Cover Estimation Methods Research Articles

Monitoring forest canopy cover change with ICESat-2 Data in fire-prone areas: A case study in Antalya, Türkiye

This study presents an innovative approach for monitoring forest canopy cover changes in Antalya, Turkey, a region characterized by rich biodiversity, recurrent forest fires, and rapid rehabilitation efforts. Employing advanced remote sensing techniques, the research integrates ICESat-2 ATL08 segment data, Landsat satellite imagery, and Esri Sentinel-2 Land Cover datasets to generate forest canopy cover (FCC) maps for the years 2019 and 2022. The study area was chosen due to its high population, significant tourism industry, and frequent forest fires, making it a critical region for biodiversity and forest resources in Turkey. The Canopy Cover Estimation Method (CCEM) was employed, achieving accuracy percentages ranging from 77% to 86%, demonstrating its efficacy in estimating FCC without the need for fieldwork. The integration of annual updates from the Esri Sentinel-2 Land Cover dataset as auxiliary data greatly enhanced the reliability and accuracy of the analyses. Despite challenges posed by the study area and differences in spatial resolution among the datasets, the study successfully demonstrated the utility of integrating ICESat-2 segment data and Landsat imagery for understanding the dynamics of FCC in a complex environment. The results contribute to more informed decision-making in forest management and conservation efforts. Future research should focus on refining the methodologies to minimize spectral misclassification, improving the spatial resolution of the FCC maps, and leveraging advancements in remote sensing technology to obtain more accurate and reliable results.

Barrier swaption pricing formulae of mean-reverting model in uncertain environment

Barrier swaption is an exotic option, in which the option purchaser has the right to decide whether the swaption will come into effect within a period and it becomes effective (invalid) only when the underlying rises (falls) to the barrier price. This paper studies four kinds of barrier swaptions based on the mean-reverting model, which are up-and-in payer swaption, down-and-in receiver swaption, down-and-out payer swaption, up-and-out receiver swaption, and the price calculation formulae are given. Then, the related parameters are calculated by the minimum cover estimation method. Finally, the examples are given.

Comparative Study of Fractional Vegetation Cover Estimation Methods Based on Fine Spatial Resolution Images for Three Vegetation Types

Comparative Study of Fractional Vegetation Cover Estimation Methods Based on Fine Spatial Resolution Images for Three Vegetation Types

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau

Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R2) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.

Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

Comparison of Visual Assessment and Digital Image Analysis for Canopy Cover Estimation

Core Ideas Cover crops provide ecosystem services linked to canopy cover development. Visual assessment of canopy cover is compared with two digital image analysis methods. Visual estimation is well correlated with image estimation but showed lower values. The choice of the canopy cover estimation method depends on the objective. Currently, in the context of agriculture, cover crops are crops cultivated with the sole aim of providing important ecosystem services such as erosion prevention. Many services offered by these crops are directly linked to the development of their vegetation, and especially of canopy cover. A proper estimation of this cover is thus necessary to evaluate cover crop performance. Many methods to estimate canopy cover exist, but differ in terms of effort and time needed to implement them. In this study, we compared visual assessment of canopy cover in the field with two methods of digital image analysis (Assess and Canopeo), for different cover crop species and vegetation types. Visual estimation was positively correlated with both type of image analysis estimations. However, it showed systematically lower values of canopy cover, especially at intermediate canopy cover values. The type of vegetation influenced the visual and digital image estimations, narrow leaf species being the most difficult to evaluate visually. This study showed that depending on its utilization, visual canopy cover assessment could be useful, especially when only relative estimation of canopy cover is needed. When absolute canopy cover estimation is needed, the use of digital image analysis should be preferred.

Comparing the accuracy of three non-destructive methods in estimating aboveground plant biomass

Aboveground plant biomass is one of the most important features of ecosystems, and it is widely used in ecosystem research. Non-destructive biomass estimation methods provide an important toolkit, because the destructive harvesting method is in many cases not feasible. However, only few studies have compared the accuracy of these methods in grassland communities to date. We studied the accuracy of three widely used methods for estimation of aboveground biomass: the visual cover estimation method, the point intercept method, and field spectroscopy. We applied them in three independent series of field samplings in semi-arid sand grasslands in Central Hungary. For each sampling method, we applied linear regression to assess the strength of the relationship between biomass proxies and actual aboveground biomass, and used coefficient of determination to evaluate accuracy. We found no evidence that the visual cover estimation, which is generally considered as a subjective method, was less accurate than point intercept method or field spectroscopy in estimating biomass. Based on our three datasets, we found that accuracy was lower for the point intercept method compared to the other two methods, while field spectroscopy and visual cover estimation were similar to each other in the semi-arid sand grassland community. We conclude that visual cover estimation can be as accurate for estimating aboveground biomass as other approaches, thus the choice amongst the methods should be based on additional pros and cons associated with each of the method and related to the specific research objective.

Comparison of field- and satellite-based vegetation cover estimation methods

BackgroundMonitoring terrestrial vegetation cover condition is important to evaluate its current condition and to identify potential vulnerabilities. Due to simplicity and low cost, point intercept method has been widely used in evaluating grassland surface and quantifying cover conditions. Field-based digital photography method is gaining popularity for the purpose of cover estimate, as it can reduce field time and enable additional analysis in the future. However, the caveats and uncertainty among field-based vegetation cover estimation methods is not well known, especially across a wide range of cover conditions. We compared cover estimates from point intercept and digital photography methods with varying sampling intensities (25, 49, and 100 points within an image), across 61 transects in typical steppe, forest steppe, and desert steppe in central Mongolia. We classified three photosynthetic groups of cover important to grassland ecosystem functioning: photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. We also acquired normalized difference vegetation index from satellite image comparison with the field-based cover.ResultsPhotosynthetic vegetation estimates by point intercept method were correlated with normalized difference vegetation index, with improvement when non-photosynthetic vegetation was combined. For digital photography method, photosynthetic and non-photosynthetic vegetation estimates showed no correlation with normalized difference vegetation index, but combining of both showed moderate and significant correlation, which slightly increased with greater sampling intensity.ConclusionsResults imply that varying greenness is playing an important role in classification accuracy confusion. We suggest adopting measures to reduce observer bias and better distinguishing greenness levels in combination with multispectral indices to improve estimates on dry matter.

Fractional Vegetation Cover Estimation Method Through Dynamic Bayesian Network Combining Radiative Transfer Model and Crop Growth Model

Fractional vegetation cover (FVC) is an important parameter for describing the conditions of land surface vegetation and is widely used for Earth surface process simulations and global change studies. Regional FVC is primarily derived from remotely sensed data. However, current FVC estimation methods are mainly employed on remotely sensed data at a single time point, which can only reflect the instantaneous physical state of the land surface and ignore the important information from the vegetation growing characteristics. The vegetation growing characteristics have great potential to capture the temporal variations of FVC and, thus, can provide complementary information to improve the FVC estimation accuracy. In this paper, a dynamic Bayesian network method was proposed to estimate FVC from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data through combining a radiative transfer model and a statistical crop growth model, which could synthetically use information from both remote sensing data and crop growing characteristics. The performance of the proposed method was investigated in a cropland area of the Heihe River Basin, covering the whole growing season of maize in 2012. The time series field FVC data were quantitatively measured using digital photography and then used to generate high-spatial-resolution FVC maps using the Advanced Spaceborne Thermal Emission and Reflection Radiometer and Compact Airborne Imaging Spectrometer data for evaluating the accuracy of FVC estimates from MODIS data. The validation results showed a satisfactory performance with a coefficient of determination $R^{2}$ of 0.956 and a root-mean-square error (RMSE) of 0.057, as compared with the performance ( $R^{2} = 0.817$ , $\text{RMSE} = 0.106$ ) of the FVC estimates using the lookup table method, which utilized the information from remote sensing data. These results indicated that the proposed method could effectively utilize the vegetation growth information and achieve reliable FVC estimates in the cropland area.

Optimal Cover Estimation Methods and Steganographic Payload Location

Cover estimation is an important part of steganalysis and has many applications. One such application is steganographic payload location using residuals, which is effective when a large number of stego images are available. In the ideal case when the cover images are available, we show that the expected number of stego images needed to perfectly locate all load-carrying pixels is approximately the logarithm of the payload size. In more practical settings when the cover images are not available, the accuracy of payload location depends primarily on the chosen cover estimation method. We present optimal, linear runtime algorithms for finding the most likely cover estimate given the stego image and experimentally demonstrate that they can be used to locate payload on both least-significant bit (LSB) replacement and LSB matching stego images. The algorithms can be extended to higher order statistical models of cover images.

Method for monitoring intertidal communities in a steep rocky shore: a combination of digital image technology and field operational strategy

This paper introduces a method for temporal studies of steep rocky intertidal communities. It combines the use of digital image technology with field methodology, so that a wide area of the community can be sampled in a short time. Two current nondestructive percent cover estimation methods (visual estimation and point intersection) were compared in terms of cost, operational advantages and data quality, with a proposed method for a sucessional study . The proposed method used sequential photos to sample multiple fixed vertical transects over time. Reproduction of the mid-intertidal transect over time was possible by overlaying temporal transects in an image editing program. This method was similar to the point intersection quadrat method used to estimate percent cover. Benefits included reduced time on field work, economic advantages and other advantages of using digital photography, such as recording. Temporal photography of transects provided measurements of recruitment, mortality and population growth, and made it possible to manufacture an animation of sucessional stages. We suggest that this is the best method for providing information and understanding on the process of succession and for monitoring benthic invertebrate intertidal communities on steep rocky shores.

Implications of the yield stress/tensile stress ratio to the SINTAP failure assessment diagrams for homogeneous materials

The treatment in structural integrity assessment procedures of materials operating in the plastic regime incorporates an inherent quantification of strain hardening behaviour through the knowledge of yield and ultimate tensile strength and, therefore, the ratio of yield stress to tensile stress, the so-called Y/ T ratio. In this paper, basic principles of the Y/ T ratio, and its treatment in codes, are first summarised. The development of the underlying equations for description of the failure assessment diagrams for the SINTAP homogeneous materials procedure is then described and the rationale behind the expressions used at the various levels of the procedure are explained. These cover estimation methods for determining Y/ T ratio, strain hardening exponent and the treatment of yield plateaus. The results of a test programme carried out to investigate the Y/ T issue on steels of varying grade, process route, thickness, and hence Y/ T ratio, are then presented with emphasis on the use of these data to validate the homogeneous materials’ procedure.

Dots on the rocks: a comparison of percent cover estimation methods

Several percent cover estimation methods have become “standards” in ecological research conducted in rocky intertidal habitats. We compare the results of five methods for estimating cover (visual estimation, evenly spaced dots, random dots, and stratified random dots on transparent plastic plates, and electronic digitizing of photographic images) for repeatability (precision), robustness against observer bias, and sensitivity (ability to detect rare species). Digitizing of photographic images was found to be most precise for species covering > ≈30% of available space. The estimates derived from the other techniques were generally not significantly different from each other for percent coverage of this magnitude. The methods were not significantly different in their abilities to estimate percent cover of less abundant species although each of the dot methods frequently failed to detect species covering < ≈ 1%. Because they require less time in the field, a combination of visual estimation and photographic documentation seems particularly effective for intertidal surveys. Observer training and validation of visual estimates by comparison to those derived from electronic digitizing of photographic images allow surveys of large numbers of plots while maximizing both coverage and reliability.