Estimate Canopy Cover Research Articles

Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method

ABSTRACT Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R 2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions.

Quantifying the precision of forest stand height and canopy cover estimates derived from air photo interpretation

Abstract Quality information on forest resources is fundamental for sustainable forest management. Manual aerial photointerpretation is used as a cost-effective source of data for forest inventories; however, the process of photointerpretation is inherently subjective and is often undertaken by multiple photointerpreters for a given forest management area. In contrast, airborne laser scanning (ALS) data enable characterization of forest structure in a systematic fashion with quantifiable levels of accuracy and precision that often exceed required targets and standards. However, the gains associated with the use of new technologies for forest inventory are difficult to measure because the quality of existing photointepreted inventories have rarely been quantified. Using ALS data as reference, the objective of this study was to quantify the precision of photointerpreted estimates of forest stand height and canopy cover (CC). We examined forest inventories from three study sites in three different forest regions of Canada. Each of the study sites was located within a different provincial jurisdiction with unique photointerpretation standards and forest ecosystems. Stand-level estimates of forest height and cover were compared to reference estimates generated from the ALS data. Overall, our results indicated that precision was greater for photointerpreted estimates of height, with a relative standard deviation ranging from 22 per cent to 29 per cent among our three sites, compared to estimates for CC, with precision ranging from 28 per cent to 59 per cent. While the relationship between photointerpreted estimates of height and ALS estimates of height were generally linear and consistent for all study sites, relationships for CC were non-linear. We found that precision for both stand height and cover varied by dominant species, inventory stand structure, age, and ALS canopy complexity, and that in the majority of cases, the differences between the photointerpreted estimate and the ALS estimate were statistically significant. It is also noted that the variability in photointerpretation precision as a function of the aforementioned factors was not consistent among our three study sites, indicating that site-specific forest conditions and photointerpretation procedures influence the precision of photointerpreted estimates. The influence of local forest conditions and interpretation procedures are therefore important considerations when seeking to quantify the potential relative gains in precision, which may be afforded by technologies such as ALS for forest inventory programs. Moreover, approaches to improve consistency in photointerpreted estimates of cover would be useful for operational inventory programs.

Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC > 60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

Analyzing urban environmental justice based on supply, demand, and access to cooling ecosystem services in Tehran, Iran

Given the important role of environmental amenities such as ecosystem services on human well-being, this study investigates the unfair distribution of access to urban cooling ecosystem services in Tehran in order to examine urban environmental justice through the following methodological steps: (1) mapping socio-economic inequality through eleven indicators reflecting socio-economic status ranked by TOPSIS, (2) exploring urban land surface temperature (LST) by applying the mono-window algorithm, (3) estimating canopy cover, and (4) applying distributional justice dimensions through demand, supply, and access to urban cooling ecosystem services. The results revealed that urban cooling ecosystem service demand was high in districts with lower socio-economic status ranking. Cooling ecosystem service supply was high in northern districts with higher socio-economic status. This study indicates that residents of Tehran are confronting urban injustice in terms of the distribution pattern of urban cooling ecosystem services.

Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

Canopy cover is one of the most important variables in ecology, hydrology, and forest management, and useful as a basis for defining forests. LiDAR is an active remote sensing method that provides the height information of an object in three-dimensional space. The method allows for the mapping of terrain, canopy height and cover. Its only setback is that it has to be integrated with Landsat to cover a large area. The main objective of this study is to generate the canopy cover estimation model using Landsat 8 OLI and LiDAR. Landsat 8 OLI vegetation indices and LiDAR-derived canopy cover estimation, through First Return Canopy Index (FRCI) method, were used to obtain a regression model. The performance of this model was then assessed using correlation, aggregate deviation, and raster display. Lastly, the best canopy cover estimation was obtained using equation, FRCI = 2.22 + 5.63Ln(NDVI), with R2 at 0.663, standard deviation at 0.161, correlation between actual and predicted value at 0.663, aggregate deviation at -0.182 and error at 56.10%.

Comparative performance of linear regression, polynomial regression and generalized additive model for canopy cover estimation in the dry deciduous forest of West Bengal

Canopy cover is an important biophysical indicator of forested habitats, but quantifying and monitoring with the accurate and cost-efficient method is always difficult in fragmented canopy cover areas. Therefore, this study was designed to explore the comparative performance of linear regression, polynomial regression and generalized additive model (GAM), and assess the important predictors for canopy cover estimation in the dry deciduous forest of West Bengal. We used the Sentinel-2 based individual bands and spectral indices as predictor variables from the different composition of imagery in the growing season (June–September) between 2018 and 2020. Canopy cover of 667 plots were measured using collect earth online (CEO) from high resolution imagery. Univariate and multiple models were fitted and assessed by 10 fold cross-validation. Root mean square error (RMSE) and the coefficient of determination (R2) was used for the evaluation of model performance. The result showed that univariate models using GAM were fitted better followed polynomial regression and linear regression model. SWIR bands and SWIR bands based indices were fitted well than other groups of variables. Individual bands based multiple prediction models performed better followed by NIR, SWIR, atmospheric and red edge indices based multiple prediction models for multiple linear regression, multiple polynomial regression and multiple GAM. Multiple GAM performed better (RMSE = 0.163, R2 = 0.777) than multiple polynomial regression (RMSE = 0.167, R2 = 0.772) and multiple linear regression model (RMSE = 0.182, R2 = 0.721). Although, the difference of performance between multiple polynomial regression model and multiple GAM was negligible. But in respect to prediction range, and overestimation and underestimation multiple GAM was superior to multiple polynomial regression model. The study demonstrates that the potential utility of GAM for canopy cover estimation and GAM can be superior to linear and polynomial regression model using suitable predictor variables.

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

High resolution aerial photogrammetry based 3D mapping of fruit crop canopies for precision inputs management

Rapid and accurate canopy attributes estimation is highly critical in fruit crops production management as this information can be used for canopy and crop load management as well as to develop nutrient/chemical prescription application maps. However, the existing ground based canopy sensing and attribute estimation methods are laborious and often involve complexity with field data collection and analysis. Manual methods can be subjective as well. Therefore, this study explores aerial photogrammetry based method of tree–row–volume (TRV), leaf–wall–area (LWA), canopy volume (CV) and canopy cover (CC) estimation for grapevine and apple canopies. Remote sensing data was collected using a consumer–grade small unmanned aerial system (UAS) with an RGB imaging sensor flying at different flight altitudes i.e., 15 m (Ground sampling distance, GSD = 0.45 cm pixel−1 at 65° sensor inclination), 30 m (0.90 and 0.85 cm pixel−1 at 65°and 75°, respectively), 45 m (1.35 and 1.27 cm pixel−1 at 65°and 75°, respectively) and 60 m (1.81 and 1.69 cm pixel−1 at 65°and 75°, respectively). Crop surface model (CSM) was derived from such data to estimate canopy height, width and foliage vigor, which are further used to estimate TRV, LWA, CV and CC. The ground measured and aerial imagery estimated TRV had a strong relationship with the data collected at the lowest GSD within grapevine canopies (R2 = 0.77 at 0.45 cm pixel−1) as well as for apple canopies (R2 = 0.82 at 0.90 cm pixel−1). Similar trends were observed for the LWA (R2 = 0.77 and 0.86), CV (R2 = 0.43 and 0.64) and CC (R2 = 0.61 and 0.68) estimates for grapevine and apple canopies, respectively. Increasing GSD (≥0.45 cm pixel−1 in grapevine and ≥ 0.90 cm pixel−1 in apple) resulted in a weak relationship between ground measurements and aerial imagery data-based estimates for grapevines (R2 ≤ 0.36) and apple canopies (R2 = 0.39–0.78). Overall, the aerial flights with lower GSD and double grid missions with RGB imaging sensor in 65° orientation aided in the development of site–specific high–quality canopy vigor maps that can be used in precision crop inputs management related decision making.

Multivariate Modelling for the Unexplained Relationships of tree Variables for Combretum hartmannianum

Tree crown information contributes to the investigation of several key forest ecosystem attributes: biodiversity, productivity, sustainability, aesthetics, forest environment and wildlife. Recent remote sensing applications utilize estimation of either canopy cover or individual tree canopy area as a first stage towards timber volume. Measurement of CR size for each tree can be time-consuming and difficult to obtain in very dense stands and for very tall trees where the base of live crown is obscured. Studies have often shown that abiotic factors such as soil and topographic characteristics play a major role for species distribution. Moreover, space has been shown to be more important than environment in determining woody plant species distribution. However, trees show considerable variation and flexibility in their size of crowns, height and stem diameters. The tree stem size has its own adaptive significance to a tree. It must be strong enough to withstand the forces that act on it (weight of the tree and the drag exerted on it by the wind). The sizes, shapes and relative locations of crowns both determine and respond to the shading and constriction effects that characterize aboveground interactions between trees. The more important issue is that, whether stem diameter-at-breast height relationships with tree height, crown height, and crown diameter are the only factors that can be used to model growth and yield of trees, especially in case of natural forests and or mixed plantations with different species and sizes. This study investigates the potential for structural indices and other spatial measures to improve the prediction of crown radius and crown length for trees in natural woodlands. Field data were collected for Cambretum hartmannianum tree species from Elgarri natural forest reserve in Blue Nile State, Sudan, to test the performance of crown radius and crown length (height) models on the basis of allometric relationships with stem dimensions. A number of spatial and non-spatial variables were incorporated into modified crown dimension models. Coefficient of determination (R2) and relative bias were used to test the performance of the inclusion of spatial or non-spatial variables into the original models (using tree dimensions alone). Predictions of crown length and radius were found to be better after the incorporation of the spatial and non-spatial. Our results also confirm that not all estimation models are suitable for all forest, but subject to the local forest condition and composition. In this regard and for the studied tree species in the selected forest, Mi index is recommended for the estimation of crown radius, and DCL index for crown length.

Quantifying the impacts of bushfire on populations of wild koalas (Phascolarctos cinereus): Insights from the 2019/20 fire season

SummaryThe impact of bushfire events on wild Koala (Phascolarctos cinereus) populations is poorly understood. Following the 2019/2020 bushfire season in eastern Australia, we resurveyed 123 field sites for which contemporaneous (current koala generation) pre‐fire survey data were available. Field sites were distributed across six fire grounds between Foster and Ballina on the north coast of New South Wales. At these sites, pre‐fire naïve occupancy levels by koalas ranged from 25% to 71% of the sampled habitat, while post‐fire naïve occupancy levels ranged from 0% to 47%. The median reduction in the naïve occupancy rate by koalas when considered across all six fire grounds was 71% when standardized against pre‐fire occupancy levels. Field data provided strong corroboration between site‐based, post‐fire foliage canopy cover estimates and modelled Google Earth Engine Burnt Area Map (GEEBAM) fire‐severity categories. In terms of GEEBAM fire‐severity categories, koala survival was five times more likely in areas where forest canopies were modelled as Unburnt or Partially burnt, compared to areas where forest canopies were Fully burnt. The capacity of bushfire‐affected koala populations to recover from the 2019/20 fire season will be conditional upon size of the original population in each fire‐affected area, the enactment and implementation of supportive, recovery‐themed management regimes, future inter‐fire intervals and associated intensities. Management actions necessary to assist recovery actions are discussed.

Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

Crop models play a paramount role in providing quantitative information on crop growth and field management. However, its prediction performance degrades significantly in the presence of unknown, uncertain parameters and noisy measurements. Consequently, simultaneous state and parameter estimation (SSPE) for crop model is required to maximize its potentials. This work aims to develop an integrated dynamic SSPE framework for the AquaCrop model by leveraging constrained particle filter, crop sensitivity analysis and UAV remote sensing. Both Monte Carlo simulation and one winter wheat experimental case study are performed to validate the proposed framework. It is shown that: (i) the proposed framework with state/parameter bound and parameter sensitivity information outperforms conventional particle filter and constrained particle filter in both state and parameter estimation in Monte Carlo simulations; (ii) in real-world experiment, the proposed approach achieves the smallest root mean squared error for canopy cover estimation among the three algorithms by using day forward-chaining validation method.

Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

A reliable estimation and monitoring of tree canopy cover or shade distribution is essential for a sustainable cocoa production via agroforestry systems. Remote sensing (RS) data offer great potential in retrieving and monitoring vegetation status at landscape scales. However, parallel advancements in image processing and analysis are required to appropriately use such data for different targeted applications. This study assessed the potential of Sentinel-1A (S-1A) C-band synthetic aperture radar (SAR) backscatter in estimating canopy cover variability in cocoa agroforestry landscapes. We investigated two landscapes, in Center and South Cameroon, which differ in predominant vegetation: forest-savannah transition and forest landscape, respectively. We estimated canopy cover using in-situ digital hemispherical photographs (DHPs) measures of gap fraction, verified the relationship with SAR backscatter intensity and assessed predictions based on three machine learning approaches: multivariate bootstrap regression, neural networks regression, and random forest regression. Our results showed that about 30% of the variance in canopy gap fraction in the cocoa production landscapes was shared by the used SAR backscatter parameters: a combination of S-1A backscatter intensity, backscatter coefficients, difference, cross ratios, and normalized ratios. Based on the model predictions, the VV (co-polarization) backscatter showed high importance in estimating canopy gap fraction; the VH (cross-polarized) backscatter was less sensitive to the estimated canopy gap. We observed that a combination of different backscatter variables was more reliable at predicting the canopy gap variability in the considered type of vegetation in this study—agroforests. Semi-variogram analysis of canopy gap fraction at the landscape scale revealed higher spatial clustering of canopy gap, based on spatial correlation, at a distance range of 18.95 m in the vegetation transition landscape, compared to a 51.12 m spatial correlation range in the forest landscape. We provide new insight on the spatial variability of canopy gaps in the cocoa landscapes which may be essential for predicting impacts of changing and extreme (drought) weather conditions on farm management and productivity. Our results contribute a proof-of-concept in using current and future SAR images to support management tools or strategies on tree inventorying and decisions regarding incentives for shade tree retention and planting in cocoa landscapes.

Influence of image pixel resolution on canopy cover estimation in poplar plantations from field, aerial and satellite optical imagery

Accurate estimates of canopy cover (CC) are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used to estimate CC from the complement of gap fraction measurements obtained with restricted-view sensors. In this short note we evaluated the influence of the image pixel resolution (ground sampling distance; GSD) on CC estimation in poplar plantations obtained from field (cover photography; GSD < 1 cm), unmanned aerial (UAV; GSD <10 cm) and satellite (Sentinel-2; GSD = 10 m) imagery. The trial was conducted in poplar tree plantations in Northern Italy, with varying age and canopy cover. Results indicated that the coarser resolution available from satellite data is suitable to obtain estimates of canopy cover, as compared with field measurements obtained from cover photography; therefore, S2 isrecommended for larger scale monitoring and routine assessment of canopy cover in poplar plantations. The higher resolution of UAV compared with Sentinel-2 allows finer assessment of canopy structure, which could also be used for calibrating metrics obtained from coarser-scale remote sensing products, avoiding the need of ground measurements.

A comparison of terrestrial and UAS sensors for measuring fuel hazard in a dry sclerophyll forest

In recent years, Unoccupied Aircraft Systems (UAS) have been used to capture information on forest structure in unprecedented detail. Pioneering studies in this field have shown that high spatial resolution images and Light Detecting And Ranging (LiDAR) data captured from these platforms provide detailed information describing the dominant tree elements of canopy cover and biomass. However, to date, few studies have investigated the arrangement of vegetation elements that contribute directly to fire propagation in UAS LiDAR point clouds; that is the surface, near-surface, elevated and intermediate-canopy vegetation. This paper begins to address this gap in the literature by exploring the use of image-based and LiDAR 3D representations collected using UAS platforms, for describing forest structure properties. Airborne and terrestrial 3D datasets were captured in a dry sclerophyll forest in south-eastern Australia. Results indicate that UAS LiDAR point clouds contain information that can describe fuel properties in all strata. Similar estimates of canopy cover (TLS: 68.27% and UAS LiDAR: 64.20%) and sub-canopy cover (Elevated cover TLS: 44.94%, UAS LiDAR: 32.27%, combined surface and near-surface cover TLS: 96.10% UAS LiDAR: 93.56%) to TLS were achieved using this technology. It was also shown that the UAS SfM photogrammetric technique significantly under performed in the representation of the canopy and below canopy structure (canopy cover - 20.31%, elevated cover 10.09%). This caused errors to be propagated in the estimate of heights in the elevated fuel layer (TLS: 0.51 m, UAS LiDAR: 0.34 m, UAS SfM: 0.15 m). A method for classifying fuel hazard layers is also presented which identifies vegetation connectivity. These results indicate that information describing the below canopy vertical structure is present within the UAS LiDAR point clouds and can be exploited through this novel classification approach for fire hazard assessment. For fire prone countries, this type of information can provide important insight into forest fuels and the potential fire behaviour and impact of fire under different scenarios.

Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in improved disease management. RGB images were acquired at four representative wheat-producing sites in the Grand Duchy of Luxembourg. Diseased leaf areas were determined based on the digital numbers (DNs) of green and red spectral bands for wheat stripe rust (WSR), and the combination of DNs of green, red, and blue spectral bands for wheat leaf rust (WLR). WSR and WLR caused alterations in the typical reflectance spectra of wheat plants between the green and red spectral channels. Overall, good agreements between UAV-based estimates and observations were found for canopy cover, WSR, and WLR severities, with statistically significant correlations (p-value (Kendall) &lt; 0.0001). Correlation coefficients were 0.92, 0.96, and 0.86 for WSR severity, WLR severity, and canopy cover, respectively. While the estimation of canopy cover was most often less accurate (correlation coefficients &lt; 0.20), WSR and WLR infected leaf areas were identified satisfactorily using the RGB imagery-derived indices during the critical period (i.e., stem elongation and booting stages) for efficacious fungicide application, while disease severities were also quantified accurately over the same period. Using such a UAV-based RGB imagery method for monitoring fungal foliar diseases throughout the cropping season can help to identify any new disease outbreak and efficaciously control its spread.

Potential for crown diameter as a predictor of canopy cover under National Park conditions

The global loss of biodiversity has led to relentless calls from conservationists to protect the environment and natural habitats. The objective of this study was to determine the effectiveness of crown diameter for prediction of canopy cover under national park conditions. The study was conducted in Etosha National Park, Namibia in 2015. Three tree species, namely Combretum imberbe, Colophospermum mopane and Terminalia prunioides were considered as representative members of the park species based on their relative population density. The study employed regression analysis to examine the nature of relationships between tree crown diameter (the dependent variable) and tree height, diameter at breast height (DBH) and distance from watering holes as independent variables. The regression coefficients for Combretum imberbe were not significant (P = 0.116; P = 0.431; P = 0.884, respectively), suggesting that crown diameter was not well estimated by these explanatory variables. On the other hand, there were significant relationships between DBH and crown diameter, as well as between crown diameter and tree height. Tree height emerged as the stronger predictor of crown diameter for both Terminalia prunioides and Colophospermum mopane. Therefore, the relationship between tree height and crown diameter as explained by cubic function can further be used to evaluate and estimate canopy cover under national park conditions.

Potential for crown diameter as a predictor of canopy cover under National Park conditions

The global loss of biodiversity has led to relentless calls from conservationists to protect the environment and natural habitats. The objective of this study was to determine the effectiveness of crown diameter for prediction of canopy cover under national park conditions. The study was conducted in Etosha National Park, Namibia in 2015. Three tree species, namely Combretum imberbe, Colophospermum mopane and Terminalia prunioides were considered as representative members of the park species based on their relative population density. The study employed regression analysis to examine the nature of relationships between tree crown diameter (the dependent variable) and tree height, diameter at breast height (DBH) and distance from watering holes as independent variables. The regression coefficients for Combretum imberbe were not significant (P = 0.116; P = 0.431; P = 0.884, respectively), suggesting that crown diameter was not well estimated by these explanatory variables. On the other hand, there were significant relationships between DBH and crown diameter, as well as between crown diameter and tree height. Tree height emerged as the stronger predictor of crown diameter for both Terminalia prunioides and Colophospermum mopane. Therefore, the relationship between tree height and crown diameter as explained by cubic function can further be used to evaluate and estimate canopy cover under national park conditions.

Determination of Critical Nitrogen Dilution Curve Based on Canopy Cover Data for Summer Maize

ABSTRACT Estimation of canopy cover (CC) can be replaced with measurement of dry matter (DM) during the growing season, which leads to saving cost and time. A variety of software programs, such as Canopeo, have recently been launched for estimating CC by processing digital images. The main objective of the present study was to validate whether the CC estimated by Canopeo could be used to determine the critical nitrogen dilution curve (CNDC) of maize. DM and critical nitrogen concentration (%Nc) of summer maize were measured during the growing seasons in 2015 and 2016. On the same dates, CC was estimated by digital images and Canopeo. The CNDC based on DM (Nc, DM) was accurately described by the equation Nc, DM = 2.9DM−0.27. Furthermore, an exponential relationship was observed between CC and DM (R2 = 0.934) that resulted in the estimation of DM based on CC (DMcc). A new CNDC based on CC was determined (Nc,CC = 4e −1.17CC) by replacing DMcc in the Nc,DM which has an acceptable accuracy in estimating Nitrogen Nutrition Index. These results demonstrate that Canopeo can be applied as a useful tool to develop CNDC and Nitrogen management for different varieties of plants under various climatic conditions.

The effects of precipitation variability on the canopy cover of forage species in arid rangelands, Iran

This research was conducted to monitor changes in canopy cover of typical species during a 10-year period in the part of arid rangelands, to find out the relationship between two important climate variables (precipitation and temperature) and canopy cover changes. For that reason, canopy cover percentages of six dominant perennials and all annual plant species combined were measured during a 10-year period at phenological maturity of plant in thirty 2 m × 2 m plots which were placed along two 250-m transect lines. The results demonstrated that the maximum canopy cover for water year 2006–2007 (wet year) and the minimum value for water year 2012–2013 (drought) were 15 and 5.5%, respectively. The canopy cover was modeled by linear regression in which precipitation and temperature variables were considered independent variables. April precipitation explained 65% of changes in the canopy cover percentage of Artemisia sieberi at 95% confidence level (RRMSE = 0.26 and MAE = 0.49). The best simple linear regression models for estimating canopy cover percentages of Stipa barbata and Zygophyllum eurypterum corresponded to cumulative 4-month precipitation from March to June and March precipitation respectively, representing 77% (at 99% confidence level) and 67% (at 95% confidence level) of changes correspondingly. Considering the dominance of A. sieberi, S. barbata, and Z. eurypterum in floristic composition of the study area, it can be concluded that most changes in canopy cover of the studied rangeland are predicted by variability of precipitation during growing seasons.