopy cover. We present a novel pipeline that uses airborne LiDAR data to train a multi-task machine learning model to generate estimates of both canopy cover and height in urban areas. We apply this to multi-source multi-spectral imagery for the case study of Chicago, USA. Our results indicate that a multi-task UNet convolutional neural network can be used to generate reliable estimates of canopy cover and height from aerial and satellite imagery. We then use these canopy estimates to allocate 75,000 trees from Chicago’s recent green initiative under four scenarios, minimizing the urban heat island effect and then optimizing for an equitable canopy distribution, comparing results when only canopy cover is used, and when both canopy cover and height are considered. Through the introduction of this novel pipeline, we show that including canopy height within decision-making processes allows the distribution of new trees to be optimised to further reduce the urban heat island effect in localities where trees have the highest cooling potential and allows trees to be more equitably distributed to communities with lower quality canopies.

Satellite‐derived prefire vegetation predicts variation in field‐based invasive annual grass cover after fire

AbstractAimsInvasion by annual grasses (IAGs) and concomitant increases in wildfire are impacting many drylands globally, and an understanding of factors that contribute to or detract from community resistance to IAGs is needed to inform postfire restoration interventions. Prefire vegetation condition is often unknown in rangelands but it likely affects variation in postfire invasion resistance across large burned scars. Whether satellite‐derived products like the Rangeland Analysis Platform (RAP) can fulfill prefire information needs and be used to parametrize models of fire recovery to inform postfire management of IAGs is a key question.MethodsWe used random forests to ask how IAG abundances in 669 field plots measured in the 2‐3 years following megafires in sagebrush steppe rangelands of western USA responded to RAP estimates of annual:perennial prefire vegetation cover, the effects of elevation, heat load, postfire treatments, soil moisture–temperature regimes, and land‐agency ratings of ecosystem resistance to invasion and resilience to disturbance.ResultsPostfire IAG cover measured in the field was % and RAP‐estimated prefire annual herbaceous cover was %. The random forest model had an R2 of 0.36 and a root‐mean‐squared error (RMSE) of 4.41. Elevation, postfire herbicide treatment, and prefire estimates from RAP for the ratio of annual:perennial and shrub cover were the most important predictors of postfire IAG cover. Threshold‐like relationships between postfire IAG cover and the predictors indicate that maintaining annual:perennial cover below 0.4 and shrub cover below <10% prior to wildfire would decrease invasion, at low elevations below 1400 m above sea level.ConclusionDespite known differences between RAP and field‐based estimates of vegetation cover, RAP was still a useful predictor of variation in IAG abundances after fire. IAG management is oftentimes reactive, but our findings indicate impactful roles for more inclusively addressing the exotic annual community, and focusing on prefire maintenance of annual:perennial herbaceous and shrub cover at low elevations.

Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images.

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

Acoustic estimation of ferromanganese crust exposure and sediment cover in a Northwest Pacific seamount using statistical analyses of shipboard multibeam acoustic data

The estimation of the distribution of ferromanganese crusts (FMCs) is determined using the threshold of backscatter intensity of the multibeam echo sounder in ground-truth data; however, this is not accurate. This study developed a method to quantitatively estimate the distribution of FMCs and thickness of the surface sediment (TSS) by determining the relationship between topographic features, backscatter intensity using statistical analyses. Thus, the exposure of FMCs and TSS was quantitatively evaluated with accuracies of 17% and 2.5 m, respectively. This method can efficiently estimate distributions of FMC and TSS of entire seamounts for the benefit of future mineral explorations.

Positive effects of tree diversity on tropical forest restoration in a field-scale experiment.

Experiments under controlled conditions have established that ecosystem functioning is generally positively related to levels of biodiversity, but it is unclear how widespread these effects are in real-world settings and whether they can be harnessed for ecosystem restoration. We used remote-sensing data from the first decade of a long-term, field-scale tropical restoration experiment initiated in 2002 to test how the diversity of planted trees affected recovery of a 500-ha area of selectively logged forest measured using multiple sources of satellite data. Replanting using species-rich mixtures of tree seedlings with higher phylogenetic and functional diversity accelerated restoration of remotely sensed estimates of aboveground biomass, canopy cover, and leaf area index. Our results are consistent with a positive relationship between biodiversity and ecosystem functioning in the lowland dipterocarp rainforests of SE Asia and demonstrate that using diverse mixtures of species can enhance their initial recovery after logging.

SiamC Transformer: Siamese coupling swin transformer Multi-Scale semantic segmentation network for vegetation extraction under shadow conditions

The accuracy of fractional vegetation cover (FVC) estimation has important significance in high-precision agricultural and ecological environment assessments. However, the presence ofshadowsunder natural light conditions can cause problems such as poor vegetation distinguishability and blurred boundaries during vegetation segmentation. This leads to missing or over-segmented vegetation inshadedareas, thus reducing the accuracy of FVC. Polarizationinformation can reflect the texture structure features, edgecharacteristics and surface state information, providing important supplementary information to light intensity information. To address the issue of shadows interfering with vegetation segmentation, a Siamese coupling swin transformer (SiamC Transformer) is proposed in this study. In this network, a dual-flow structure is used to simultaneously retrieve the feature information of vegetation degree of linear polarization (DoLP) images and light intensity images simultaneously to obtain multi-dimensional global semantic information of multi-scale vegetation. In the feature fusion stage, two fusion modules are proposed: an adaptive fusion module (AFM) and an adaptive fusion plus module (AFPM), which can fuse shallow spatial information, texture information and deep semantic information. The AFM and AFPM enable the network to better achieve object localization, region activation and edge sharpening, while improving the segmentation accuracy of small objects. The experimental results show that the mean intersection over union (mIoU) of this network is as high as 97.46% on vegetation datasets consisting of light intensity and polarization images, which is better than that of other algorithms. This network has higher accuracy and adaptability for shadow scenes and improves the accuracy of FVC calculation under shadow conditions.

Unraveling the Relationship between Percentage Cover and Biomass of Various Herbaceous Species in the Dry Tropical Grassland

The estimation of biomass and cover is an ideal variable for determining ecosystem productivity, vegetation abundance, and community structure of any ecosystem. Biomass estimation by harvest method causes a huge loss of biomass and biodiversity. Non-destructive methods are helpful for repeated and regular sampling of the same plot to measure any change in biomass at the fixed time interval. There are already several regression equations established between biomass and cover in various ecosystems for finding above-ground biomass but there was an urgent need for such studies in dry tropical grasslands. The experiment was performed in the entire campus of Banaras Hindu University, Varanasi. 115 Quadrats were sampled in the entire university campus during year 2019-2020. For each quadrat, species-wise individual numbers were recorded and above-ground biomass was estimated by harvest method. Herbage cover was recorded for each species and measured by gridding each 1×1 m2 quadrat into 100 cells of 10 × 10 cm cells, each representing 1% cover. We found 59 herbaceous species of 28 different families. The family Asteraceae was the most common while only single species represented the other seventeen families. Most of the species like Dichanthium annulatum, Sida acuta, Ageratum conyzoides, Malvastrum coromandelianum, Rungia pectinate, and Vernonia cinerea showed linear regression equation, Parthenium hysterophorus, Alternenthera sessilis, Boerhavia diffusa showed quadratic polynomial trendlines. Species like Zephyranthes citrina and Ruellia tuberosa showed a power regression equation. Only Andrographis paniculata, and Chenopodium album showed an exponential regression equation. A power regression equation was found between pooled biomass and cover. Using the regression equations biomass of the listed 59 species could be calculated easily without disturbing the vegetation of the study area which will eventually help in the conservation of nature.

Amazonian pollen assemblages reflect biogeographic gradients and forest cover

AbstractAimPollen assemblages are commonly used to reconstruct past climates yet have not yet been used to reconstruct past human activities, including deforestation. We aim to assess (i) how pollen assemblages vary across biogeographic and environmental gradients, (ii) the source area of pollen assemblages from lake sediment samples and (iii) which pollen taxa can best be used to quantify deforested landscapes.LocationAmazonia.TaxonPlantae.MethodsPollen assemblages (N = 65) from mud‐water interface samples (representing modern conditions) of lake sediment cores were compared with modern gradients of temperature, precipitation and elevation. Pollen assemblages were also compared with local‐scale estimates of forest cover at 1, 2, 5, 10, 20 and 40 km buffers around each lake.ResultsOver 250 pollen types were identified in the samples, and pollen assemblages were able to accurately differentiate biogeographic regions across the basin, corresponding with gradients in temperature and precipitation. Poaceae percentages were the best predictor of deforestation, and had a significant negative relationship with forest cover estimates. These relationships were strongest for the 1 km buffer area, weakening as buffer sizes increased.Main conclusionsThe diverse Amazonian pollen assemblages strongly reflect environmental gradients, and percentages of Poaceae best reflect local‐scale variability in forest cover. Our results of modern pollen‐landscape relationships can be used to provide a foundation for quantitative reconstructions of climate and deforestation in Amazonia.

Forest Canopy Cover Inversion Exploration Using Multi-Source Optical Data and Combined Methods

An accurate estimation of canopy cover can provide an important basis for forest ecological management by understanding the forest status and change patterns. The aim of this paper is to investigate the four methods of the random forest (RF), support vector regression (SVR), k-nearest neighbor (KNN), and k-nearest neighbor with fast iterative features selection (KNN-FIFS) for modeling forest canopy cover, and to evaluate three mainstream optical data sources—Landsat8 OLI, Sentinel-2A, Gaofen-1 (GF-1)—and three types of data combined comparatively by selecting the optimal modeling method. The paper uses the Daxinganling Ecological Station of Genhe City, Inner Mongolia, as the research area, and is based on three types of multispectral remote sensing data, extracting spectral characteristics, textural characteristics, terrain characteristics; the Kauth–Thomas transform (K-T transform); and color transformation characteristics (HIS). The optimal combination of features was selected using three feature screening methods, namely stepwise regression, RF, and KNN-FIFS, and the four methods: RF, SVR KNN, and KNN-FIFS, were combined to carry out the evaluation analysis regarding the accuracy of forest canopy cover modeling: (1) In this study, a variety of remote sensing features were introduced, and the feature variables were selected by different parameter preference methods and then employed in modeling. Based on the four modeling inversion methods, the KNN-FIFS model achieves the best accuracy: the Landsat8 OLI with R2 = 0.60, RMSE = 0.11, and RMSEr = 14.64% in the KNN-FIFS model; the Sentinel-2A with R2 = 0.80, RMSE = 0.08, and RMSEr = 11.63% in the KNN-FIFS model; the GF-1 with R2 = 0.55, RMSE = 0.12, and RMSEr = 15.04% in the KNN-FIFS model; and the federated data with R2 = 0.82, RMSE = 0.08, and RMSEr = 10.40% in the KNN-FIFS model; (2) the three multispectral datasets have the ability to estimate forest canopy cover, and the modeling accuracy superior under the combination of multi-source data features; (3) under different optical data, KNN- FIFS achieves the best accuracy in the established nonparametric model, and its feature optimization method is better than that of the random forest optimization method. For the same model, the estimation result of the joint data is better than the single optical data; thus, the KNN-FIFS model, with specific parameters, can significantly improve the inversion accuracy and efficiency of forest canopy cover evaluation from different data sources.

Modeling global indices for estimating non-photosynthetic vegetation cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

A national assessment of urban forest carbon storage and sequestration in Canada

During a time of rapid urban growth and development, it is becoming ever more important to monitor the carbon fluxes of our cities. Unlike Canada’s commercially managed forests that have a long history of inventory and modelling tools, there is both a lack of coordinated data and considerable uncertainty on assessment procedures for urban forest carbon. Nonetheless, independent studies have been carried out across Canada. To improve upon Canada’s federal government reporting on carbon storage and sequestration by urban forests, this study builds on existing data to develop an updated assessment of carbon storage and sequestration for Canada’s urban forests. Using canopy cover estimates derived from ortho-imagery and satellite imagery ranging from 2008 to 2012 and field-based urban forest inventory and assessment data from 16 Canadian cities and one US city, this study found that Canadian urban forests store approximately 27,297.8 kt C (− 37%, + 45%) in above and belowground biomass and sequester approximately 1497.7 kt C year−1 (− 26%, + 28%). In comparison with the previous national assessment of urban forest carbon, this study suggested that in urban areas carbon storage has been overestimated and carbon sequestration has been underestimated. Maximizing urban forest carbon sinks will contribute to Canada’s mitigation efforts and, while being a smaller carbon sink compared to commercial forests, will also provide important ecosystem services and co-benefits to approximately 83% of Canadian people.

Should we estimate plant cover in percent or on ordinal scales?

Question: We explored the error resulting from different methods for recording the cover of plants in vegetation plots, specifically the direct estimation of percent cover vs. the use of ordinal cover scales (7-step Braun-Blanquet and 5-step Hult-Sernander-Du Rietz). Methods: We simulated 121 plant species of different cover, sampled with 13 different levels of estimation precision. Estimation precision was either based on a constant coefficient of variation (0.1–1.0) across all cover values or on empirical data from Hatton et al. (1986, Journal of Range Management 39: 91–92) (× 0.5, × 1.0, × 1.5). Each sampling was repeated 10 times. Subsequently, we determined the mean relative and absolute errors that occurred in the data used for ensuing numerical analyses. Results: Except for few cases with unrealistic settings (very high estimation error and ignorance of species with lower cover values), direct estimation in percent yielded better results than the use of ordinal scales. Based on the empirical values of estimation accuracy, the use of ordinal scales inflated the mean absolute and relative errors nearly 2-fold in case of the 7-step Braun-Blanquet scale and about 1.5-fold in case of the Hult-Sernander-Du Rietz scale if only considering cover values above 1%. Conclusions: From our personal experience, the careful application of an ordinal scale is not faster than the direct estimation of percent cover. For this reason, we see no plausible argument supporting the use of ordinal cover scales when essentially all subsequent analyses are numeric. Abbreviations: Br.-Bl. = 7-step variant of the Braun-Blanquet scale and its numerical replacement as in Table 2; CV = coefficient of variation; H.-S. = Hult-Sernander-Du Rietz scale and its numerical replacement as shown in Table 1.

Patterns of regional site index across a North American boreal forest gradient

Forest structure—the height, cover, vertical complexity, and spatial patterns of trees—is a key indicator of productivity variation across forested extents. During the 2017 and 2019 growing seasons, NASA’s Arctic-Boreal Vulnerability Experiment collected full-waveform airborne LiDAR using the land, vegetation and imaging sensor, sampling boreal and tundra landscapes across a variety of ecological regions from central Canada westward through Alaska. Here, we compile and archive a geo-referenced gridded suite of these data that include vertical structure estimates and novel horizontal cover estimates of vegetation canopy cover derived from vegetation’s vertical LiDAR profile. We validate these gridded estimates with small footprint airborne LiDAR, and link >36 million of them with stand age estimates from a Landsat time-series of tree-canopy cover that we confirm with plot-level disturbance year data. We quantify the regional magnitude and variability in site index, the age-dependent rates of forest growth, across 15 boreal ecoregions in North America. With this open archive suite of forest structure data linked to stand age, we bound current forest productivity estimates across a boreal structure gradient whose response to key bioclimatic drivers may change with stand age. These results, derived from a reduction of a large archive of airborne LiDAR and a Landsat time series, quantify forest productivity bounds for input into forest and ecosystem growth models, to update forecasts of changes in North America’s boreal forests by improving the regional parametrization of forest growth rates.

Tree canopy density thresholds for improved forests cover estimation in protected areas of Madagascar

Abstract The rich endemic biodiversity of Madagascar is concentrated in different types of natural forests primarily conserved within the network of protected areas (PAs). Since 1990, remote sensing has been utilized to monitor forest cover. The latest forest cover map generated using these techniques provides accurate estimates of natural forest cover within the PAs network. However, the standardized application of Tree Canopy Density (TCD), as used in global assessments of forest cover, yields erroneous estimates for different forest types in Madagascar because the standard TCD cannot be globally applied to all types of forests. Our study aims to utilize global remote sensing data at the scale of PAs to identify specific TCD thresholds for individual PAs. Starting from the year 2000 data, the application of these thresholds will allow us to estimate deforestation in subsequent years at reduced costs. We used the official PA boundaries, a reliable forest cover map at the national scale, and the TCDs published at a global scale to infer the values of TCD to be applied in each PA. The standard TCD threshold above 30% overestimates humid and dry forests and underestimates dry spiny forests in Madagascar. Our specific TCD thresholds inferred for each PA accurately estimate the forest cover in the vast majority of PAs. Using these specific TCD thresholds will allow for improved monitoring of forest cover within the network of PAs. The methodology detailed here can also be applied in other geographic regions, and future improvements in data on forest cover—both remotely sensed and field-collected—will enhance our ability to estimate forest cover and its changes over time.

Using machine learning to achieve simultaneous, georeferenced surveys of fish and benthic communities on shallow coral reefs

AbstractSurveying coastal systems to estimate distribution and abundance of fish and benthic organisms is labor‐intensive, often resulting in spatially limited data that are difficult to scale up to an entire reef or island. We developed a method that leverages the automation of a machine learning platform, CoralNet, to efficiently and cost‐effectively allow a single observer to simultaneously generate georeferenced data on abundances of fish and benthic taxa over large areas in shallow coastal environments. Briefly, a researcher conducts a fish survey while snorkeling on the surface and towing a float equipped with a handheld GPS and a downward‐facing GoPro, passively taking ~ 10 photographs per meter of benthos. Photographs and surveys are later georeferenced and photographs are automatically annotated by CoralNet. We found that this method provides similar biomass and density values for common fishes as traditional scuba‐based fish counts on fixed transects, with the advantage of covering a larger area. Our CoralNet validation determined that while photographs automatically annotated by CoralNet are less accurate than photographs annotated by humans at the level of a single image, the automated approach provides comparable or better estimations of the percent cover of the benthic substrates at the level of a minute of survey (~ 50 m2 of reef) due to the volume of photographs that can be automatically annotated, providing greater spatial coverage of the site. This method can be used in a variety of shallow systems and is particularly advantageous when spatially explicit data or surveys of large spatial extents are necessary.

Differences in Canopy Cover Estimations from ALS Data and Their Effect on Fire Prediction

Canopy cover is a primary attribute used in empirical wildfire models for certain fuel types. Accurate estimation of canopy cover is a key to ensuring accurate prediction of fire spread and behaviour in these fuels. Airborne Laser Scanning (ALS) is a promising active remote sensing technology for estimating canopy cover in natural ecosystems since it can penetrate and measure the vegetation canopy. Various methods have been developed to estimate canopy cover from ALS data. However, little attention has been given to the evaluation of algorithms used to calculate canopy cover and the subsequent influence these algorithms can have on wildfire behaviour models. In this study we evaluate the effect of using different algorithms to calculate canopy cover on the performance of the Australian Mallee-heath fire spread model. ALS data was used to derive five canopy cover models. Fire spread metrics including burned area, unburned area within the fire extent, and extent of fire were compared for different model run times. The results show that these metrics are strongly influenced by choice of algorithm used to calculate canopy cover. The results from this study may provide practical guidance for the optimal selection of estimation methods in canopy cover mapping.

Recovery of forest structure dynamics following selective logging in lowland dipterocarp Peninsular Malaysia

The selective management system (SMS) practised in Malaysia has emerged to optimise the sustainability of permanently reserved forest management. SMS requires a management regime (felling) to ensure economical harvesting and appropriate residual stands for the logging cycle, including ecological balance and environmental quality. SMS includes a Post-Felling Forest Inventory (Post-F) sequence. Post-F is used to obtain information on the remaining stands and other plants to determine the silvicultural treatment of a logged area. However, data gathered from Post-F are insufficient to track and collect information on forest structure dynamics recovery after logging. Thus, this study is to further understand the applicability of remote sensing technologies for forest recovery structure assessment after selective logging in the lowland dipterocarp forest of Peninsular Malaysia. Understanding the structural and composition changes occurring in lowland dipterocarp forests is vital for forecasting these ecosystems in the future. This study uses temporal dynamics (5, 9, 16, 26 and 32 months) of canopy cover images obtained from Landsat 8 after selective logging. Using the Carnegie Landsat Analysis System (CLASlite), this study applies Automated Monte Carlo Unmixing Analysis (AutoMCU) algorithm to derive per-pixel fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil. The relationship between these three indicators has shown the dynamic growth pattern of the forest area after logging. Differences can be seen via changes in the PV, NPV and bare soil image over various time periods. The information derived from this study is vital for forest conservation strategies after logging, thus enhancing Sustainable Forest Management in Peninsular Malaysia.

Testing the Effect of Relative Pollen Productivity on the REVEALS Model: A Validated Reconstruction of Europe-Wide Holocene Vegetation

Reliable quantitative vegetation reconstructions for Europe during the Holocene are crucial to improving our understanding of landscape dynamics, making it possible to assess the past effects of environmental variables and land-use change on ecosystems and biodiversity, and mitigating their effects in the future. We present here the most spatially extensive and temporally continuous pollen-based reconstructions of plant cover in Europe (at a spatial resolution of 1° × 1°) over the Holocene (last 11.7 ka BP) using the ‘Regional Estimates of VEgetation Abundance from Large Sites’ (REVEALS) model. This study has three main aims. First, to present the most accurate and reliable generation of REVEALS reconstructions across Europe so far. This has been achieved by including a larger number of pollen records compared to former analyses, in particular from the Mediterranean area. Second, to discuss methodological issues in the quantification of past land cover by using alternative datasets of relative pollen productivities (RPPs), one of the key input parameters of REVEALS, to test model sensitivity. Finally, to validate our reconstructions with the global forest change dataset. The results suggest that the RPPs.st1 (31 taxa) dataset is best suited to producing regional vegetation cover estimates for Europe. These reconstructions offer a long-term perspective providing unique possibilities to explore spatial-temporal changes in past land cover and biodiversity.

Comparing methods to quantify cover in turfgrass research

AbstractTurfgrass cover can be assessed qualitatively using visual ratings, but quantitative turfgrass cover measurements are desired for producing unbiased data. Digital image analysis and point intercept are two quantitative percent cover data collection methods used in turfgrass research. A potential weakness of digital image analysis is the difficulty in evaluating color variation. Considering this, a series of controlled environment and field experiments were conducted to evaluate the accuracy of the point intercept method compared to using a digital grid, digital image analysis, and visual ratings when utilized in turfgrass research. To explore this topic, four experiments were conducted in Corvallis, OR, and one in New Brunswick, NJ. Results from research conducted in Corvallis, OR, determined that the closest point intercept spacing (2.5 cm) resulted in a percent cover that was not statistically different from the known cover and the lowest variance, but the greatest amount of time required to collect the data. Digital image analysis was the most consistent method for measuring percent cover when a known percent cover was being quantified. The estimate of the percent dollar spot cover was reduced by a single pigment application regardless of the data collection method. In the absence of pigment applications, the point intercept frame, digital image analysis, and visual ratings were well‐correlated methods used to assess dollar spot cover. Digital image analysis was not correlated with the other data collection methods when used to evaluate percent anthracnose cover. The research conducted in New Brunswick, NJ, determined that the point intercept method and digital image analysis are well‐correlated methods for quantifying turfgrass establishment from seed.

Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.